code
stringlengths 1
13.8M
|
|---|
if (!require("shiny"))
stop("This demo requires shiny.")
library(rgl)
options(rgl.useNULL = TRUE)
ui <- fluidPage(
mainPanel(
tabsetPanel(
tabPanel("red",
rglwidgetOutput('thewidget1')),
tabPanel("green",
rglwidgetOutput('thewidget2'))
))
)
server <- function(input, output, session) {
x <- rnorm(100)
y <- 2*rnorm(100)
z <- 10*rnorm(100)
open3d()
plot3d(x, y, z, col = "red")
scene1 <- scene3d()
plot3d(z, y, x, col = "green")
scene2 <- scene3d()
close3d()
save <- options(rgl.inShiny = TRUE)
on.exit(options(save))
output$thewidget1 <- renderRglwidget(
rglwidget(scene1)
)
output$thewidget2 <- renderRglwidget(
rglwidget(scene2)
)
}
if (interactive())
shinyApp(ui = ui, server = server) |
acontext("Segment size")
df <- data.frame(x=c(0, 0),
y=c(0, 1),
xend=c(1, 1),
yend=c(1, 0),
size=c(5, 10))
test_that("segment size translates to stroke-width", {
viz <- list(segs=ggplot()+
geom_segment(aes(x, y, xend=xend, yend=yend),
data=df, size=1))
info <- animint2HTML(viz)
expect_styles(info$html, list("stroke-width"="^1[a-z]*$"))
})
test_that("segment size range translates to stroke-width", {
viz <- list(segs=ggplot()+
geom_segment(aes(x, y, xend=xend, yend=yend, size=size),
data=df)+
scale_size_identity())
info <- animint2HTML(viz)
expect_styles(info$html, list("stroke-width"=c("^5[a-z]*$", "^10[a-z]*$")))
}) |
flash_clean_sprint_events <- function(df, wide_format_sprint) {
if (wide_format_sprint == FALSE) {
df <- df %>%
data.frame() %>%
dplyr::mutate(Time = stringr::str_remove(Time, "(?<!D)[Q|q]")) %>%
dplyr::mutate(dplyr::across(
dplyr::matches("[0-9]"),
~ stringr::str_remove(.x, " ?\\[(.*)\\]")
))
varying_cols <-
names(df)[grep("(^X\\d)|(^Lap)|(^L\\d)", names(df))]
if (length(varying_cols) > 0) {
df <- flash_pivot_longer(df, varying = varying_cols)
}
} else {
df <- df %>%
flash_col_names() %>%
dplyr::mutate(dplyr::across(
dplyr::matches("Split_"),
~ stringr::str_remove(.x, " ?\\[(.*)\\]")
))
}
clean_sprint_data <- df %>%
dplyr::mutate(Time = stringr::str_remove(Time, "(?<!D)[Q|q]")) %>%
dplyr::rename("Result" = "Time") %>%
dplyr::mutate(
Tiebreaker = dplyr::case_when(
stringr::str_detect(Result, "\\(\\d{1,2}\\.\\d{3}\\)") == TRUE ~ stringr::str_extract(Result, "\\d{1,2}\\.\\d{3}"),
TRUE ~ "NA"
)
) %>%
dplyr::mutate(Result = stringr::str_remove(Result, "\\(\\d{1,2}\\.\\d{3}\\)")) %>%
dplyr::mutate(Result = stringr::str_remove(Result, "\\\n[:upper:]{1,2}")) %>%
dplyr::na_if("NA") %>%
dplyr::mutate(dplyr::across(where(is.character), stringr::str_trim))
if ("Team" %in% names(clean_sprint_data) == FALSE) {
clean_sprint_data <- clean_sprint_data %>%
dplyr::mutate(
Team = stringr::str_split_fixed(Name, "\\\n", 3)[, 2],
Name = stringr::str_split_fixed(Name, "\\\n", 3)[, 1]
)
}
return(clean_sprint_data)
}
sprint_events <- flash_clean_sprint_events |
methods::setClass("dbartsTreePrior")
methods::setClass("dbartsCGMPrior", contains = "dbartsTreePrior",
slots = list(power = "numeric", base = "numeric"))
methods::setValidity("dbartsCGMPrior",
function(object) {
if (object@power <= 0.0) return("'power' must be positive")
if (object@base <= 0.0 || object@base >= 1.0) return("'base' must be in (0, 1)")
TRUE
})
methods::setClass("dbartsNodeHyperprior")
methods::setClass("dbartsChiHyperprior", contains = "dbartsNodeHyperprior",
slots = list(degreesOfFreedom = "numeric", scale = "numeric"))
methods::setValidity("dbartsChiHyperprior",
function(object) {
if (object@degreesOfFreedom <= 0.0) return("'degreesOfFreedom' must be positive")
if (object@scale <= 0.0) return("'scale' must be positive")
TRUE
})
methods::setClass("dbartsFixedHyperprior", contains = "dbartsNodeHyperprior",
slots = list(k = "numeric"),
prototype = list(k = 2))
methods::setValidity("dbartsFixedHyperprior",
function(object) {
if (object@k <= 0.0) return("'k' must be positive")
TRUE
})
methods::setClass("dbartsNodePrior")
methods::setClass("dbartsNormalPrior", contains = "dbartsNodePrior")
methods::setClass("dbartsResidPrior")
methods::setClass("dbartsChiSqPrior", contains = "dbartsResidPrior",
slots = list(df = "numeric", quantile = "numeric"))
methods::setValidity("dbartsChiSqPrior",
function(object) {
if (object@df <= 0.0) return("'df' must be positive")
if (object@quantile <= 0.0) return("'quantile' must be positive")
TRUE
})
methods::setClass("dbartsFixedPrior", contains = "dbartsResidPrior",
slots = list(value = "numeric"))
methods::setValidity("dbartsFixedPrior",
function(object) {
if (object@value <= 0.0) return("'value' must be positive")
TRUE
})
methods::setClass("dbartsControl",
slots =
list(binary = "logical",
verbose = "logical",
keepTrainingFits = "logical",
useQuantiles = "logical",
keepTrees = "logical",
n.samples = "integer",
n.burn = "integer",
n.trees = "integer",
n.chains = "integer",
n.threads = "integer",
n.thin = "integer",
printEvery = "integer",
printCutoffs = "integer",
rngKind = "character",
rngNormalKind = "character",
rngSeed = "integer",
updateState = "logical",
call = "language"),
prototype =
list(binary = FALSE,
verbose = FALSE,
keepTrainingFits = TRUE,
useQuantiles = FALSE,
keepTrees = FALSE,
n.samples = NA_integer_,
n.burn = 200L,
n.trees = 75L,
n.chains = 4L,
n.threads = 1L,
n.thin = 1L,
printEvery = 100L,
printCutoffs = 0L,
rngKind = "default",
rngNormalKind = "default",
rngSeed = NA_integer_,
updateState = TRUE,
call = quote(call("NA")))
)
methods::setValidity("dbartsControl",
function(object) {
if (length(object@verbose) != 1L) return("'verbose' must be of length 1")
if (length(object@keepTrainingFits) != 1L) return("'keepTrainingFits' must be of length 1")
if (length(object@useQuantiles) != 1L) return("'useQuantiles' must be of length 1")
if (length(object@keepTrees) != 1L) return("'keepTrees' must be of length 1")
if (length([email protected]) != 1L) return("'n.burn' must be of length 1")
if (length([email protected]) != 1L) return("'n.trees' must be of length 1")
if (length([email protected]) != 1L) return("'n.threads' must be of length 1")
if (length([email protected]) != 1L) return("'n.thin' must be of length 1")
if (length(object@printEvery) != 1L) return("'printEvery' must be of length 1")
if (length(object@printCutoffs) != 1L) return("'printCutoffs' must be of length 1")
if (length(object@updateState) != 1L) return("'updateState' must be of length 1")
if (length([email protected]) != 1L) return("'n.samples' must be of length 1")
if (length(object@rngSeed) != 1L) return("'rngSeed' must be of length 1")
if (is.na(object@verbose)) return("'verbose' must be TRUE/FALSE")
if (is.na(object@keepTrainingFits)) return("'keepTrainingFits' must be TRUE/FALSE")
if (is.na(object@useQuantiles)) return("'useQuantiles' must be TRUE/FALSE")
if (is.na(object@keepTrees)) return("'keepTrees' must be TRUE/FALSE")
if ([email protected] < 0L) return("'n.burn' must be a non-negative integer")
if ([email protected] <= 0L) return("'n.trees' must be a positive integer")
if ([email protected] <= 0L) return("'n.chains' must be a positive integer")
if ([email protected] <= 0L) return("'n.threads' must be a positive integer")
if ([email protected] < 0L) return("'n.thin' must be a non-negative integer")
if (object@printEvery < 0L) return("'printEvery' must be a non-negative integer")
if (object@printCutoffs < 0L) return("'printCutoffs' must be a non-negative integer")
rngKind <- parse(text = deparse(RNGkind)[-1L])[[1L]]
rngKinds <- character(0L)
rngNormalKinds <- character(0L)
for (i in seq_along(rngKind)) {
rngKind.i <- as.character(rngKind[[i]])
if (any(grepl("Mersenne", rngKind.i)))
rngKinds <- eval(parse(text = rngKind.i[which(grepl("Mersenne", rngKind.i))]))
if (any(grepl("Inversion", rngKind.i)))
rngNormalKinds <- eval(parse(text = rngKind.i[which(grepl("Inversion", rngKind.i))]))
}
if (length(rngKinds) == 0L || length(rngNormalKinds) == 0L) {
oldKind <- RNGkind()
oldSeed <- .Random.seed
tryResult <- tryCatch(RNGkind(object@rngKind, object@rngNormalKind), error = function(e) e)
if (inherits(tryResult, "error")) return(paste0("unrecognized rng kind ('", object@rngKind, "', '", object@rngNormalKind, "')"))
object@rngKind <- RNGkind()[1L]
object@rngNormalKind <- RNGkind()[2L]
RNGkind(oldKind[1L], oldKind[2L])
.Random.seed <- oldSeed
} else {
if (!(object@rngKind %in% rngKinds)) return(paste0("unrecognized rng kind '", object@rngKind, "'"))
if (!(object@rngNormalKind %in% rngNormalKinds)) return(paste0("unrecognized rng normal kind '", object@rngNormalKind, "'"))
}
if (is.na(object@updateState)) return("'updateState' must be TRUE/FALSE")
if (!is.na([email protected]) && [email protected] < 0L) return("'n.samples' must be a non-negative integer")
TRUE
})
methods::setClass("dbartsModel",
slots =
list(p.birth_death = "numeric",
p.swap = "numeric",
p.change = "numeric",
p.birth = "numeric",
node.scale = "numeric",
tree.prior = "dbartsTreePrior",
node.prior = "dbartsNodePrior",
node.hyperprior = "dbartsNodeHyperprior",
resid.prior = "dbartsResidPrior"),
prototype =
list(p.birth_death = 1.0,
p.swap = 0.0,
p.change = 0.0,
p.birth = 0.5,
node.scale = 0.5,
tree.prior = new("dbartsCGMPrior"),
node.prior = new("dbartsNormalPrior"),
node.hyperprior = new("dbartsFixedHyperprior"),
resid.prior = new("dbartsChiSqPrior")))
methods::setValidity("dbartsModel",
function(object) {
proposalProbs <- c([email protected]_death, [email protected], [email protected])
if (any(proposalProbs < 0.0) || any(proposalProbs > 1.0)) return("rule proposal probabilities must be in [0, 1]")
if (abs(sum(proposalProbs) - 1.0) >= 1e-10) return("rule proposal probabilities must sum to 1")
if ([email protected] <= 0.0 || [email protected] >= 1.0) return("birth probability for birth/death step must be in (0, 1)")
if ([email protected] <= 0.0) return("node.scale must be > 0")
TRUE
})
methods::setClassUnion("matrixOrNULL", c("matrix", "NULL"))
methods::setClassUnion("numericOrNULL", c("numeric", "NULL"))
methods::setClass("dbartsData",
slots =
list(y = "numeric",
x = "matrix",
varTypes = "integer",
x.test = "matrixOrNULL",
weights = "numericOrNULL",
offset = "numericOrNULL",
offset.test = "numericOrNULL",
n.cuts = "integer",
sigma = "numeric",
testUsesRegularOffset = "logical"),
prototype =
list(y = numeric(0),
x = matrix(0, 0, 0),
varTypes = integer(0),
x.test = NULL,
weights = NULL,
offset = NULL,
offset.test = NULL,
n.cuts = integer(0),
sigma = NA_real_,
testUsesRegularOffset = NA
)
)
methods::setValidity("dbartsData",
function(object) {
numObservations <- length(object@y)
if (nrow(object@x) != numObservations) return("number of rows of 'x' must equal length of 'y'")
if (length(object@varTypes) > 0 &&
any(object@varTypes != ORDINAL_VARIABLE & object@varTypes != CATEGORICAL_VARIABLE))
return("variable types must all be ordinal or categorical")
if (!is.null([email protected]) && ncol([email protected]) != ncol(object@x)) return("'x.test' must be null or have number of columns equal to 'x'")
if (!is.null(object@weights)) {
if (length(object@weights) != numObservations) return("'weights' must be null or have length equal to that of 'y'")
if (anyNA(object@weights)) return("'weights' cannot be NA")
if (any(object@weights < 0.0)) return("'weights' must all be non-negative")
if (any(object@weights == 0.0)) warning("'weights' of 0 will be ignored but increase computation time")
}
if (!is.null(object@offset) && length(object@offset) != numObservations) return("'offset' must be null or have length equal to that of 'y'")
if (!anyNA([email protected]) && length([email protected]) != ncol(object@x)) return("length of 'n.cuts' must equal number of columns in 'x'")
if (!is.na(object@sigma) && object@sigma <= 0.0) return("'sigma' must be positive")
TRUE
})
methods::setClass("dbartsState",
slots = list(trees = "integer",
treeFits = "numeric",
savedTrees = "integer",
sigma = "numeric",
k = "numericOrNULL",
rng.state = "integer")) |
knitr::opts_chunk$set(
collapse = TRUE,
comment = "
)
fam <- binomial()
class(fam)
names(fam)
set.seed(1)
x <- matrix(rnorm(500), ncol = 5)
y <- rowSums(x[, 1:2]) + rnorm(100)
library(glmnet)
oldfit <- glmnet(x, y, family = "gaussian")
newfit <- glmnet(x, y, family = gaussian())
thresh <- 1e-18
oldfit <- glmnet(x, y, family="gaussian", thresh = thresh)
newfit <- glmnet(x, y, family = gaussian(), thresh = thresh)
library(testthat)
for (key in c("a0", "beta", "df", "dim", "lambda", "dev.ratio",
"nulldev", "offset", "nobs")) {
expect_equal(oldfit[[key]], newfit[[key]])
}
biny <- ifelse(y > 0, 1, 0)
cnty <- ceiling(exp(y))
oldfit <- glmnet(x, biny, family = "binomial")
newfit <- glmnet(x, biny, family = binomial())
oldfit <- glmnet(x, cnty, family = "poisson")
newfit <- glmnet(x, cnty, family = poisson())
newfit <- glmnet(x, biny, family = binomial(link = "probit"))
newfit <- glmnet(x, cnty, family = quasipoisson())
class(newfit)
fit <- glmnet(x, y, family = "gaussian")
class(fit)
set.seed(2020)
n <- 100
p <- 4
x <- matrix(runif(n * p, 5, 10), n)
y <- rpois(n, exp(rowMeans(x)))
glmfit <- glm(y ~ x - 1, family = poisson)
coef(glmfit)
oldfit <- glmnet(x, y, family = "poisson", standardize = FALSE,
intercept = FALSE, lambda = 0)
coef(oldfit)
glmnet.control(mxitnr = 50)
newfit <- glmnet(x, y, family = poisson(), standardize = FALSE,
intercept = FALSE, lambda = 0)
coef(newfit)
thresh <- 1e-15
glmfit <- glm(y ~ x-1, family = poisson,
control = list(epsilon = thresh, maxit = 100))
newfit <- glmnet(x, y, family = poisson(), standardize = FALSE, intercept = FALSE,
lambda = 0, thresh = thresh)
expect_equal(as.numeric(coef(glmfit)),
as.numeric(coef(newfit))[2:5]) |
'ursa_read' <- function(fname,verbose=FALSE) {
if (envi_exists(fname)) {
return(read_envi(fname))
}
if (!.lgrep("\\.zip$",fname)) {
return(read_gdal(fname=fname,verbose=verbose))
}
list1 <- unzip(fname,exdir=tempdir());on.exit(file.remove(list1))
ind <- .grep("\\.tif(f)*$",list1)
if (length(ind)) {
aname <- .gsub("\\.tif(f)*","",basename(list1[ind]))
if (TRUE) {
res <- vector("list",length(aname))
names(res) <- aname
res <- lapply(list1[ind],.read_gdal,verbose=verbose)
names(res) <- aname
g <- lapply(res,ursa_grid)
if (all(sapply(head(g,-1),function(g2) all.equal(g[[1]],g2))))
res <- as.ursa(res)
return(res)
}
for (i in sample(seq_along(aname))) {
a <- .read_gdal(fname=list1[ind][i],verbose=verbose)
if (!exists("res"))
res <- ursa(bandname=aname)
res[i] <- a
}
return(res)
}
NULL
}
'read_gdal' <- function(fname,resetGrid=TRUE,band=NULL
,engine=c("native","rgdal","sf"),verbose=FALSE,...) {
engine <- match.arg(engine)
if (accepted_changes <- TRUE) {
if ((!is.null(band))||(engine %in% c("native","rgdal")[1:2])) {
isSF <- FALSE
}
else if (engine %in% c("native","sf")[2])
isSF <- TRUE
else {
loaded <- loadedNamespaces()
if ("sf" %in% loaded)
isSF <- TRUE
else if (("sp" %in% loaded)||("rgdal" %in% loaded))
isSF <- FALSE
else
isSF <- requireNamespace("sf",quietly=.isPackageInUse())
}
}
else
isSF <- FALSE
if (verbose)
print(c(isSF=isSF))
if (isSF) {
res <- as_ursa(sf::gdal_read(fname))
}
else {
obj <- open_gdal(fname,verbose=verbose)
if (is.null(obj))
return(NULL)
res <- if (!is.null(band)) obj[band] else obj[]
close(obj)
}
if (T & length(grep("^\\d{8}\\.s1ab\\.1km\\.n\\.mos[13]d\\.jpg$",basename(fname)))) {
g0 <- ursa_grid(res)
if ((g0$columns==4500L)&&(g0$rows==5500L)) {
xy <- .project(c(-176.682000,61.327000),spatial_crs(3413))
g1 <- .grid.skeleton()
g1$resx <- g1$resy <- 1004.1
g1$crs <- spatial_crs(3413)
g1$columns <- g0$columns
g1$rows <- g0$rows
g1$minx <- round(xy[,1])-g1$resx/2
g1$maxy <- round(xy[,2])
g1$maxx <- g1$minx+g1$resx*g1$columns
g1$miny <- g1$maxy-g1$resy*g1$rows
ursa_grid(res) <- g1
}
}
if (resetGrid)
session_grid(res)
res
}
'.read_gdal' <- function(fname,fileout=NULL,verbose=!FALSE,...) {
if (!is.character(fname))
return(NULL)
requireNamespace("rgdal",quietly=.isPackageInUse())
if (verbose)
.elapsedTime("rgdal has been loaded")
op <- options(warn=0-!verbose)
a <- try(rgdal::GDALinfo(fname,returnStats=FALSE,returnRAT=FALSE
,returnColorTable=TRUE,returnCategoryNames=TRUE))
options(op)
if (inherits(a,"try-error")) {
fname <- normalizePath(fname)
op <- options(warn=0-!verbose)
a <- try(rgdal::GDALinfo(fname,returnStats=FALSE,returnRAT=FALSE
,returnColorTable=TRUE,returnCategoryNames=TRUE))
options(op)
if (verbose)
str(a)
if (inherits(a,"try-error")) {
if (verbose) {
message("It looks like file ",.dQuote(fname)
," is not found or not GDAL-recognized")
}
return(NULL)
}
}
a1 <- as.numeric(a)
g1 <- regrid()
g1$rows <- as.integer(a1[1])
g1$columns <- as.integer(a1[2])
nl <- as.integer(a1[3])
g1$minx <- a1[4]
g1$miny <- a1[5]
g1$resx <- a1[6]
g1$resy <- a1[7]
g1$maxx <- with(g1,minx+resx*columns)
g1$maxy <- with(g1,miny+resy*rows)
g1$crs <- attr(a,"projection")
if (is.na(g1$crs))
g1$crs <- ""
b1 <- attr(a,"mdata")
ln <- .gsub("^Band_\\d+=\\t*(.+)$","\\1",.grep("band",b1,value=TRUE))
c1 <- attr(a,"df")
hasndv <- unique(c1$hasNoDataValue)
nodata <- unique(c1$NoDataValue)
nodata <- if ((length(hasndv)==1)&&(length(nodata)==1)&&(hasndv)) nodata
else NA
ct <- attr(a,"ColorTable")
if ((length(ct))&&(!is.null(ct[[1]]))) {
ct <- ct[[1]]
ca <- attr(a,"CATlist")
if ((length(ca))&&(!is.null(ca[[1]]))) {
nval <- ca[[1]]
ct <- ct[seq(length(nval))]
}
else
nval <- NULL
names(ct) <- nval
}
else
ct <- character()
class(ct) <- "ursaColorTable"
session_grid(g1)
dset <- methods::new("GDALReadOnlyDataset",fname)
if (!length(ln)) {
dima <- dim(dset)
ln <- paste("Band",if (length(dima)==3) seq(dima[3]) else 1L)
}
if (!is.character(fileout)) {
val <- rgdal::getRasterData(dset)
dima <- dim(val)
if (length(dima)==2)
dim(val) <- c(dima,1L)
val <- val[,rev(seq(dim(val)[2])),,drop=FALSE]
res <- as.ursa(value=val,bandname=ln,ignorevalue=nodata)
}
else {
res <- create_envi(fileout,bandname=ln,ignorevalue=nodata,...)
for (i in seq_along(ln))
{
res[i]$value[] <- rgdal::getRasterData(dset,band=i)
}
}
rgdal::closeDataset(dset)
res$colortable <- ct
class(res$value) <- ifelse(length(ct),"ursaCategory","ursaNumeric")
res
} |
move_jitter_grob <- function(loon.grob, index, swap, jitterxy, temporary, ...) {
obj <- character(0)
class(obj) <- names(loon.grob$children)
UseMethod("move_jitter_grob", obj)
}
move_jitter_grob.l_plot <- function(loon.grob, index, swap, jitterxy, temporary = FALSE, ...) {
if(length(index) == 0) return(loon.grob)
args <- list(...)
pointsTreeName <- args$pointsTreeName
if(pointsTreeName != "points: missing glyphs") {
x <- jitterxy$x
y <- jitterxy$y
newGrob <- grid::getGrob(loon.grob, pointsTreeName)
if(!temporary & swap) {
lapply(index,
function(i) {
if(grepl(newGrob$children[[i]]$name,pattern = "primitive_glyph")) {
newGrob$children[[i]] <<- grid::editGrob(
grob = newGrob$children[[i]],
y = unit(x[which(index %in% i)], "native"),
x = unit(y[which(index %in% i)], "native")
)
} else if(grepl(newGrob$children[[i]]$name, pattern = "serialaxes_glyph")) {
polyline_grob <- grid::getGrob(newGrob$children[[i]], "polyline")
if(is.null(polyline_grob)) {
polyline_grob <- grid::getGrob(newGrob$children[[i]], "polyline: showArea")
polyline_grob_name <- "polyline: showArea"
} else polyline_grob_name <- "polyline"
polyline_grob <- grid::editGrob(
polyline_grob,
y = unit(x[which(index %in% i)], "native") +
get_unit(polyline_grob$x,
is.unit = FALSE,
as.numeric = FALSE),
x = unit(y[which(index %in% i)], "native") +
get_unit(polyline_grob$y,
is.unit = FALSE,
as.numeric = FALSE)
)
newGrob$children[[i]] <<- grid::setGrob(
gTree = newGrob$children[[i]],
gPath = polyline_grob_name,
newGrob = polyline_grob
)
} else if(grepl(newGrob$children[[i]]$name, pattern = "polygon_glyph")) {
newGrob$children[[i]] <<- grid::editGrob(
grob = newGrob$children[[i]],
y = unit(x[which(index %in% i)], "native") +
get_unit(newGrob$children[[i]]$x,
is.unit = FALSE,
as.numeric = FALSE),
x = unit(y[which(index %in% i)], "native") +
get_unit(newGrob$children[[i]]$y,
is.unit = FALSE,
as.numeric = FALSE)
)
} else if(grepl(newGrob$children[[i]]$name, pattern = "pointrange_glyph")) {
pointGrob <- grid::getGrob(newGrob$children[[i]], "point")
line_grob <- grid::getGrob(newGrob$children[[i]], "range")
pointGrob <- grid::editGrob(
pointGrob,
y = unit(x[which(index %in% i)], "native"),
x = unit(y[which(index %in% i)], "native")
)
range <- diff(sort(as.numeric(line_grob$x)))/2
line_grob <- grid::editGrob(
line_grob,
y = unit(rep(x[which(index %in% i)], 2), "native"),
x = unit(c(y[which(index %in% i)] - range, y[which(index %in% i)] + range), "native")
)
tmpGrob <- grid::setGrob(
gTree = newGrob$children[[i]],
gPath = "point",
newGrob = pointGrob
)
newGrob$children[[i]] <<- grid::setGrob(
gTree = tmpGrob,
gPath = "range",
newGrob = line_grob
)
} else if(grepl(newGrob$children[[i]]$name, pattern = "text_glyph")) {
newGrob$children[[i]] <<- grid::editGrob(
grob = newGrob$children[[i]],
y = unit(x[which(index %in% i)], "native"),
x = unit(y[which(index %in% i)], "native")
)
} else if(grepl(newGrob$children[[i]]$name, pattern = "image_glyph")) {
imageBorderGrob <- grid::getGrob(newGrob$children[[i]], "image_border")
imageGrob <- grid::getGrob(newGrob$children[[i]], "image")
imageBorderGrob <- grid::editGrob(
imageBorderGrob,
y = unit(x[which(index %in% i)], "native"),
x = unit(y[which(index %in% i)], "native")
)
imageGrob <- grid::editGrob(
imageGrob,
y = unit(x[which(index %in% i)], "native"),
x = unit(y[which(index %in% i)], "native")
)
tmpGrob <- grid::setGrob(
gTree = newGrob$children[[i]],
gPath = "image_border",
newGrob = imageBorderGrob
)
newGrob$children[[i]] <<- grid::setGrob(
gTree = tmpGrob,
gPath = "image",
newGrob = imageGrob
)
} else stop("not implemented")
}
)
} else {
lapply(index,
function(i) {
if(grepl(newGrob$children[[i]]$name, pattern = "primitive_glyph")) {
newGrob$children[[i]] <<- grid::editGrob(
grob = newGrob$children[[i]],
x = unit(x[which(index %in% i)], "native"),
y = unit(y[which(index %in% i)], "native")
)
} else if(grepl(newGrob$children[[i]]$name, pattern = "serialaxes_glyph")) {
polyline_grob <- grid::getGrob(newGrob$children[[i]], "polyline")
if(is.null(polyline_grob)) {
polyline_grob <- grid::getGrob(newGrob$children[[i]], "polyline: showArea")
polyline_grob_name <- "polyline: showArea"
} else polyline_grob_name <- "polyline"
polyline_grob <- grid::editGrob(
polyline_grob,
x = unit(x[which(index %in% i)], "native") +
get_unit(polyline_grob$x,
is.unit = FALSE,
as.numeric = FALSE),
y = unit(y[which(index %in% i)], "native") +
get_unit(polyline_grob$y,
is.unit = FALSE,
as.numeric = FALSE)
)
newGrob$children[[i]] <<- grid::setGrob(
gTree = newGrob$children[[i]],
gPath = polyline_grob_name,
newGrob = polyline_grob
)
} else if(grepl(newGrob$children[[i]]$name, pattern = "polygon_glyph")) {
newGrob$children[[i]] <<- grid::editGrob(
grob = newGrob$children[[i]],
x = unit(x[which(index %in% i)], "native") +
get_unit(newGrob$children[[i]]$x,
is.unit = FALSE,
as.numeric = FALSE),
y = unit(y[which(index %in% i)], "native") +
get_unit(newGrob$children[[i]]$y,
is.unit = FALSE,
as.numeric = FALSE)
)
} else if(grepl(newGrob$children[[i]]$name, pattern = "pointrange_glyph")) {
pointGrob <- grid::getGrob(newGrob$children[[i]], "point")
line_grob <- grid::getGrob(newGrob$children[[i]], "range")
pointGrob <- grid::editGrob(
pointGrob,
x = unit(x[which(index %in% i)], "native"),
y = unit(y[which(index %in% i)], "native")
)
range <- diff(sort(as.numeric(line_grob$y)))/2
line_grob <- grid::editGrob(
line_grob,
x = unit(rep(x[which(index %in% i)], 2), "native"),
y = unit(c(y[which(index %in% i)] - range, y[which(index %in% i)] + range), "native")
)
tmpGrob <- grid::setGrob(
gTree = newGrob$children[[i]],
gPath = "point",
newGrob = pointGrob
)
newGrob$children[[i]] <<- grid::setGrob(
gTree = tmpGrob,
gPath = "range",
newGrob = line_grob
)
} else if(grepl(newGrob$children[[i]]$name, pattern = "text_glyph")) {
newGrob$children[[i]] <<- grid::editGrob(
grob = newGrob$children[[i]],
x = unit(x[which(index %in% i)], "native"),
y = unit(y[which(index %in% i)], "native")
)
} else if(grepl(newGrob$children[[i]]$name, pattern = "image_glyph")) {
imageBorderGrob <- grid::getGrob(newGrob$children[[i]], "image_border")
imageGrob <- grid::getGrob(newGrob$children[[i]], "image")
imageBorderGrob <- grid::editGrob(
imageBorderGrob,
x = unit(x[which(index %in% i)], "native"),
y = unit(y[which(index %in% i)], "native")
)
imageGrob <- grid::editGrob(
imageGrob,
x = unit(x[which(index %in% i)], "native"),
y = unit(y[which(index %in% i)], "native")
)
tmpGrob <- grid::setGrob(
gTree = newGrob$children[[i]],
gPath = "image_border",
newGrob = imageBorderGrob
)
newGrob$children[[i]] <<- grid::setGrob(
gTree = tmpGrob,
gPath = "image",
newGrob = imageGrob
)
} else stop("not implemented")
}
)
}
grid::setGrob(
gTree = loon.grob,
gPath = pointsTreeName,
newGrob = newGrob
)
} else loon.grob
}
move_jitter_grob.l_graph <- function(loon.grob, index, swap, jitterxy, temporary = FALSE, ...) {
if(length(index) == 0) return(loon.grob)
jx <- jitterxy$x
jy <- jitterxy$y
nodesGrob <- grid::getGrob(loon.grob, "graph nodes")
labelsGrob <- grid::getGrob(loon.grob, "graph labels")
edgesGrob <- grid::getGrob(loon.grob, "graph edges")
if(!temporary & swap) {
lapply(index,
function(i) {
nodesGrob$children[[i]] <<- grid::editGrob(
grob = nodesGrob$children[[i]],
x = unit(jy[which(index == i)], "native"),
y = unit(jx[which(index == i)], "native")
)
}
)
loon.grob <- grid::setGrob(
gTree = loon.grob,
gPath = "graph nodes",
newGrob = nodesGrob
)
if(!grepl(grobName(labelsGrob), pattern = "null")) {
lapply(index,
function(i) {
grobi <- labelsGrob$children[[i]]
labelsGrob$children[[i]] <<- grid::editGrob(
grob = grobi,
x = unit(jy[which(index == i)], "native") +
get_unit(grobi$y,
is.unit = FALSE,
as.numeric = FALSE),
y = unit(jx[which(index == i)], "native") +
get_unit(grobi$x,
is.unit = FALSE,
as.numeric = FALSE)
)
}
)
loon.grob <- grid::setGrob(
gTree = loon.grob,
gPath = "graph labels",
newGrob = labelsGrob
)
}
lapply(1:length(edgesGrob$children),
function(i) {
grobi <- edgesGrob$children[[i]]
if(!grepl(grobi$name,pattern = "missing")) {
to_id <- grobi$id
num_line <- length(to_id)/2
edgesGrob$children[[i]] <<- if(i %in% index) {
x <- c(rep(jy[which(index == i)], num_line), c(grobi$x)[(num_line + 1) : (2*num_line)])
y <- c(rep(jx[which(index == i)], num_line), c(grobi$y)[(num_line + 1) : (2*num_line)])
change_id <- which(to_id %in% index)[which(to_id %in% index) > num_line]
if(length(change_id) > 0) {
x[change_id] <- jy[which(index %in% to_id[change_id])]
y[change_id] <- jx[which(index %in% to_id[change_id])]
grid::editGrob(
grobi,
x = unit(x, "native"),
y = unit(y, "native")
)
} else {
grid::editGrob(
grobi,
x = unit(x, "native"),
y = unit(y, "native")
)
}
} else {
change_id <- which(to_id %in% index)[which(to_id %in% index) > num_line]
x <- c(grobi$x)
y <- c(grobi$y)
if(length(change_id) > 0) {
x[change_id] <- jy[which(index %in% to_id[change_id])]
y[change_id] <- jx[which(index %in% to_id[change_id])]
grid::editGrob(
grobi,
x = unit(x, "native"),
y = unit(y, "native")
)
} else grobi
}
}
}
)
loon.grob <- grid::setGrob(
gTree = loon.grob,
gPath = "graph edges",
newGrob = edgesGrob
)
} else {
lapply(index,
function(i) {
nodesGrob$children[[i]] <<- grid::editGrob(
grob = nodesGrob$children[[i]],
x = unit(jx[which(index == i)], "native"),
y = unit(jy[which(index == i)], "native")
)
}
)
loon.grob <- grid::setGrob(
gTree = loon.grob,
gPath = "graph nodes",
newGrob = nodesGrob
)
if(!grepl(grobName(labelsGrob),pattern = "null")) {
lapply(index,
function(i) {
grobi <- labelsGrob$children[[i]]
labelsGrob$children[[i]] <<- grid::editGrob(
grob = grobi,
y = unit(jy[which(index == i)], "native") +
get_unit(grobi$y,
is.unit = FALSE,
as.numeric = FALSE),
x = unit(jx[which(index == i)], "native") +
get_unit(grobi$x,
is.unit = FALSE,
as.numeric = FALSE)
)
}
)
loon.grob <- grid::setGrob(
gTree = loon.grob,
gPath = "graph labels",
newGrob = labelsGrob
)
}
lapply(1:length(edgesGrob$children),
function(i) {
grobi <- edgesGrob$children[[i]]
if(!grepl(grobi$name, pattern = "missing")) {
to_id <- grobi$id
num_line <- length(to_id)/2
edgesGrob$children[[i]] <<- if(i %in% index) {
y <- c(rep(jy[which(index == i)], num_line), c(grobi$y)[(num_line + 1) : (2*num_line)])
x <- c(rep(jx[which(index == i)], num_line), c(grobi$x)[(num_line + 1) : (2*num_line)])
change_id <- which(to_id %in% index)[which(to_id %in% index) > num_line]
if(length(change_id) > 0) {
x[change_id] <- jx[which(index %in% to_id[change_id])]
y[change_id] <- jy[which(index %in% to_id[change_id])]
grid::editGrob(
grobi,
x = unit(x, "native"),
y = unit(y, "native")
)
} else {
grid::editGrob(
grobi,
x = unit(x, "native"),
y = unit(y, "native")
)
}
} else {
change_id <- which(to_id %in% index)[which(to_id %in% index) > num_line]
x <- c(grobi$x)
y <- c(grobi$y)
if(length(change_id) > 0) {
x[change_id] <- jx[which(index %in% to_id[change_id])]
y[change_id] <- jy[which(index %in% to_id[change_id])]
grid::editGrob(
grobi,
x = unit(x, "native"),
y = unit(y, "native")
)
} else grobi
}
}
}
)
loon.grob <- grid::setGrob(
gTree = loon.grob,
gPath = "graph edges",
newGrob = edgesGrob
)
}
loon.grob
}
jitter_coord <- function(x, y, index) {
if(length(index) == 1) {
jitter_x <- x[index]
jitter_y <- y[index]
} else {
diff_x <- diff(sort(x[index]))
diff_y <- diff(sort(y[index]))
diff_x <- diff_x[diff_x > 1e-2]
diff_y <- diff_y[diff_y > 1e-2]
dx <- if(length(diff_x) == 0)
1e-2
else
min(diff_x)
dy <- if(length(diff_y) == 0)
1e-2
else
min(diff_y)
jitter_x <- rnorm(length(index), x[index], dx/5)
jitter_y <- rnorm(length(index), y[index], dx/5)
}
list(
x = jitter_x,
y = jitter_y
)
} |
library(RagGrid)
m = data.frame(a = 1, b = 2, c = 3)
aggrid(m)
aggrid(as.matrix(m)) |
library(LearnBayes)
data(cancermortality)
mycontour(betabinexch0,c(.0001,.003,1,20000),cancermortality,
xlab="eta",ylab="K")
S=readline(prompt="Type <Return> to continue : ")
windows()
mycontour(betabinexch,c(-8,-4.5,3,16.5),cancermortality,
xlab="logit eta",ylab="log K") |
multiverse_engine <- function(options) {
if(is.null(options$inside)) stop("A multiverse object should be specified with",
"a multiverse code block using the `inside` argument")
.multiverse_name = options$inside
if (is.character(.multiverse_name)) {
if ( !(.multiverse_name %in% ls(envir = knit_global()))) {
stop(
"Multiverse object `", .multiverse_name, "` was not found.\n",
"You may need to execute `", .multiverse_name, " <- multiverse()` to create the multiverse\n",
"before executing a multiverse code block."
)
}
.multiverse = get(.multiverse_name, envir = knit_global())
} else if (is.multiverse(.multiverse_name)) {
.multiverse = .multiverse_name
}
.c = multiverse_block_code(.multiverse, options$label, options$code)
if(is.null(getOption("knitr.in.progress"))) {
if (!is.null(getOption("execute"))) {
if (getOption("execute") == "all") {
execute_multiverse(.multiverse)
} else if (getOption("execute") == "default") {
execute_universe(.multiverse)
}
}
multiverse_default_block_exec(.c, options)
} else {
multiverse_default_block_exec(options$code, options, TRUE)
}
}
multiverse_block_code <- function(.multiverse, .label, .code) {
if (strsplit(.label, "-[0-9]+") == "unnamed-chunk") {
stop("Please provide a label to your multiverse code block")
}
if (!is(.multiverse, "multiverse")) {
stop("Objects passed to inside should be a multiverse object")
}
pasted <- paste(.code, collapse = "\n")
.expr <- parse(text = c("{", pasted, "}"), keep.source = FALSE)[[1]]
add_and_parse_code(.multiverse, .expr, .label)
.m_list = attr(.multiverse, "multiverse")$multiverse_diction$as_list()
if ( is.list(parameters(.multiverse)) & length(parameters(.multiverse)) == 0 ) {
.c = get_code_universe(.m_list = .m_list, .uni = 1, .level = length(.m_list))
} else {
idx = 1
.c = get_code_universe(.m_list = .m_list, .uni = idx, .level = length(.m_list))
}
deparse(.c[[.label]])
}
multiverse_default_block_exec <- function(.code, options, knit = FALSE) {
if (knit && options$eval) {
.multiverse = options$inside
options$engine = "R"
options$comment = ""
options$dev = 'png'
options_list <- lapply(1:size(.multiverse), function(x) {
temp_options <- options
temp_options$code = tidy_source(text = map_chr(
tail(head(deparse(expand(.multiverse)[[".code"]][[x]][[options$label]]), -1), -1),
~ gsub(pattern = " ", replacement = "", x = .)
))$text.tidy
if (x == 1) {
temp_options$class.source = paste0("multiverse universe-", x, " default")
temp_options$class.output = paste0("multiverse universe-", x, " default")
} else {
temp_options$class.source = paste0("multiverse universe-", x, "")
temp_options$class.output = paste0("multiverse universe-", x, "")
}
temp_options
})
eng_r = knit_engines$get("R")
unlist(lapply(options_list, eng_r))
} else {
code = .code[-c(1, length(.code))]
outputs = evaluate::evaluate(
code,
new_device = FALSE,
envir = knit_global()
)
outputs[sapply(outputs, function(x) is.character(x) || is_condition(x))]
}
}
knitr::knit_engines$set(multiverse = multiverse_engine) |
test_that("function_to_pmml('1+2') outputs correct xml", {
current <- function_to_pmml("1 + 2")
node <- newXMLNode(name = "Apply", attrs = c("function" = "+"))
c1 <- newXMLNode(name = "Constant", attrs = c("dataType" = "double"), text = "1")
c2 <- newXMLNode(name = "Constant", attrs = c("dataType" = "double"), text = "2")
expected <- addChildren(node, kids = c(c1, c2))
current_split <- strsplit(saveXML(current), split = "")[[1]]
expected_split <- strsplit(saveXML(expected), split = "")[[1]]
expect_equal(current_split, expected_split)
})
test_that("function_to_pmml('foo(bar(baz))') outputs correct xml", {
current <- function_to_pmml("foo(bar(baz))")
node <- newXMLNode(name = "Apply", attrs = c("function" = "foo"))
c1 <- newXMLNode(name = "Apply", attrs = c("function" = "bar"))
c2 <- newXMLNode(name = "FieldRef", attrs = c("field" = "baz"))
expected <- addChildren(node, addChildren(c1, c2))
current_split <- strsplit(saveXML(current), split = "")[[1]]
expected_split <- strsplit(saveXML(expected), split = "")[[1]]
expect_equal(current_split, expected_split)
})
test_that("function_to_pmml('1(2)') throws unexpected end of input error", {
expect_error(function_to_pmml("1(2"), regexp = "unexpected end of input")
})
test_that("function_to_pmml('-3') outputs correct xml", {
current <- function_to_pmml("-3")
node <- newXMLNode(name = "Apply", attrs = c("function" = "-"))
c1 <- newXMLNode(name = "Constant", attrs = c("dataType" = "double"), text = "0")
c2 <- newXMLNode(name = "Constant", attrs = c("dataType" = "double"), text = "3")
expected <- addChildren(node, kids = c(c1, c2))
current_split <- strsplit(saveXML(current), split = "")[[1]]
expected_split <- strsplit(saveXML(expected), split = "")[[1]]
expect_equal(current_split, expected_split)
})
test_that("function_to_pmml('-(44*a)') outputs correct xml", {
current <- function_to_pmml("-(44*a)")
node <- newXMLNode(name = "Apply", attrs = c("function" = "-"))
c1 <- newXMLNode(name = "Constant", attrs = c("dataType" = "double"), text = "0")
c1node <- newXMLNode(name = "Apply", attrs = c("function" = "*"))
c2 <- newXMLNode(name = "Constant", attrs = c("dataType" = "double"), text = "44")
c3 <- newXMLNode(name = "FieldRef", attrs = c("field" = "a"))
addChildren(c1node, kids = c(c2, c3))
expected <- addChildren(node, kids = c(c1, c1node))
current_split <- strsplit(saveXML(current), split = "")[[1]]
expected_split <- strsplit(saveXML(expected), split = "")[[1]]
expect_equal(current_split, expected_split)
})
test_that("function_to_pmml('-a') outputs correct xml", {
current <- function_to_pmml("-a")
node <- newXMLNode(name = "Apply", attrs = c("function" = "-"))
c1 <- newXMLNode(name = "Constant", attrs = c("dataType" = "double"), text = "0")
c2 <- newXMLNode(name = "FieldRef", attrs = c("field" = "a"))
expected <- addChildren(node, kids = c(c1, c2))
current_split <- strsplit(saveXML(current), split = "")[[1]]
expected_split <- strsplit(saveXML(expected), split = "")[[1]]
expect_equal(current_split, expected_split)
})
test_that("function_to_pmml('?3') throws error when ? is * or /", {
expect_error(function_to_pmml("*3"), regexp = "<text>:1:1: unexpected '*'")
expect_error(function_to_pmml("/3"), regexp = "<text>:1:1: unexpected '/'")
})
test_that("function_to_pmml outputs boolean TRUE/FALSE for if function", {
current <- function_to_pmml("if(out < t){TRUE} else {FALSE}")
c0node <- newXMLNode(name = "Apply", attrs = c("function" = "if"))
c1node <- newXMLNode(name = "Apply", attrs = c("function" = "lessThan"))
c2 <- newXMLNode(name = "FieldRef", attrs = c("field" = "out"))
c3 <- newXMLNode(name = "FieldRef", attrs = c("field" = "t"))
addChildren(c1node, kids = c(c2, c3))
c3 <- newXMLNode(name = "Constant", attrs = c("dataType" = "boolean"), text = TRUE)
c4 <- newXMLNode(name = "Constant", attrs = c("dataType" = "boolean"), text = FALSE)
expected <- addChildren(c0node, kids = c(c1node, c3, c4))
current_split <- strsplit(saveXML(current), split = "")[[1]]
expected_split <- strsplit(saveXML(expected), split = "")[[1]]
expect_equal(current_split, expected_split)
}) |
sar_mmf <- function(data, start = NULL,
grid_start = "partial", grid_n = NULL,
normaTest = "none", homoTest =
"none", homoCor = "spearman", verb = TRUE){
.Deprecated()
if (!(is.matrix(data) | is.data.frame(data)))
stop('data must be a matrix or dataframe')
if (is.matrix(data)) data <- as.data.frame(data)
if (anyNA(data)) stop('NAs present in data')
normaTest <- match.arg(normaTest, c('none', 'shapiro', 'kolmo',
'lillie'))
homoTest <- match.arg(homoTest, c('none', 'cor.area',
'cor.fitted'))
if (homoTest != 'none'){
homoCor <- match.arg(homoCor, c('spearman', 'pearson',
'kendall'))
}
if (!(grid_start %in% c('none', 'partial', 'exhaustive'))){
stop('grid_start should be one of none, partial or exhaustive')
}
if (grid_start == 'exhaustive'){
if (!is.numeric(grid_n))
stop('grid_n should be numeric if grid_start == exhaustive')
}
if (!is.logical(verb)){
stop('verb should be logical')
}
data <- data[order(data[,1]),]
colnames(data) <- c('A','S')
xr <- range(data$S)/mean(data$S)
if (isTRUE(all.equal(xr[1], xr[2]))) {
if (data$S[1] == 0){
warning('All richness values are zero: parameter estimates of',
' non-linear models should be interpreted with caution')
} else{
warning('All richness values identical')
}}
model <- list(
name=c("MMF"),
formula=expression(S==d/(1+c*A^(-z))),
exp=expression(d/(1+c*A^(-z))),
shape="convex/sigmoid",
asymp=function(pars)pars["d"],
parLim = c("Rplus","Rplus","Rplus"),
custStart=function(data)c(max(data$S),5,.25),
init=function(data){
if(any(data$S==0)){data=data[data$S!=0,]}
d=(max(data$S)*4)
newVar = log((d/data$S) - 1)
reg = stats::lm(newVar~log(data$A))
c=exp(reg$coefficients[1])
z=-reg$coefficients[2]
c(d,c,z)
}
)
model <- compmod(model)
fit <- get_fit(model = model, data = data, start = start,
grid_start = grid_start, grid_n = grid_n, algo = 'Nelder-Mead',
normaTest = normaTest, homoTest = homoTest,
homoCor = homoCor, verb = verb)
if(is.na(fit$value)){
return(list(value = NA))
}else{
obs <- obs_shape(fit, verb = verb)
fit$observed_shape <- obs$fitShape
fit$asymptote <- obs$asymp
fit$neg_check <- any(fit$calculated < 0)
class(fit) <- 'sars'
attr(fit, 'type') <- 'fit'
return(fit)
}
} |
delay.pert<-function(duration,prec1and2=matrix(0),prec3and4=matrix(0),observed.duration,delta=NULL,cost.function=NULL){
or1<-order(organize(prec1and2,prec3and4)$Order[,2])
or2<-organize(prec1and2,prec3and4)$Order[,2]
precedence<-organize(prec1and2,prec3and4)$Precedence
duration<-duration[or2]
observed.duration<-observed.duration[or2]
activities<-1:length(duration)
n<-length(activities)
SD<-numeric(length(activities))
w<-numeric(2^n-1)
tiempo.early<-rep(0,n)
ii<-as.logical(colSums(precedence))
iii<-activities[ii]
nn<-length(iii)
if(nn>0){
prec<-matrix(0,nrow=nn,ncol=n-1)
for(j in 1:length(iii)){
prec[j,1:length(which(precedence[,iii[j]]==1))]<-which(precedence[,iii[j]]==1)
}
prec<-prec[,as.logical(colSums(prec)),drop=FALSE]
for(i in 1:length(iii)) {
tiempo.early[iii[i]]=max(tiempo.early[prec[i,]]+duration[prec[i,]]);
}
}
tiempo<-max(tiempo.early+duration)
if(is.null(delta)==FALSE){tiempo<-delta}
if(nn>0){
for(i in 1:nn) {
tiempo.early[iii[i]]=max(tiempo.early[prec[i,]]+observed.duration[prec[i,]]);
}
}
tiempo.observado<-max(tiempo.early+observed.duration)
{
if(is.null(cost.function)==FALSE){
cat("There has been a delay of = ", cost.function(tiempo.observado), "\n")
Prop<-((observed.duration-duration)/sum(observed.duration-duration))*(cost.function(tiempo.observado))
TProp<-(pmin(observed.duration-duration,cost.function(tiempo.observado))/sum(pmin(observed.duration-duration,cost.function(tiempo.observado))))*(cost.function(tiempo.observado))
}
else{
cat("There has been a delay of = ", tiempo.observado-tiempo, "\n")
Prop<-((observed.duration-duration)/sum(observed.duration-duration))*(tiempo.observado-tiempo)
TProp<-(pmin(observed.duration-duration,tiempo.observado-tiempo)/sum(pmax(observed.duration-duration,tiempo.observado-tiempo)))*(tiempo.observado-tiempo)
}
}
for(j in 1:length(activities)){
duracion1<-duration
duracion1[j]<-observed.duration[j]
if(nn>0){
for(i in 1:nn) {
tiempo.early[iii[i]]=max(tiempo.early[prec[i,]]+duracion1[prec[i,]]);
}
}
if(is.null(cost.function)==FALSE){w[j]<-cost.function(max(tiempo.early+duracion1))}
SD[j]<-max(tiempo.early+duracion1)
}
{
if(length(activities)>10){
cat("shapley need some time to compute a", n, "player game \n")
cat("Continue calculation of the Shapley value based on sampling? (Y = Yes, N = No) \n")
continue <- scan(what = "character", n = 1)
if(continue=="Y"){
contador<-0
sh<-numeric(length(activities))
while(contador<=1000){
x<-sample(length(activities))
for(i in 1:n){
duracion1<-duration
duracion1[x[1:i]]<-observed.duration[x[1:i]]
if(nn>0){
for(j in 1:nn) {
tiempo.early[iii[j]]=max(tiempo.early[prec[j,]]+duracion1[prec[j,]])
}
}
{
if(is.null(cost.function)==FALSE){
tiempo2[i]<-cost.function(max(tiempo.early+duracion1))
}
else{
tiempo2[i]<-max(tiempo.early+duracion1)-tiempo
}
}
}
sh[x[1]]<-sh[x[1]]+tiempo2[1]
sh[x[2:n]]<-sh[x[2:n]]+tiempo2[2:n]-tiempo2[1:(n-1)]
contador<-contador+1
}
sh<-sh/contador
}
}
else{
v<-numeric(2^n-1)
v[1:n]<-SD[1:n]
continue<-"Y"
p<-n+1
for(j in 2:n){
con<-as.matrix(combn(c(1:n),j))
for(z in 1:dim(con)[2]){
duracion1<-duration
duracion1[con[,z]]<-observed.duration[con[,z]]
if(nn>0){
for(i in 1:length(iii)) {
tiempo.early[iii[i]]=max(tiempo.early[prec[i,]]+duracion1[prec[i,]])
}
}
if(is.null(cost.function)==FALSE){w[p]<-cost.function(max(tiempo.early+duracion1))}
v[p]<-max(tiempo.early+duracion1)
p<-p+1
}
}
v<-v-tiempo
{
if(is.null(cost.function)==FALSE){
z<-as.matrix(DefineGame(n,w)$Lex)
z<-as.vector(z)
coalitions <- set.func(c(0, z))
sh <- Shapley.value(coalitions)
}
else{
z<-as.matrix(DefineGame(n,v)$Lex)
z<-as.vector(z)
coalitions <- set.func(c(0, z))
sh <- Shapley.value(coalitions)
}
}
}
}
{
if(continue=="Y"){
A<-matrix(c(Prop[or1],TProp[or1],sh[or1]),ncol=length(activities),byrow=TRUE)
colnames(A)=c(activities)
rownames(A)=c("The proportional payment ","The truncated proportional payment ", "Shapley rule")
cat(" ","\n")
return(round(A,5))
}
else{
A<-matrix(c(Prop[or1],TProp[or1]),ncol=length(activities),byrow=TRUE)
colnames(A)=c(activities)
rownames(A)=c("The proportional payment ","The truncated proportional payment ")
cat(" ","\n")
return(round(A,5))
}
}
} |
summary.mvProbitMargEff <- function( object, ... ) {
nObs <- nrow( object )
nMargEff <- ncol( object )
result <- matrix( NA, nrow = nObs * nMargEff, ncol = 4 )
if( nObs == 1 ) {
rownames( result ) <- colnames( object )
} else {
rownames( result ) <- paste(
rep( rownames( object ), each = nMargEff ), ": ",
rep( colnames( object ), nObs ), sep = "" )
}
colnames( result ) <- c( "Estimate", "Std. error", "z value", "Pr(> z)" )
result[ , 1 ] <- c( t( as.matrix( object ) ) )
margEffVCov <- attr( object, "vcov" )
if( !is.null( margEffVCov ) ) {
for( i in 1:nObs ) {
result[ ( (i-1) * nMargEff + 1 ):( i * nMargEff ), 2 ] <-
sqrt( diag( margEffVCov[ i, , ] ) )
}
result[ , 3 ] <- result[ , 1 ] / result[ , 2 ]
result[ , 4 ] <- 2 * pnorm( -abs( result[ , 3 ] ) )
}
class( result ) <- c( "summary.mvProbitMargEff", class( result ) )
return( result )
} |
getEurostatRaw <-
function(kod = "educ_iste", rowRegExp=NULL, colRegExp=NULL, strip.white = TRUE) {
adres <- paste("http://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing?sort=1&file=data%2F",kod,".tsv.gz",sep="")
tfile <- tempfile()
download.file(adres, tfile)
dat <- read.table(gzfile(tfile), sep="\t", na.strings = ": ", header=FALSE, stringsAsFactors=FALSE, strip.white = strip.white)
unlink(tfile)
colnames(dat) <- as.character(dat[1,])
dat <- dat[-1,]
if (!is.null(rowRegExp)) {
dat <- dat[grep(dat[,1], pattern=rowRegExp),,drop=FALSE]
}
if (!is.null(colRegExp)) {
dat <- dat[,union(1, grep(colnames(dat), pattern=colRegExp)),drop=FALSE]
}
for (i in 2:ncol(dat)) {
tmp <- sapply(strsplit(as.character(dat[,i]), split = ' '), `[`, 1)
tmp[tmp==":"] = NA
dat[,i] <-as.numeric(tmp)
}
dat
} |
PipeOpEncodeImpact = R6Class("PipeOpEncodeImpact",
inherit = PipeOpTaskPreprocSimple,
public = list(
initialize = function(id = "encodeimpact", param_vals = list()) {
ps = ParamSet$new(params = list(
ParamDbl$new("smoothing", 0, Inf, tags = c("train", "required")),
ParamLgl$new("impute_zero", tags = c("train", "required"))
))
ps$values = list(smoothing = 1e-4, impute_zero = FALSE)
super$initialize(id, param_set = ps, param_vals = param_vals, tags = "encode", feature_types = c("factor", "ordered"))
}
),
private = list(
.get_state_dt = function(dt, levels, target) {
task_type = if (is.numeric(target)) "regr" else "classif"
state = list()
smoothing = self$param_set$values$smoothing
list(impact = switch(task_type,
classif = sapply(dt, function(col)
sapply(levels(target), function(tl) {
tprop = (sum(target == tl) + smoothing) / (length(target) + 2 * smoothing)
tplogit = log(tprop / (1 - tprop))
map_dbl(c(stats::setNames(levels(col), levels(col)), c(.TEMP.MISSING = NA)),
function(cl) {
if (!self$param_set$values$impute_zero && is.na(cl)) return(NA_real_)
condprob = (sum(target[is.na(cl) | col == cl] == tl, na.rm = TRUE) + smoothing) /
(sum(is.na(cl) | col == cl, na.rm = TRUE) + 2 * smoothing)
cplogit = log(condprob / (1 - condprob))
cplogit - tplogit
})
}), simplify = FALSE),
regr = {
meanimp = mean(target)
sapply(dt, function(col)
t(t(c(sapply(levels(col), function(lvl) {
(sum(target[col == lvl], na.rm = TRUE) + smoothing * meanimp) /
(sum(col == lvl, na.rm = TRUE) + smoothing) - meanimp
}), if (self$param_set$values$impute_zero) c(.TEMP.MISSING = 0) else c(.TEMP.MISSING = NA)))), simplify = FALSE)
}))
},
.transform_dt = function(dt, levels) {
impact = self$state$impact
imap(dt, function(curdat, idx) {
curdat = as.character(curdat)
curdat[is.na(curdat)] = ".TEMP.MISSING"
curdat[curdat %nin% rownames(impact[[idx]])] = ".TEMP.MISSING"
impact[[idx]][match(curdat, rownames(impact[[idx]])), , drop = is.null(colnames(impact[[idx]]))]
})
}
)
)
mlr_pipeops$add("encodeimpact", PipeOpEncodeImpact) |
require(graph)
require(eulerian)
g <- new("graphNEL", nodes=as.character(1:10), edgemode="directed")
g <- addEdge(graph=g, from="1", to="10")
g <- addEdge(graph=g, from="2", to="1")
g <- addEdge(graph=g, from="2", to="6")
g <- addEdge(graph=g, from="3", to="2")
g <- addEdge(graph=g, from="4", to="2")
g <- addEdge(graph=g, from="5", to="4")
g <- addEdge(graph=g, from="6", to="5")
g <- addEdge(graph=g, from="6", to="8")
g <- addEdge(graph=g, from="7", to="9")
g <- addEdge(graph=g, from="8", to="7")
g <- addEdge(graph=g, from="9", to="6")
g <- addEdge(graph=g, from="10", to="3")
testNum <- 1
cat("Test-", testNum, ": ", sep="")
has <- hasEulerianPath(g)
msg <- ifelse(has==TRUE, "passed", "failed!!!")
cat(msg, "\n")
cat("Test-", testNum <- testNum + 1, ": ", sep="")
has <- hasEulerianCycle(g)
msg <- ifelse(has==TRUE, "passed", "failed!!!")
cat(msg, "\n")
cat("Test-", testNum <- testNum + 1, ": ", sep="")
epath <- eulerian(g)
msg <- ifelse(length(epath)==numEdges(g)+1, "passed", "failed!!!")
cat(msg, "\n")
cat("
g <- addEdge(graph=g, from="5", to="6")
cat("Test-", testNum <- testNum + 1, ": ", sep="")
has <- hasEulerianPath(g)
msg <- ifelse(has==TRUE, "passed", "failed!!!")
cat(msg, "\n")
cat("Test-", testNum <- testNum + 1, ": ", sep="")
has <- hasEulerianPath(g, "5")
msg <- ifelse(has==TRUE, "passed", "failed!!!")
cat(msg, "\n")
cat("Test-", testNum <- testNum + 1, ": ", sep="")
has <- hasEulerianPath(g, "6")
msg <- ifelse(has==FALSE, "passed", "failed!!!")
cat(msg, "\n")
cat("Test-", testNum <- testNum + 1, ": ", sep="")
has <- hasEulerianCycle(g)
msg <- ifelse(has==FALSE, "passed", "failed!!!")
cat(msg, "\n")
cat("Test-", testNum <- testNum + 1, ": ", sep="")
epath <- eulerian(g, "5")
msg <- ifelse(length(epath)==numEdges(g)+1, "passed", "failed!!!")
cat(msg, "\n")
cat("Test-", testNum <- testNum + 1, ": ", sep="")
epath <- tryCatch(eulerian(g, "7"), error = function(e) NA);
msg <- ifelse(is.na(epath), "passed", "failed!!!")
cat(msg, "\n")
cat("
g <- new("graphNEL", nodes=LETTERS[6:1], edgemode="undirected")
g <- addEdge(graph=g, from=c("A","B","B","C","D"), to=c("B","C","D","E","E"))
cat("Test-", testNum <- testNum + 1, ": ", sep="")
has <- hasEulerianPath(g)
msg <- ifelse(has==TRUE, "passed", "failed!!!")
cat(msg, "\n")
cat("Test-", testNum <- testNum + 1, ": ", sep="")
has <- hasEulerianPath(g, "A")
msg <- ifelse(has==TRUE, "passed", "failed!!!")
cat(msg, "\n")
cat("Test-", testNum <- testNum + 1, ": ", sep="")
has <- hasEulerianPath(g, "B")
msg <- ifelse(has==TRUE, "passed", "failed!!!")
cat(msg, "\n")
cat("Test-", testNum <- testNum + 1, ": ", sep="")
has <- hasEulerianPath(g, "C")
msg <- ifelse(has==FALSE, "passed", "failed!!!")
cat(msg, "\n")
cat("Test-", testNum <- testNum + 1, ": ", sep="")
has <- hasEulerianPath(g, "F")
msg <- ifelse(has==FALSE, "passed", "failed!!!")
cat(msg, "\n")
cat("Test-", testNum <- testNum + 1, ": ", sep="")
has <- hasEulerianCycle(g)
msg <- ifelse(has==FALSE, "passed", "failed!!!")
cat(msg, "\n")
cat("Test-", testNum <- testNum + 1, ": ", sep="")
epath <- eulerian(g, "B")
msg <- ifelse(length(epath)==numEdges(g)+1 && epath[1]=="B", "passed", "failed!!!")
cat(msg, "\n")
cat("Test-", testNum <- testNum + 1, ": ", sep="")
epath <- tryCatch(eulerian(g, "C"), error = function(e) NA);
msg <- ifelse(is.na(epath), "passed", "failed!!!")
cat(msg, "\n")
cat("
g <- new("graphNEL", nodes=LETTERS[1:4], edgemode="undirected")
g <- addEdge(graph=g, from=c("A","B","C","D"), to=c("B","A","D","C"))
cat("Test-", testNum <- testNum + 1, ": ", sep="")
has <- hasEulerianPath(g)
msg <- ifelse(has==FALSE, "passed", "failed!!!")
cat(msg, "\n")
cat("Test-", testNum <- testNum + 1, ": ", sep="")
has <- hasEulerianCycle(g)
msg <- ifelse(has==FALSE, "passed", "failed!!!")
cat(msg, "\n")
cat("Test-", testNum <- testNum + 1, ": ", sep="")
epath <- tryCatch(eulerian(g, "C"), error = function(e) NA);
msg <- ifelse(is.na(epath), "passed", "failed!!!")
cat(msg, "\n")
cat("
g <- new("graphNEL", nodes=LETTERS[1:5], edgemode="undirected")
g <- addEdge(graph=g, from=c("A","B","B","C", "C", "D", "C","C"), to=c("B","C","D","E", "C", "E","C","C"))
cat("Test-", testNum <- testNum + 1, ": ", sep="")
has <- hasEulerianPath(g)
msg <- ifelse(has==TRUE, "passed", "failed!!!")
cat(msg, "\n")
cat("Test-", testNum <- testNum + 1, ": ", sep="")
has <- hasEulerianCycle(g)
msg <- ifelse(has==FALSE, "passed", "failed!!!")
cat(msg, "\n")
cat("Test-", testNum <- testNum + 1, ": ", sep="")
epath <- tryCatch(eulerian(g, "B"), error = function(e) NA);
msg <- ifelse(length(epath)==numEdges(g)+1 && epath[1]=="B", "passed", "failed!!!")
cat(msg, "\n") |
"greenhouse_gases" |
na.bootstrap <- function(.x, ... ) {
na.replace( .x, .na=function(x, ...) sample( na.omit(x), replace=TRUE, ...) )
}
na.resample <- na.bootstrap |
context("Latexify")
test_that("Escaped hash", expect_true(latexify("\\
test_that("Escaped caret", expect_true(latexify("\\^") == "\\textasciicircum{}"))
test_that("Escaped underscore", expect_true(latexify("_") == "\\_"))
test_that("Escaped backtick", expect_true(latexify("\\`") == "\\textasciigrave{}"))
test_that("Escaped tilde", expect_true(latexify("\\~") == "\\textasciitilde{}"))
test_that("percent", expect_true(latexify("100%") == "100\\%"))
test_that("endash", expect_true(latexify("--") == "\\textendash{}"))
test_that("emdash", expect_true(latexify("---") == "\\textemdash{}"))
test_that("ellipsis", expect_true(latexify("...") == "\\ldots{}"))
test_that("greater", expect_true(latexify(">") == "\\textgreater{}"))
test_that("less", expect_true(latexify("<") == "\\textless{}"))
test_that("strikethrough", expect_true(latexify("This is ~~text~~!") == "This is \\sout{text}!"))
test_that("subscript", expect_true(latexify("See footnote~123~.") == "See footnote\\textsubscript{123}."))
test_that("superscript", expect_true(latexify("See authors^123^.") == "See authors\\textsuperscript{123}."))
test_that("header 1", expect_true(latexify("
test_that("header 2", expect_true(latexify("
test_that("header 3", expect_true(latexify("
test_that("header 4", expect_true(latexify("
test_that("header 5", expect_true(latexify("
test_that("header 6", expect_true(latexify("
test_that("regex specials", expect_true(latexify(".?+()-") == ".?+()-")) |
??? Try to find explanation of this -- too messy as is.
L9<-40
m<-40
if (m>L9) stop("Not enough data points")
lo<-c(0.0001, 0.001, 0.0001, -100, 0.0001, 0.0001)
up<-c(1, 100, 100, -0.001, 1, 1)
start<-c(b1=0.100, b2=2, b3=2, b4=-3, b5=-0.2, b6=2)
y1<-c(0.2, 0.4, 0.8, 1, 1.6, 2, 3, 4, 0.2, 0.4, 0.8, 1,
1.6, 2, 3, 4, 0.2, 0.4, 0.8, 1, 1.6, 2, 3, 4, 0.2,
0.4, 0.8, 1, 1.6, 2, 3, 4, 0.2, 0.4, 0.8, 1, 1.6, 2, 3, 4)
y2<-c(0.027, 0.052, 0.09, 0.098, 0.1, 0.099, 0.096, 0.092,
0.024, 0.045, 0.076, 0.081, 0.083, 0.082, 0.08, 0.078,
0.02, 0.038, 0.061, 0.063, 0.065, 0.065, 0.064, 0.064,
0.016, 0.029, 0.043, 0.045, 0.047, 0.047, 0.048, 0.049,
0.011, 0.019, 0.027, 0.028, 0.03, 0.031, 0.033, 0.035)
y3<-c(0, 0, 0, 0, 0, 0, 0, 0, -0.5, -0.5, -0.5, -0.5, -0.5,
-0.5, -0.5, -0.5, -1, -1, -1, -1, -1, -1, -1, -1,
-1.5, -1.5, -1.5, -1.5, -1.5, -1.5, -1.5, -1.5,
-2, -2, -2, -2, -2, -2, -2, -2)
kandat<-data.frame(y1=y1, y2=y2, y3=y3)
formula <- "y
3500 LET I3=0: REM start with computable function
3510 LET Z1=Y(I,1)-Y(I,2)*(b2+b3): REM VDS-ADJUSTED
3515 LET Z2=b4+b5*Z1
3520 IF ABS(Z2)<.00001 THEN 3800: REM AVOID ZERO DIVIDE
3530 LET Z3=Y(I,3)-Y(I,2)*b2-Z2
3540 IF ABS(Z3)<0.00001 THEN 3830
3550 LET Z4=b6*(Y(I,1)-Y(I,2)*(b2+b3))
3560 LET Z5=Z4/Z3
3565 IF ABS(Z5)>30 THEN 3860: REM AVOID LARGE EXPONENT
3570 LET Z6=EXP(Z5)
3575 LET Z7=1/Z6
3580 LET Z8=(Z6-Z7)/(Z6+Z7)
3590 LET Z9=1-(Y(I,3)-Y(I,2)*b2)/Z2
3600 LET Z0=b1*Z9*Z9*Z8
3610 LET Y(I,5)=Z0: REM SAVE MODEL VALUE
3620 LET R1=(Z0-Y(I,2))*Y(I,4)
3625 LET YEXP=Z0*Y(I,4): REM note scaling
3630 LET Y(I,6)=R1: REM SAVE RESIDUAL
3640 RETURN
3800 PRINT "Vp0 + gamma*(Vds-Id*(Rs+Rd)) near zero =";Z2;
3802 PRINT " data point ";I
3810 PRINT
3812 PRINT
3820 GOTO 3950
3830 PRINT "Denominator of tanh argument near zero = ";Z3;
3832 PRINT " data point ";I
3840 PRINT
3842 PRINT
3850 GOTO 3950
3860 PRINT "Argument of exp() too large = ";Z5;" data point ";I
3870 PRINT
3880 GOTO 3950
3950 LET I3=1: REM cannot compute the residual
3960 RETURN
4000 PRINT "DERIVATIVES NOT DEFINED"
4010 PRINT
4030 STOP
4500 PRINT "Model for small Vds"
4520 PRINT
4530 PRINT "Data","Model","Residual"
4540 PRINT
4550 FOR I=1 TO 40 STEP 8
4560 PRINT Y(I,2),Y(I,5),Y(I,6)
4570 PRINT
4580 NEXT I
4590 PRINT
4600 PRINT
4633 PRINT "number of data points available =";L9
4634 PRINT
4635 INPUT "How many points to use in fit ";M
4636 IF M>L9 THEN 3035
4637 PRINT
4640 LET N=6: REM 3 parameters
4650 RETURN |
model_goodness_for_algo_crm <- function(mod, num=1){
actpred <- actual_vs_predicted_crm(mod, num)
dif_vals <- apply(actpred, 1, function(x) abs(diff(x)) )
len <- 100
arr <- rep(0, len)
for(i in 1:len){
k <- (i-1)/100
arr[i] <- length(which(dif_vals<=k))/length(dif_vals)
}
x <- seq(0, 1, length.out = (len))
y <- c(arr)
auc <- pracma::trapz(x, y)
mse <- mean(dif_vals^2)
out <- list()
out$xy <- cbind(x,y)
out$auc <- auc
out$mse <- mse
return(out)
}
actual_vs_predicted_crm <- function(mod, num=1, min_item=0, max_item=1){
dat <- mod$data
algo_vals <- dat[ ,num]
act_prd <- matrix(0, ncol=2, nrow=length(algo_vals))
act_prd[ ,1] <- algo_vals
colnames(act_prd) <- c("Actual", "Preds")
paras <- mod$param
alpha <- paras[num, 1]
beta <- paras[num, 2]
gamma <- paras[num, 3]
min.item <- rep(min_item, dim(dat)[2])
max.item <- rep(max_item, dim(dat)[2])
persons <- EstCRM::EstCRMperson(dat,paras,min.item,max.item)
scores <- persons$thetas[ ,2]
z_vals <- log(algo_vals/(max_item - algo_vals))
theta <- seq(from = min(scores), to=max(scores), by=0.01)
z <- seq(from = -6, to=6, by=0.01)
theta_z <- expand.grid(theta, z)
colnames(theta_z) <- c("theta", "z")
pdf <- alpha*gamma/sqrt(2*pi)*exp(-alpha^2/2*(theta_z[ ,1]-beta-gamma*theta_z[ ,2])^2)
pdfdf <- cbind.data.frame(theta_z, pdf)
for(i in 1:length(scores)){
ind1 <- which.min(abs(pdfdf[ ,1] - scores[i]))
min_val <- pdfdf[ind1, 1]
inds <- which(pdfdf[ ,1] == min_val)
df2 <- pdfdf[inds, ]
val <- df2[which.max(df2[, 3]) ,2]
act_prd[i, 2] <- max_item*exp(val)/(1 + exp(val))
}
return(act_prd)
}
model_goodness_crm <- function(mod){
dd <- dim(mod$data)[2]
mse <- acc <- matrix(0, ncol=1, nrow=dd)
for(i in 1:dd){
oo <- model_goodness_for_algo_crm(mod, num=i)
acc[i, 1] <- oo$auc
mse[i, 1] <- oo$mse
if(i==1){
curves <- matrix(0, ncol= (dd+1), nrow=dim(oo$xy)[1])
curves[ ,1] <- oo$xy[ ,1]
}
curves[ ,(i+1)] <- oo$xy[ ,2]
}
colnames(curves) <- c("x", rownames(mod$param))
rownames(acc) <-rownames(mod$param)
out <- list()
out$goodnessAUC <- acc
out$mse <- mse
out$curves <- curves
return(out)
} |
"GetCurves" <- function (x, xnewdata, xtfnewdata, tgrid = NULL, ygrid = NULL,
frail = NULL, CI = 0.95, PLOT = TRUE, ...) {
if(is(x,"anovaDDP")){
estimates = plot.anovaDDP(x, xnewdata, tgrid=tgrid, CI=CI, PLOT=PLOT, ...)
}else if(is(x,"spCopulaDDP")){
estimates = plot.spCopulaDDP(x, xnewdata, tgrid=tgrid, CI=CI, PLOT=PLOT, ...)
}else if(is(x,"indeptCoxph")){
estimates = plot.indeptCoxph(x, xnewdata, tgrid=tgrid, CI=CI, PLOT=PLOT, ...)
}else if(is(x,"spCopulaCoxph")){
estimates = plot.spCopulaCoxph(x, xnewdata, tgrid=tgrid, CI=CI, PLOT=PLOT, ...)
}else if(is(x, "survregbayes")){
estimates = plot.survregbayes(x, xnewdata, tgrid=tgrid, frail=frail, CI=CI, PLOT=PLOT, ...)
}else if(is(x, "frailtyGAFT")){
estimates = plot.frailtyGAFT(x, xnewdata, xtfnewdata, tgrid=tgrid, frail=frail, CI=CI, PLOT=PLOT, ...)
}else if(is(x, "SuperSurvRegBayes")){
estimates = plot.SuperSurvRegBayes(x, xnewdata, tgrid=tgrid, CI=CI, PLOT=PLOT, ...)
}else if(is(x, "SpatDensReg")){
estimates = plot.SpatDensReg(x, xnewdata, ygrid=ygrid, CI=CI, PLOT=PLOT, ...)
}
invisible(estimates)
} |
cpg.GC <-
function(x) {
if(class(x)%in% c("cpg","cpg.perm")) {
holm.values<-p.adjust(x$results$gc.p.value,"holm")
num.holm<-sum(holm.values<.05,na.rm=TRUE)
holm.ind<-ifelse(holm.values<.05,TRUE,FALSE)
if(sum(is.na(x$results[,3]))>0) {
holm.ind[which(is.na(x$results[,3]))]<-FALSE
}
fdr.method<-x$info$FDR.method
if (fdr.method=="qvalue") {
if (!requireNamespace("qvalue", quietly = TRUE)) {
stop("qvalue needed for this to work. Please install it.",
call. = FALSE)
}
fdr.adj<-tryCatch(qvalue::qvalue(x$results$gc.p.value), error = function(e) NULL)
if(is.null(fdr.adj)) {
fdr.adj <- tryCatch(qvalue::qvalue(x$results$gc.p.value, pi0.method = "bootstrap"),
error = function(e) NULL)
if(is.null(fdr.adj)) {
fdr.method="BH"
}}}
if(fdr.method!="qvalue") {
fdr.adj<-p.adjust(x$results$gc.p.value,fdr.method)
}
num.fdr<-sum(fdr.adj<.05,na.rm=TRUE)
beta.col<-nrow(x$results)
levin<-is.factor(x$indep)
n1<-coef(x)[1,1]
gcvalue<-median(n1*ifelse(rep(levin,beta.col),x$results[,2],x$results[,2]**2),na.rm=TRUE)/qchisq(.5,n1)
gcvalue<-ifelse(gcvalue<1,1,gcvalue)
if(levin) {
adj.test.stat<-x$results[,2]/gcvalue
}
else{
adj.test.stat<-sqrt(x$results[,2]**2/gcvalue)*sign(x$results[,2])
}
gc.results<-data.frame(x$results[,1],adj.test.stat,x$results$gc.p.value,holm.ind,fdr.adj,stringsAsFactors=FALSE)
names(gc.results)<-c("CPG.Labels","GC.Adjusted","Adjust.P.value","Adj.Holm","Adj.FDR")
gc.info<-data.frame(num.holm=num.holm,FDR.method=fdr.method,num.fdr=num.fdr,gcvalue=gcvalue,stringsAsFactors=FALSE)
gc.ev<-list(gc.results=gc.results,gc.info=gc.info,coefficients=coef(x))
if(class(x) %in% "cpg.perm") {
perm.p.gc<-sum(x$gc.permutation.matrix[,1] <= min(gc.results[,3],na.rm=TRUE))/x$perm.p.values$nperm
perm.holm.gc<-sum(x$gc.permutation.matrix[,2] >= gc.info[,1])/x$perm.p.values$nperm
perm.fdr.gc<-sum(x$gc.permutation.matrix[,3] >= gc.info[,3])/x$perm.p.values$nperm
gc.info<-data.frame(gc.info,perm.p.gc,perm.holm.gc,perm.fdr.gc,stringsAsFactors=FALSE)
gc.ev<-list(gc.results=gc.results,gc.info=gc.info,coefficients=coef(x))
}
class(gc.ev)<-ifelse(class(x)=="cpg","cpg.gc","cpg.perm.gc")
gc.ev
}
} |
NULL
setMethod("initialize", "Table", function(.Object, ...) {
args <- list(...)
args <- .emptyDefault(args, .TableSelected, NA_character_)
args <- .emptyDefault(args, .TableSearch, "")
do.call(callNextMethod, c(list(.Object), args))
})
setValidity2("Table", function(object) {
msg <- character(0)
msg <- .singleStringError(msg, object, .TableSelected)
msg <- .validStringError(msg, object, .TableSearch)
if (length(msg)) {
return(msg)
}
TRUE
})
setMethod(".refineParameters", "Table", function(x, se) {
x <- callNextMethod()
if (is.null(x)) {
return(NULL)
}
x
})
setMethod(".multiSelectionCommands", "Table", function(x, index) {
search <- slot(x, .TableSearch)
searchcols <- slot(x, .TableColSearch)
sprintf("selected <- rownames(contents)[iSEE::filterDT(contents, global=%s,\n column=%s)]",
deparse(search),
.deparse_for_viewing(searchcols, indent=2))
})
setMethod(".multiSelectionActive", "Table", function(x) {
if (slot(x, .TableSearch)!="" || any(slot(x, .TableColSearch)!="")) {
list(Search=slot(x, .TableSearch), ColumnSearch=slot(x, .TableColSearch))
} else {
NULL
}
})
setMethod(".multiSelectionRestricted", "Table", function(x) TRUE)
setMethod(".singleSelectionValue", "Table", function(x, contents) {
slot(x, .TableSelected)
})
setMethod(".defineOutput", "Table", function(x) {
tagList(
dataTableOutput(.getEncodedName(x)),
uiOutput(paste0(.getEncodedName(x), "_", .tableExtraInfo)),
hr()
)
})
setMethod(".createObservers", "Table", function(x, se, input, session, pObjects, rObjects) {
callNextMethod()
panel_name <- .getEncodedName(x)
.create_table_observers(panel_name, input=input,
session=session, pObjects=pObjects, rObjects=rObjects)
.createUnprotectedParameterObservers(.getEncodedName(x), .TableHidden, input,
pObjects, rObjects, ignoreNULL=FALSE)
})
setMethod(".renderOutput", "Table", function(x, se, ..., output, pObjects, rObjects) {
.create_table_output(.getEncodedName(x), se=se, output=output, pObjects=pObjects, rObjects=rObjects)
callNextMethod()
})
setMethod(".generateOutput", "Table", function(x, se, ..., all_memory, all_contents) {
.define_table_commands(x, se, all_memory=all_memory, all_contents=all_contents)
})
setMethod(".exportOutput", "Table", function(x, se, all_memory, all_contents) {
contents <- .generateOutput(x, se, all_memory=all_memory, all_contents=all_contents)
newpath <- paste0(.getEncodedName(x), ".csv")
write.csv(file=newpath, contents$contents)
newpath
})
setMethod(".defineDataInterface", "Table", function(x, se, select_info) {
hidden <- slot(x, .TableHidden)
.addSpecificTour(class(x), .TableHidden, function(tab_name) {
data.frame(
element=paste0("
intro="Here, we can hide particular columns in the table.
This is helpful for hiding uninformative annotations so that we don't have to keep on scrolling left/right to see the interesting bits.
Any number of column names can be specified here."
)
})
c(
callNextMethod(),
list(
.selectInput.iSEE(x, .TableHidden,
choices=hidden, selected=hidden,
label="Hidden columns:", multiple=TRUE)
)
)
})
setMethod(".hideInterface", "Table", function(x, field) {
if (field %in% c(.multiSelectHistory, .selectColRestrict, .selectRowRestrict)) {
TRUE
} else {
callNextMethod()
}
})
setMethod(".definePanelTour", "Table", function(x) {
mdim <- .multiSelectionDimension(x)
rbind(
callNextMethod(),
c(paste0("
)
})
setMethod("updateObject", "Table", function(object, ..., verbose=FALSE) {
if (!.is_latest_version(object)) {
update.2.1 <- is(try(slot(object, .plotHoverInfo), silent=TRUE), "try-error")
object <- callNextMethod()
if (update.2.1){
.Deprecated(msg=sprintf("detected outdated '%s' instance, run 'updateObject(<%s>)'", class(object)[1], class(object)[1]))
object[[.TableHidden]] <- character(0)
}
}
object
}) |
require("knitr",quietly=TRUE)
opts_chunk$set(fig.path="figs/ag2-", fig.align="center",
fig.width=7, fig.height=7, comment="")
knit_hooks$set(output = function(x, options) {
paste('\\begin{Soutput}\n', x, '\\end{Soutput}\n', sep = '')
})
options(width=90)
par( omi=c(0,0,0,0), mai=c(0.2,0.2,0.2,0.2) )
if(!file.exists("figs")) dir.create("figs")
library( munsellinterpol )
par( omi=c(0,0,0,0), mai=c(0.5,0.5,0.1,0.1) )
plot( c(0,100), c(0,10), type='n', xlab='', ylab='', las=1, tcl=0,
lab=c(10,8,7), mgp=c(3,0.25,0) )
title( xlab='Y', line=1.5 ) ; title( ylab='Value', line=1.5 )
grid( lty=1 ) ; abline( h=0, v=0 )
V = seq( 0, 10, by=0.125 )
color = unlist( list(ASTM='black',OSA='black',MgO='black',Munsell='red',Priest='blue') )
for( w in names(color) )
lines( YfromV(V,w), V, col=color[w], lty=ifelse(w=='MgO',2,1), lwd=0.75 )
lty = ifelse( names(color)=='MgO', 2, 1 )
legend( "bottomright", names(color), bty='n', lty=lty, lwd=1.5, col=color, inset=0.1 )
par( omi=c(0,0,0,0), mai=c(0.5,1,0.1,0.1) )
Y = seq( 0, 100, by=0.5 )
delta = VfromY(Y,'OSA') - VfromY(Y,'ASTM')
plot( range(Y), range(delta), type='n', xlab='', ylab='', las=1, tcl=0,
lab=c(10,8,7), mgp=c(3,0.25,0) )
title( xlab='Y', line=1.5 ) ; title( ylab='{OSA Value} - {ASTM Value}', line=3 )
grid( lty=1 ) ; abline( h=0, v=0 )
lines( Y, delta, lwd=0.75 )
Lightness_from_linear <- function( Y ) {
ifelse( Y < (24/116)^3, (116/12)^3 * Y, 116*Y^(1/3) - 16 )
}
par( omi=c(0,0,0,0), mai=c(0.5,0.75,0.1,0.1) )
Y = (0:100)/100
L = Lightness_from_linear( Y )
plot( range(Y), range(L), type='n', xlab='', ylab='', las=1, tcl=0,
lab=c(10,8,7), mgp=c(3,0.25,0) )
title( xlab='Y (luminance factor)', line=1.5 ); title( ylab='Lightness', line=2 )
grid( lty=1 ) ; abline( h=0, v=0 )
lines( Y, L, lwd=0.75 )
V = VfromY( 100 * Y, 'ASTM' )
lines( Y, 10*V, lty=2 )
legend( "bottomright", c("Lightness (CIE)","10*Value (ASTM)"), lty=c(1,2),
bty='n', inset=0.1 )
par( omi=c(0,0,0,0), mai=c(0.5,0.75,0.1,0.1) )
quotient = L / V
plot( range(Y), range(quotient,na.rm=T), type='n', xlab='', ylab='', las=1, tcl=0,
lab=c(10,8,7), mgp=c(3,0.25,0) )
title( xlab='Y (luminance factor)', line=1.5 )
title( ylab='Lightness / Value', line=3 )
grid( lty=1 ) ; abline( h=0, v=0 )
lines( Y, quotient )
knit_hooks$set(output = function(x, options) { x })
toLatex(sessionInfo(), locale=FALSE) |
lslxModel$set("public",
"initialize",
function(model,
numeric_variable,
ordered_variable,
weight_variable,
auxiliary_variable,
group_variable,
reference_group,
level_group,
nlevel_ordered) {
private$initialize_model(model = model)
private$initialize_specification()
private$initialize_tag(
numeric_variable = numeric_variable,
ordered_variable = ordered_variable,
weight_variable = weight_variable,
auxiliary_variable = auxiliary_variable,
group_variable = group_variable,
reference_group = reference_group,
level_group = level_group,
nlevel_ordered = nlevel_ordered
)
private$organize_specification()
private$expand_specification()
})
lslxModel$set("private",
"initialize_model",
function(model) {
model <-
gsub(pattern = "[[:blank:]]",
replacement = "",
x = model)
model <-
gsub(pattern = "\\$|\\?|\\\\|\\^|%|&|
replacement = "",
x = model)
model <-
gsub(pattern = ";",
replacement = "\n",
x = model)
model <-
gsub(pattern = "\n{2,}",
replacement = "\n",
x = model)
model <-
unlist(x = strsplit(x = model,
split = "\n"),
use.names = FALSE)
model <-
gsub(pattern = "=~",
replacement = ":=>",
x = model)
model <-
gsub(pattern = "~~",
replacement = "<=>",
x = model)
model <-
gsub(pattern = "~\\*~",
replacement = "\\*=\\*",
x = model)
model <-
ifelse(
!grepl(pattern = "\\|=|=\\||\\|~|~\\|",
x = model),
gsub(
pattern = "\\|",
replacement = "|=",
x = model
),
model
)
model <-
ifelse(
!grepl(pattern = "<~|<~:|~>|:~>|<~>|\\|~|~\\|",
x = model),
gsub(
pattern = "~",
replacement = "<=",
x = model
),
model
)
self$model <- model
})
lslxModel$set("private",
"initialize_specification",
function() {
operator <- c("|=",
"=|",
"|~",
"~|",
"*=*",
"*~*",
"<=:",
"<~:",
":=>",
":~>",
"<=",
"<~",
"=>",
"~>",
"<=>",
"<~>")
self$specification <-
do.call(what = rbind,
args = lapply(
X = self$model,
FUN = function(model_i) {
operator_i <- operator[sapply(
X = c(
"\\|=",
"=\\|",
"\\|~",
"~\\|",
"\\*=\\*",
"\\*~\\*",
"<=:",
"<~:",
":=>",
":~>",
"<=[^:>]",
"<~[^:>]",
"[^:<]=>",
"[^:<]~>",
"<=>",
"<~>"
),
FUN = function(pattern) {
grepl(pattern, model_i)
}
)]
if (length(operator_i) > 0) {
model_i_split <-
strsplit(x = model_i, split = operator_i, fixed = TRUE)[[1]]
if (operator_i %in%
c(":=>", ":~>", "=>", "~>", "=|", "~|")) {
if (operator_i %in%
c(":=>", ":~>", "=>", "~>")) {
operator_i <-
paste0(rev(gsub(
pattern = ">",
replacement = "<",
x = substring(operator_i,
1:nchar(operator_i),
1:nchar(operator_i))
)),
collapse = "")
} else {
operator_i <-
paste0(rev(substring(
operator_i,
1:nchar(operator_i),
1:nchar(operator_i)
)),
collapse = "")
}
model_i <-
c(left = model_i_split[2],
operator = operator_i,
right = model_i_split[1])
} else {
model_i <-
c(left = model_i_split[1],
operator = operator_i,
right = model_i_split[2])
}
left_i <-
unlist(strsplit(x = model_i[["left"]],
split = "\\+"),
use.names = FALSE)
right_i <-
unlist(strsplit(x = model_i[["right"]],
split = "\\+"),
use.names = FALSE)
model_i <-
expand.grid(
relation = NA_character_,
left = left_i,
right = right_i,
operator = operator_i,
KEEP.OUT.ATTRS = FALSE,
stringsAsFactors = FALSE
)
left_i_split <-
strsplit(model_i$left, split = "\\*")
right_i_split <-
strsplit(model_i$right, split = "\\*")
model_i$left <-
sapply(
left_i_split,
FUN = function(i)
getElement(i, length(i))
)
model_i$right <-
sapply(
right_i_split,
FUN = function(i)
getElement(i, length(i))
)
if (any(model_i$right[model_i$operator %in%
c("<=>", "<~>")] == "1") |
any(model_i$left[model_i$operator %in%
c("<=>", "<~>")] == "1")) {
stop(
"Intercept term '1' cannot present at any side of expression for covariance specification."
)
}
if (any(model_i$left[!(model_i$operator %in%
c("<=>", "<~>"))] == "1")) {
stop("Intercept term '1' cannot present at the arrow side of expression.")
}
if (any(model_i$right[model_i$operator %in%
c("|=", "|~")] == "1") |
any(model_i$left[model_i$operator %in%
c("|=", "|~")] == "1")) {
stop(
"Intercept term '1' cannot present at any side of expression for threshold specification."
)
}
if (any(model_i$right[model_i$operator %in%
c("*=*", "*~*")] == "1") |
any(model_i$left[model_i$operator %in%
c("*=*", "*~*")] == "1")) {
stop(
"Intercept term '1' cannot present at any side of expression for scale specification."
)
}
model_i$left_prefix <-
sapply(
left_i_split,
FUN = function(i) {
ifelse(length(i) == 1L, NA_character_, i[1])
}
)
model_i$right_prefix <-
sapply(
right_i_split,
FUN = function(i) {
ifelse(length(i) == 1L, NA_character_, i[1])
}
)
if (any(!(is.na(model_i$left_prefix) |
is.na(model_i$right_prefix)))) {
stop("Prefix before '*' cannot simultaneously present at both side of expression.")
} else if (any(!is.na(model_i$left_prefix))) {
model_i$prefix <-
model_i$left_prefix
} else if (any(!is.na(model_i$right_prefix))) {
model_i$prefix <-
model_i$right_prefix
} else {
model_i$prefix <- NA_character_
}
model_i$left_prefix <- NULL
model_i$right_prefix <- NULL
model_i$relation <-
paste0(model_i$left,
ifelse(
model_i$operator %in%
c("<=:", "<~:", "<=", "<~"),
"<-",
ifelse(model_i$operator %in%
c("<=>", "<~>"),
"<->",
ifelse(model_i$operator %in%
c("|=", "|~"),
"|",
"**"))),
model_i$right)
model_i$operator <-
ifelse(
model_i$right == "1",
ifelse(
model_i$operator == "<=:",
"<=",
ifelse(model_i$operator == "<~:",
"<~",
model_i$operator)
),
model_i$operator
)
} else {
model_i <- data.frame()
}
return(model_i)
}
))
if (any(grepl(
pattern = "[[:digit:]]",
x = substr(
x = setdiff(x = unique(
c(self$specification$left,
self$specification$right)
),
y = c("1")),
start = 1,
stop = 1
)
))) {
stop(
"Names of variable(s) or factor(s) cannot start with numbers.",
"\n Please check the specified 'model'."
)
}
})
lslxModel$set("private",
"initialize_tag",
function(numeric_variable,
ordered_variable,
weight_variable,
auxiliary_variable,
group_variable,
reference_group,
level_group,
nlevel_ordered) {
self$numeric_variable <- numeric_variable
self$ordered_variable <- ordered_variable
self$weight_variable <- weight_variable
self$auxiliary_variable <- auxiliary_variable
self$group_variable <- group_variable
self$reference_group <- reference_group
self$level_group <- level_group
self$name_factor <-
unique(self$specification[self$specification$operator %in%
c("<=:", "<~:"),
"right"])
self$name_response <-
setdiff(x = unique(unlist(self$specification[!(self$specification$operator %in%
c("|=", "|~")),
c("left", "right")])),
y = c(self$name_factor, "1"))
if (!all(self$name_response %in%
union(x = self$numeric_variable,
y = self$ordered_variable))) {
stop(
"Some response variable in 'model' cannot be found in 'data' or 'sample_cov'.",
"\n Response variables specified by 'model' are ",
do.call(paste, as.list(self$name_response)),
".",
"\n Column names of 'data' or 'sample_cov' are ",
do.call(paste, as.list(union(x = self$numeric_variable,
y = self$ordered_variable))),
"."
)
}
self$numeric_variable <-
intersect(x = self$name_response,
y = self$numeric_variable)
self$ordered_variable <-
intersect(x = self$name_response,
y = self$ordered_variable)
if (length(self$ordered_variable) > 0) {
self$nlevel_ordered <-
nlevel_ordered[self$ordered_variable]
self$name_threshold <- paste0("t", 1:(max(nlevel_ordered) - 1))
} else {
self$nlevel_ordered <- numeric(0)
self$name_threshold <- character(0)
}
self$auxiliary_variable <-
setdiff(x = self$auxiliary_variable,
y = self$name_response)
self$name_eta <-
c(self$name_response, self$name_factor)
self$name_endogenous <-
unique(self$specification[(self$specification$operator %in%
c("<=:", "<~:", "<=", "<~")) &
(self$specification$right != "1"),
"left"])
self$name_exogenous <-
unique(setdiff(x = self$name_eta,
y = self$name_endogenous))
})
lslxModel$set("private",
"organize_specification",
function() {
self$specification <-
do.call(what = rbind,
args = lapply(
split(self$specification, 1:nrow(self$specification)),
FUN = function(specification_i) {
if ((specification_i[["operator"]] %in% c("<=>", "<~>")) &
(
match(specification_i[["left"]], self$name_eta) <
match(specification_i[["right"]], self$name_eta)
)) {
specification_i <-
data.frame(
relation = paste0(specification_i[["right"]],
"<->",
specification_i[["left"]]),
left = specification_i[["right"]],
right = specification_i[["left"]],
operator = specification_i[["operator"]],
prefix = specification_i[["prefix"]]
)
}
return(specification_i)
}
))
self$specification <-
self$specification[!duplicated(self$specification$relation,
fromLast = TRUE),]
if (length(self$ordered_variable) > 0) {
relation_gamma <-
unlist(mapply(
FUN = function(ordered_variable_i,
nlevel_ordered_i) {
paste0(ordered_variable_i,
"|",
paste0("t", 1:(nlevel_ordered_i - 1)))
},
self$ordered_variable,
self$nlevel_ordered),
use.names = FALSE)
if (!all(self$specification[(self$specification$operator %in%
c("|=", "|~")),
"relation"] %in% (relation_gamma))) {
stop("Some specification for thresholds are not recognized.")
}
relation_gamma <-
setdiff(x = relation_gamma, y = self$specification$relation)
} else {
relation_gamma <- character(0)
}
if (length(relation_gamma) > 0) {
specification_gamma <-
data.frame(
relation = relation_gamma,
left = substr(
relation_gamma,
start = 1,
stop = regexpr("\\|", relation_gamma) - 1
),
right = substr(
relation_gamma,
start = regexpr("\\|", relation_gamma) + 1,
stop = nchar(relation_gamma)
),
operator = "|=",
prefix = NA_character_,
stringsAsFactors = FALSE
)
} else {
specification_gamma = data.frame()
}
if (length(self$ordered_variable) > 0) {
relation_psi <- paste0(self$ordered_variable,
"**",
self$ordered_variable)
if (!all(self$specification[(self$specification$operator %in%
c("*=*", "*~*")),
"relation"] %in% (relation_psi))) {
stop("Some specification for scale are not recognized.")
}
relation_psi <-
setdiff(x = relation_psi, y = self$specification$relation)
} else {
relation_psi <- character(0)
}
if (length(relation_psi) > 0) {
specification_psi <-
data.frame(
relation = relation_psi,
left = substr(
relation_psi,
start = 1,
stop = regexpr("\\*\\*", relation_psi) - 1
),
right = substr(
relation_psi,
start = regexpr("\\*\\*", relation_psi) + 2,
stop = nchar(relation_psi)
),
operator = "*=*",
prefix = 1,
stringsAsFactors = FALSE
)
} else {
specification_psi = data.frame()
}
if (any(self$specification$right == "1")) {
if (length(intersect(x = self$numeric_variable,
y = self$name_exogenous)) > 0) {
relation_alpha <-
setdiff(
x = paste(
intersect(
x = self$numeric_variable,
y = self$name_exogenous
),
"1",
sep = "<-"
),
y = self$specification$relation
)
} else {
relation_alpha <- character()
}
} else {
if (length(self$numeric_variable) > 0) {
relation_alpha <-
setdiff(
x = paste(self$numeric_variable,
"1",
sep = "<-"),
y = self$specification$relation
)
} else {
relation_alpha <- character()
}
}
if (length(relation_alpha) > 0) {
specification_alpha <- data.frame(
relation = relation_alpha,
left = substr(
relation_alpha,
start = 1,
stop = regexpr("<-", relation_alpha) - 1
),
right = "1",
operator = "<=",
prefix = NA_character_,
stringsAsFactors = FALSE
)
} else {
specification_alpha = data.frame()
}
if (length(self$name_exogenous) > 1) {
relation_phi <-
setdiff(x = c(
paste0(self$name_eta,
"<->",
self$name_eta),
paste0(apply(
combn(rev(self$name_exogenous), 2),
2,
FUN = function(i) {
paste(i[1], i[2], sep = "<->")
}
))
),
y = self$specification$relation)
} else {
relation_phi <-
setdiff(
x = paste0(self$name_eta,
"<->",
self$name_eta),
y = self$specification$relation
)
}
if (length(relation_phi) > 0) {
specification_phi <-
data.frame(
relation = relation_phi,
left = substr(
relation_phi,
start = 1,
stop = regexpr("<->", relation_phi) - 1
),
right = substr(
relation_phi,
start = regexpr("<->", relation_phi) + 3,
stop = nchar(relation_phi)
),
operator = "<=>",
prefix = NA_character_,
stringsAsFactors = FALSE
)
} else {
specification_phi = data.frame()
}
self$specification <-
rbind(
self$specification,
specification_gamma,
specification_alpha,
specification_phi,
specification_psi,
make.row.names = FALSE,
stringsAsFactors = FALSE
)
})
lslxModel$set("private",
"expand_specification",
function() {
prefix_split <-
lapply(X = self$specification$prefix,
FUN = function(prefix_i) {
if (!is.na(prefix_i)) {
if ((substr(prefix_i,
start = 1,
stop = 2) == "c(") &
(substr(prefix_i,
start = nchar(prefix_i),
stop = nchar(prefix_i)) == ")")) {
prefix_i <-
substr(prefix_i, start = 3, stop = (nchar(prefix_i) - 1))
prefix_i_split <- character(0)
while (nchar(prefix_i) > 0) {
idx_left_bracket <- regexpr("\\(", prefix_i)[1]
idx_right_bracket <- regexpr("\\)", prefix_i)[1]
idx_comma <- regexpr(",", prefix_i)[1]
if (idx_comma == -1) {
prefix_i_split <- c(prefix_i_split, prefix_i)
prefix_i <- ""
} else {
if ((idx_left_bracket == -1) &
(idx_right_bracket == -1)) {
prefix_i_split <- c(prefix_i_split, strsplit(prefix_i, ",")[[1]])
prefix_i <- ""
} else {
if ((idx_left_bracket < idx_comma) &
(idx_right_bracket > idx_comma)) {
prefix_i_split <- c(prefix_i_split,
substr(prefix_i,
start = 1,
stop = idx_right_bracket))
if (idx_right_bracket < nchar(prefix_i)) {
prefix_i <-
substr(prefix_i,
start = (idx_right_bracket + 2),
stop = nchar(prefix_i))
} else {
prefix_i <- ""
}
} else {
prefix_i_split <- c(prefix_i_split,
substr(prefix_i,
start = 1,
stop = (idx_comma - 1)))
prefix_i <-
substr(prefix_i,
start = (idx_comma + 1),
stop = nchar(prefix_i))
}
}
}
}
} else {
prefix_i_split <- prefix_i
}
} else {
prefix_i_split <- prefix_i
}
prefix_i_split <-
ifelse(gsub(pattern = "\\(.*$", replacement = "", x = prefix_i_split) %in%
c("free", "fix", "pen", "start", "lab"),
prefix_i_split,
ifelse(is.na(prefix_i_split),
prefix_i_split,
ifelse(prefix_i_split == "NA",
NA,
ifelse(suppressWarnings(!is.na(as.numeric(prefix_i_split))),
paste0("fix", "(", prefix_i_split, ")"),
paste0("lab", "(", prefix_i_split, ")")))))
return(prefix_i_split)
})
if (anyNA(self$reference_group)) {
if (any(sapply(X = prefix_split,
FUN = function(prefix_split_i) {
return(length(prefix_split_i) > 1)
}))) {
stop("When 'reference_group = NA', vectorized prefix cannot be used.")
}
if (length(self$ordered_variable) > 0) {
stop("When 'reference_group = NA', response variable cannot be ordered.")
}
self$specification <-
do.call(what = rbind,
args = lapply(
X = c("<NA>", self$level_group),
FUN = function(level_group_i) {
specification_i <- self$specification
specification_i$group <- level_group_i
specification_i$reference <-
ifelse(level_group_i == "<NA>", TRUE, FALSE)
specification_i$operator <-
ifelse(specification_i$group == "<NA>",
specification_i$operator,
gsub(pattern = "=",
replacement = "~",
x = specification_i$operator))
specification_i$prefix <-
sapply(
X = prefix_split,
FUN = function(prefix_split_j) {
prefix_split_j_verb <-
gsub(pattern = "\\(.*$",
replacement = "",
x = prefix_split_j)
if (prefix_split_j_verb %in% "pen") {
prefix_split_j <-
eval(parse(text = prefix_split_j))
}
if (level_group_i != "<NA>") {
if (prefix_split_j_verb %in% "fix") {
prefix_split_j <- "fix(0)"
} else {
prefix_split_j <- NA
}
}
return(prefix_split_j)
}
)
rownames(specification_i) <-
paste0(specification_i$relation,
"/",
specification_i$group)
return(specification_i)
}
))
} else {
if (any(sapply(X = prefix_split,
FUN = function(prefix_split_i) {
return((length(prefix_split_i) > 1) &
(length(prefix_split_i) != length(self$level_group)))
}))) {
stop("The length of prefix vector should be 1 or equal to to the number of groups.")
}
self$specification <-
do.call(what = rbind,
args = lapply(
X = self$level_group,
FUN = function(level_group_i) {
specification_i <- self$specification
specification_i$group <- level_group_i
specification_i$reference <-
ifelse(
is.null(self$reference_group),
FALSE,
ifelse(level_group_i == self$reference_group,
TRUE,
FALSE)
)
specification_i$prefix <-
sapply(
X = prefix_split,
FUN = function(prefix_split_j) {
if (length(prefix_split_j) == 1) {
prefix_split_j_verb <-
gsub(pattern = "\\(.*$",
replacement = "",
x = prefix_split_j)
if (prefix_split_j_verb %in% "pen") {
prefix_split_j <-
eval(parse(text = prefix_split_j))
}
if (!is.null(self$reference_group)) {
if (level_group_i != self$reference_group) {
if (prefix_split_j_verb %in%
c("free", "fix", "start")) {
prefix_split_j <-
paste0(prefix_split_j_verb, "(", 0, ")")
} else if (prefix_split_j_verb %in% "pen") {
prefix_split_j <-
paste0(substr(prefix_split_j,
start = 1,
stop = regexpr("=", prefix_split_j)[1]),
0,
substr(prefix_split_j,
start = regexpr(",", prefix_split_j)[1],
stop = nchar(prefix_split_j)))
} else if (prefix_split_j_verb %in% c("lab")) {
prefix_split_j <- "fix(0)"
} else {
prefix_split_j <- NA
}
}
}
} else {
prefix_split_j <-
prefix_split_j[level_group_i == self$level_group]
prefix_split_j_verb <-
gsub(pattern = "\\(.*$",
replacement = "",
x = prefix_split_j)
if (prefix_split_j_verb %in% "pen") {
prefix_split_j <-
eval(parse(text = prefix_split_j))
}
}
return(prefix_split_j)
}
)
rownames(specification_i) <-
paste0(specification_i$relation,
"/",
specification_i$group)
return(specification_i)
}
))
}
self$specification$matrix <-
factor(
x = ifelse(
self$specification$operator %in% c("|=", "|~"),
"gamma",
ifelse(self$specification$operator %in% c("*=*", "*~*"),
"psi",
ifelse(
self$specification$operator %in% c("<=>", "<~>"),
"phi",
ifelse(self$specification$right == "1",
"alpha",
"beta")
))),
levels = c("gamma", "alpha", "beta", "phi", "psi")
)
self$specification$block <-
with(self$specification, {
block_left <-
ifelse(left %in% self$name_response,
"y",
"f")
block_right <-
ifelse(matrix %in% "gamma",
"t",
ifelse(
right %in% self$name_response,
"y",
ifelse(
right %in% self$name_factor,
"f",
"1"
)
))
block_middle <-
ifelse(matrix %in% "gamma",
"|",
ifelse(matrix %in% "psi",
"**",
ifelse(matrix %in% c("alpha", "beta"),
"<-",
"<->")))
paste0(block_left, block_middle, block_right)
})
self$specification$type <-
with(self$specification, {
prefix_verb <-
gsub(pattern = "\\(.*$", replacement = "", x = prefix)
type <-
ifelse(prefix_verb %in% "free",
"free",
ifelse(prefix_verb %in% "fix",
"fixed",
ifelse(prefix_verb %in% "pen",
"pen",
ifelse(
operator %in% c("|=", "*=*", "<=:", "<=", "<=>"),
"free",
"pen"))))
return(type)
})
self$specification$type <-
ifelse(self$specification$relation %in%
c(paste0(self$ordered_variable,
"<->",
self$ordered_variable)),
"fixed",
self$specification$type)
self$specification$start <-
with(self$specification, {
prefix_verb <-
gsub(pattern = "\\(.*$", replacement = "", x = prefix)
prefix_value <-
ifelse(prefix_verb %in% c("free", "fix", "start"),
substr(x = prefix,
start = (regexpr("\\(", prefix) + 1),
stop = (nchar(prefix) - 1)),
ifelse(prefix_verb %in% "pen",
sapply(X = strsplit(prefix, split = "=|,|\\(|\\)"),
FUN = function(x) {
x[3]},
simplify = TRUE, USE.NAMES = FALSE),
NA_real_))
start <-
ifelse(prefix_verb %in% c("free", "fix", "pen", "start"),
suppressWarnings(as.numeric(prefix_value)),
NA_real_)
return(start)
})
self$specification$start <-
ifelse(self$specification$relation %in%
paste0(self$ordered_variable,
"<->",
self$ordered_variable),
NA_real_,
self$specification$start)
self$specification$label <-
with(self$specification, {
prefix_verb <-
gsub(pattern = "\\(.*$", replacement = "", x = prefix)
label <-
ifelse(prefix_verb %in% "lab",
substr(x = prefix,
start = (regexpr("\\(", prefix) + 1),
stop = (nchar(prefix) - 1)),
NA_character_)
return(label)
})
self$specification$penalty <-
with(self$specification, {
prefix_verb <-
gsub(pattern = "\\(.*$", replacement = "", x = prefix)
penalty <-
ifelse(prefix_verb %in% "pen",
sapply(X = strsplit(prefix, split = "=|,|\\(|\\)"),
FUN = function(x) {
x[5]},
simplify = TRUE, USE.NAMES = FALSE),
ifelse(type == "pen",
"default",
"none"))
return(penalty)
})
self$specification$set <-
with(self$specification, {
prefix_verb <-
gsub(pattern = "\\(.*$", replacement = "", x = prefix)
set <-
ifelse(prefix_verb %in% "pen",
sapply(X = strsplit(prefix, split = "=|,|\\(|\\)"),
FUN = function(x) {
as.numeric(x[7])},
simplify = TRUE, USE.NAMES = FALSE),
ifelse(type == "pen", 1, 0))
return(set)
})
self$specification$weight <-
with(self$specification, {
prefix_verb <-
gsub(pattern = "\\(.*$", replacement = "", x = prefix)
weight <-
ifelse(prefix_verb %in% "pen",
sapply(X = strsplit(prefix, split = "=|,|\\(|\\)"),
FUN = function(x) {
x[9]},
simplify = TRUE, USE.NAMES = FALSE),
ifelse(type == "pen", 1, 0))
return(as.numeric(weight))
})
self$specification$operator <- NULL
self$specification$prefix <- NULL
if (length(self$ordered_variable) > 0) {
specification_gamma <- self$specification[self$specification$matrix == "gamma", ]
specification_non_gamma <- self$specification[self$specification$matrix != "gamma", ]
specification_gamma <-
specification_gamma[order(
specification_gamma$reference,
specification_gamma$group,
specification_gamma$matrix,
specification_gamma$block,
match(specification_gamma$left, self$name_eta),
match(specification_gamma$right, c("1", self$name_threshold, self$name_eta)),
method = "radix"
),]
specification_non_gamma <-
specification_non_gamma[order(
specification_non_gamma$reference,
specification_non_gamma$group,
specification_non_gamma$matrix,
specification_non_gamma$block,
match(specification_non_gamma$right, c("1", self$name_threshold, self$name_eta)),
match(specification_non_gamma$left, self$name_eta),
method = "radix"
),]
self$specification <- rbind(specification_gamma,
specification_non_gamma)
self$specification <-
self$specification[order(
self$specification$reference,
self$specification$group,
self$specification$matrix,
self$specification$block,
method = "radix"
),]
} else {
self$specification <-
self$specification[order(
self$specification$reference,
self$specification$group,
self$specification$matrix,
self$specification$block,
match(self$specification$right, c("1", self$name_threshold, self$name_eta)),
match(self$specification$left, self$name_eta),
method = "radix"
),]
}
self$specification <-
self$specification[order(
self$specification$reference,
decreasing = TRUE,
method = "radix"
), ]
}) |
loopboot <-
function(j=NULL,pred.x,pred.y,xresid,yresid,ti,obs,n,m,extended.classical,cbb,joint,period){
if (is.numeric(cbb)==TRUE) {
index <- sample(1:(obs+3),3,replace=FALSE)
xresid2 <- c(xresid,xresid)
yresid2 <- c(xresid,xresid)
k <- obs/cbb
xblocks <- sample(1:(obs+3),k,replace=TRUE)
if (joint==FALSE) yblocks <- sample(1:(obs+3),k,replace=TRUE)
else yblocks <- xblocks
xressamp <- c(t(outer(xblocks,0:(cbb-1),FUN="+")))
yressamp <- c(t(outer(yblocks,0:(cbb-1),FUN="+")))
y<-yresid2[yressamp]+pred.y[-index]
x<-xresid2[xressamp]+pred.x[-index]
}
else {
index <- sample(1:(obs+3),3,replace=FALSE)
if (joint==FALSE) {
y<-sample(yresid,obs,replace=T)+pred.y[-index]
x<-sample(xresid,obs,replace=T)+pred.x[-index]
}
else {
resid.sampler <- sample(1:(obs+3),obs,replace=TRUE)
y<-yresid[resid.sampler]+pred.y[-index]
x<-xresid[resid.sampler]+pred.x[-index]
}
}
Ta.lm<-lm.fit(cbind(rep(1,obs),sin(ti[-index]),cos(ti[-index])),x)
b.x<-sqrt(coef(Ta.lm)[[2]]^2+coef(Ta.lm)[[3]]^2)
phase.angle<- atan2(coef(Ta.lm)[[3]],coef(Ta.lm)[[2]])-pi/2
rad<-ti[-index]+phase.angle
cx<-coef(Ta.lm)[[1]]
if (extended.classical==FALSE) {
Tb.lm<-lm.fit(cbind(rep(1,obs),sin(rad)^m,cos(rad)^n),y)}
else {
direc <- sign(cos(rad))
Tb.lm<-lm.fit(cbind(rep(1,obs),sin(rad)^m,direc*abs(cos(rad))^n),y)}
b.y<-coef(Tb.lm)[[3]]
retention<- coef(Tb.lm)[[2]]
cy<-coef(Tb.lm)[[1]]
if (n==1) beta.split.angle<-atan2(b.y,b.x)*180/pi
else if (n >= 2) beta.split.angle <- 0
else beta.split.angle<-NA
hysteresis.x <- 1/sqrt(1+(b.y/retention)^(2/m))
coercion <- hysteresis.x*b.x
hysteresis.y <- retention/b.y
lag<-abs(atan2(retention,b.y))*period/(pi*2)
area <- (0.5/(beta((m+3)/2,(m+3)/2)*(m+2))+1/beta((m+1)/2,(m+1)/2)-1/beta((m+3)/2,(m-1)/2))/(2^m)*pi*abs(retention*b.x)
z <- c("n"=n, "m"=m,"b.x"=b.x,"b.y"=b.y,"phase.angle"=as.vector(phase.angle),"cx"=cx,"cy"=cy,"retention"=retention,
"coercion"=coercion,"area"=area,"lag"=lag, "beta.split.angle"=beta.split.angle,"hysteresis.x"=hysteresis.x, "hysteresis.y"=hysteresis.y)
z
} |
hzeta.control <- function(save.weights = TRUE, ...) {
list(save.weights = save.weights)
}
hzeta <-
function(lshape = "logloglink", ishape = NULL, nsimEIM = 100) {
stopifnot(ishape > 0)
stopifnot(nsimEIM > 10,
length(nsimEIM) == 1,
nsimEIM == round(nsimEIM))
lshape <- as.list(substitute(lshape))
eshape <- link2list(lshape)
lshape <- attr(eshape, "function.name")
new("vglmff",
blurb = c("Haight's Zeta distribution f(y) = (2y-1)^(-shape) - ",
"(2y+1)^(-shape),\n",
" shape>0, y = 1, 2,....\n\n",
"Link: ",
namesof("shape", lshape, earg = eshape), "\n\n",
"Mean: (1-2^(-shape)) * zeta(shape) if shape>1",
"\n",
"Variance: (1-2^(1-shape)) * zeta(shape-1) - mean^2 if shape>2"),
infos = eval(substitute(function(...) {
list(M1 = 1,
Q1 = 1,
expected = FALSE,
multipleResponses = FALSE,
parameters.names = c("shape"),
lshape = .lshape ,
nsimEIM = .nsimEIM )
}, list( .nsimEIM = nsimEIM, .lshape = lshape ))),
initialize = eval(substitute(expression({
w.y.check(w = w, y = y,
Is.integer.y = TRUE,
Is.positive.y = TRUE)
predictors.names <-
namesof("shape", .lshape , earg = .eshape , tag = FALSE)
if (!length(etastart)) {
a.init <- if (length( .ishape)) .ishape else {
if ((meany <- weighted.mean(y, w)) < 1.5) 3.0 else
if (meany < 2.5) 1.4 else 1.1
}
a.init <- rep_len(a.init, n)
etastart <- theta2eta(a.init, .lshape , earg = .eshape )
}
}), list( .lshape = lshape, .eshape = eshape, .ishape = ishape ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
shape <- eta2theta(eta, .lshape , earg = .eshape )
mu <- (1-2^(-shape)) * zeta(shape)
mu[shape <= 1] <- Inf
mu
}, list( .lshape = lshape, .eshape = eshape ))),
last = eval(substitute(expression({
misc$link <- c(shape = .lshape)
misc$earg <- list(shape = .eshape )
misc$nsimEIM <- .nsimEIM
}), list( .lshape = lshape, .eshape = eshape, .nsimEIM = nsimEIM ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL,
summation = TRUE) {
shape <- eta2theta(eta, .lshape , earg = .eshape )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <- c(w) * dhzeta(x = y, shape = shape, log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lshape = lshape, .eshape = eshape ))),
vfamily = c("hzeta"),
validparams = eval(substitute(function(eta, y, extra = NULL) {
shape <- eta2theta(eta, .lshape , earg = .eshape )
okay1 <- all(is.finite(shape)) && all(0 < shape)
okay1
}, list( .lshape = lshape, .eshape = eshape ))),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- [email protected]) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
shape <- eta2theta(eta, .lshape , earg = .eshape )
rhzeta(nsim * length(shape), shape = shape)
}, list( .lshape = lshape, .eshape = eshape ))),
deriv = eval(substitute(expression({
shape <- eta2theta(eta, .lshape , earg = .eshape )
dshape.deta <- dtheta.deta(shape, .lshape , earg = .eshape )
d3 <- deriv3(~ log((2*y-1)^(-shape) - (2*y+1)^(-shape)),
"shape", hessian = FALSE)
eval.d3 <- eval(d3)
dl.dshape <- attr(eval.d3, "gradient")
c(w) * dl.dshape * dshape.deta
}), list( .lshape = lshape, .eshape = eshape ))),
weight = eval(substitute(expression({
sd3 <- deriv3(~ log((2*ysim-1)^(-shape) - (2*ysim+1)^(-shape)),
"shape", hessian = FALSE)
run.var <- 0
for (ii in 1:( .nsimEIM )) {
ysim <- rhzeta(n, shape = shape)
eval.sd3 <- eval(sd3)
dl.dshape <- attr(eval.d3, "gradient")
rm(ysim)
temp3 <- dl.dshape
run.var <- ((ii-1) * run.var + temp3^2) / ii
}
wz <- if (intercept.only)
matrix(colMeans(cbind(run.var)),
n, dimm(M), byrow = TRUE) else cbind(run.var)
wz <- wz * dshape.deta^2
c(w) * wz
}), list( .nsimEIM = nsimEIM ))))
}
dhzeta <- function(x, shape, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
if (!is.Numeric(shape, positive = TRUE))
stop("'shape' must be numeric and have positive values")
nn <- max(length(x), length(shape))
if (length(x) != nn) x <- rep_len(x, nn)
if (length(shape) != nn) shape <- rep_len(shape, nn)
ox <- !is.finite(x)
zero <- ox | round(x) != x | x < 1
ans <- rep_len(0, nn)
ans[!zero] <- (2*x[!zero]-1)^(-shape[!zero]) -
(2*x[!zero]+1)^(-shape[!zero])
if (log.arg) log(ans) else ans
}
phzeta <- function(q, shape, log.p = FALSE) {
nn <- max(length(q), length(shape))
q <- rep_len(q, nn)
shape <- rep_len(shape, nn)
oq <- !is.finite(q)
zero <- oq | q < 1
q <- floor(q)
ans <- 0 * q
ans[!zero] <- 1 - (2*q[!zero]+1)^(-shape[!zero])
ans[q == -Inf] <- 0
ans[q == Inf] <- 1
ans[shape <= 0] <- NaN
if (log.p) log(ans) else ans
}
qhzeta <- function(p, shape) {
if (!is.Numeric(p, positive = TRUE) ||
any(p >= 1))
stop("argument 'p' must have values inside the interval (0,1)")
nn <- max(length(p), length(shape))
p <- rep_len(p, nn)
shape <- rep_len(shape, nn)
ans <- (((1 - p)^(-1/shape) - 1) / 2)
ans[shape <= 0] <- NaN
floor(ans + 1)
}
rhzeta <- function(n, shape) {
use.n <- if ((length.n <- length(n)) > 1) length.n else
if (!is.Numeric(n, integer.valued = TRUE,
length.arg = 1, positive = TRUE))
stop("bad input for argument 'n'") else n
shape <- rep_len(shape, use.n)
ans <- (runif(use.n)^(-1/shape) - 1) / 2
ans[shape <= 0] <- NaN
floor(ans + 1)
}
dkumar <- function(x, shape1, shape2, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
N <- max(length(x), length(shape1), length(shape2))
if (length(x) != N) x <- rep_len(x, N)
if (length(shape1) != N) shape1 <- rep_len(shape1, N)
if (length(shape2) != N) shape2 <- rep_len(shape2, N)
logdensity <- rep_len(log(0), N)
xok <- (0 <= x & x <= 1)
logdensity[xok] <- log(shape1[xok]) + log(shape2[xok]) +
(shape1[xok] - 1) * log(x[xok]) +
(shape2[xok] - 1) * log1p(-x[xok]^shape1[xok])
logdensity[shape1 <= 0] <- NaN
logdensity[shape2 <= 0] <- NaN
if (log.arg) logdensity else exp(logdensity)
}
rkumar <- function(n, shape1, shape2) {
ans <- (1 - (runif(n))^(1/shape2))^(1/shape1)
ans[(shape1 <= 0) | (shape2 <= 0)] <- NaN
ans
}
qkumar <- function(p, shape1, shape2,
lower.tail = TRUE, log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
if (lower.tail) {
if (log.p) {
ln.p <- p
ans <- (-expm1((1/shape2) * log(-expm1(ln.p))))^(1/shape1)
ans[ln.p > 0] <- NaN
} else {
ans <- (-expm1((1/shape2) * log1p(-p)))^(1/shape1)
ans[p < 0] <- NaN
ans[p == 0] <- 0
ans[p == 1] <- 1
ans[p > 1] <- NaN
}
} else {
if (log.p) {
ln.p <- p
ans <- (-expm1(ln.p / shape2))^(1/shape1)
ans[ln.p > 0] <- NaN
ans
} else {
ans <- (-expm1((1/shape2) * log(p)))^(1/shape1)
ans[p < 0] <- NaN
ans[p == 0] <- 1
ans[p == 1] <- 0
ans[p > 1] <- NaN
}
}
ans[(shape1 <= 0) | (shape2 <= 0)] = NaN
ans
}
pkumar <- function(q, shape1, shape2,
lower.tail = TRUE, log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
if (lower.tail) {
if (log.p) {
ans <- log(-expm1(shape2 * log1p(-q^shape1)))
ans[q <= 0 ] <- -Inf
ans[q >= 1] <- 0
} else {
ans <- -expm1(shape2 * log1p(-q^shape1))
ans[q <= 0] <- 0
ans[q >= 1] <- 1
}
} else {
if (log.p) {
ans <- shape2 * log1p(-q^shape1)
ans[q <= 0] <- 0
ans[q >= 1] <- -Inf
} else {
ans <- exp(shape2 * log1p(-q^shape1))
ans[q <= 0] <- 1
ans[q >= 1] <- 0
}
}
ans[(shape1 <= 0) | (shape2 <= 0)] <- NaN
ans
}
kumar <-
function(lshape1 = "loglink", lshape2 = "loglink",
ishape1 = NULL, ishape2 = NULL,
gshape1 = exp(2*ppoints(5) - 1), tol12 = 1.0e-4,
zero = NULL) {
lshape1 <- as.list(substitute(lshape1))
eshape1 <- link2list(lshape1)
lshape1 <- attr(eshape1, "function.name")
lshape2 <- as.list(substitute(lshape2))
eshape2 <- link2list(lshape2)
lshape2 <- attr(eshape2, "function.name")
if (length(ishape1) &&
(!is.Numeric(ishape1, length.arg = 1, positive = TRUE)))
stop("bad input for argument 'ishape1'")
if (length(ishape2) && !is.Numeric(ishape2))
stop("bad input for argument 'ishape2'")
if (!is.Numeric(tol12, length.arg = 1, positive = TRUE))
stop("bad input for argument 'tol12'")
if (!is.Numeric(gshape1, positive = TRUE))
stop("bad input for argument 'gshape1'")
new("vglmff",
blurb = c("Kumaraswamy distribution\n\n",
"Links: ",
namesof("shape1", lshape1, eshape1, tag = FALSE), ", ",
namesof("shape2", lshape2, eshape2, tag = FALSE), "\n",
"Mean: shape2 * beta(1 + 1 / shape1, shape2)"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero , M = M,
predictors.names = predictors.names,
M1 = 2)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
expected = TRUE,
multipleResponses = TRUE,
parameters.names = c("shape1", "shape2"),
lshape1 = .lshape1 ,
lshape2 = .lshape2 ,
zero = .zero )
}, list( .zero = zero, .lshape1 = lshape1, .lshape2 = lshape2 ))),
initialize = eval(substitute(expression({
checklist <- w.y.check(w = w, y = y, Is.positive.y = TRUE,
ncol.w.max = Inf, ncol.y.max = Inf,
out.wy = TRUE, colsyperw = 1, maximize = TRUE)
w <- checklist$w
y <- checklist$y
if (any((y <= 0) | (y >= 1)))
stop("the response must be in (0, 1)")
extra$ncoly <- ncoly <- ncol(y)
extra$M1 <- M1 <- 2
M <- M1 * ncoly
mynames1 <- param.names("shape1", ncoly, skip1 = TRUE)
mynames2 <- param.names("shape2", ncoly, skip1 = TRUE)
predictors.names <-
c(namesof(mynames1, .lshape1 , earg = .eshape1 , tag = FALSE),
namesof(mynames2, .lshape2 , earg = .eshape2 , tag = FALSE))[
interleave.VGAM(M, M1 = M1)]
if (!length(etastart)) {
kumar.Loglikfun <- function(shape1, y, x, w, extraargs) {
mediany <- colSums(y * w) / colSums(w)
shape2 <- log(0.5) / log1p(-(mediany^shape1))
sum(c(w) * dkumar(y, shape1 = shape1, shape2 = shape2, log = TRUE))
}
shape1.grid <- as.vector( .gshape1 )
shape1.init <- if (length( .ishape1 )) .ishape1 else
grid.search(shape1.grid, objfun = kumar.Loglikfun,
y = y, x = x, w = w)
shape1.init <- matrix(shape1.init, n, ncoly, byrow = TRUE)
mediany <- colSums(y * w) / colSums(w)
shape2.init <- if (length( .ishape2 )) .ishape2 else
log(0.5) / log1p(-(mediany^shape1.init))
shape2.init <- matrix(shape2.init, n, ncoly, byrow = TRUE)
etastart <-
cbind(theta2eta(shape1.init, .lshape1 , earg = .eshape1 ),
theta2eta(shape2.init, .lshape2 , earg = .eshape2 ))[,
interleave.VGAM(M, M1 = M1)]
}
}), list( .lshape1 = lshape1, .lshape2 = lshape2,
.ishape1 = ishape1, .ishape2 = ishape2,
.eshape1 = eshape1, .eshape2 = eshape2,
.gshape1 = gshape1 ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
shape1 <- eta2theta(eta[, c(TRUE, FALSE)], .lshape1 , earg = .eshape1 )
shape2 <- eta2theta(eta[, c(FALSE, TRUE)], .lshape2 , earg = .eshape2 )
shape2 * (base::beta(1 + 1/shape1, shape2))
}, list( .lshape1 = lshape1, .lshape2 = lshape2,
.eshape1 = eshape1, .eshape2 = eshape2 ))),
last = eval(substitute(expression({
misc$link <- c(rep_len( .lshape1 , ncoly),
rep_len( .lshape2 , ncoly))[interleave.VGAM(M, M1 = M1)]
temp.names <- c(mynames1, mynames2)[interleave.VGAM(M, M1 = M1)]
names(misc$link) <- temp.names
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names
for (ii in 1:ncoly) {
misc$earg[[M1*ii-1]] <- .eshape1
misc$earg[[M1*ii ]] <- .eshape2
}
}), list( .lshape1 = lshape1, .lshape2 = lshape2,
.eshape1 = eshape1, .eshape2 = eshape2 ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL, summation = TRUE) {
shape1 <- eta2theta(eta[, c(TRUE, FALSE)], .lshape1 , earg = .eshape1 )
shape2 <- eta2theta(eta[, c(FALSE, TRUE)], .lshape2 , earg = .eshape2 )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <- c(w) * dkumar(x = y, shape1, shape2, log = TRUE)
if (summation) sum(ll.elts) else ll.elts
}
}, list( .lshape1 = lshape1, .lshape2 = lshape2,
.eshape1 = eshape1, .eshape2 = eshape2 ))),
vfamily = c("kumar"),
validparams = eval(substitute(function(eta, y, extra = NULL) {
shape1 <- eta2theta(eta[, c(TRUE, FALSE)], .lshape1 , earg = .eshape1 )
shape2 <- eta2theta(eta[, c(FALSE, TRUE)], .lshape2 , earg = .eshape2 )
okay1 <- all(is.finite(shape1)) && all(0 < shape1) &&
all(is.finite(shape2)) && all(0 < shape2)
okay1
}, list( .lshape1 = lshape1, .lshape2 = lshape2,
.eshape1 = eshape1, .eshape2 = eshape2 ))),
simslot = eval(substitute(
function(object, nsim) {
eta <- predict(object)
shape1 <- eta2theta(eta[, c(TRUE, FALSE)], .lshape1 , earg = .eshape1 )
shape2 <- eta2theta(eta[, c(FALSE, TRUE)], .lshape2 , earg = .eshape2 )
rkumar(nsim * length(shape1), shape1 = shape1, shape2 = shape2)
}, list( .lshape1 = lshape1, .lshape2 = lshape2,
.eshape1 = eshape1, .eshape2 = eshape2 ))),
deriv = eval(substitute(expression({
shape1 <- eta2theta(eta[, c(TRUE, FALSE)], .lshape1 , earg = .eshape1 )
shape2 <- eta2theta(eta[, c(FALSE, TRUE)], .lshape2 , earg = .eshape2 )
dshape1.deta <- dtheta.deta(shape1, link = .lshape1 , earg = .eshape1 )
dshape2.deta <- dtheta.deta(shape2, link = .lshape2 , earg = .eshape2 )
dl.dshape1 <- 1 / shape1 + log(y) - (shape2 - 1) * log(y) *
(y^shape1) / (1 - y^shape1)
dl.dshape2 <- 1 / shape2 + log1p(-y^shape1)
dl.deta <- c(w) * cbind(dl.dshape1 * dshape1.deta,
dl.dshape2 * dshape2.deta)
dl.deta[, interleave.VGAM(M, M1 = M1)]
}), list( .lshape1 = lshape1, .lshape2 = lshape2,
.eshape1 = eshape1, .eshape2 = eshape2 ))),
weight = eval(substitute(expression({
ned2l.dshape11 <- (1 + (shape2 / (shape2 - 2)) *
((digamma(shape2) - digamma(2))^2 -
(trigamma(shape2) - trigamma(2)))) / shape1^2
ned2l.dshape22 <- 1 / shape2^2
ned2l.dshape12 <-
(digamma(2) - digamma(1 + shape2)) / ((shape2 - 1) * shape1)
index1 <- (abs(shape2 - 1) < .tol12 )
ned2l.dshape12[index1] <- -trigamma(2) / shape1[index1]
index2 <- (abs(shape2 - 2) < .tol12 )
ned2l.dshape11[index2] <-
(1 - 2 * psigamma(2, deriv = 2)) / shape1[index2]^2
wz <- array(c(c(w) * ned2l.dshape11 * dshape1.deta^2,
c(w) * ned2l.dshape22 * dshape2.deta^2,
c(w) * ned2l.dshape12 * dshape1.deta * dshape2.deta),
dim = c(n, M / M1, 3))
wz <- arwz2wz(wz, M = M, M1 = M1)
wz
}), list( .lshape1 = lshape1, .lshape2 = lshape2,
.eshape1 = eshape1, .eshape2 = eshape2, .tol12 = tol12 ))))
}
drice <- function(x, sigma, vee, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
N <- max(length(x), length(vee), length(sigma))
if (length(x) != N) x <- rep_len(x, N)
if (length(vee) != N) vee <- rep_len(vee , N)
if (length(sigma ) != N) sigma <- rep_len(sigma , N)
logdensity <- rep_len(log(0), N)
xok <- (x > 0)
x.abs <- abs(x[xok] * vee[xok] / sigma[xok]^2)
logdensity[xok] <- log(x[xok]) - 2 * log(sigma[xok]) +
(-(x[xok]^2+vee[xok]^2)/(2*sigma[xok]^2)) +
log(besselI(x.abs, nu = 0, expon.scaled = TRUE)) +
x.abs
logdensity[sigma <= 0] <- NaN
logdensity[vee < 0] <- NaN
logdensity[is.infinite(x)] <- -Inf
if (log.arg) logdensity else exp(logdensity)
}
rrice <- function(n, sigma, vee) {
theta <- 1
X <- rnorm(n, mean = vee * cos(theta), sd = sigma)
Y <- rnorm(n, mean = vee * sin(theta), sd = sigma)
sqrt(X^2 + Y^2)
}
marcumQ <- function(a, b, m = 1,
lower.tail = TRUE, log.p = FALSE, ... ) {
pchisq(b^2, df = 2*m, ncp = a^2,
lower.tail = lower.tail, log.p = log.p, ... )
}
price <- function(q, sigma, vee,
lower.tail = TRUE, log.p = FALSE, ...) {
ans <- marcumQ(vee/sigma, q/sigma, m = 1,
lower.tail = lower.tail, log.p = log.p, ... )
ans
}
qrice <- function(p, sigma, vee,
lower.tail = TRUE, log.p = FALSE, ... ) {
sqrt(qchisq(p, df = 2, ncp = (vee/sigma)^2,
lower.tail = lower.tail, log.p = log.p, ... )) * sigma
}
riceff.control <- function(save.weights = TRUE, ...) {
list(save.weights = save.weights)
}
riceff <- function(lsigma = "loglink", lvee = "loglink",
isigma = NULL, ivee = NULL,
nsimEIM = 100, zero = NULL, nowarning = FALSE) {
lvee <- as.list(substitute(lvee))
evee <- link2list(lvee)
lvee <- attr(evee, "function.name")
lsigma <- as.list(substitute(lsigma))
esigma <- link2list(lsigma)
lsigma <- attr(esigma, "function.name")
if (length(ivee) && !is.Numeric(ivee, positive = TRUE))
stop("bad input for argument 'ivee'")
if (length(isigma) && !is.Numeric(isigma, positive = TRUE))
stop("bad input for argument 'isigma'")
if (!is.Numeric(nsimEIM, length.arg = 1,
integer.valued = TRUE) ||
nsimEIM <= 50)
stop("'nsimEIM' should be an integer greater than 50")
new("vglmff",
blurb = c("Rice distribution\n\n",
"Links: ",
namesof("sigma", lsigma, earg = esigma, tag = FALSE), ", ",
namesof("vee", lvee, earg = evee, tag = FALSE), "\n",
"Mean: ",
"sigma*sqrt(pi/2)*exp(z/2)*((1-z)*",
"besselI(-z/2, nu = 0) - z * besselI(-z/2, nu = 1)) ",
"where z=-vee^2/(2*sigma^2)"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero , M = M,
predictors.names = predictors.names,
M1 = 2)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
expected = FALSE,
multipleResponses = FALSE,
parameters.names = c("sigma", "vee"),
nsimEIM = .nsimEIM,
lsigma = .lsigma ,
lvee = .lvee ,
zero = .zero )
}, list( .zero = zero, .lsigma = lsigma, .lvee = lvee,
.nsimEIM = nsimEIM ))),
initialize = eval(substitute(expression({
temp5 <-
w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = 1,
ncol.y.max = 1,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
w <- temp5$w
y <- temp5$y
predictors.names <-
c(namesof("sigma", .lsigma , earg = .esigma , tag = FALSE),
namesof("vee", .lvee , earg = .evee , tag = FALSE))
if (!length(etastart)) {
riceff.Loglikfun <- function(vee, y, x, w, extraargs) {
sigma.init <- sd(rep(y, w))
sum(c(w) * (log(y) - 2*log(sigma.init) +
log(besselI(y*vee/sigma.init^2, nu = 0)) -
(y^2 + vee^2) / (2*sigma.init^2)))
}
vee.grid <-
seq(quantile(rep(y, w), probs = seq(0, 1, 0.2))["20%"],
quantile(rep(y, w), probs = seq(0, 1, 0.2))["80%"], len = 11)
vee.init <- if (length( .ivee )) .ivee else
grid.search(vee.grid, objfun = riceff.Loglikfun,
y = y, x = x, w = w)
vee.init <- rep_len(vee.init, length(y))
sigma.init <- if (length( .isigma )) .isigma else
sqrt(max((weighted.mean(y^2, w) - vee.init^2)/2, 0.001))
sigma.init <- rep_len(sigma.init, length(y))
etastart <-
cbind(theta2eta(sigma.init, .lsigma , earg = .esigma ),
theta2eta(vee.init, .lvee , earg = .evee ))
}
}), list( .lvee = lvee, .lsigma = lsigma,
.ivee = ivee, .isigma = isigma,
.evee = evee, .esigma = esigma ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
vee <- eta2theta(eta[, 1], link = .lvee , earg = .evee )
sigma <- eta2theta(eta[, 2], link = .lsigma , earg = .esigma )
temp9 <- -vee^2 / (2*sigma^2)
sigma * sqrt(pi/2) *
((1-temp9) * besselI(-temp9/2, nu = 0, expon = TRUE) -
temp9 * besselI(-temp9/2, nu = 1, expon = TRUE))
}, list( .lvee = lvee, .lsigma = lsigma,
.evee = evee, .esigma = esigma ))),
last = eval(substitute(expression({
misc$link <- c("sigma" = .lsigma , "vee" = .lvee )
misc$earg <- list("sigma" = .esigma , "vee" = .evee )
misc$expected <- TRUE
misc$nsimEIM <- .nsimEIM
misc$multipleResponses <- FALSE
}), list( .lvee = lvee, .lsigma = lsigma,
.evee = evee, .esigma = esigma, .nsimEIM = nsimEIM ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL,
summation = TRUE) {
sigma <- eta2theta(eta[, 1], link = .lsigma , earg = .esigma )
vee <- eta2theta(eta[, 2], link = .lvee , earg = .evee )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <- c(w) * drice(x = y, sigma = sigma, vee = vee, log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lvee = lvee, .lsigma = lsigma,
.evee = evee, .esigma = esigma ))),
vfamily = c("riceff"),
validparams = eval(substitute(function(eta, y, extra = NULL) {
sigma <- eta2theta(eta[, 1], link = .lsigma , earg = .esigma )
vee <- eta2theta(eta[, 2], link = .lvee , earg = .evee )
okay1 <- all(is.finite(sigma)) && all(0 < sigma) &&
all(is.finite(vee )) && all(0 < vee )
okay1
}, list( .lvee = lvee, .lsigma = lsigma,
.evee = evee, .esigma = esigma ))),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- [email protected]) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
sigma <- eta2theta(eta[, 1], link = .lsigma , earg = .esigma )
vee <- eta2theta(eta[, 2], link = .lvee , earg = .evee )
rrice(nsim * length(vee),
vee = vee, sigma = sigma)
}, list( .lvee = lvee, .lsigma = lsigma,
.evee = evee, .esigma = esigma ))),
deriv = eval(substitute(expression({
sigma <- eta2theta(eta[, 1], link = .lsigma , earg = .esigma )
vee <- eta2theta(eta[, 2], link = .lvee , earg = .evee )
dvee.deta <- dtheta.deta(vee, link = .lvee , earg = .evee )
dsigma.deta <- dtheta.deta(sigma, link = .lsigma , earg = .esigma )
temp8 <- y * vee / sigma^2
dl.dvee <- -vee/sigma^2 + (y/sigma^2) *
besselI(temp8, nu = 1) / besselI(temp8, nu = 0)
dl.dsigma <- -2/sigma + (y^2 + vee^2)/(sigma^3) -
(2 * temp8 / sigma) *
besselI(temp8, nu = 1) / besselI(temp8, nu = 0)
c(w) * cbind(dl.dsigma * dsigma.deta,
dl.dvee * dvee.deta)
}), list( .lvee = lvee, .lsigma = lsigma,
.evee = evee, .esigma = esigma, .nsimEIM = nsimEIM ))),
weight = eval(substitute(expression({
run.var <- run.cov <- 0
for (ii in 1:( .nsimEIM )) {
ysim <- rrice(n, vee = vee, sigma = sigma)
temp8 <- ysim * vee / sigma^2
dl.dvee <- -vee/sigma^2 + (ysim/sigma^2) *
besselI(temp8, nu = 1) / besselI(temp8, nu = 0)
dl.dsigma <- -2/sigma + (ysim^2 + vee^2)/(sigma^3) -
(2 * temp8 / sigma) *
besselI(temp8, nu = 1) / besselI(temp8, nu = 0)
rm(ysim)
temp3 <- cbind(dl.dsigma, dl.dvee)
run.var <- ((ii-1) * run.var + temp3^2) / ii
run.cov <- ((ii-1) * run.cov + temp3[, 1] * temp3[, 2]) / ii
}
wz <- if (intercept.only)
matrix(colMeans(cbind(run.var, run.cov)),
n, dimm(M), byrow = TRUE) else cbind(run.var, run.cov)
dtheta.detas <- cbind(dsigma.deta, dvee.deta)
index0 <- iam(NA_real_, NA_real_, M = M, both = TRUE, diag = TRUE)
wz <- wz * dtheta.detas[, index0$row] *
dtheta.detas[, index0$col]
c(w) * wz
}), list( .lvee = lvee, .lsigma = lsigma,
.evee = evee, .esigma = esigma, .nsimEIM = nsimEIM ))))
}
dskellam <- function(x, mu1, mu2, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
L <- max(length(x), length(mu1), length(mu2))
if (length(x) != L) x <- rep_len(x, L)
if (length(mu1) != L) mu1 <- rep_len(mu1, L)
if (length(mu2) != L) mu2 <- rep_len(mu2, L)
ok2 <- is.finite(mu1) & is.finite(mu2) & (mu1 >= 0) & (mu2 >= 0)
ok3 <- (mu1 == 0) & (mu2 > 0)
ok4 <- (mu1 > 0) & (mu2 == 0)
ok5 <- (mu1 == 0) & (mu2 == 0)
if (log.arg) {
ans <- -mu1 - mu2 + 2 * sqrt(mu1*mu2) +
0.5 * x * log(mu1) - 0.5 * x * log(mu2) +
log(besselI(2 * sqrt(mu1*mu2),
nu = abs(x),
expon.scaled = TRUE))
ans[ok3] <- dpois(x = -x[ok3], lambda = mu2[ok3], log = TRUE)
ans[ok4] <- dpois(x = -x[ok4], lambda = mu1[ok4], log = TRUE)
ans[ok5] <- dpois(x = x[ok5], lambda = 0.0, log = TRUE)
ans[x != round(x)] = log(0.0)
} else {
ans <- (mu1/mu2)^(x/2) * exp(-mu1-mu2 + 2 * sqrt(mu1*mu2)) *
besselI(2 * sqrt(mu1*mu2),
nu = abs(x),
expon.scaled = TRUE)
ans[ok3] <- dpois(x = -x[ok3], lambda = mu2[ok3])
ans[ok4] <- dpois(x = -x[ok4], lambda = mu1[ok4])
ans[ok5] <- dpois(x = x[ok5], lambda = 0.0)
ans[x != round(x)] <- 0.0
}
ans[!ok2] <- NaN
ans
}
rskellam <- function(n, mu1, mu2) {
rpois(n, mu1) - rpois(n, mu2)
}
skellam.control <- function(save.weights = TRUE, ...) {
list(save.weights = save.weights)
}
skellam <- function(lmu1 = "loglink", lmu2 = "loglink",
imu1 = NULL, imu2 = NULL,
nsimEIM = 100, parallel = FALSE, zero = NULL) {
lmu1 <- as.list(substitute(lmu1))
emu1 <- link2list(lmu1)
lmu1 <- attr(emu1, "function.name")
lmu2 <- as.list(substitute(lmu2))
emu2 <- link2list(lmu2)
lmu2 <- attr(emu2, "function.name")
if (length(imu1) &&
!is.Numeric(imu1, positive = TRUE))
stop("bad input for argument 'imu1'")
if (length(imu2) &&
!is.Numeric(imu2, positive = TRUE))
stop("bad input for argument 'imu2'")
if (!is.Numeric(nsimEIM, length.arg = 1,
integer.valued = TRUE) ||
nsimEIM <= 50)
stop("argument 'nsimEIM' should be an integer greater than 50")
new("vglmff",
blurb = c("Skellam distribution\n\n",
"Links: ",
namesof("mu1", lmu1, earg = emu1, tag = FALSE), ", ",
namesof("mu2", lmu2, earg = emu2, tag = FALSE), "\n",
"Mean: mu1-mu2", "\n",
"Variance: mu1+mu2"),
constraints = eval(substitute(expression({
constraints <- cm.VGAM(matrix(1, M, 1), x = x,
bool = .parallel ,
constraints = constraints,
apply.int = TRUE)
constraints <- cm.zero.VGAM(constraints, x = x, .zero , M = M,
predictors.names = predictors.names,
M1 = 2)
}), list( .parallel = parallel, .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
expected = FALSE,
multipleResponses = FALSE,
parameters.names = c("mu1", "mu2"),
nsimEIM = .nsimEIM,
lmu1 = .lmu1 ,
lmu2 = .lmu2 ,
zero = .zero )
}, list( .zero = zero, .lmu1 = lmu1, .lmu2 = lmu2,
.nsimEIM = nsimEIM ))),
initialize = eval(substitute(expression({
temp5 <-
w.y.check(w = w, y = y,
ncol.w.max = 1,
ncol.y.max = 1,
Is.integer.y = TRUE,
out.wy = TRUE,
maximize = TRUE)
w <- temp5$w
y <- temp5$y
predictors.names <- c(
namesof("mu1", .lmu1, earg = .emu1, tag = FALSE),
namesof("mu2", .lmu2, earg = .emu2, tag = FALSE))
if (!length(etastart)) {
junk <- lm.wfit(x = x, y = c(y), w = c(w))
var.y.est <- sum(c(w) * junk$resid^2) / junk$df.residual
mean.init <- weighted.mean(y, w)
mu1.init <- max((var.y.est + mean.init) / 2, 0.01)
mu2.init <- max((var.y.est - mean.init) / 2, 0.01)
mu1.init <- rep_len(if (length( .imu1 )) .imu1 else mu1.init, n)
mu2.init <- rep_len(if (length( .imu2 )) .imu2 else mu2.init, n)
etastart <- cbind(theta2eta(mu1.init, .lmu1, earg = .emu1 ),
theta2eta(mu2.init, .lmu2, earg = .emu2 ))
}
}), list( .lmu1 = lmu1, .lmu2 = lmu2,
.imu1 = imu1, .imu2 = imu2,
.emu1 = emu1, .emu2 = emu2 ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
mu1 <- eta2theta(eta[, 1], link = .lmu1, earg = .emu1 )
mu2 <- eta2theta(eta[, 2], link = .lmu2, earg = .emu2 )
mu1 - mu2
}, list( .lmu1 = lmu1, .lmu2 = lmu2,
.emu1 = emu1, .emu2 = emu2 ))),
last = eval(substitute(expression({
misc$link <- c("mu1" = .lmu1, "mu2" = .lmu2)
misc$earg <- list("mu1" = .emu1, "mu2" = .emu2 )
misc$expected <- TRUE
misc$nsimEIM <- .nsimEIM
}), list( .lmu1 = lmu1, .lmu2 = lmu2,
.emu1 = emu1, .emu2 = emu2,
.nsimEIM = nsimEIM ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL,
summation = TRUE) {
mu1 <- eta2theta(eta[, 1], link = .lmu1, earg = .emu1 )
mu2 <- eta2theta(eta[, 2], link = .lmu2, earg = .emu2 )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <-
if ( is.logical( .parallel ) &&
length( .parallel ) == 1 &&
.parallel )
c(w) * log(besselI(2*mu1, nu = y, expon = TRUE)) else
c(w) * (-mu1 - mu2 +
0.5 * y * log(mu1) -
0.5 * y * log(mu2) +
2 * sqrt(mu1*mu2) +
log(besselI(2 * sqrt(mu1*mu2), nu = y, expon = TRUE)))
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lmu1 = lmu1, .lmu2 = lmu2,
.emu1 = emu1, .emu2 = emu2,
.parallel = parallel ))),
vfamily = c("skellam"),
validparams = eval(substitute(function(eta, y, extra = NULL) {
mu1 <- eta2theta(eta[, 1], link = .lmu1, earg = .emu1 )
mu2 <- eta2theta(eta[, 2], link = .lmu2, earg = .emu2 )
okay1 <- all(is.finite(mu1)) && all(0 < mu1) &&
all(is.finite(mu2)) && all(0 < mu2)
okay1
}, list( .lmu1 = lmu1, .lmu2 = lmu2,
.emu1 = emu1, .emu2 = emu2 ))),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- [email protected]) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
mu1 <- eta2theta(eta[, 1], link = .lmu1, earg = .emu1 )
mu2 <- eta2theta(eta[, 2], link = .lmu2, earg = .emu2 )
rskellam(nsim * length(mu1), mu1, mu2)
}, list( .lmu1 = lmu1, .lmu2 = lmu2,
.emu1 = emu1, .emu2 = emu2,
.parallel = parallel ))),
deriv = eval(substitute(expression({
mu1 <- eta2theta(eta[, 1], link = .lmu1, earg = .emu1 )
mu2 <- eta2theta(eta[, 2], link = .lmu2, earg = .emu2 )
dmu1.deta <- dtheta.deta(mu1, link = .lmu1, earg = .emu1 )
dmu2.deta <- dtheta.deta(mu2, link = .lmu2, earg = .emu2 )
temp8 <- 2 * sqrt(mu1*mu2)
temp9 <- besselI(temp8, nu = y , expon = TRUE)
temp7 <- (besselI(temp8, nu = y-1, expon = TRUE) +
besselI(temp8, nu = y+1, expon = TRUE)) / 2
temp6 <- temp7 / temp9
dl.dmu1 <- -1 + 0.5 * y / mu1 + sqrt(mu2/mu1) * temp6
dl.dmu2 <- -1 - 0.5 * y / mu2 + sqrt(mu1/mu2) * temp6
c(w) * cbind(dl.dmu1 * dmu1.deta,
dl.dmu2 * dmu2.deta)
}), list( .lmu1 = lmu1, .lmu2 = lmu2,
.emu1 = emu1, .emu2 = emu2,
.nsimEIM = nsimEIM ))),
weight = eval(substitute(expression({
run.var <- run.cov <- 0
for (ii in 1:( .nsimEIM )) {
ysim <- rskellam(n, mu1=mu1, mu2=mu2)
temp9 <- besselI(temp8, nu = ysim, expon = TRUE)
temp7 <- (besselI(temp8, nu = ysim-1, expon = TRUE) +
besselI(temp8, nu = ysim+1, expon = TRUE)) / 2
temp6 <- temp7 / temp9
dl.dmu1 <- -1 + 0.5 * ysim/mu1 + sqrt(mu2/mu1) * temp6
dl.dmu2 <- -1 - 0.5 * ysim/mu2 + sqrt(mu1/mu2) * temp6
rm(ysim)
temp3 <- cbind(dl.dmu1, dl.dmu2)
run.var <- ((ii-1) * run.var + temp3^2) / ii
run.cov <- ((ii-1) * run.cov + temp3[, 1] * temp3[, 2]) / ii
}
wz <- if (intercept.only)
matrix(colMeans(cbind(run.var, run.cov)),
n, dimm(M), byrow = TRUE) else
cbind(run.var, run.cov)
dtheta.detas <- cbind(dmu1.deta, dmu2.deta)
index0 <- iam(NA_real_, NA_real_, M = M, both = TRUE, diag = TRUE)
wz <- wz * dtheta.detas[, index0$row] *
dtheta.detas[, index0$col]
c(w) * wz
}), list( .lmu1 = lmu1, .lmu2 = lmu2,
.emu1 = emu1, .emu2 = emu2,
.nsimEIM = nsimEIM ))))
}
dyules <- function(x, shape, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
LLL <- max(length(x), length(shape))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
bad0 <- !is.finite(shape) | shape <= 0
bad <- bad0 | !is.finite(x) | x < 1 | x != round(x)
logpdf <- x + shape
if (any(!bad)) {
logpdf[!bad] <- log(shape[!bad]) + lbeta(x[!bad], shape[!bad] + 1)
}
logpdf[!bad0 & is.infinite(x)] <- log(0)
logpdf[!bad0 & x < 1 ] <- log(0)
logpdf[!bad0 & x != round(x) ] <- log(0)
logpdf[ bad0] <- NaN
if (log.arg) logpdf else exp(logpdf)
}
pyules <- function(q, shape, lower.tail = TRUE, log.p = FALSE) {
tq <- trunc(q)
if (lower.tail) {
ans <- 1 - tq * beta(abs(tq), shape+1)
ans[q < 1] <- 0
ans[is.infinite(q) & 0 < q] <- 1
} else {
ans <- tq * beta(abs(tq), shape+1)
ans[q < 1] <- 1
ans[is.infinite(q) & 0 < q] <- 0
}
ans[shape <= 0] <- NaN
if (log.p) log(ans) else ans
ans
}
qyules <- function(p, shape) {
LLL <- max(length(p), length(shape))
if (length(p) != LLL) p <- rep_len(p, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
ans <- p + shape
bad0 <- !is.finite(shape) | shape <= 0
bad <- bad0 | !is.finite(p) | p <= 0 | 1 <= p
lo <- rep_len(1, LLL) - 0.5
approx.ans <- lo
hi <- 2 * lo + 10.5
dont.iterate <- bad
done <- dont.iterate | p <= pyules(hi, shape)
iter <- 0
max.iter <- round(log2(.Machine$double.xmax)) - 2
max.iter <- round(log2(1e300)) - 2
while (!all(done) && iter < max.iter) {
lo[!done] <- hi[!done]
hi[!done] <- 2 * hi[!done] + 10.5
done[!done] <- (p[!done] <= pyules(hi[!done],
shape = shape[!done]))
iter <- iter + 1
}
foo <- function(q, shape, p)
pyules(q, shape) - p
lhs <- dont.iterate | (p <= dyules(1, shape))
approx.ans[!lhs] <-
bisection.basic(foo, lo[!lhs], hi[!lhs], tol = 1/16,
shape = shape[!lhs], p = p[!lhs])
faa <- floor(approx.ans[!lhs])
tmp <-
ifelse(pyules(faa, shape = shape[!lhs]) < p[!lhs] &
p[!lhs] <= pyules(faa+1, shape = shape[!lhs]),
faa+1, faa)
ans[!lhs] <- tmp
vecTF <- !bad0 & !is.na(p) &
p <= dyules(1, shape)
ans[vecTF] <- 1
ans[!bad0 & !is.na(p) & p == 0] <- 1
ans[!bad0 & !is.na(p) & p == 1] <- Inf
ans[!bad0 & !is.na(p) & p < 0] <- NaN
ans[!bad0 & !is.na(p) & p > 1] <- NaN
ans[ bad0] <- NaN
ans
}
ryules <- function(n, shape) {
rgeom(n, prob = exp(-rexp(n, rate = shape))) + 1
}
yulesimon.control <- function(save.weights = TRUE, ...) {
list(save.weights = save.weights)
}
yulesimon <- function(lshape = "loglink",
ishape = NULL, nsimEIM = 200,
zero = NULL) {
if (length(ishape) &&
!is.Numeric(ishape, positive = TRUE))
stop("argument 'ishape' must be > 0")
lshape <- as.list(substitute(lshape))
eshape <- link2list(lshape)
lshape <- attr(eshape, "function.name")
if (!is.Numeric(nsimEIM, length.arg = 1,
integer.valued = TRUE) ||
nsimEIM <= 50)
stop("argument 'nsimEIM' should be an integer greater than 50")
new("vglmff",
blurb = c("Yule-Simon distribution ",
"f(y) = shape * beta(y, shape + 1), ",
"shape > 0, y = 1, 2,..\n\n",
"Link: ",
namesof("shape", lshape, earg = eshape), "\n\n",
"Mean: shape / (shape - 1), provided shape > 1\n",
"Variance: shape^2 / ((shape - 1)^2 * (shape - 2)), ",
"provided shape > 2"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero , M = M,
predictors.names = predictors.names,
M1 = 1)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 1,
Q1 = 1,
expected = TRUE,
multipleResponses = TRUE,
nsimEIM = .nsimEIM ,
parameters.names = c("shape"),
zero = .zero )
}, list( .zero = zero,
.nsimEIM = nsimEIM ))),
initialize = eval(substitute(expression({
temp5 <-
w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
Is.integer.y = TRUE,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
w <- temp5$w
y <- temp5$y
ncoly <- ncol(y)
M1 <- 1
extra$ncoly <- ncoly
extra$M1 <- M1
M <- M1 * ncoly
mynames1 <- param.names("shape", ncoly, skip1 = TRUE)
predictors.names <-
namesof(mynames1, .lshape , earg = .eshape , tag = FALSE)
if (!length(etastart)) {
wmeany <- colSums(y * w) / colSums(w) + 1/8
shape.init <- wmeany / (wmeany - 1)
shape.init <- matrix(if (length( .ishape )) .ishape else
shape.init, n, M, byrow = TRUE)
etastart <- theta2eta(shape.init, .lshape , earg = .eshape )
}
}), list( .lshape = lshape, .eshape = eshape, .ishape = ishape ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
ans <- shape <- eta2theta(eta, .lshape , earg = .eshape )
ans[shape > 1] <- shape / (shape - 1)
ans[shape <= 1] <- NA
ans
}, list( .lshape = lshape, .eshape = eshape ))),
last = eval(substitute(expression({
M1 <- extra$M1
misc$link <- c(rep_len( .lshape , ncoly))
names(misc$link) <- mynames1
misc$earg <- vector("list", M)
names(misc$earg) <- mynames1
for (ii in 1:ncoly) {
misc$earg[[ii]] <- .eshape
}
misc$M1 <- M1
misc$ishape <- .ishape
misc$nsimEIM <- .nsimEIM
}), list( .lshape = lshape, .eshape = eshape, .nsimEIM = nsimEIM,
.ishape = ishape ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL,
summation = TRUE) {
shape <- eta2theta(eta, .lshape , earg = .eshape )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <- c(w) * dyules(x = y, shape = shape, log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lshape = lshape, .eshape = eshape ))),
vfamily = c("yulesimon"),
validparams = eval(substitute(function(eta, y, extra = NULL) {
shape <- eta2theta(eta, .lshape , earg = .eshape )
okay1 <- all(is.finite(shape)) && all(0 < shape)
okay1
}, list( .lshape = lshape, .eshape = eshape ))),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- [email protected]) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
shape <- eta2theta(eta, .lshape , earg = .eshape )
ryules(nsim * length(shape), shape = shape)
}, list( .lshape = lshape, .eshape = eshape ))),
deriv = eval(substitute(expression({
M1 <- 1
shape <- eta2theta(eta, .lshape , earg = .eshape )
dl.dshape <- 1/shape + digamma(1+shape) - digamma(1+shape+y)
dshape.deta <- dtheta.deta(shape, .lshape , earg = .eshape )
c(w) * dl.dshape * dshape.deta
}), list( .lshape = lshape, .eshape = eshape ))),
weight = eval(substitute(expression({
run.var <- 0
for (ii in 1:( .nsimEIM )) {
ysim <- ryules(n, shape <- shape)
dl.dshape <- 1/shape + digamma(1+shape) - digamma(1+shape+ysim)
rm(ysim)
temp3 <- dl.dshape
run.var <- ((ii-1) * run.var + temp3^2) / ii
}
wz <- if (intercept.only)
matrix(colMeans(cbind(run.var)),
n, M, byrow = TRUE) else cbind(run.var)
wz <- wz * dshape.deta^2
c(w) * wz
}), list( .nsimEIM = nsimEIM ))))
}
dlind <- function(x, theta, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
if ( log.arg ) {
ans <- 2 * log(theta) + log1p(x) - theta * x - log1p(theta)
ans[x < 0 | is.infinite(x)] <- log(0)
} else {
ans <- theta^2 * (1 + x) * exp(-theta * x) / (1 + theta)
ans[x < 0 | is.infinite(x)] <- 0
}
ans[theta <= 0] <- NaN
ans
}
plind <- function(q, theta, lower.tail = TRUE, log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
if (lower.tail) {
if (log.p) {
ans <- log(-expm1(-theta * q + log1p(q / (1 + 1/theta))))
ans[q <= 0 ] <- -Inf
ans[q == Inf] <- 0
} else {
ans <- -expm1(-theta * q + log1p(q / (1 + 1/theta)))
ans[q <= 0] <- 0
ans[q == Inf] <- 1
}
} else {
if (log.p) {
ans <- -theta * q + log1p(q / (1 + 1/theta))
ans[q <= 0] <- 0
ans[q == Inf] <- -Inf
} else {
ans <- exp(-theta * q + log1p(q / (1 + 1/theta)))
ans[q <= 0] <- 1
ans[q == Inf] <- 0
}
}
ans[theta <= 0] <- NaN
ans
}
rlind <- function(n, theta) {
use.n <- if ((length.n <- length(n)) > 1) length.n else
if (!is.Numeric(n, integer.valued = TRUE,
length.arg = 1, positive = TRUE))
stop("bad input for argument 'n'") else n
ifelse(runif(use.n) < rep_len(1 / (1 + 1/theta), use.n),
rexp(use.n, theta),
rgamma(use.n, shape = 2, scale = 1 / theta))
}
lindley <- function(link = "loglink",
itheta = NULL, zero = NULL) {
if (length(itheta) &&
!is.Numeric(itheta, positive = TRUE))
stop("argument 'itheta' must be > 0")
link <- as.list(substitute(link))
earg <- link2list(link)
link <- attr(earg, "function.name")
new("vglmff",
blurb = c("Lindley distribution f(y) = ",
"theta^2 * (1 + y) * exp(-theta * y) / (1 + theta), ",
"theta > 0, y > 0,\n\n",
"Link: ",
namesof("theta", link, earg = earg), "\n\n",
"Mean: (theta + 2) / (theta * (theta + 1))\n",
"Variance: (theta^2 + 4 * theta + 2) / (theta * (theta + 1))^2"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero , M = M,
predictors.names = predictors.names,
M1 = 1)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 1,
Q1 = 1,
expected = TRUE,
hadof = TRUE,
multipleResponses = TRUE,
parameters.names = c("theta"),
zero = .zero )
}, list( .zero = zero ))),
initialize = eval(substitute(expression({
temp5 <-
w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
w <- temp5$w
y <- temp5$y
ncoly <- ncol(y)
M1 <- 1
extra$ncoly <- ncoly
extra$M1 <- M1
M <- M1 * ncoly
mynames1 <- param.names("theta", ncoly, skip1 = TRUE)
predictors.names <- namesof(mynames1, .link , earg = .earg ,
tag = FALSE)
if (!length(etastart)) {
wmeany <- colSums(y * w) / colSums(w) + 1/8
theta.init <- 1 / (wmeany + 1)
theta.init <- matrix(if (length( .itheta )) .itheta else
theta.init, n, M, byrow = TRUE)
etastart <- theta2eta(theta.init, .link , earg = .earg )
}
}), list( .link = link, .earg = earg, .itheta = itheta ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
theta <- eta2theta(eta, .link , earg = .earg )
(theta + 2) / (theta * (theta + 1))
}, list( .link = link, .earg = earg ))),
last = eval(substitute(expression({
M1 <- extra$M1
misc$link <- c(rep_len( .link , ncoly))
names(misc$link) <- mynames1
misc$earg <- vector("list", M)
names(misc$earg) <- mynames1
for (ii in 1:ncoly) {
misc$earg[[ii]] <- .earg
}
misc$M1 <- M1
misc$itheta <- .itheta
}), list( .link = link, .earg = earg,
.itheta = itheta ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL,
summation = TRUE) {
theta <- eta2theta(eta, .link , earg = .earg )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <- c(w) * dlind(x = y, theta = theta, log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .link = link, .earg = earg ))),
vfamily = c("lindley"),
hadof = eval(substitute(
function(eta, extra = list(), deriv = 1,
linpred.index = 1,
w = 1, dim.wz = c(NROW(eta), NCOL(eta) * (NCOL(eta)+1)/2),
...) {
theta <- eta2theta(eta, .link , earg = .earg )
numer <- theta^2 + 4 * theta + 2
denom <- (theta * (1 + theta))^2
ans <- c(w) *
switch(as.character(deriv),
"0" = numer / denom,
"1" = (2 * theta + 4 - numer *
2 * theta * (1 + theta) * (1 + 2 * theta) / denom) / denom,
"2" = NA * theta,
"3" = NA * theta,
stop("argument 'deriv' must be 0, 1, 2 or 3"))
if (deriv == 0) ans else
retain.col(ans, linpred.index)
}, list( .link = link, .earg = earg ))),
validparams = eval(substitute(function(eta, y, extra = NULL) {
theta <- eta2theta(eta, .link , earg = .earg )
okay1 <- all(is.finite(theta)) && all(0 < theta)
okay1
}, list( .link = link, .earg = earg ))),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- [email protected]) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
theta <- eta2theta(eta, .link , earg = .earg )
rlind(nsim * length(theta), theta = theta)
}, list( .link = link, .earg = earg ))),
deriv = eval(substitute(expression({
M1 <- 1
theta <- eta2theta(eta, .link , earg = .earg )
dl.dtheta <- 2 / theta - 1 / (1 + theta) - y
DTHETA.DETA <- dtheta.deta(theta, .link , earg = .earg )
c(w) * dl.dtheta * DTHETA.DETA
}), list( .link = link, .earg = earg ))),
weight = eval(substitute(expression({
ned2l.dtheta2 <- (theta^2 + 4 * theta + 2) / (theta * (1 + theta))^2
c(w) * ned2l.dtheta2 * DTHETA.DETA^2
}), list( .zero = zero ))))
}
dpoislindley <- function(x, theta, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
if ( log.arg ) {
ans <- 2 * log(theta) + log(theta + 2 + x) -
(x+3) * log1p(theta)
ans[(x != round(x)) | (x < 0)] <- log(0)
} else {
ans <- theta^2 * (theta + 2 + x) / (theta + 1)^(x+3)
ans[(x != round(x)) | (x < 0)] <- 0
}
ans[
(theta <= 0)] <- NA
ans
}
dslash <- function(x, mu = 0, sigma = 1, log = FALSE,
smallno = .Machine$double.eps * 1000) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
if (!is.Numeric(sigma) || any(sigma <= 0))
stop("argument 'sigma' must be positive")
L <- max(length(x), length(mu), length(sigma))
if (length(x) != L) x <- rep_len(x, L)
if (length(mu) != L) mu <- rep_len(mu, L)
if (length(sigma) != L) sigma <- rep_len(sigma, L)
zedd <- (x-mu)/sigma
if (log.arg) {
ifelse(abs(zedd) < smallno,
-log(2*sigma*sqrt(2*pi)),
log1p(-exp(-zedd^2/2)) - log(sqrt(2*pi)*sigma*zedd^2))
} else {
ifelse(abs(zedd) < smallno,
1/(2*sigma*sqrt(2*pi)),
-expm1(-zedd^2/2)/(sqrt(2*pi)*sigma*zedd^2))
}
}
pslash <- function(q, mu = 0, sigma = 1, very.negative = -10000,
lower.tail = TRUE, log.p = FALSE) {
if (anyNA(q))
stop("argument 'q' must have non-missing values")
if (!is.Numeric(mu))
stop("argument 'mu' must have finite and non-missing values")
if (!is.Numeric(sigma, positive = TRUE))
stop("argument 'sigma' must have positive finite non-missing values")
if (!is.Numeric(very.negative, length.arg = 1) ||
(very.negative >= 0))
stop("argument 'very.negative' must be quite negative")
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
L <- max(length(q), length(mu), length(sigma))
if (length(q) != L) q <- rep_len(q, L)
if (length(mu) != L) mu <- rep_len(mu, L)
if (length(sigma) != L) sigma <- rep_len(sigma, L)
zedd <- (q - mu)/sigma
ans <- as.numeric(q * NA)
extreme.q <- FALSE
for (ii in 1:L) {
use.trick <- (-abs(zedd[ii]) <= very.negative)
if (use.trick) {
ans[ii] <- ifelse(zedd[ii] < 0, 0.0, 1.0)
extreme.q <- TRUE
} else
if ((zedd[ii] >= very.negative) &&
zedd[ii] <= 0.0) {
temp2 <- integrate(dslash, lower = q[ii], upper = mu[ii],
mu = mu[ii], sigma = sigma[ii])
if (temp2$message != "OK")
warning("integrate() failed on 'temp2'")
ans[ii] <- 0.5 - temp2$value
} else {
temp1 <- integrate(dslash, lower = mu[ii], upper = q[ii],
mu = mu[ii], sigma = sigma[ii])
if (temp1$message != "OK")
warning("integrate() failed")
ans[ii] <- 0.5 + temp1$value
}
}
if (extreme.q)
warning("returning 0 or 1 values for extreme values of argument 'q'")
if (lower.tail) {
if (log.p) log(ans) else ans
} else {
if (log.p) log1p(-ans) else -expm1(log(ans))
}
}
rslash <- function (n, mu = 0, sigma = 1) {
rnorm(n = n, mean = mu, sd = sigma) / runif(n = n)
}
slash.control <- function(save.weights = TRUE, ...) {
list(save.weights = save.weights)
}
slash <- function(lmu = "identitylink", lsigma = "loglink",
imu = NULL, isigma = NULL,
gprobs.y = ppoints(8),
nsimEIM = 250, zero = NULL,
smallno = .Machine$double.eps * 1000) {
lmu <- as.list(substitute(lmu))
emu <- link2list(lmu)
lmu <- attr(emu, "function.name")
lsigma <- as.list(substitute(lsigma))
esigma <- link2list(lsigma)
lsigma <- attr(esigma, "function.name")
if (length(isigma) &&
!is.Numeric(isigma, positive = TRUE))
stop("argument 'isigma' must be > 0")
if (!is.Numeric(nsimEIM, length.arg = 1,
integer.valued = TRUE) ||
nsimEIM <= 50)
stop("argument 'nsimEIM' should be an integer greater than 50")
if (!is.Numeric(gprobs.y, positive = TRUE) ||
max(gprobs.y) >= 1)
stop("bad input for argument 'gprobs.y'")
if (!is.Numeric(smallno, positive = TRUE) ||
smallno > 0.1)
stop("bad input for argument 'smallno'")
new("vglmff",
blurb = c("Slash distribution\n\n",
"Links: ",
namesof("mu", lmu, earg = emu, tag = FALSE), ", ",
namesof("sigma", lsigma, earg = esigma, tag = FALSE), "\n",
paste(
"1-exp(-(((y-mu)/sigma)^2)/2))/(sqrt(2*pi)*",
"sigma*((y-mu)/sigma)^2)",
"\ty!=mu",
"\n1/(2*sigma*sqrt(2*pi))",
"\t\t\t\t\t\t\ty=mu\n")),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero , M = M,
predictors.names = predictors.names,
M1 = 2)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
expected = TRUE,
multipleResponses = FALSE,
parameters.names = c("mu", "sigma"),
lmu = .lmu ,
lsigma = .lsigma ,
zero = .zero )
}, list( .zero = zero, .lmu = lmu, .lsigma = lsigma ))),
initialize = eval(substitute(expression({
temp5 <-
w.y.check(w = w, y = y,
ncol.w.max = 1,
ncol.y.max = 1,
out.wy = TRUE,
maximize = TRUE)
w <- temp5$w
y <- temp5$y
predictors.names <- c(
namesof("mu", .lmu , earg = .emu, tag = FALSE),
namesof("sigma", .lsigma , earg = .esigma, tag = FALSE))
if (!length(etastart)) {
slash.Loglikfun <- function(mu, y, x, w, extraargs) {
sigma <- if (is.Numeric(.isigma)) .isigma else
max(0.01,
((quantile(rep(y, w), prob = 0.75)/2)-mu)/qnorm(0.75))
zedd <- (y-mu)/sigma
sum(c(w) * ifelse(abs(zedd)<.smallno,
-log(2*sigma*sqrt(2*pi)),
log1p(-exp(-zedd^2/2)) -
log(sqrt(2*pi) * sigma * zedd^2)))
}
gprobs.y <- .gprobs.y
mu.grid <- quantile(rep(y, w), probs = gprobs.y)
mu.grid <- seq(mu.grid[1], mu.grid[2], length=100)
mu.init <- if (length( .imu )) .imu else
grid.search(mu.grid, objfun = slash.Loglikfun,
y = y, x = x, w = w)
sigma.init <- if (is.Numeric(.isigma)) .isigma else
max(0.01,
((quantile(rep(y, w), prob = 0.75)/2) -
mu.init) / qnorm(0.75))
mu.init <- rep_len(mu.init, length(y))
etastart <- matrix(0, n, 2)
etastart[, 1] <- theta2eta(mu.init, .lmu , earg = .emu )
etastart[, 2] <- theta2eta(sigma.init, .lsigma , earg = .esigma )
}
}), list( .lmu = lmu, .lsigma = lsigma,
.imu = imu, .isigma = isigma,
.emu = emu, .esigma = esigma,
.gprobs.y = gprobs.y, .smallno = smallno))),
linkinv = eval(substitute(function(eta, extra = NULL) {
NA * eta2theta(eta[, 1], link = .lmu , earg = .emu )
}, list( .lmu = lmu, .emu = emu ))),
last = eval(substitute(expression({
misc$link <- c("mu" = .lmu , "sigma" = .lsigma )
misc$earg <- list("mu" = .emu , "sigma" = .esigma )
misc$expected <- TRUE
misc$nsimEIM <- .nsimEIM
}), list( .lmu = lmu, .lsigma = lsigma,
.emu = emu, .esigma = esigma, .nsimEIM = nsimEIM ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL,
summation = TRUE) {
mu <- eta2theta(eta[, 1], link = .lmu , earg = .emu )
sigma <- eta2theta(eta[, 2], link = .lsigma , earg = .esigma )
zedd <- (y - mu) / sigma
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <- c(w) * dslash(x = y, mu = mu, sigma = sigma, log = TRUE,
smallno = .smallno)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lmu = lmu, .lsigma = lsigma,
.emu = emu, .esigma = esigma, .smallno = smallno ))),
vfamily = c("slash"),
validparams = eval(substitute(function(eta, y, extra = NULL) {
mu <- eta2theta(eta[, 1], link = .lmu , earg = .emu )
sigma <- eta2theta(eta[, 2], link = .lsigma , earg = .esigma )
okay1 <- all(is.finite(mu)) &&
all(is.finite(sigma)) && all(0 < sigma)
okay1
}, list( .lmu = lmu, .lsigma = lsigma,
.emu = emu, .esigma = esigma ))),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- [email protected]) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
mu <- eta2theta(eta[, 1], link = .lmu , earg = .emu )
sigma <- eta2theta(eta[, 2], link = .lsigma , earg = .esigma )
rslash(nsim * length(sigma), mu = mu, sigma = sigma)
}, list( .lmu = lmu, .lsigma = lsigma,
.emu = emu, .esigma = esigma, .smallno = smallno ))),
deriv = eval(substitute(expression({
mu <- eta2theta(eta[, 1], link = .lmu , earg = .emu )
sigma <- eta2theta(eta[, 2], link = .lsigma , earg = .esigma )
dmu.deta <- dtheta.deta(mu, link = .lmu , earg = .emu )
dsigma.deta <- dtheta.deta(sigma, link = .lsigma , earg = .esigma )
zedd <- (y - mu) / sigma
d3 <- deriv3(~ w * log(1 - exp(-(((y - mu) / sigma)^2) / 2)) -
log(sqrt(2 * pi) * sigma * ((y - mu) / sigma)^2),
c("mu", "sigma"))
eval.d3 <- eval(d3)
dl.dthetas <- attr(eval.d3, "gradient")
dl.dmu <- dl.dthetas[, 1]
dl.dsigma <- dl.dthetas[, 2]
ind0 <- (abs(zedd) < .smallno)
dl.dmu[ind0] <- 0
dl.dsigma[ind0] <- -1 / sigma[ind0]
c(w) * cbind(dl.dmu * dmu.deta, dl.dsigma * dsigma.deta)
}), list( .lmu = lmu, .lsigma = lsigma,
.emu = emu, .esigma = esigma, .smallno = smallno ))),
weight = eval(substitute(expression({
run.varcov <- 0
ind1 <- iam(NA_real_, NA_real_, M = M, both = TRUE, diag = TRUE)
sd3 <- deriv3(~ w * log(1 - exp(-(((ysim - mu) / sigma)^2) / 2))-
log(sqrt(2 * pi) * sigma * ((ysim - mu) / sigma)^2),
c("mu", "sigma"))
for (ii in 1:( .nsimEIM )) {
ysim <- rslash(n, mu = mu, sigma = sigma)
seval.d3 <- eval(sd3)
dl.dthetas <- attr(seval.d3, "gradient")
dl.dmu <- dl.dthetas[, 1]
dl.dsigma <- dl.dthetas[, 2]
temp3 <- cbind(dl.dmu, dl.dsigma)
run.varcov <- run.varcov + temp3[, ind1$row] * temp3[, ind1$col]
}
run.varcov <- run.varcov / .nsimEIM
wz <- if (intercept.only)
matrix(colMeans(run.varcov, na.rm = FALSE),
n, ncol(run.varcov), byrow = TRUE) else run.varcov
dthetas.detas <- cbind(dmu.deta, dsigma.deta)
wz <- wz * dthetas.detas[, ind1$row] * dthetas.detas[, ind1$col]
c(w) * wz
}), list( .lmu = lmu, .lsigma = lsigma,
.emu = emu, .esigma = esigma,
.nsimEIM = nsimEIM, .smallno = smallno ))))
}
dnefghs <- function(x, tau, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
N <- max(length(x), length(tau))
if (length(x) != N) x <- rep_len(x, N)
if (length(tau) != N) tau <- rep_len(tau, N)
logdensity <- log(sin(pi*tau)) + (1-tau)*x - log(pi) - log1pexp(x)
logdensity[tau < 0] <- NaN
logdensity[tau > 1] <- NaN
if (log.arg) logdensity else exp(logdensity)
}
nefghs <- function(link = "logitlink",
itau = NULL, imethod = 1) {
if (length(itau) &&
!is.Numeric(itau, positive = TRUE) ||
any(itau >= 1))
stop("argument 'itau' must be in (0, 1)")
link <- as.list(substitute(link))
earg <- link2list(link)
link <- attr(earg, "function.name")
if (!is.Numeric(imethod, length.arg = 1,
integer.valued = TRUE, positive = TRUE) ||
imethod > 2)
stop("argument 'imethod' must be 1 or 2")
new("vglmff",
blurb = c("Natural exponential family generalized hyperbolic ",
"secant distribution\n",
"f(y) = sin(pi*tau)*exp((1-tau)*y)/(pi*(1+exp(y))\n\n",
"Link: ",
namesof("tau", link, earg = earg), "\n\n",
"Mean: pi / tan(pi * tau)\n"),
infos = eval(substitute(function(...) {
list(M1 = 1,
Q1 = 1,
expected = TRUE,
multipleResponses = FALSE,
parameters.names = c("tau"),
ltau = .link )
}, list( .link = link ))),
initialize = eval(substitute(expression({
temp5 <-
w.y.check(w = w, y = y,
ncol.w.max = 1,
ncol.y.max = 1,
out.wy = TRUE,
maximize = TRUE)
w <- temp5$w
y <- temp5$y
predictors.names <-
namesof("tau", .link , earg = .earg , tag = FALSE)
if (!length(etastart)) {
wmeany <- if ( .imethod == 1) weighted.mean(y, w) else
median(rep(y, w))
if (abs(wmeany) < 0.01)
wmeany <- 0.01
tau.init <- atan(pi / wmeany) / pi + 0.5
tau.init[tau.init < 0.03] <- 0.03
tau.init[tau.init > 0.97] <- 0.97
tau.init <- rep_len(if (length( .itau )) .itau else tau.init, n)
etastart <- theta2eta(tau.init, .link , earg = .earg )
}
}), list( .link = link, .earg = earg,
.itau = itau,
.imethod = imethod ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
tau <- eta2theta(eta, .link , earg = .earg )
pi / tan(pi * tau)
}, list( .link = link, .earg = earg ))),
last = eval(substitute(expression({
misc$link <- c(tau = .link )
misc$earg <- list(tau = .earg )
misc$expected <- TRUE
misc$imethod <- .imethod
}), list( .link = link, .earg = earg,
.imethod = imethod ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL,
summation = TRUE) {
tau <- eta2theta(eta, .link , earg = .earg )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <- c(w) * dnefghs(x = y, tau = tau, log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .link = link, .earg = earg ))),
vfamily = c("nefghs"),
validparams = eval(substitute(function(eta, y, extra = NULL) {
tau <- eta2theta(eta, .link , earg = .earg )
okay1 <- all(is.finite(tau)) && all(0 < tau)
okay1
}, list( .link = link, .earg = earg ))),
deriv = eval(substitute(expression({
tau <- eta2theta(eta, .link , earg = .earg )
dl.dtau <- pi / tan(pi * tau) - y
dtau.deta <- dtheta.deta(tau, .link , earg = .earg )
w * dl.dtau * dtau.deta
}), list( .link = link, .earg = earg ))),
weight = eval(substitute(expression({
ned2l.dtau2 <- (pi / sin(pi * tau))^2
wz <- ned2l.dtau2 * dtau.deta^2
c(w) * wz
}), list( .link = link ))))
}
dlogF <- function(x, shape1, shape2, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
logdensity <- shape1*x - lbeta(shape1, shape2) -
(shape1 + shape2) * log1pexp(x)
logdensity[is.infinite(x)] <- -Inf
if (log.arg) logdensity else exp(logdensity)
}
logF <- function(lshape1 = "loglink", lshape2 = "loglink",
ishape1 = NULL, ishape2 = 1,
imethod = 1) {
if (length(ishape1) &&
!is.Numeric(ishape1, positive = TRUE))
stop("argument 'ishape1' must be positive")
if (
!is.Numeric(ishape2, positive = TRUE))
stop("argument 'ishape2' must be positive")
lshape1 <- as.list(substitute(lshape1))
eshape1 <- link2list(lshape1)
lshape1 <- attr(eshape1, "function.name")
lshape2 <- as.list(substitute(lshape2))
eshape2 <- link2list(lshape2)
lshape2 <- attr(eshape2, "function.name")
if (!is.Numeric(imethod, length.arg = 1,
integer.valued = TRUE, positive = TRUE) ||
imethod > 2)
stop("argument 'imethod' must be 1 or 2")
new("vglmff",
blurb = c("log F distribution\n",
"f(y) = exp(-shape2 * y) / (beta(shape1, shape2) * ",
"(1 + exp(-y))^(shape1 + shape2))\n\n",
"Link: ",
namesof("shape1", lshape1, earg = eshape1), ", ",
namesof("shape2", lshape2, earg = eshape2), "\n\n",
"Mean: digamma(shape1) - digamma(shape2)"),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
expected = TRUE,
multipleResponses = FALSE,
parameters.names = c("shape1", "shape2"),
lshape1 = .lshape1 ,
lshape2 = .lshape2 ,
imethod = .imethod )
}, list( .lshape1 = lshape1, .imethod = imethod,
.lshape2 = lshape2 ))),
initialize = eval(substitute(expression({
temp5 <-
w.y.check(w = w, y = y,
ncol.w.max = 1,
ncol.y.max = 1,
out.wy = TRUE,
maximize = TRUE)
w <- temp5$w
y <- temp5$y
predictors.names <- c(
namesof("shape1", .lshape1 , earg = .eshape1 , tag = FALSE),
namesof("shape2", .lshape2 , earg = .eshape2 , tag = FALSE))
if (!length(etastart)) {
wmeany <- if ( .imethod == 1) weighted.mean(y, w) else
median(rep(y, w))
shape1.init <- shape2.init <- rep_len( .ishape2 , n)
shape1.init <- if (length( .ishape1)) rep_len( .ishape1, n) else {
index1 <- (y > wmeany)
shape1.init[ index1] <- shape2.init[ index1] + 1/1
shape1.init[!index1] <- shape2.init[!index1] - 1/1
shape1.init <- pmax(shape1.init, 1/8)
shape1.init
}
etastart <-
cbind(theta2eta(shape1.init, .lshape1 , earg = .eshape1 ),
theta2eta(shape2.init, .lshape2 , earg = .eshape2 ))
}
}), list( .lshape1 = lshape1, .lshape2 = lshape2,
.eshape1 = eshape1, .eshape2 = eshape2,
.ishape1 = ishape1, .ishape2 = ishape2,
.imethod = imethod ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
shape1 <- eta2theta(eta[, 1], .lshape1 , earg = .eshape1 )
shape2 <- eta2theta(eta[, 2], .lshape2 , earg = .eshape2 )
digamma(shape1) - digamma(shape2)
}, list( .lshape1 = lshape1, .lshape2 = lshape2,
.eshape1 = eshape1, .eshape2 = eshape2 ))),
last = eval(substitute(expression({
misc$link <- c(shape1 = .lshape1 , shape2 = .lshape2 )
misc$earg <- list(shape1 = .eshape1 , shape2 = .eshape2 )
extra$percentile <- numeric(ncol(y))
locat <- cbind(digamma(shape1) - digamma(shape2))
for (ii in 1:ncol(y)) {
y.use <- if (ncol(y) > 1) y[, ii] else y
extra$percentile[ii] <-
100 * weighted.mean(y.use <= locat[, ii], w[, min(ii, ncol(w))])
}
misc$expected <- TRUE
misc$imethod <- .imethod
}), list( .lshape1 = lshape1, .lshape2 = lshape2,
.eshape1 = eshape1, .eshape2 = eshape2,
.imethod = imethod ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL, summation = TRUE) {
shape1 <- eta2theta(eta[, 1], .lshape1 , earg = .eshape1 )
shape2 <- eta2theta(eta[, 2], .lshape2 , earg = .eshape2 )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <- c(w) * dlogF(x = y, shape1 = shape1,
shape2 = shape2, log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lshape1 = lshape1, .lshape2 = lshape2,
.eshape1 = eshape1, .eshape2 = eshape2 ))),
vfamily = c("logF"),
validparams = eval(substitute(function(eta, y, extra = NULL) {
shape1 <- eta2theta(eta[, 1], .lshape1 , earg = .eshape1 )
shape2 <- eta2theta(eta[, 2], .lshape2 , earg = .eshape2 )
okay1 <- all(is.finite(shape1)) && all(0 < shape1) &&
all(is.finite(shape2)) && all(0 < shape2)
okay1
}, list( .lshape1 = lshape1, .lshape2 = lshape2,
.eshape1 = eshape1, .eshape2 = eshape2 ))),
deriv = eval(substitute(expression({
shape1 <- eta2theta(eta[, 1], .lshape1 , earg = .eshape1 )
shape2 <- eta2theta(eta[, 2], .lshape2 , earg = .eshape2 )
tmp888 <- digamma(shape1 + shape2) - log1pexp(-y)
dl.dshape1 <- tmp888 - digamma(shape1)
dl.dshape2 <- tmp888 - digamma(shape2) - y
dshape1.deta <- dtheta.deta(shape1, .lshape1 , earg = .eshape1 )
dshape2.deta <- dtheta.deta(shape2, .lshape2 , earg = .eshape2 )
c(w) * cbind(dl.dshape1 * dshape1.deta,
dl.dshape2 * dshape2.deta)
}), list( .lshape1 = lshape1, .lshape2 = lshape2,
.eshape1 = eshape1, .eshape2 = eshape2 ))),
weight = eval(substitute(expression({
tmp888 <- trigamma(shape1 + shape2)
ned2l.dshape12 <- trigamma(shape1) - tmp888
ned2l.dshape22 <- trigamma(shape2) - tmp888
ned2l.dshape1shape2 <- -tmp888
wz <- matrix(0, n, dimm(M))
wz[, iam(1, 1, M = M)] <- ned2l.dshape12 * dshape1.deta^2
wz[, iam(2, 2, M = M)] <- ned2l.dshape22 * dshape2.deta^2
wz[, iam(1, 2, M = M)] <- ned2l.dshape1shape2 *
dshape1.deta * dshape2.deta
c(w) * wz
}), list( .lshape1 = lshape1, .lshape2 = lshape2,
.eshape1 = eshape1, .eshape2 = eshape2 ))))
}
dbenf <- function(x, ndigits = 1, log = FALSE) {
if (!is.Numeric(ndigits, length.arg = 1,
positive = TRUE, integer.valued = TRUE) ||
ndigits > 2)
stop("argument 'ndigits' must be 1 or 2")
lowerlimit <- ifelse(ndigits == 1, 1, 10)
upperlimit <- ifelse(ndigits == 1, 9, 99)
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
ans <- x * NA
indexTF <- is.finite(x) & (x >= lowerlimit)
ans[indexTF] <- log10(1 + 1/x[indexTF])
ans[!is.na(x) & !is.nan(x) &
((x < lowerlimit) |
(x > upperlimit) |
(x != round(x)))] <- 0.0
if (log.arg) log(ans) else ans
}
rbenf <- function(n, ndigits = 1) {
if (!is.Numeric(ndigits, length.arg = 1,
positive = TRUE, integer.valued = TRUE) ||
ndigits > 2)
stop("argument 'ndigits' must be 1 or 2")
lowerlimit <- ifelse(ndigits == 1, 1, 10)
upperlimit <- ifelse(ndigits == 1, 9, 99)
use.n <- if ((length.n <- length(n)) > 1) length.n else
if (!is.Numeric(n, integer.valued = TRUE,
length.arg = 1, positive = TRUE))
stop("bad input for argument 'n'") else n
myrunif <- runif(use.n)
ans <- rep_len(lowerlimit, use.n)
for (ii in (lowerlimit+1):upperlimit) {
indexTF <- (pbenf(ii-1, ndigits = ndigits) < myrunif) &
(myrunif <= pbenf(ii, ndigits = ndigits))
ans[indexTF] <- ii
}
ans
}
pbenf <- function(q, ndigits = 1, lower.tail = TRUE, log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
if (!is.Numeric(ndigits, length.arg = 1,
positive = TRUE, integer.valued = TRUE) ||
ndigits > 2)
stop("argument 'ndigits' must be 1 or 2")
lowerlimit <- ifelse(ndigits == 1, 1, 10)
upperlimit <- ifelse(ndigits == 1, 9, 99)
ans <- q * NA
floorq <- floor(q)
indexTF <- is.finite(q) & (floorq >= lowerlimit)
if (ndigits == 1) {
if (lower.tail) {
if (log.p) {
ans[indexTF] <- log(log10(1 + floorq[indexTF]))
ans[q < lowerlimit ] <- -Inf
ans[q >= upperlimit] <- 0
} else {
ans[indexTF] <- log10(1 + floorq[indexTF])
ans[q < lowerlimit] <- 0
ans[q >= upperlimit] <- 1
}
} else {
if (log.p) {
ans[indexTF] <- log1p(-log10(1 + floorq[indexTF]))
ans[q < lowerlimit] <- 0
ans[q >= upperlimit] <- -Inf
} else {
ans[indexTF] <- log10(10 / (1 + floorq[indexTF]))
ans[q < lowerlimit] <- 1
ans[q >= upperlimit] <- 0
}
}
} else {
if (lower.tail) {
if (log.p) {
ans[indexTF] <- log(log10((1 + floorq[indexTF])/10))
ans[q < lowerlimit ] <- -Inf
ans[q >= upperlimit] <- 0
} else {
ans[indexTF] <- log10((1 + floorq[indexTF])/10)
ans[q < lowerlimit] <- 0
ans[q >= upperlimit] <- 1
}
} else {
if (log.p) {
ans[indexTF] <- log(log10(100/(1 + floorq[indexTF])))
ans[q < lowerlimit] <- 0
ans[q >= upperlimit] <- -Inf
} else {
ans[indexTF] <- log10(100/(1 + floorq[indexTF]))
ans[q < lowerlimit] <- 1
ans[q >= upperlimit] <- 0
}
}
}
ans
}
if (FALSE)
qbenf <- function(p, ndigits = 1) {
if (!is.Numeric(ndigits, length.arg = 1,
positive = TRUE, integer.valued = TRUE) ||
ndigits > 2)
stop("argument 'ndigits' must be 1 or 2")
lowerlimit <- ifelse(ndigits == 1, 1, 10)
upperlimit <- ifelse(ndigits == 1, 9, 99)
bad <- !is.na(p) & !is.nan(p) & ((p < 0) | (p > 1))
if (any(bad))
stop("bad input for argument 'p'")
ans <- rep_len(lowerlimit, length(p))
for (ii in (lowerlimit+1):upperlimit) {
indexTF <- is.finite(p) &
(pbenf(ii-1, ndigits = ndigits) < p) &
(p <= pbenf(ii, ndigits = ndigits))
ans[indexTF] <- ii
}
ans[ is.na(p) | is.nan(p)] <- NA
ans[!is.na(p) & !is.nan(p) & (p == 0)] <- lowerlimit
ans[!is.na(p) & !is.nan(p) & (p == 1)] <- upperlimit
ans
}
qbenf <- function(p, ndigits = 1,
lower.tail = TRUE, log.p = FALSE) {
if (!is.Numeric(ndigits, length.arg = 1,
positive = TRUE, integer.valued = TRUE) ||
ndigits > 2)
stop("argument 'ndigits' must be 1 or 2")
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
if (log.p) {
bad <- ((p > 0) | is.na(p) | is.nan(p))
} else {
bad <- ((p < 0) | (p > 1) | is.na(p) | is.nan(p))
}
if (any(bad))
stop("bad input for argument 'p'")
lowerlimit <- ifelse(ndigits == 1, 1, 10)
upperlimit <- ifelse(ndigits == 1, 9, 99)
ans <- rep_len(lowerlimit, length(p))
if (lower.tail) {
for (ii in (lowerlimit+1):upperlimit) {
indexTF <- is.finite(p) &
(pbenf(ii-1, ndigits = ndigits,
lower.tail = lower.tail, log.p = log.p) < p) &
(p <= pbenf(ii, ndigits = ndigits,
lower.tail = lower.tail, log.p = log.p))
ans[indexTF] <- ii
}
} else {
for (ii in (lowerlimit+1):upperlimit) {
indexTF <- is.finite(p) &
(pbenf(ii-1, ndigits = ndigits,
lower.tail = lower.tail, log.p = log.p) >= p) &
(p > pbenf(ii, ndigits = ndigits,
lower.tail = lower.tail, log.p = log.p))
ans[indexTF] <- ii
}
}
if (lower.tail) {
if (log.p) {
ans[p > 0] <- NaN
ans[p == -Inf] <- lowerlimit
} else {
ans[p < 0] <- NaN
ans[p == 0] <- lowerlimit
ans[p == 1] <- upperlimit
ans[p > 1] <- NaN
}
} else {
if (log.p) {
ans[p > 0] <- NaN
ans[p == -Inf] <- upperlimit
} else {
ans[p < 0] <- NaN
ans[p == 0] <- upperlimit
ans[p == 1] <- lowerlimit
ans[p > 1] <- NaN
}
}
ans
}
truncgeometric <-
function(upper.limit = Inf,
link = "logitlink", expected = TRUE,
imethod = 1, iprob = NULL, zero = NULL) {
if (is.finite(upper.limit) &&
!is.Numeric(upper.limit, integer.valued = TRUE,
positive = TRUE))
stop("bad input for argument 'upper.limit'")
if (any(upper.limit < 0))
stop("bad input for argument 'upper.limit'")
if (!is.logical(expected) || length(expected) != 1)
stop("bad input for argument 'expected'")
link <- as.list(substitute(link))
earg <- link2list(link)
link <- attr(earg, "function.name")
if (!is.Numeric(imethod, length.arg = 1,
integer.valued = TRUE, positive = TRUE) ||
imethod > 3)
stop("argument 'imethod' must be 1 or 2 or 3")
uu.ll <- min(upper.limit)
new("vglmff",
blurb = c("Truncated geometric distribution ",
"(P[Y=y] =\n",
" ",
"prob * (1 - prob)^y / [1-(1-prob)^",
uu.ll+1, "], y = 0,1,...,",
uu.ll, ")\n",
"Link: ",
namesof("prob", link, earg = earg), "\n",
"Mean: mu = 1 / prob - 1 ",
ifelse(is.finite(upper.limit),
paste("- (", upper.limit+1, ") * (1 - prob)^",
upper.limit+1, " / (1 - ",
"(1 - prob)^", upper.limit+1, ")", sep = ""),
"")),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero , M = M,
predictors.names = predictors.names,
M1 = 1)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 1,
Q1 = 1,
expected = .expected ,
imethod = .imethod ,
multipleResponses = TRUE,
parameters.names = c("prob"),
upper.limit = .upper.limit ,
zero = .zero )
}, list( .zero = zero,
.expected = expected,
.imethod = imethod,
.upper.limit = upper.limit ))),
initialize = eval(substitute(expression({
temp5 <-
w.y.check(w = w, y = y,
Is.nonnegative.y = TRUE,
Is.integer.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
w <- temp5$w
y <- temp5$y
ncoly <- ncol(y)
M1 <- 1
extra$ncoly <- ncoly
extra$M1 <- M1
M <- M1 * ncoly
extra$upper.limit <- matrix( .upper.limit , n, ncoly, byrow = TRUE)
if (any(y > extra$upper.limit))
stop("some response values greater than argument 'upper.limit'")
mynames1 <- param.names("prob", ncoly, skip1 = TRUE)
predictors.names <-
namesof(mynames1, .link , earg = .earg , tag = FALSE)
if (!length(etastart)) {
prob.init <- if ( .imethod == 2)
1 / (1 + y + 1/16) else
if ( .imethod == 3)
1 / (1 + apply(y, 2, median) + 1/16) else
1 / (1 + colSums(y * w) / colSums(w) + 1/16)
if (!is.matrix(prob.init))
prob.init <- matrix(prob.init, n, M, byrow = TRUE)
if (length( .iprob ))
prob.init <- matrix( .iprob , n, M, byrow = TRUE)
etastart <- theta2eta(prob.init, .link , earg = .earg )
}
}), list( .link = link, .earg = earg,
.upper.limit = upper.limit,
.imethod = imethod, .iprob = iprob ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
prob <- eta2theta(eta, .link , earg = .earg )
QQQ <- 1 - prob
upper.limit <- extra$upper.limit
tmp1 <- QQQ^(upper.limit+1)
answer <- 1 / prob - 1 - (upper.limit+1) * tmp1 / (1 - tmp1)
answer[!is.finite(answer)] <- 1 / prob[!is.finite(answer)] - 1
answer
}, list( .link = link, .earg = earg ))),
last = eval(substitute(expression({
M1 <- extra$M1
misc$link <- c(rep_len( .link , ncoly))
names(misc$link) <- mynames1
misc$earg <- vector("list", M)
names(misc$earg) <- mynames1
for (ii in 1:ncoly) {
misc$earg[[ii]] <- .earg
}
misc$M1 <- M1
misc$multipleResponses <- TRUE
misc$expected <- .expected
misc$imethod <- .imethod
misc$iprob <- .iprob
}), list( .link = link, .earg = earg,
.iprob = iprob,
.upper.limit = upper.limit,
.expected = expected, .imethod = imethod ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL,
summation = TRUE) {
prob <- eta2theta(eta, .link , earg = .earg )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
upper.limit <- extra$upper.limit
ll.elts <- c(w) * (dgeom(x = y, prob = prob, log = TRUE) -
log1p(-(1.0 - prob)^(1 + upper.limit)))
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .link = link, .earg = earg ))),
vfamily = c("truncgeometric"),
validparams = eval(substitute(function(eta, y, extra = NULL) {
prob <- eta2theta(eta, .link , earg = .earg )
okay1 <- all(is.finite(prob)) && all(0 < prob & prob < 1)
okay1
}, list( .link = link, .earg = earg ))),
deriv = eval(substitute(expression({
prob <- eta2theta(eta, .link , earg = .earg )
sss <- upper.limit <- extra$upper.limit
QQQ <- 1 - prob
tmp1 <- QQQ^(upper.limit + 1)
dl.dprob <- 1 / prob + (0 - y) / (1 - prob) -
(1+upper.limit) * QQQ^(upper.limit - 0) / (1 - tmp1)
dl.dprob[!is.finite(upper.limit)] <-
1 / prob[!is.finite(upper.limit)] +
(0 - y[!is.finite(upper.limit)]) / (1 - prob[!is.finite(upper.limit)])
dprobdeta <- dtheta.deta(prob, .link , earg = .earg )
c(w) * cbind(dl.dprob * dprobdeta)
}), list( .link = link, .earg = earg,
.upper.limit = upper.limit,
.expected = expected ))),
weight = eval(substitute(expression({
eim.oim.fun <- function(mu.y, sss)
ifelse(is.finite(sss),
1/prob^2 + (0 + mu.y) / QQQ^2 - (1+sss) *
((sss-0) * QQQ^(sss-1) / (1 - tmp1) +
(1+sss) * QQQ^(2*sss) / (1 - tmp1)^2),
1 / (prob^2 * (1 - prob)))
ned2l.dprob2 <- if ( .expected ) {
eim.oim.fun(mu, sss)
} else {
eim.oim.fun(y, sss)
}
wz <- ned2l.dprob2 * dprobdeta^2
if ( !( .expected ))
wz <- wz - dl.dprob * d2theta.deta2(prob, .link , earg = .earg )
c(w) * wz
}), list( .link = link, .earg = earg,
.expected = expected ))))
}
betaff <-
function(A = 0, B = 1,
lmu = "logitlink",
lphi = "loglink",
imu = NULL, iphi = NULL,
gprobs.y = ppoints(8),
gphi = exp(-3:5)/4,
zero = NULL) {
if (!is.Numeric(A, length.arg = 1) ||
!is.Numeric(B, length.arg = 1) || A >= B)
stop("A must be < B, and both must be of length one")
stdbeta <- (A == 0 && B == 1)
lmu <- as.list(substitute(lmu))
emu <- link2list(lmu)
lmu <- attr(emu, "function.name")
lphi <- as.list(substitute(lphi))
ephi <- link2list(lphi)
lphi <- attr(ephi, "function.name")
if (length(imu) && (!is.Numeric(imu, positive = TRUE) ||
any(imu <= A) || any(imu >= B)))
stop("bad input for argument 'imu'")
if (length(iphi) && !is.Numeric(iphi, positive = TRUE))
stop("bad input for argument 'iphi'")
new("vglmff",
blurb = c("Beta distribution parameterized by mu and a ",
"precision parameter\n",
if (stdbeta) paste("f(y) = y^(mu*phi-1) * (1-y)^((1-mu)*phi-1)",
"/ beta(mu*phi,(1-mu)*phi),\n",
" 0<y<1, 0<mu<1, phi>0\n\n") else
paste("f(y) = (y-",A,")^(mu1*phi-1) * (",B,
"-y)^(((1-mu1)*phi)-1) / \n(beta(mu1*phi,(1-mu1)*phi) * (",
B, "-", A, ")^(phi-1)),\n",
A," < y < ",B, ", ", A," < mu < ",B,
", mu = ", A, " + ", (B-A), " * mu1",
", phi > 0\n\n", sep = ""),
"Links: ",
namesof("mu", lmu, earg = emu), ", ",
namesof("phi", lphi, earg = ephi)),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero , M = M,
predictors.names = predictors.names,
M1 = 2)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
expected = TRUE,
multipleResponses = FALSE,
parameters.names = c("mu", "phi"),
A = .A ,
B = .B ,
zero = .zero )
}, list( .zero = zero,
.A = A, .B = B ))),
initialize = eval(substitute(expression({
if (min(y) <= .A || max(y) >= .B)
stop("data not within (A, B)")
temp5 <-
w.y.check(w = w, y = y,
out.wy = TRUE,
maximize = TRUE)
w <- temp5$w
y <- temp5$y
extra$A <- .A
extra$B <- .B
predictors.names <- c(namesof("mu", .lmu , .emu , short = TRUE),
namesof("phi", .lphi , .ephi, short = TRUE))
if (!length(etastart)) {
NOS <- 1
muu.init <-
phi.init <- matrix(NA_real_, n, NOS)
gprobs.y <- .gprobs.y
gphi <- if (length( .iphi )) .iphi else .gphi
betaff.Loglikfun <- function(muu, phi, y, x, w, extraargs) {
zedd <- (y - extraargs$A) / ( extraargs$B - extraargs$A)
m1u <- (muu - extraargs$A) / ( extraargs$B - extraargs$A)
shape1 <- phi * m1u
shape2 <- (1 - m1u) * phi
sum(c(w) * (dbeta(x = zedd, shape1, shape2, log = TRUE) -
log(abs( extraargs$B - extraargs$A ))))
}
for (jay in 1:NOS) {
gmuu <- if (length( .imu )) .imu else
quantile(y[, jay], probs = gprobs.y)
try.this <-
grid.search2(gmuu, gphi,
objfun = betaff.Loglikfun,
y = y[, jay],
w = w[, jay],
extraargs = list(A = .A , B = .B ),
ret.objfun = TRUE)
muu.init[, jay] <- try.this["Value1"]
phi.init[, jay] <- try.this["Value2"]
}
if (FALSE) {
mu.init <- if (is.Numeric( .imu )) .imu else {
if ( .imethod == 1) weighted.mean(y, w) else
(y + weighted.mean(y, w)) / 2
}
mu1.init <- (mu.init - .A ) / ( .B - .A )
phi.init <- if (is.Numeric( .iphi )) .iphi else
max(0.01, -1 + ( .B - .A )^2 * mu1.init*(1-mu1.init)/var(y))
}
etastart <- matrix(0, n, 2)
etastart[, 1] <- theta2eta(muu.init, .lmu , earg = .emu )
etastart[, 2] <- theta2eta(phi.init, .lphi , earg = .ephi )
}
}), list( .lmu = lmu, .lphi = lphi, .imu = imu, .iphi = iphi,
.A = A, .B = B, .emu = emu, .ephi = ephi,
.gprobs.y = gprobs.y, .gphi = gphi ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
mu <- eta2theta(eta[, 1], .lmu , .emu )
mu
}, list( .lmu = lmu, .emu = emu, .A = A, .B = B))),
last = eval(substitute(expression({
misc$link <- c(mu = .lmu , phi = .lphi )
misc$earg <- list(mu = .emu , phi = .ephi )
misc$limits <- c( .A , .B )
misc$stdbeta <- .stdbeta
}), list( .lmu = lmu, .lphi = lphi, .A = A, .B = B,
.emu = emu, .ephi = ephi,
.stdbeta = stdbeta ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL,
summation = TRUE) {
mu <- eta2theta(eta[, 1], .lmu , earg = .emu )
phi <- eta2theta(eta[, 2], .lphi , earg = .ephi )
m1u <- if ( .stdbeta ) mu else (mu - .A ) / ( .B - .A )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
shape1 <- phi * m1u
shape2 <- (1 - m1u) * phi
zedd <- (y - .A) / ( .B - .A)
ll.elts <-
c(w) * (dbeta(x = zedd, shape1 = shape1, shape2 = shape2,
log = TRUE) -
log( abs( .B - .A )))
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lmu = lmu, .lphi = lphi, .A = A, .B = B,
.emu = emu, .ephi = ephi,
.stdbeta = stdbeta ))),
vfamily = "betaff",
validparams = eval(substitute(function(eta, y, extra = NULL) {
mu <- eta2theta(eta[, 1], .lmu , .emu )
phi <- eta2theta(eta[, 2], .lphi , .ephi )
okay1 <- all(is.finite(mu )) && all(extra$A < mu & mu < extra$B) &&
all(is.finite(phi)) && all(0 < phi)
okay1
}, list( .lmu = lmu, .lphi = lphi, .A = A, .B = B,
.emu = emu, .ephi = ephi ))),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- [email protected]) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
mu <- eta2theta(eta[, 1], .lmu , earg = .emu )
phi <- eta2theta(eta[, 2], .lphi , earg = .ephi )
m1u <- if ( .stdbeta ) mu else (mu - .A ) / ( .B - .A )
shape1 <- phi * m1u
shape2 <- (1 - m1u) * phi
.A + ( .B - .A ) *
rbeta(nsim * length(shape1), shape1 = shape1, shape2 = shape2)
}, list( .lmu = lmu, .lphi = lphi, .A = A, .B = B,
.emu = emu, .ephi = ephi,
.stdbeta = stdbeta ))),
deriv = eval(substitute(expression({
mu <- eta2theta(eta[, 1], .lmu , .emu )
phi <- eta2theta(eta[, 2], .lphi , .ephi )
m1u <- if ( .stdbeta ) mu else (mu - .A) / ( .B - .A)
dmu.deta <- dtheta.deta(mu, .lmu , .emu )
dmu1.dmu <- 1 / ( .B - .A )
dphi.deta <- dtheta.deta(phi, .lphi , .ephi )
temp1 <- m1u*phi
temp2 <- (1-m1u)*phi
if ( .stdbeta ) {
dl.dmu1 <- phi * (digamma(temp2) -
digamma(temp1) + log(y) - log1p(-y))
dl.dphi <- digamma(phi) - mu*digamma(temp1) -
(1-mu)*digamma(temp2) +
mu*log(y) + (1-mu)*log1p(-y)
} else {
dl.dmu1 <- phi*(digamma(temp2) - digamma(temp1) +
log(y-.A) - log( .B-y))
dl.dphi <- digamma(phi) - m1u*digamma(temp1) -
(1-m1u)*digamma(temp2) +
m1u*log(y-.A) + (1-m1u)*log( .B-y) - log( .B -.A)
}
c(w) * cbind(dl.dmu1 * dmu1.dmu * dmu.deta,
dl.dphi * dphi.deta)
}), list( .lmu = lmu, .lphi = lphi,
.emu = emu, .ephi = ephi,
.A = A, .B = B,
.stdbeta = stdbeta ))),
weight = eval(substitute(expression({
ned2l.dmu12 <- (trigamma(temp1) + trigamma(temp2)) * phi^2
ned2l.dphi2 <- -trigamma(phi) + trigamma(temp1) * m1u^2 +
trigamma(temp2) * (1-m1u)^2
ned2l.dmu1phi <- temp1 * trigamma(temp1) - temp2 * trigamma(temp2)
wz <- matrix(NA_real_, n, dimm(M))
wz[, iam(1, 1, M)] <- ned2l.dmu12 * dmu1.dmu^2 * dmu.deta^2
wz[, iam(2, 2, M)] <- ned2l.dphi2 * dphi.deta^2
wz[, iam(1, 2, M)] <- ned2l.dmu1phi * dmu1.dmu * dmu.deta * dphi.deta
c(w) * wz
}), list( .A = A, .B = B ))))
}
betaR <-
function(lshape1 = "loglink", lshape2 = "loglink",
i1 = NULL, i2 = NULL, trim = 0.05,
A = 0, B = 1, parallel = FALSE, zero = NULL) {
lshape1 <- as.list(substitute(lshape1))
eshape1 <- link2list(lshape1)
lshape1 <- attr(eshape1, "function.name")
lshape2 <- as.list(substitute(lshape2))
eshape2 <- link2list(lshape2)
lshape2 <- attr(eshape2, "function.name")
if (length( i1 ) && !is.Numeric( i1, positive = TRUE))
stop("bad input for argument 'i1'")
if (length( i2 ) && !is.Numeric( i2, positive = TRUE))
stop("bad input for argument 'i2'")
if (!is.Numeric(A, length.arg = 1) ||
!is.Numeric(B, length.arg = 1) ||
A >= B)
stop("A must be < B, and both must be of length one")
stdbeta <- (A == 0 && B == 1)
new("vglmff",
blurb = c("Two-parameter Beta distribution ",
"(shape parameters parameterization)\n",
if (stdbeta)
paste("y^(shape1-1) * (1-y)^(shape2-1) / B(shape1,shape2),",
"0 <= y <= 1, shape1>0, shape2>0\n\n") else
paste("(y-",A,")^(shape1-1) * (",B,
"-y)^(shape2-1) / [B(shape1,shape2) * (",
B, "-", A, ")^(shape1+shape2-1)], ",
A," <= y <= ",B," shape1>0, shape2>0\n\n", sep = ""),
"Links: ",
namesof("shape1", lshape1, earg = eshape1), ", ",
namesof("shape2", lshape2, earg = eshape2)),
constraints = eval(substitute(expression({
constraints <- cm.VGAM(matrix(1, M, 1), x = x,
bool = .parallel ,
constraints, apply.int = TRUE)
constraints <- cm.zero.VGAM(constraints, x = x, .zero , M = M,
predictors.names = predictors.names,
M1 = 2)
}), list( .parallel = parallel, .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
A = .A,
B = .B,
multipleResponses = FALSE,
zero = .zero )
}, list( .A = A, .B = B,
.zero = zero ))),
initialize = eval(substitute(expression({
if (min(y) <= .A || max(y) >= .B)
stop("data not within (A, B)")
if (NCOL(y) != 1)
stop("response must be a vector or a one-column matrix")
w.y.check(w = w, y = y)
predictors.names <-
c(namesof("shape1", .lshape1 , earg = .eshape1 , short = TRUE),
namesof("shape2", .lshape2 , earg = .eshape2 , short = TRUE))
if (!length(etastart)) {
mu1d <- mean(y, trim = .trim )
uu <- (mu1d - .A) / ( .B - .A)
DD <- ( .B - .A)^2
pinit <- max(0.01, uu^2 * (1 - uu) * DD / var(y) - uu)
qinit <- max(0.01, pinit * (1 - uu) / uu)
etastart <- matrix(0, n, 2)
etastart[, 1] <- theta2eta( pinit, .lshape1 , earg = .eshape1 )
etastart[, 2] <- theta2eta( qinit, .lshape2 , earg = .eshape2 )
}
if (is.Numeric( .i1 ))
etastart[, 1] <- theta2eta( .i1 , .lshape1 , earg = .eshape1 )
if (is.Numeric( .i2 ))
etastart[, 2] <- theta2eta( .i2 , .lshape2 , earg = .eshape2 )
}), list( .lshape1 = lshape1, .lshape2 = lshape2,
.i1 = i1, .i2 = i2, .trim = trim, .A = A, .B = B,
.eshape1 = eshape1, .eshape2 = eshape2 ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
shapes <- cbind(eta2theta(eta[, 1], .lshape1 , earg = .eshape1 ),
eta2theta(eta[, 2], .lshape2 , earg = .eshape2 ))
.A + ( .B - .A ) * shapes[, 1] / (shapes[, 1] + shapes[, 2])
}, list( .lshape1 = lshape1, .lshape2 = lshape2, .A = A, .B = B,
.eshape1 = eshape1, .eshape2 = eshape2 ))),
last = eval(substitute(expression({
misc$link <- c(shape1 = .lshape1 , shape2 = .lshape2 )
misc$earg <- list(shape1 = .eshape1 , shape2 = .eshape2 )
misc$limits <- c( .A , .B )
}), list( .lshape1 = lshape1, .lshape2 = lshape2,
.A = A, .B = B,
.eshape1 = eshape1, .eshape2 = eshape2 ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta, extra = NULL,
summation = TRUE) {
shapes <- cbind(eta2theta(eta[, 1], .lshape1 , earg = .eshape1 ),
eta2theta(eta[, 2], .lshape2 , earg = .eshape2 ))
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
zedd <- (y - .A ) / ( .B - .A )
ll.elts <-
c(w) * (dbeta(x = zedd, shape1 = shapes[, 1],
shape2 = shapes[, 2],
log = TRUE) - log( abs( .B - .A )))
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lshape1 = lshape1, .lshape2 = lshape2, .A = A, .B = B,
.eshape1 = eshape1, .eshape2 = eshape2 ))),
vfamily = "betaR",
validparams = eval(substitute(function(eta, y, extra = NULL) {
shapes <- cbind(eta2theta(eta[, 1], .lshape1 , earg = .eshape1 ),
eta2theta(eta[, 2], .lshape2 , earg = .eshape2 ))
okay1 <- all(is.finite(shapes)) && all(0 < shapes)
okay1
}, list( .lshape1 = lshape1, .lshape2 = lshape2, .A = A, .B = B,
.eshape1 = eshape1, .eshape2 = eshape2 ))),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- [email protected]) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
shapes <- cbind(eta2theta(eta[, 1], .lshape1 , earg = .eshape1 ),
eta2theta(eta[, 2], .lshape2 , earg = .eshape2 ))
.A + ( .B - .A ) *
rbeta(nsim * length(shapes[, 1]),
shape1 = shapes[, 1], shape2 = shapes[, 2])
}, list( .lshape1 = lshape1, .lshape2 = lshape2, .A = A, .B = B,
.eshape1 = eshape1, .eshape2 = eshape2 ))),
deriv = eval(substitute(expression({
shapes <- cbind(eta2theta(eta[, 1], .lshape1 , earg = .eshape1 ),
eta2theta(eta[, 2], .lshape2 , earg = .eshape2 ))
dshapes.deta <-
cbind(dtheta.deta(shapes[, 1], .lshape1 , earg = .eshape1),
dtheta.deta(shapes[, 2], .lshape2 , earg = .eshape2))
dl.dshapes <- cbind(log(y - .A ), log( .B - y)) -
digamma(shapes) +
digamma(shapes[, 1] + shapes[, 2]) - log( .B - .A )
c(w) * dl.dshapes * dshapes.deta
}), list( .lshape1 = lshape1, .lshape2 = lshape2, .A = A, .B = B,
.eshape1 = eshape1, .eshape2 = eshape2 ))),
weight = expression({
trig.sum <- trigamma(shapes[, 1] + shapes[, 2])
ned2l.dshape12 <- trigamma(shapes[, 1]) - trig.sum
ned2l.dshape22 <- trigamma(shapes[, 2]) - trig.sum
ned2l.dshape1shape2 <- -trig.sum
wz <- matrix(NA_real_, n, dimm(M))
wz[, iam(1, 1, M)] <- ned2l.dshape12 * dshapes.deta[, 1]^2
wz[, iam(2, 2, M)] <- ned2l.dshape22 * dshapes.deta[, 2]^2
wz[, iam(1, 2, M)] <- ned2l.dshape1shape2 * dshapes.deta[, 1] *
dshapes.deta[, 2]
c(w) * wz
}))
}
betaprime <-
function(lshape = "loglink", ishape1 = 2, ishape2 = NULL,
zero = NULL) {
lshape <- as.list(substitute(lshape))
eshape <- link2list(lshape)
lshape <- attr(eshape, "function.name")
new("vglmff",
blurb = c("Beta-prime distribution\n",
"y^(shape1-1) * (1+y)^(-shape1-shape2) / Beta(shape1,shape2),",
" y>0, shape1>0, shape2>0\n\n",
"Links: ",
namesof("shape1", lshape, earg = eshape), ", ",
namesof("shape2", lshape, earg = eshape), "\n",
"Mean: shape1/(shape2-1) provided shape2>1"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero , M = M,
predictors.names = predictors.names,
M1 = 2)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
expected = TRUE,
multipleResponses = FALSE,
parameters.names = c("shape1", "shape2"),
lshape1 = .lshape ,
lshape2 = .lshape ,
zero = .zero )
}, list( .zero = zero, .lshape = lshape ))),
initialize = eval(substitute(expression({
w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = 1,
ncol.y.max = 1)
predictors.names <-
c(namesof("shape1", .lshape , earg = .eshape , short = TRUE),
namesof("shape2", .lshape , earg = .eshape , short = TRUE))
if (is.numeric( .ishape1) && is.numeric( .ishape2 )) {
vec <- c( .ishape1, .ishape2 )
vec <- c(theta2eta(vec[1], .lshape , earg = .eshape ),
theta2eta(vec[2], .lshape , earg = .eshape ))
etastart <- matrix(vec, n, 2, byrow = TRUE)
}
if (!length(etastart)) {
init1 <- if (length( .ishape1 ))
rep_len( .ishape1 , n) else rep_len(1, n)
init2 <- if (length( .ishape2 ))
rep_len( .ishape2 , n) else 1 + init1 / (y + 0.1)
etastart <-
matrix(theta2eta(c(init1, init2), .lshape , earg = .eshape ),
n, 2, byrow = TRUE)
}
}), list( .lshape = lshape, .eshape = eshape,
.ishape1 = ishape1, .ishape2 = ishape2 ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
shapes <- eta2theta(eta, .lshape , earg = .eshape )
ifelse(shapes[, 2] > 1, shapes[, 1] / (shapes[, 2] - 1), NA)
}, list( .lshape = lshape, .eshape = eshape ))),
last = eval(substitute(expression({
misc$link <- c(shape1 = .lshape , shape2 = .lshape )
misc$earg <- list(shape1 = .eshape , shape2 = .eshape )
}), list( .lshape = lshape, .eshape = eshape ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL,
summation = TRUE) {
shapes <- eta2theta(eta, .lshape , earg = .eshape )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <-
c(w) * ((shapes[, 1]-1) * log(y) -
lbeta(shapes[, 1], shapes[, 2]) -
(shapes[, 2] + shapes[, 1]) * log1p(y))
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lshape = lshape, .eshape = eshape ))),
vfamily = "betaprime",
validparams = eval(substitute(function(eta, y, extra = NULL) {
shapes <- eta2theta(eta, .lshape , earg = .eshape )
okay1 <- all(is.finite(shapes)) && all(0 < shapes)
okay1
}, list( .lshape = lshape, .eshape = eshape ))),
deriv = eval(substitute(expression({
shapes <- eta2theta(eta, .lshape , earg = .eshape )
dshapes.deta <- dtheta.deta(shapes, .lshape , earg = .eshape )
dl.dshapes <- cbind(log(y) - log1p(y) - digamma(shapes[, 1]) +
digamma(shapes[, 1] + shapes[, 2]),
- log1p(y) - digamma(shapes[, 2]) +
digamma(shapes[, 1] + shapes[, 2]))
c(w) * dl.dshapes * dshapes.deta
}), list( .lshape = lshape, .eshape = eshape ))),
weight = expression({
temp2 <- trigamma(shapes[, 1] + shapes[, 2])
ned2l.dshape12 <- trigamma(shapes[, 1]) - temp2
ned2l.dshape22 <- trigamma(shapes[, 2]) - temp2
ned2l.dshape1shape2 <- -temp2
wz <- matrix(NA_real_, n, dimm(M))
wz[, iam(1, 1, M)] <- ned2l.dshape12 * dshapes.deta[, 1]^2
wz[, iam(2, 2, M)] <- ned2l.dshape22 * dshapes.deta[, 2]^2
wz[, iam(1, 2, M)] <- ned2l.dshape1shape2 *
dshapes.deta[, 1] * dshapes.deta[, 2]
c(w) * wz
}))
}
zoabetaR <-
function(lshape1 = "loglink", lshape2 = "loglink",
lpobs0 = "logitlink", lpobs1 = "logitlink",
ishape1 = NULL, ishape2 = NULL, trim = 0.05,
type.fitted = c("mean", "pobs0", "pobs1", "beta.mean"),
parallel.shape = FALSE,
parallel.pobs = FALSE,
zero = NULL) {
A <- 0
B <- 1
lshape1 <- as.list(substitute(lshape1))
eshape1 <- link2list(lshape1)
lshape1 <- attr(eshape1, "function.name")
lshape2 <- as.list(substitute(lshape2))
eshape2 <- link2list(lshape2)
lshape2 <- attr(eshape2, "function.name")
lprobb0 <- as.list(substitute(lpobs0))
eprobb0 <- link2list(lprobb0)
lprobb0 <- attr(eprobb0, "function.name")
lprobb1 <- as.list(substitute(lpobs1))
eprobb1 <- link2list(lprobb1)
lprobb1 <- attr(eprobb1, "function.name")
if (length( ishape1 ) && !is.Numeric( ishape1, positive = TRUE))
stop("bad input for argument 'ishape1'")
if (length( ishape2 ) && !is.Numeric( ishape2, positive = TRUE))
stop("bad input for argument 'ishape2'")
if (!is.Numeric(A, length.arg = 1) ||
!is.Numeric(B, length.arg = 1) ||
A >= B)
stop("A must be < B, and both must be of length one")
stdbeta <- (A == 0 && B == 1)
type.fitted <- match.arg(type.fitted,
c("mean", "pobs0", "pobs1", "beta.mean"))[1]
new("vglmff",
blurb = c("Standard Beta distribution with 0- and \n",
"1-inflation ",
"(shape parameters parameterization)\n",
if (stdbeta)
paste("y^(shape1-1) * (1-y)^(shape2-1) / beta(shape1,shape2),",
"0 <= y <= 1, shape1>0, shape2>0\n\n") else
paste("(y-",A,")^(shape1-1) * (",B,
"-y)^(shape2-1) / [beta(shape1,shape2) * (",
B, "-", A, ")^(shape1+shape2-1)], ",
A," <= y <= ",B," shape1>0, shape2>0, ",
"0 < pobs0 < 1, 0 < pobs1 < 1 \n\n", sep = ""),
"Links: ",
namesof("shape1", lshape1, earg = eshape1), ", ",
namesof("shape2", lshape2, earg = eshape2), ", ",
namesof("pobs0", lprobb0, earg = eprobb0), ", ",
namesof("pobs1", lprobb1, earg = eshape1)),
constraints = eval(substitute(expression({
constraints.orig <- constraints
if (is.logical( .parallel.probb ) && .parallel.probb &&
(cind0[1] + cind1[1] <= 1))
warning("argument 'parallel.pobs' specified when there is only ",
"one of 'pobs0' and 'pobs1'")
cmk.s <- kronecker(matrix(1, NOS, 1), rbind(1, 1, 0, 0))
cmk.S <- kronecker(diag(NOS), rbind(diag(2), 0*diag(2)))
con.s <- cm.VGAM(cmk.s, x = x,
bool = .parallel.shape ,
constraints = constraints.orig,
apply.int = TRUE,
cm.default = cmk.S,
cm.intercept.default = cmk.S)
print("con.s")
print( con.s )
cmk.p <- kronecker(matrix(1, NOS, 1), rbind(0, 0, 1, 1))
cmk.P <- kronecker(diag(NOS), rbind(0*diag(2), diag(2)))
con.p <- cm.VGAM(cmk.p,
x = x,
bool = .parallel.probb ,
constraints = constraints.orig,
apply.int = TRUE,
cm.default = cmk.P,
cm.intercept.default = cmk.P)
print("con.p")
print( con.p )
con.use <- con.s
for (klocal in seq_along(con.s)) {
print("klocal")
print( klocal )
con.use[[klocal]] <-
cbind(con.s[[klocal]], con.p[[klocal]])
print("con.use")
print( con.use )
if (!cind0[1]) {
print("hi1a")
vec.use <- rep_len(c(TRUE, TRUE, FALSE, TRUE),
nrow(con.use[[klocal]]))
con.use[[klocal]] <-
(con.use[[klocal]])[vec.use, ]
}
if (!cind1[1]) {
print("hi1b")
vec.use <- rep_len(c(TRUE, TRUE, TRUE, FALSE),
nrow(con.use[[klocal]]))
con.use[[klocal]] <-
(con.use[[klocal]])[vec.use, ]
}
col.delete <- apply(con.use[[klocal]], 2,
function(HkCol) all(HkCol == 0))
print("col.delete")
print( col.delete )
con.use[[klocal]] <- (con.use[[klocal]])[, !col.delete]
}
print("con.use1")
print( con.use )
constraints <- con.use
constraints <- cm.zero.VGAM(constraints, x = x, .zero , M = M,
predictors.names = predictors.names,
M1 = M1)
}), list( .parallel.shape = parallel.shape,
.parallel.probb = parallel.pobs,
.zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = NA,
Q1 = 1,
A = .A ,
B = .B ,
expected = TRUE,
multipleResponses = TRUE,
type.fitted = .type.fitted ,
zero = .zero )
}, list( .A = A, .B = B,
.type.fitted = type.fitted,
.zero = zero ))),
initialize = eval(substitute(expression({
if (min(y) < .A || max(y) > .B)
stop("data not within [A, B]")
temp5 <-
w.y.check(w = w, y = y,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
w <- temp5$w
y <- temp5$y
ncoly <- NOS <- ncol(y)
if (ncoly > 1 && !( .stdbeta ))
stop("can only input multiple responses with the standard beta")
cind0 <- colSums(ind0 <- y == 0) > 0
cind1 <- colSums(ind1 <- y == 1) > 0
if (!any(cind0 | cind1))
stop("no 0s or 1s in the responses to perform 0- and/or ",
"1-inflation! ",
"Try using betaff() or betaR() instead.")
if (ncoly > 1 && !all(cind0 == cind0[1]) &&
!all(cind0 == cind0[1]))
stop("with multiple responses, cannot have 0-inflation in ",
"some responses and 1-inflation in other responses")
M1 <- 2 + cind0[1] + cind1[1]
M <- M1 * NOS
print("c(cind0, cind1)")
print( c(cind0, cind1) )
mynames1 <- param.names("shape1", ncoly, skip1 = TRUE)
mynames2 <- param.names("shape2", ncoly, skip1 = TRUE)
mynames3 <- param.names("pobs0", ncoly, skip1 = TRUE)
mynames4 <- param.names("pobs1", ncoly, skip1 = TRUE)
predictors.names <-
c(namesof(mynames1, .lshape1 , earg = .eshape1 , short = TRUE),
namesof(mynames2, .lshape2 , earg = .eshape2 , short = TRUE),
if (cind0[1])
namesof(mynames3, .lprobb0 , earg = .eprobb0 , short = TRUE) else
NULL,
if (cind1[1])
namesof(mynames4, .lprobb1 , earg = .eprobb1 , short = TRUE) else
NULL)[interleave.VGAM(M, M1 = M1)]
extra$type.fitted <- .type.fitted
extra$colnames.y <- colnames(y)
extra$M1 <- M1
extra$cind0 <- cind0
extra$cind1 <- cind1
if (!length(etastart)) {
p0init <- matrix(colMeans(ind0), n, ncoly, byrow = TRUE)
p1init <- matrix(colMeans(ind1), n, ncoly, byrow = TRUE)
mu1d <- matrix(NA_real_, n, NOS)
for (jay in 1:ncoly) {
yy <- y[, jay]
yy <- yy[ .A < yy & yy < .B ]
mu1d[, jay] <- weighted.mean(yy, trim = .trim )
}
uu <- (mu1d - .A ) / ( .B - .A )
DD <- ( .B - .A )^2
p.init <- if (is.Numeric( .ishape1 ))
matrix( .ishape1 , n, ncoly, byrow = TRUE) else
uu^2 * (1 - uu) * DD / var(yy) - uu
p.init[p.init < 0.01] <- 0.01
q.init <- if (is.Numeric( .ishape2 ))
matrix( .ishape2 , n, ncoly, byrow = TRUE) else
p.init * (1 - uu) / uu
q.init[q.init < 0.01] <- 0.01
etastart <- cbind(
theta2eta(p.init, .lshape1 , earg = .eshape1 ),
theta2eta(q.init, .lshape2 , earg = .eshape2 ),
if (cind0[1])
theta2eta(p0init, .lprobb0 , earg = .eprobb0 )
else NULL,
if (cind1[1])
theta2eta(p1init, .lprobb1 , earg = .eprobb1 )
else NULL)[,
interleave.VGAM(M, M1 = M1)]
}
}), list( .lshape1 = lshape1, .lshape2 = lshape2,
.eshape1 = eshape1, .eshape2 = eshape2,
.lprobb0 = lprobb0, .lprobb1 = lprobb1,
.eprobb0 = eprobb0, .eprobb1 = eprobb1,
.ishape1 = ishape1, .ishape2 = ishape2,
.trim = trim, .A = A, .B = B,
.type.fitted = type.fitted,
.stdbeta = stdbeta ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
M1 <- extra$M1
cind0 <- extra$cind0
cind1 <- extra$cind1
NOS <- ncol(eta) / M1
shape1 <- eta2theta(eta[, c(TRUE, rep(FALSE, M1 - 1)), drop = FALSE],
.lshape1 , earg = .eshape1 )
shape2 <- eta2theta(eta[, c(FALSE, TRUE,
rep(FALSE, M1 - 2)), drop = FALSE],
.lshape2 , earg = .eshape2 )
probb0 <- if (cind0[1])
eta2theta(eta[, c(FALSE, FALSE, TRUE,
if (cind1[1]) FALSE else NULL), drop = FALSE],
.lprobb0 , earg = .eprobb0 ) else 0
probb1 <- if (cind1[1])
eta2theta(eta[, c(FALSE, FALSE,
if (cind0[1]) FALSE else NULL, TRUE),
drop = FALSE],
.lprobb1 , earg = .eprobb1 ) else 0
type.fitted <- match.arg(extra$type.fitted,
c("mean", "pobs0", "pobs1", "beta.mean"))[1]
ans <-
switch(type.fitted,
"mean" = (1 - probb0) * shape1 / (shape1 + shape2) +
probb1 * shape2 / (shape1 + shape2),
"beta.mean" = shape1/(shape1+shape2),
"pobs0" = probb0,
"pobs1" = probb1)
label.cols.y(ans, colnames.y = extra$colnames.y, NOS = NOS)
}, list( .lshape1 = lshape1, .lshape2 = lshape2, .A = A, .B = B,
.eshape1 = eshape1, .eshape2 = eshape2,
.lprobb0 = lprobb0, .lprobb1 = lprobb1,
.eprobb0 = eprobb0, .eprobb1 = eprobb1 ))),
last = eval(substitute(expression({
misc$link <- rep_len( c( .lshape1 , .lshape2 ,
if (cind0[1]) .lprobb0 else NULL,
if (cind1[1]) .lprobb1 else NULL), M)
names(misc$link) <- c(mynames1, mynames2,
if (cind0[1]) mynames3 else NULL,
if (cind1[1]) mynames4 else NULL)[
interleave.VGAM(M, M1 = M1)]
misc$earg <- vector("list", M)
names(misc$earg) <- names(misc$link)
jay <- 1
while (jay <= M) {
misc$earg[[jay]] <- .eshape1
jay <- jay + 1
misc$earg[[jay]] <- .eshape2
jay <- jay + 1
if (cind0[1]) {
misc$earg[[jay]] <- .eprobb0
jay <- jay + 1
}
if (cind1[1]) {
misc$earg[[jay]] <- .eprobb1
jay <- jay + 1
}
}
misc$supportlimits <- c( .A , .B )
}), list( .lshape1 = lshape1, .lshape2 = lshape2,
.eshape1 = eshape1, .eshape2 = eshape2,
.lprobb0 = lprobb0, .lprobb1 = lprobb1,
.eprobb0 = eprobb0, .eprobb1 = eprobb1,
.A = A, .B = B ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta, extra = NULL,
summation = TRUE) {
M1 <- 4
M1 <- extra$M1
cind0 <- extra$cind0
cind1 <- extra$cind1
NOS <- ncol(eta) / M1
shape1 <- eta2theta(eta[, c(TRUE, rep(FALSE, M1 - 1)), drop = FALSE],
.lshape1 , earg = .eshape1 )
shape2 <- eta2theta(eta[, c(FALSE, TRUE,
rep(FALSE, M1 - 2)), drop = FALSE],
.lshape2 , earg = .eshape2 )
probb0 <- if (cind0[1])
eta2theta(eta[, c(FALSE, FALSE, TRUE,
if (cind1[1]) FALSE else NULL), drop = FALSE],
.lprobb0 , earg = .eprobb0 ) else 0
probb1 <- if (cind1[1])
eta2theta(eta[, c(FALSE, FALSE,
if (cind0[1]) FALSE else NULL, TRUE),
drop = FALSE],
.lprobb1 , earg = .eprobb1 ) else 0
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
zedd <- (y - .A ) / ( .B - .A )
ll.elts <-
c(w) * (dzoabeta(x = zedd, shape1 = shape1, shape2 = shape2,
pobs0 = probb0, pobs1 = probb1,
log = TRUE) - log( abs( .B - .A )))
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lshape1 = lshape1, .lshape2 = lshape2, .A = A, .B = B,
.eshape1 = eshape1, .eshape2 = eshape2,
.lprobb0 = lprobb0, .lprobb1 = lprobb1,
.eprobb0 = eprobb0, .eprobb1 = eprobb1 ))),
vfamily = "zoabetaR",
validparams = eval(substitute(function(eta, y, extra = NULL) {
M1 <- 4
M1 <- extra$M1
cind0 <- extra$cind0
cind1 <- extra$cind1
NOS <- ncol(eta) / M1
shape1 <- eta2theta(eta[, c(TRUE, rep(FALSE, M1 - 1)), drop = FALSE],
.lshape1 , earg = .eshape1 )
shape2 <- eta2theta(eta[, c(FALSE, TRUE,
rep(FALSE, M1 - 2)), drop = FALSE],
.lshape2 , earg = .eshape2 )
probb0 <- if (cind0[1])
eta2theta(eta[, c(FALSE, FALSE, TRUE,
if (cind1[1]) FALSE else NULL), drop = FALSE],
.lprobb0 , earg = .eprobb0 ) else 0.5
probb1 <- if (cind1[1])
eta2theta(eta[, c(FALSE, FALSE,
if (cind0[1]) FALSE else NULL, TRUE), drop = FALSE],
.lprobb1 , earg = .eprobb1 ) else 0.5
okay1 <- all(is.finite(shape1)) && all(0 < shape1) &&
all(is.finite(shape2)) && all(0 < shape2) &&
all(is.finite(probb0)) && all(0 < probb0 & probb0 < 1) &&
all(is.finite(probb1)) && all(0 < probb1 & probb1 < 1)
okay1
}, list( .lshape1 = lshape1, .lshape2 = lshape2, .A = A, .B = B,
.eshape1 = eshape1, .eshape2 = eshape2,
.lprobb0 = lprobb0, .lprobb1 = lprobb1,
.eprobb0 = eprobb0, .eprobb1 = eprobb1 ))),
deriv = eval(substitute(expression({
M1 <- 4
M1 <- extra$M1
cind0 <- extra$cind0
cind1 <- extra$cind1
NOS <- ncol(eta) / M1
shape1 <- eta2theta(eta[, c(TRUE, rep(FALSE, M1 - 1)), drop = FALSE],
.lshape1 , earg = .eshape1 )
shape2 <- eta2theta(eta[, c(FALSE, TRUE,
rep(FALSE, M1 - 2)), drop = FALSE],
.lshape2 , earg = .eshape2 )
probb0 <- if (cind0[1])
eta2theta(eta[, c(FALSE, FALSE, TRUE,
if (cind1[1]) FALSE else NULL), drop = FALSE],
.lprobb0 , earg = .eprobb0 ) else 0
probb1 <- if (cind1[1])
eta2theta(eta[, c(FALSE, FALSE,
if (cind0[1]) FALSE else NULL, TRUE), drop = FALSE],
.lprobb1 , earg = .eprobb1 ) else 0
dshape1.deta <- dtheta.deta(shape1, .lshape1 , earg = .eshape1 )
dshape2.deta <- dtheta.deta(shape2, .lshape2 , earg = .eshape2 )
dprobb0.deta <- dtheta.deta(probb0, .lprobb0 , earg = .eprobb0 )
dprobb1.deta <- dtheta.deta(probb1, .lprobb1 , earg = .eprobb1 )
index0 <- y == 0
index1 <- y == 1
indexi <- !index0 & !index1
dig.sum <- digamma(shape1 + shape2)
QQ <- 1 - probb0 - probb1
if (cind0[1]) {
dl.dprobb0 <- -1 / QQ
dl.dprobb0[index0] <- 1 / probb0[index0]
dl.dprobb0[index1] <- 0
}
if (cind1[1]) {
dl.dprobb1 <- -1 / QQ
dl.dprobb1[index0] <- 0
dl.dprobb1[index1] <- 1 / probb1[index1]
}
dl.dshape1 <- log(y) - digamma(shape1) + dig.sum
dl.dshape2 <- log1p(-y) - digamma(shape2) + dig.sum
dl.dshape1[!indexi] <- 0
dl.dshape2[!indexi] <- 0
myderiv <- c(w) *
cbind(dl.dshape1 * dshape1.deta,
dl.dshape2 * dshape2.deta,
if (cind0[1]) dl.dprobb0 * dprobb0.deta else NULL,
if (cind1[1]) dl.dprobb1 * dprobb1.deta else NULL)
colnames(myderiv) <- NULL
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lshape1 = lshape1, .lshape2 = lshape2, .A = A, .B = B,
.eshape1 = eshape1, .eshape2 = eshape2,
.lprobb0 = lprobb0, .lprobb1 = lprobb1,
.eprobb0 = eprobb0, .eprobb1 = eprobb1 ))),
weight = expression({
trig.sum <- trigamma(shape1 + shape2)
ned2l.dshape12 <- (trigamma(shape1) - trig.sum) * QQ
ned2l.dshape22 <- (trigamma(shape2) - trig.sum) * QQ
ned2l.dprobb02 <- (1 - probb1) / (probb0 * QQ)
ned2l.dprobb12 <- (1 - probb0) / (probb1 * QQ)
ned2l.dshape1shape2 <- -trig.sum * QQ
ned2l.dshape2probb0 <- 0
ned2l.dprobb0probb1 <- 1 / QQ
ned2l.dshape1probb0 <- 0
ned2l.dshape2probb1 <- 0
ned2l.dshape1probb1 <- 0
ned2l.dshape1probb0 <- 0
wz <- array(c(c(w) * ned2l.dshape12 * dshape1.deta^2,
c(w) * ned2l.dshape22 * dshape2.deta^2,
if (cind0[1]) c(w) * ned2l.dprobb02 * dprobb0.deta^2 else NULL,
if (cind1[1]) c(w) * ned2l.dprobb12 * dprobb1.deta^2 else NULL,
c(w) * ned2l.dshape1shape2 * dshape1.deta * dshape2.deta,
if (cind0[1]) c(w) * ned2l.dshape2probb0 * dshape2.deta * dprobb0.deta,
c(w) * ned2l.dprobb0probb1 * dprobb0.deta * dprobb1.deta,
if (cind0[1]) c(w) * ned2l.dshape1probb0 * dshape1.deta * dprobb0.deta,
if (cind1[1]) c(w) * ned2l.dshape2probb1 * dshape2.deta * dprobb1.deta,
if (cind1[1]) c(w) * ned2l.dshape1probb1 * dshape1.deta * dprobb1.deta),
dim = c(n, M / M1, M1*(M1+1)/2))
wz <- arwz2wz(wz, M = M, M1 = M1)
wz
}))
}
dtopple <- function(x, shape, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
L <- max(length(x), length(shape))
if (length(x) != L) x <- rep_len(x, L)
if (length(shape) != L) shape <- rep_len(shape, L)
logdensity <- rep_len(log(0), L)
xok <- (0 <= x) & (x <= 1)
logdensity[xok] <-
log(2) + log(shape[xok]) + log1p(-x[xok]) +
(shape[xok] - 1) * (log(x[xok]) + log(2) + log1p(-x[xok]/2))
logdensity[shape >= 1] <- NaN
if (log.arg) logdensity else exp(logdensity)
}
ptopple <- function(q, shape, lower.tail = TRUE, log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
if (lower.tail) {
if (log.p) {
ans <- shape * (log(q) + log(2) + log1p(-q/2))
ans[q <= 0 ] <- -Inf
ans[q >= 1] <- 0
} else {
ans <- (q * (2 - q))^shape
ans[q <= 0] <- 0
ans[q >= 1] <- 1
}
} else {
if (log.p) {
ans <- log1p(-(q * (2 - q))^shape)
ans[q <= 0] <- 0
ans[q >= 1] <- -Inf
} else {
ans <- exp(log1p(-(q * (2 - q))^shape))
ans[q <= 0] <- 1
ans[q >= 1] <- 0
}
}
ans[shape <= 0] <- NaN
ans[shape >= 1] <- NaN
ans
}
qtopple <- function(p, shape) {
ans <- -expm1(0.5 * log1p(-p^(1/shape)))
ans[shape <= 0] <- NaN
ans[shape >= 1] <- NaN
ans
}
rtopple <- function(n, shape) {
qtopple(runif(n), shape)
}
topple <-
function(lshape = "logitlink", zero = NULL,
gshape = ppoints(8),
parallel = FALSE,
type.fitted = c("mean", "percentiles", "Qlink"),
percentiles = 50) {
type.fitted <- match.arg(type.fitted,
c("mean", "percentiles", "Qlink"))[1]
lshape <- as.list(substitute(lshape))
eshape <- link2list(lshape)
lshape <- attr(eshape, "function.name")
new("vglmff",
blurb = c("Topp-Leone distribution ",
"F(y; shape) = (y * (2 - y))^shape, ",
"0 < y < 1, 0 < shape < 1\n",
"Link: ",
namesof("shape", lshape, earg = eshape)),
constraints = eval(substitute(expression({
constraints <- cm.VGAM(matrix(1, M, 1), x = x,
bool = .parallel ,
constraints, apply.int = FALSE)
constraints <- cm.zero.VGAM(constraints, x = x, .zero , M = M,
predictors.names = predictors.names,
M1 = 1)
}), list( .parallel = parallel,
.zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 1,
Q1 = 1,
expected = TRUE,
hadof = TRUE,
multipleResponses = TRUE,
parallel = .parallel ,
parameters.names = "shape",
percentiles = .percentiles ,
type.fitted = .type.fitted ,
zero = .zero )
}, list( .parallel = parallel,
.percentiles = percentiles ,
.type.fitted = type.fitted,
.zero = zero ))),
initialize = eval(substitute(expression({
temp5 <-
w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
w <- temp5$w
y <- temp5$y
if (any(y >= 1))
stop("response must be in (0, 1)")
ncoly <- ncol(y)
M1 <- 1
M <- M1 * ncoly
extra$ncoly <- ncoly
extra$type.fitted <- .type.fitted
extra$colnames.y <- colnames(y)
extra$percentiles <- .percentiles
extra$M1 <- M1
if ((NOS <- M / M1) > 1 && length( .percentiles ) > 1)
stop("can only have one response when 'percentiles' is a ",
"vector longer than unity")
mynames1 <- param.names("shape", ncoly, skip1 = TRUE)
predictors.names <-
namesof(mynames1, .lshape , earg = .eshape , tag = FALSE)
if (!length(etastart)) {
shape.init <- matrix(0, nrow(x), ncoly)
gshape <- .gshape
topple.Loglikfun <- function(shape, y, x = NULL,
w, extraargs = NULL) {
sum(c(w) * dtopple(x = y, shape = shape, log = TRUE))
}
for (jay in 1:ncoly) {
shape.init[, jay] <- grid.search(gshape,
objfun = topple.Loglikfun,
y = y[, jay], w = w[, jay])
}
etastart <- theta2eta(shape.init, .lshape , earg = .eshape )
}
}), list( .lshape = lshape, .gshape = gshape,
.eshape = eshape,
.percentiles = percentiles,
.type.fitted = type.fitted
))),
linkinv = eval(substitute(function(eta, extra = NULL) {
type.fitted <-
if (length(extra$type.fitted)) {
extra$type.fitted
} else {
warning("cannot find 'type.fitted'. Returning the 'mean'.")
"mean"
}
type.fitted <- match.arg(type.fitted,
c("mean", "percentiles", "Qlink"))[1]
if (type.fitted == "Qlink") {
eta2theta(eta, link = "logitlink")
} else {
shape <- eta2theta(eta, .lshape , earg = .eshape )
pcent <- extra$percentiles
perc.mat <- matrix(pcent, NROW(eta), length(pcent),
byrow = TRUE) / 100
fv <-
switch(type.fitted,
"mean" = 1 - (gamma(1 + shape))^2 *
4^shape / gamma(2 * (1 + shape)),
"percentiles" = qtopple(perc.mat,
shape = matrix(shape, nrow(perc.mat), ncol(perc.mat))))
if (type.fitted == "percentiles")
fv <- label.cols.y(fv, colnames.y = extra$colnames.y,
NOS = NCOL(eta), percentiles = pcent,
one.on.one = FALSE)
fv
}
}, list( .lshape = lshape, .eshape = eshape ))),
last = eval(substitute(expression({
misc$earg <- vector("list", M)
names(misc$earg) <- mynames1
for (ilocal in 1:ncoly) {
misc$earg[[ilocal]] <- .eshape
}
misc$link <- rep_len( .lshape , ncoly)
names(misc$link) <- mynames1
}), list( .lshape = lshape, .eshape = eshape ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL,
summation = TRUE) {
shape <- eta2theta(eta, .lshape , earg = .eshape )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <- c(w) * dtopple(x = y, shape = shape, log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lshape = lshape, .eshape = eshape ))),
vfamily = c("topple"),
validparams = eval(substitute(function(eta, y, extra = NULL) {
shape <- eta2theta(eta, .lshape , earg = .eshape )
okay1 <- all(is.finite(shape)) && all(0 < shape & shape < 1)
okay1
}, list( .lshape = lshape, .eshape = eshape ))),
hadof = eval(substitute(
function(eta, extra = list(), deriv = 1,
linpred.index = 1,
w = 1, dim.wz = c(NROW(eta), NCOL(eta) * (NCOL(eta)+1)/2),
...) {
shape <- eta2theta(eta, .lshape , earg = .eshape )
ans <- c(w) *
switch(as.character(deriv),
"0" = 1 / shape^2,
"1" = -2 / shape^3,
"2" = 6 / shape^4,
"3" = -24 / shape^5,
stop("argument 'deriv' must be 0, 1, 2 or 3"))
if (deriv == 0) ans else retain.col(ans, linpred.index)
}, list( .lshape = lshape, .eshape = eshape ))),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- [email protected]) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
shape <- eta2theta(eta, .lshape , earg = .eshape )
rtopple(nsim * length(shape), shape = c(shape))
}, list( .lshape = lshape,
.eshape = eshape ))),
deriv = eval(substitute(expression({
shape <- eta2theta(eta, .lshape , earg = .eshape )
dl.dshape <- 1 / shape + log(y) + log(2) + log1p(-y/2)
dshape.deta <- dtheta.deta(shape, .lshape , earg = .eshape )
c(w) * dl.dshape * dshape.deta
}), list( .lshape = lshape, .eshape = eshape ))),
weight = eval(substitute(expression({
ned2l.dshape2 <- 1 / shape^2
wz <- c(w) * ned2l.dshape2 * dshape.deta^2
wz
}), list( .lshape = lshape, .eshape = eshape ))))
}
dzeta <- function(x, shape, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
LLL <- max(length(shape), length(x))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
ox <- !is.finite(x)
zero <- ox | round(x) != x | x < 1
ans <- rep_len(if (log.arg) log(0) else 0, LLL)
if (any(!zero)) {
if (log.arg) {
ans[!zero] <- (-shape[!zero]-1) * log(x[!zero]) -
log(zeta(shape[!zero] + 1))
} else {
ans[!zero] <- x[!zero]^(-shape[!zero]-1) / zeta(shape[!zero]+1)
}
}
if (any(ox))
ans[ox] <- if (log.arg) log(0) else 0
ans[shape <= 0] <- NaN
ans
}
pzeta <- function(q, shape, lower.tail = TRUE) {
LLL <- max(lenq <- length(q), lens <- length(shape))
if (length(q) != LLL) q <- rep_len(q, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
ans <- rep_len(0, LLL)
aa <- 12
qfloor <- floor(q)
for (nn in 1:(aa-1))
ans <- ans + as.numeric(nn <= qfloor) / nn^(shape+1)
vecTF <- (aa-1 <= qfloor)
if (lower.tail) {
if (any(vecTF))
ans[vecTF] <- zeta(shape[vecTF]+1) -
Zeta.aux(shape[vecTF]+1, qfloor[vecTF] )
} else {
ans <- zeta(shape+1) - ans
if (any(vecTF))
ans[vecTF] <- Zeta.aux(shape[vecTF]+1, qfloor[vecTF] )
}
ans / zeta(shape+1)
}
qzeta <- function(p, shape) {
LLL <- max(lenp <- length(p), lens <- length(shape))
if (length(p) != LLL) p <- rep_len(p, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
ans <- rep_len(0, LLL)
lowsup <- 1
lo <- rep_len(lowsup - 0.5, LLL)
approx.ans <- lo
hi <- 2 * lo + 10.5
dont.iterate <- p == 1 | shape <= 0
done <- p <= pzeta(hi, shape) | dont.iterate
while (!all(done)) {
lo[!done] <- hi[!done]
hi[!done] <- 2 * hi[!done] + 10.5
done[!done] <- (p[!done] <= pzeta(hi[!done], shape[!done]))
}
foo <- function(q, shape, p)
pzeta(q, shape) - p
lhs <- (p <= dzeta(1, shape)) | dont.iterate
approx.ans[!lhs] <-
bisection.basic(foo, lo[!lhs], hi[!lhs], tol = 1/16,
shape = shape[!lhs], p = p[!lhs])
faa <- floor(approx.ans)
ans <- ifelse(pzeta(faa, shape) < p &
p <= pzeta(faa+1, shape), faa+1, faa)
ans[p == 1] <- Inf
ans[shape <= 0] <- NaN
ans
}
rzeta <- function(n, shape) {
qzeta(runif(n), shape)
}
zetaff <-
function(lshape = "loglink",
ishape = NULL,
gshape = 1 + exp(-seq(7)),
zero = NULL) {
if (length(ishape) && !is.Numeric(ishape, positive = TRUE))
stop("argument 'ishape' must be > 0")
lshape <- as.list(substitute(lshape))
eshape <- link2list(lshape)
lshape <- attr(eshape, "function.name")
new("vglmff",
blurb = c("Zeta distribution ",
"f(y; shape) = 1/(y^(shape+1) zeta(shape+1)), ",
"shape>0, y = 1, 2,..\n\n",
"Link: ",
namesof("shape", lshape, earg = eshape), "\n\n",
"Mean: zeta(shape) / zeta(shape+1), provided shape>1\n",
"Variance: zeta(shape-1) / zeta(shape+1) - mean^2, if shape>2"),
infos = eval(substitute(function(...) {
list(M1 = 1,
Q1 = 1,
multipleResponses = TRUE,
parameters.names = "shape",
zero = .zero ,
lshape = .lshape )
}, list( .lshape = lshape,
.zero = zero ))),
initialize = eval(substitute(expression({
temp5 <-
w.y.check(w = w, y = y,
ncol.w.max = Inf,
ncol.y.max = Inf,
Is.integer.y = TRUE,
Is.positive.y = TRUE,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
w <- temp5$w
y <- temp5$y
ncoly <- ncol(y)
mynames1 <- param.names("shape", ncoly, skip1 = TRUE)
predictors.names <-
namesof(mynames1, .lshape , earg = .eshape , tag = FALSE)
M1 <- 1
extra$ncoly <- ncoly
extra$M1 <- M1
M <- M1 * ncoly
if (!length(etastart)) {
zetaff.Loglikfun <- function(shape, y, x, w, extraargs) {
sum(c(w) * dzeta(x = y, shape, log = TRUE))
}
gshape <- .gshape
if (!length( .ishape )) {
shape.init <- matrix(NA_real_, n, M, byrow = TRUE)
for (jay in 1:ncoly) {
shape.init[, jay] <- grid.search(gshape,
objfun = zetaff.Loglikfun,
y = y[, jay], x = x, w = w[, jay])
}
} else {
shape.init <- matrix( .ishape , n, M, byrow = TRUE)
}
etastart <- theta2eta(shape.init, .lshape , earg = .eshape )
}
}), list( .lshape = lshape, .eshape = eshape,
.ishape = ishape, .gshape = gshape ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
ans <- pp <- eta2theta(eta, .lshape , earg = .eshape )
ans[pp > 1] <- zeta(pp[pp > 1]) / zeta(pp[pp > 1] + 1)
ans[pp <= 1] <- NA
ans
}, list( .lshape = lshape, .eshape = eshape ))),
last = eval(substitute(expression({
misc$link <- rep_len( .lshape , ncoly)
names(misc$link) <- mynames1
misc$earg <- vector("list", M)
names(misc$earg) <- mynames1
for (jay in 1:ncoly) {
misc$earg[[jay]] <- .eshape
}
}), list( .lshape = lshape, .eshape = eshape ))),
loglikelihood = eval(substitute( function(mu, y, w, residuals = FALSE,
eta, extra = NULL, summation = TRUE) {
shape <- eta2theta(eta, .lshape , earg = .eshape )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <- c(w) * dzeta(x = y, shape, log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lshape = lshape, .eshape = eshape ))),
vfamily = c("zetaff"),
hadof = eval(substitute(
function(eta, extra = list(), deriv = 1,
linpred.index = 1,
w = 1, dim.wz = c(NROW(eta), NCOL(eta) * (NCOL(eta)+1)/2),
...) {
shape <- eta2theta(eta, .lshape , earg = .eshape )
fred0 <- zeta(shape+1)
fred1 <- zeta(shape+1, deriv = 1)
fred2 <- zeta(shape+1, deriv = 2)
ans <- c(w) *
switch(as.character(deriv),
"0" = fred2 / fred0 - (fred1/fred0)^2,
"1" = (zeta(shape + 1, deriv = 3) -
fred2 * fred1 / fred0) / fred0 -
2 * (fred1 / fred0) * (
fred2 /fred0 - (fred1/fred0)^2),
"2" = NA * theta,
"3" = NA * theta,
stop("argument 'deriv' must be 0, 1, 2 or 3"))
if (deriv == 0) ans else
retain.col(ans, linpred.index)
}, list( .lshape = lshape, .eshape = eshape ))),
validparams = eval(substitute(function(eta, y, extra = NULL) {
shape <- eta2theta(eta, .lshape , earg = .eshape )
okay1 <- all(is.finite(shape)) && all(0 < shape)
okay1
}, list( .lshape = lshape, .eshape = eshape ))),
deriv = eval(substitute(expression({
shape <- eta2theta(eta, .lshape , earg = .eshape )
fred0 <- zeta(shape + 1)
fred1 <- zeta(shape + 1, deriv = 1)
dl.dshape <- -log(y) - fred1 / fred0
dshape.deta <- dtheta.deta(shape, .lshape , earg = .eshape )
c(w) * dl.dshape * dshape.deta
}), list( .lshape = lshape, .eshape = eshape ))),
weight = expression({
NOS <- NCOL(y)
ned2l.dshape2 <- zeta(shape + 1, deriv = 2) / fred0 -
(fred1 / fred0)^2
wz <- ned2l.dshape2 * dshape.deta^2
w.wz.merge(w = w, wz = wz, n = n, M = M, ndepy = NOS)
}))
}
gharmonic2 <- function(n, shape = 1) {
if (!is.Numeric(n, integer.valued = TRUE, positive = TRUE))
stop("bad input for argument 'n'")
LLL <- max(length(n), length(shape))
if (length(n) != LLL) n <- rep_len(n, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
aa <- 12
ans <- rep_len(0, LLL)
for (ii in 1:aa)
ans <- ans + as.numeric(ii <= n) / ii^shape
vecTF <- (aa < n)
if (any(vecTF))
ans[vecTF] <- zeta(shape[vecTF]) -
Zeta.aux(shape[vecTF], 1 + n[vecTF])
ans
}
gharmonic <- function(n, shape = 1, deriv = 0) {
if (!is.Numeric(n, integer.valued = TRUE, positive = TRUE))
stop("bad input for argument 'n'")
if (!is.Numeric(deriv, length.arg = 1, integer.valued = TRUE) ||
deriv < 0)
stop("bad input for argument 'deriv'")
lognexponent <- deriv
sign <- ifelse(deriv %% 2 == 0, 1, -1)
ans <-
if (length(n) == 1 && length(shape) == 1) {
if (lognexponent != 0) sum(log(1:n)^lognexponent * (1:n)^(-shape)) else
sum((1:n)^(-shape))
} else {
LEN <- max(length(n), length(shape))
n <- rep_len(n, LEN)
ans <- shape <- rep_len(shape, LEN)
if (lognexponent != 0) {
for (ii in 1:LEN)
ans[ii] <- sum(log(1:n[ii])^lognexponent * (1:n[ii])^(-shape[ii]))
} else {
for (ii in 1:LEN)
ans[ii] <- sum((1:n[ii])^(-shape[ii]))
}
ans
}
sign * ans
}
dzipf <- function(x, N, shape, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
if (!is.Numeric(x))
stop("bad input for argument 'x'")
if (!is.Numeric(N, integer.valued = TRUE, positive = TRUE))
stop("bad input for argument 'N'")
if (!is.Numeric(shape, positive = TRUE))
stop("bad input for argument 'shape'")
nn <- max(length(x), length(N), length(shape))
if (length(x) != nn) x <- rep_len(x, nn)
if (length(N) != nn) N <- rep_len(N, nn)
if (length(shape) != nn) shape <- rep_len(shape, nn)
ox <- !is.finite(x)
zero <- ox | round(x) != x | x < 1 | x > N
ans <- (if (log.arg) log(0) else 0) * x
if (any(!zero))
if (log.arg) {
ans[!zero] <- (-shape[!zero]) * log(x[!zero]) -
log(gharmonic2(N[!zero], shape[!zero]))
} else {
ans[!zero] <- x[!zero]^(-shape[!zero]) / gharmonic2(N[!zero],
shape[!zero])
}
ans
}
pzipf <- function(q, N, shape, log.p = FALSE) {
if (!is.Numeric(N, integer.valued = TRUE, positive = TRUE))
stop("bad input for argument 'N'")
nn <- max(length(q), length(N), length(shape))
if (length(q) != nn) q <- rep_len(q, nn)
if (length(N) != nn) N <- rep_len(N, nn)
if (length(shape) != nn) shape <- rep_len(shape, nn)
oq <- !is.finite(q)
dont.iterate <- shape <= 0
zeroOR1 <- oq | q < 1 | N <= q | dont.iterate
floorq <- floor(q)
ans <- 0 * floorq
ans[oq | q >= N] <- 1
if (any(!zeroOR1))
ans[!zeroOR1] <- gharmonic2(floorq[!zeroOR1], shape[!zeroOR1]) /
gharmonic2( N[!zeroOR1], shape[!zeroOR1])
ans[shape <= 0] <- NaN
if (log.p) log(ans) else ans
}
qzipf <- function(p, N, shape) {
if (!is.Numeric(p))
stop("bad input for argument 'p'")
if (!is.Numeric(N, integer.valued = TRUE, positive = TRUE))
stop("bad input for argument 'N'")
if (!is.Numeric(shape, positive = TRUE))
stop("bad input for argument 'shape'")
nn <- max(length(p), length(N), length(shape))
if (length(p) != nn) p <- rep_len(p, nn)
if (length(N) != nn) N <- rep_len(N, nn)
if (length(shape) != nn) shape <- rep_len(shape, nn)
a <- rep_len(1, nn)
b <- rep_len(N, nn)
approx.ans <- a
foo <- function(q, N, shape, p)
pzipf(q, N, shape) - p
dont.iterate <- p == 1 | shape <= 0
lhs <- (p <= dzipf(1, N, shape)) | dont.iterate
approx.ans[!lhs] <-
bisection.basic(foo, a[!lhs], b[!lhs], shape = shape[!lhs],
tol = 1/16, p = p[!lhs], N = N[!lhs])
faa <- floor(approx.ans)
ans <- ifelse(pzipf(faa, N, shape) < p & p <= pzipf(faa+1, N, shape),
faa+1, faa)
ans[shape <= 0] <- NaN
ans[p == 1] <- N
ans
}
rzipf <- function(n, N, shape) {
qzipf(runif(n), N, shape)
}
zipf <- function(N = NULL, lshape = "loglink", ishape = NULL) {
if (length(N) &&
(!is.Numeric(N, positive = TRUE,
integer.valued = TRUE, length.arg = 1) ||
N <= 1))
stop("bad input for argument 'N'")
enteredN <- length(N)
if (length(ishape) && !is.Numeric(ishape, positive = TRUE))
stop("argument 'ishape' must be > 0")
lshape <- as.list(substitute(lshape))
eshape <- link2list(lshape)
lshape <- attr(eshape, "function.name")
new("vglmff",
blurb = c("Zipf distribution f(y;s) = y^(-s) / sum((1:N)^(-s)),",
" s > 0, y = 1, 2,...,N",
ifelse(enteredN, paste(" = ", N, sep = ""), ""),
"\n\n",
"Link: ",
namesof("shape", lshape, earg = eshape),
"\n\n",
"Mean: gharmonic(N, shape-1) / gharmonic(N, shape)"),
infos = eval(substitute(function(...) {
list(M1 = 1,
Q1 = 1,
multipleResponses = FALSE,
parameters.names = "shape",
N = enteredN,
lshape = .lshape )
}, list( .lshape = lshape,
.enteredN = enteredN
))),
initialize = eval(substitute(expression({
w.y.check(w = w, y = y,
Is.integer.y = TRUE)
predictors.names <- namesof("shape", .lshape , earg = .eshape ,
tag = FALSE)
NN <- .N
if (!is.Numeric(NN, length.arg = 1,
positive = TRUE, integer.valued = TRUE))
NN <- max(y)
if (max(y) > NN)
stop("maximum of the response is greater than argument 'N'")
if (any(y < 1))
stop("all response values must be in 1, 2, 3,...,N( = ", NN,")")
extra$N <- NN
if (!length(etastart)) {
llfun <- function(shape, y, N, w) {
sum(c(w) * dzipf(x = y, N = extra$N, shape = shape, log = TRUE))
}
shape.init <- if (length( .ishape )) .ishape else
getInitVals(gvals = seq(0.1, 3, length.out = 19),
llfun = llfun,
y = y, N = extra$N, w = w)
shape.init <- rep_len(shape.init, length(y))
if ( .lshape == "logloglink") shape.init[shape.init <= 1] <- 1.2
etastart <- theta2eta(shape.init, .lshape , earg = .eshape )
}
}), list( .lshape = lshape, .eshape = eshape,
.ishape = ishape, .N = N ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
shape <- eta2theta(eta, .lshape , earg = .eshape )
gharmonic2(extra$N,
shape = shape - 1) / gharmonic2(extra$N, shape = shape)
}, list( .lshape = lshape, .eshape = eshape ))),
last = eval(substitute(expression({
misc$expected <- FALSE
misc$link <- c(shape = .lshape)
misc$earg <- list(shape = .eshape )
misc$N <- extra$N
}), list( .lshape = lshape, .eshape = eshape ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL,
summation = TRUE) {
shape <- eta2theta(eta, .lshape , earg = .eshape )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <- c(w) * dzipf(x = y, N = extra$N, shape = shape,
log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lshape = lshape, .eshape = eshape ))),
vfamily = c("zipf"),
validparams = eval(substitute(function(eta, y, extra = NULL) {
shape <- eta2theta(eta, .lshape , earg = .eshape )
okay1 <- all(is.finite(shape)) && all(0 < shape)
okay1
}, list( .lshape = lshape, .eshape = eshape ))),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- [email protected]) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
extra <- object@extra
shape <- eta2theta(eta, .lshape , earg = .eshape )
rzipf(nsim * length(shape), N = extra$N, shape = shape)
}, list( .lshape = lshape, .eshape = eshape ))),
deriv = eval(substitute(expression({
shape <- eta2theta(eta, .lshape , earg = .eshape )
fred1 <- gharmonic(extra$N, shape, deriv = 1)
fred0 <- gharmonic2(extra$N, shape)
dl.dshape <- -log(y) - fred1 / fred0
dshape.deta <- dtheta.deta(shape, .lshape , earg = .eshape )
d2shape.deta2 <- d2theta.deta2(shape, .lshape , earg = .eshape )
c(w) * dl.dshape * dshape.deta
}), list( .lshape = lshape, .eshape = eshape ))),
weight = expression({
d2l.dshape <- gharmonic(extra$N, shape, deriv = 2) / fred0 -
(fred1/fred0)^2
wz <- c(w) * (dshape.deta^2 * d2l.dshape - d2shape.deta2 * dl.dshape)
wz
}))
}
ddiffzeta <- function(x, shape, start = 1, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
LLL <- max(length(shape), length(x), length(start))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(start) != LLL) start <- rep_len(start, LLL)
ox <- !is.finite(x)
zero <- ox | round(x) != x | x < start
ans <- rep_len(if (log.arg) log(0) else 0, LLL)
if (any(!zero)) {
ans[!zero] <- (start[!zero] / x[!zero]) ^(shape[!zero]) -
(start[!zero] / (1 + x[!zero]))^(shape[!zero])
if (log.arg)
ans[!zero] <- log(ans[!zero])
}
if (any(ox))
ans[ox] <- if (log.arg) log(0) else 0
ans[shape <= 0] <- NaN
ans[start != round(start) | start < 1] <- NaN
ans
}
pdiffzeta <- function(q, shape, start = 1, lower.tail = TRUE) {
LLL <- max(length(shape), length(q), length(start))
if (length(q) != LLL) q <- rep_len(q, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(start) != LLL) start <- rep_len(start, LLL)
if (lower.tail) {
ans <- 1 - (start / floor(1 + q))^shape
} else {
ans <- (start / floor(1 + q))^shape
}
ans[q < start] <- if (lower.tail) 0 else 1
ans[shape <= 0] <- NaN
ans[start != round(start) | start < 1] <- NaN
ans
}
qdiffzeta <- function(p, shape, start = 1) {
LLL <- max(length(p), length(shape), length(start))
if (length(p) != LLL) p <- rep_len(p, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(start) != LLL) start <- rep_len(start, LLL)
lo <- rep_len(start, LLL)
approx.ans <- lo
hi <- 2 * lo + 10.5
dont.iterate <- p == 1 | shape <= 0 | start != round(start) | start < 1
done <- p <= pdiffzeta(hi, shape, start = start) | dont.iterate
max.iter <- 100
iter <- 0
while (!all(done) && iter < max.iter) {
lo[!done] <- hi[!done]
hi[!done] <- 2 * hi[!done] + 10.5
done[!done] <- is.infinite(hi[!done]) |
(p[!done] <= pdiffzeta(hi[!done], shape[!done],
start[!done]))
iter <- iter + 1
}
foo <- function(q, shape, start, p)
pdiffzeta(q, shape, start) - p
lhs <- (p <= ddiffzeta(start, shape, start = start)) | dont.iterate
approx.ans[!lhs] <-
bisection.basic(foo, lo[!lhs], hi[!lhs], tol = 1/16,
shape = shape[!lhs],
start = start[!lhs], p = p[!lhs])
faa <- floor(approx.ans)
ans <- ifelse(pdiffzeta(faa , shape, start = start) < p &
p <= pdiffzeta(faa+1, shape, start = start), faa+1, faa)
ans[p == 1] <- Inf
ans[shape <= 0] <- NaN
ans[start != round(start) | start < 1] <- NaN
ans
}
rdiffzeta <- function(n, shape, start = 1) {
rr <- runif(n)
qdiffzeta(rr, shape, start = start)
}
diffzeta <- function(start = 1, lshape = "loglink", ishape = NULL) {
if (!is.Numeric(start, positive = TRUE,
integer.valued = TRUE, length.arg = 1))
stop("bad input for argument 'start'")
enteredstart <- length(start)
if (length(ishape) && !is.Numeric(ishape, positive = TRUE))
stop("argument 'ishape' must be > 0")
lshape <- as.list(substitute(lshape))
eshape <- link2list(lshape)
lshape <- attr(eshape, "function.name")
new("vglmff",
blurb = c("Difference in 2 Zipf distributions ",
"f(y; shape) = y^(-shape) / sum((1:start)^(-shape)), ",
"shape > 0, start, start+1,...",
ifelse(enteredstart, paste("start = ", start, sep = ""), ""),
"\n\n",
"Link: ",
namesof("shape", lshape, earg = eshape),
"\n\n",
"Mean: gharmonic(start, shape-1) / gharmonic(start, shape)"),
infos = eval(substitute(function(...) {
list(M1 = 1,
Q1 = 1,
expected = TRUE,
multipleResponses = TRUE,
start = .start ,
parameters.names = "shape")
}, list( .start = start ))),
initialize = eval(substitute(expression({
start <- .start
temp5 <-
w.y.check(w = w, y = y,
ncol.w.max = Inf,
ncol.y.max = Inf,
Is.integer.y = TRUE,
Is.positive.y = TRUE,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
w <- temp5$w
y <- temp5$y
if (any(y < start))
stop("some response values less than 'start'")
predictors.names <- namesof("shape", .lshape , earg = .eshape ,
tag = FALSE)
extra$start <- start
if (!length(etastart)) {
llfun <- function(shape, y, start, w) {
sum(c(w) * ddiffzeta(x = y, start = extra$start,
shape = shape, log = TRUE))
}
shape.init <- if (length( .ishape )) .ishape else
getInitVals(gvals = seq(0.1, 3.0, length.out = 19),
llfun = llfun,
y = y, start = extra$start, w = w)
shape.init <- rep_len(shape.init, length(y))
if ( .lshape == "logloglink") shape.init[shape.init <= 1] <- 1.2
etastart <- theta2eta(shape.init, .lshape , earg = .eshape )
}
}), list( .lshape = lshape,
.eshape = eshape, .ishape = ishape, .start = start ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
shape <- eta2theta(eta, .lshape , earg = .eshape )
aa <- extra$start
if (length(aa) != 1 || aa < 1 || round(aa) != aa)
stop("the 'start' variable must be of unit length")
if (aa == 1)
return(zeta(shape))
mymat <- matrix(1:aa, NROW(eta), aa, byrow = TRUE)
temp1 <- rowSums(1 / mymat^shape)
(aa^shape) * (zeta(shape) - temp1 + 1 / aa^(shape-1))
}, list( .lshape = lshape, .eshape = eshape ))),
last = eval(substitute(expression({
misc$expected <- FALSE
misc$link <- c(shape = .lshape )
misc$earg <- list(shape = .eshape )
misc$start <- extra$start
}), list( .lshape = lshape, .eshape = eshape ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL,
summation = TRUE) {
shape <- eta2theta(eta, .lshape , earg = .eshape )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <- c(w) * ddiffzeta(x = y, start = extra$start,
shape = shape, log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lshape = lshape, .eshape = eshape ))),
vfamily = c("diffzeta"),
validparams = eval(substitute(function(eta, y, extra = NULL) {
shape <- eta2theta(eta, .lshape , earg = .eshape )
okay1 <- all(is.finite(shape)) && all(0 < shape)
okay1
}, list( .lshape = lshape, .eshape = eshape ))),
deriv = eval(substitute(expression({
shape <- eta2theta(eta, .lshape , earg = .eshape )
temp1 <- extra$start / y
temp2 <- extra$start / (y+1)
AA <- temp1^shape - temp2^shape
Aprime <- log(temp1) * temp1^shape -
log(temp2) * temp2^shape
dl.dshape <- Aprime / AA
dshape.deta <- dtheta.deta(shape, .lshape , earg = .eshape )
c(w) * dl.dshape * dshape.deta
}), list( .lshape = lshape, .eshape = eshape ))),
weight = expression({
ned2l.dshape <- (Aprime / AA)^2
wz <- c(w) * ned2l.dshape * dshape.deta^2
wz
}))
}
ddiffzeta <- function(x, shape, start = 1, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
LLL <- max(length(shape), length(x), length(start))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(start) != LLL) start <- rep_len(start, LLL)
ox <- !is.finite(x)
zero <- ox | round(x) != x | x < start
ans <- rep_len(if (log.arg) log(0) else 0, LLL)
if (any(!zero)) {
ans[!zero] <- (start[!zero] / x[!zero]) ^(shape[!zero]) -
(start[!zero] / (1 + x[!zero]))^(shape[!zero])
if (log.arg)
ans[!zero] <- log(ans[!zero])
}
if (any(ox))
ans[ox] <- if (log.arg) log(0) else 0
ans[shape <= 0] <- NaN
ans[start != round(start) | start < 1] <- NaN
ans
}
pdiffzeta <- function(q, shape, start = 1, lower.tail = TRUE) {
LLL <- max(length(shape), length(q), length(start))
if (length(q) != LLL) q <- rep_len(q, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(start) != LLL) start <- rep_len(start, LLL)
if (lower.tail) {
ans <- 1 - (start / floor(1 + q))^shape
} else {
ans <- (start / floor(1 + q))^shape
}
ans[q < start] <- if (lower.tail) 0 else 1
ans[shape <= 0] <- NaN
ans[start != round(start) | start < 1] <- NaN
ans
}
qdiffzeta <- function(p, shape, start = 1) {
LLL <- max(length(p), length(shape), length(start))
if (length(p) != LLL) p <- rep_len(p, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(start) != LLL) start <- rep_len(start, LLL)
lo <- rep_len(start, LLL)
approx.ans <- lo
hi <- 2 * lo + 10.5
dont.iterate <- p == 1 | shape <= 0 | start != round(start) | start < 1
done <- p <= pdiffzeta(hi, shape, start = start) | dont.iterate
max.iter <- 100
iter <- 0
while (!all(done) && iter < max.iter) {
lo[!done] <- hi[!done]
hi[!done] <- 2 * hi[!done] + 10.5
done[!done] <- is.infinite(hi[!done]) |
(p[!done] <= pdiffzeta(hi[!done], shape[!done],
start[!done]))
iter <- iter + 1
}
foo <- function(q, shape, start, p)
pdiffzeta(q, shape, start) - p
lhs <- (p <= ddiffzeta(start, shape, start = start)) | dont.iterate
approx.ans[!lhs] <-
bisection.basic(foo, lo[!lhs], hi[!lhs], tol = 1/16,
shape = shape[!lhs],
start = start[!lhs], p = p[!lhs])
faa <- floor(approx.ans)
ans <- ifelse(pdiffzeta(faa , shape, start = start) < p &
p <= pdiffzeta(faa+1, shape, start = start), faa+1, faa)
ans[p == 1] <- Inf
ans[shape <= 0] <- NaN
ans[start != round(start) | start < 1] <- NaN
ans
}
rdiffzeta <- function(n, shape, start = 1) {
rr <- runif(n)
qdiffzeta(rr, shape, start = start)
}
diffzeta <- function(start = 1, lshape = "loglink", ishape = NULL) {
if (!is.Numeric(start, positive = TRUE,
integer.valued = TRUE, length.arg = 1))
stop("bad input for argument 'start'")
enteredstart <- length(start)
if (length(ishape) && !is.Numeric(ishape, positive = TRUE))
stop("argument 'ishape' must be > 0")
lshape <- as.list(substitute(lshape))
eshape <- link2list(lshape)
lshape <- attr(eshape, "function.name")
new("vglmff",
blurb = c("Difference in 2 Zipf distributions ",
"f(y; shape) = y^(-shape) / sum((1:start)^(-shape)), ",
"shape > 0, start, start+1,...",
ifelse(enteredstart, paste("start = ", start, sep = ""), ""),
"\n\n",
"Link: ",
namesof("shape", lshape, earg = eshape),
"\n\n",
"Mean: gharmonic(start, shape-1) / gharmonic(start, shape)"),
infos = eval(substitute(function(...) {
list(M1 = 1,
Q1 = 1,
expected = TRUE,
multipleResponses = TRUE,
start = .start ,
parameters.names = "shape")
}, list( .start = start ))),
initialize = eval(substitute(expression({
start <- .start
temp5 <-
w.y.check(w = w, y = y,
ncol.w.max = Inf,
ncol.y.max = Inf,
Is.integer.y = TRUE,
Is.positive.y = TRUE,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
w <- temp5$w
y <- temp5$y
if (any(y < start))
stop("some response values less than 'start'")
predictors.names <- namesof("shape", .lshape , earg = .eshape ,
tag = FALSE)
extra$start <- start
if (!length(etastart)) {
llfun <- function(shape, y, start, w) {
sum(c(w) * ddiffzeta(x = y, start = extra$start,
shape = shape, log = TRUE))
}
shape.init <- if (length( .ishape )) .ishape else
getInitVals(gvals = seq(0.1, 3.0, length.out = 19),
llfun = llfun,
y = y, start = extra$start, w = w)
shape.init <- rep_len(shape.init, length(y))
if ( .lshape == "logloglink") shape.init[shape.init <= 1] <- 1.2
etastart <- theta2eta(shape.init, .lshape , earg = .eshape )
}
}), list( .lshape = lshape,
.eshape = eshape, .ishape = ishape, .start = start ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
shape <- eta2theta(eta, .lshape , earg = .eshape )
aa <- extra$start
if (length(aa) != 1 || aa < 1 || round(aa) != aa)
stop("the 'start' variable must be of unit length")
if (aa == 1)
return(zeta(shape))
mymat <- matrix(1:aa, NROW(eta), aa, byrow = TRUE)
temp1 <- rowSums(1 / mymat^shape)
(aa^shape) * (zeta(shape) - temp1 + 1 / aa^(shape-1))
}, list( .lshape = lshape, .eshape = eshape ))),
last = eval(substitute(expression({
misc$expected <- FALSE
misc$link <- c(shape = .lshape )
misc$earg <- list(shape = .eshape )
misc$start <- extra$start
}), list( .lshape = lshape, .eshape = eshape ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL,
summation = TRUE) {
shape <- eta2theta(eta, .lshape , earg = .eshape )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <- c(w) * ddiffzeta(x = y, start = extra$start,
shape = shape, log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lshape = lshape, .eshape = eshape ))),
vfamily = c("diffzeta"),
validparams = eval(substitute(function(eta, y, extra = NULL) {
shape <- eta2theta(eta, .lshape , earg = .eshape )
okay1 <- all(is.finite(shape)) && all(0 < shape)
okay1
}, list( .lshape = lshape, .eshape = eshape ))),
deriv = eval(substitute(expression({
shape <- eta2theta(eta, .lshape , earg = .eshape )
temp1 <- extra$start / y
temp2 <- extra$start / (y+1)
AA <- temp1^shape - temp2^shape
Aprime <- log(temp1) * temp1^shape -
log(temp2) * temp2^shape
dl.dshape <- Aprime / AA
dshape.deta <- dtheta.deta(shape, .lshape , earg = .eshape )
c(w) * dl.dshape * dshape.deta
}), list( .lshape = lshape, .eshape = eshape ))),
weight = expression({
ned2l.dshape <- (Aprime / AA)^2
wz <- c(w) * ned2l.dshape * dshape.deta^2
wz
}))
} |
smallFilter <- function(dat, threshold = 1000L) {
dat[dat$size >= threshold, ]
} |
f.star.test<-function(means,variances,ns){
MSB.s=sum(ns*(means-mean(means))^2)
MSW.s=sum((1-ns/sum(ns))*variances)
F.s=MSB.s/MSW.s
gs=((1-ns/sum(ns))*variances)/MSW.s
f=1/(sum(gs^2/(ns-1)))
p.value=1-pf(F.s,length(means)-1,f)
f.squared=(length(means)-1)*F.s/sum(ns)
return(list("statistic"=F.s,"p.value"=p.value,"est.f.squared"=f.squared))
} |
.make_file_resource <- function(path = "/data/CNSIM1.csv", format = "csv") {
newResource(
name = "test",
url = paste0("file://", path),
format = format
)
}
test_that("file resource resolver works", {
res <- .make_file_resource()
resolver <- TidyFileResourceResolver$new()
expect_true(resolver$isFor(res))
res <- newResource(
name = "CNSIM1",
url = "app+https://app.example.org/files/data/CNSIM1.csv",
secret = "DSDFrezerFgbgBC",
format = "csv"
)
expect_false(resolver$isFor(res))
})
test_that("file resource resolver is loaded", {
res <- .make_file_resource()
registerResourceResolver(TidyFileResourceResolver$new())
resolver <- resolveResource(res)
expect_false(is.null(resolver))
client <- newResourceClient(res)
expect_false(is.null(client))
})
test_that("file resource client factory, file not found", {
res <- .make_file_resource()
resolver <- TidyFileResourceResolver$new()
client <- resolver$newClient(res)
expect_equal(class(client), c("TidyFileResourceClient", "FileResourceClient", "ResourceClient", "R6"))
expect_equal(client$downloadFile(), "/data/CNSIM1.csv")
expect_error(client$asDataFrame())
})
test_that("file resource client factory, csv file", {
res <- .make_file_resource("./data/dataset.csv")
resolver <- TidyFileResourceResolver$new()
client <- resolver$newClient(res)
expect_equal(class(client), c("TidyFileResourceClient", "FileResourceClient", "ResourceClient", "R6"))
expect_equal(client$downloadFile(), "data/dataset.csv")
df <- client$asDataFrame()
expect_false(is.null(df))
expect_true("data.frame" %in% class(df))
expect_true("tbl" %in% class(df))
client$close()
})
test_that("file resource client factory, spss file", {
res <- .make_file_resource("./data/dataset.sav", format = "spss")
resolver <- TidyFileResourceResolver$new()
client <- resolver$newClient(res)
expect_equal(class(client), c("TidyFileResourceClient", "FileResourceClient", "ResourceClient", "R6"))
expect_equal(client$downloadFile(), "data/dataset.sav")
df <- client$asDataFrame()
expect_false(is.null(df))
expect_true("data.frame" %in% class(df))
expect_true("tbl" %in% class(df))
client$close()
})
test_that("csv file resource coercing to data.frame", {
res <- .make_file_resource("./data/dataset.csv")
registerResourceResolver(TidyFileResourceResolver$new())
df <- as.data.frame(res)
expect_false(is.null(df))
expect_true("data.frame" %in% class(df))
expect_true("tbl" %in% class(df))
})
test_that("csv file resource client coercing to data.frame", {
res <- .make_file_resource("./data/dataset.csv")
registerResourceResolver(TidyFileResourceResolver$new())
client <- newResourceClient(res)
df <- as.data.frame(client)
expect_false(is.null(df))
expect_true("data.frame" %in% class(df))
expect_true("tbl" %in% class(df))
}) |
setGeneric("existsSheet",
function(object, name) standardGeneric("existsSheet"))
setMethod("existsSheet",
signature(object = "workbook"),
function(object, name) {
xlcCall(object, "existsSheet", name)
}
) |
context("class level lsm_c_core_cv metric")
landscapemetrics_class_landscape_value <- lsm_c_core_cv(landscape)
test_that("lsm_c_core_cv is typestable", {
expect_is(lsm_c_core_cv(landscape), "tbl_df")
expect_is(lsm_c_core_cv(landscape_stack), "tbl_df")
expect_is(lsm_c_core_cv(landscape_brick), "tbl_df")
expect_is(lsm_c_core_cv(landscape_list), "tbl_df")
})
test_that("lsm_c_core_cv returns the desired number of columns", {
expect_equal(ncol(landscapemetrics_class_landscape_value), 6)
})
test_that("lsm_c_core_cv returns in every column the correct type", {
expect_type(landscapemetrics_class_landscape_value$layer, "integer")
expect_type(landscapemetrics_class_landscape_value$level, "character")
expect_type(landscapemetrics_class_landscape_value$class, "integer")
expect_type(landscapemetrics_class_landscape_value$id, "integer")
expect_type(landscapemetrics_class_landscape_value$metric, "character")
expect_type(landscapemetrics_class_landscape_value$value, "double")
}) |
flamingo <-
structure(list(LOS = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9,
9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11,
11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11,
11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11,
11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11,
11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12,
12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,
12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,
12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,
12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13,
13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,
13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,
14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,
14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,
14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,
14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,
14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,
14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,
14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,
14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,
14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,
14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,
14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15,
15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16,
16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 18,
18, 19, 19, 19, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 22, 22,
23, 23, 23, 28, 30), year = c(2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2020,
2020, 2020, 2020, 2020, 2020, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2019, 2019, 2019, 2019,
2020, 2020, 2020, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2020, 2020, 2019, 2019, 2020, 2019, 2019, 2020, 2019, 2019, 2020,
2020, 2019, 2019, 2019, 2020, 2020, 2020, 2019, 2019, 2019, 2019,
2019, 2019, 2019), arrdate = structure(c(18143, 18156, 18160,
18018, 18020, 18023, 18023, 18024, 18028, 18031, 18034, 18034,
18034, 18034, 18038, 18038, 18039, 18040, 18041, 18042, 18042,
18045, 18045, 18046, 18046, 18047, 18047, 18048, 18048, 18048,
18048, 18049, 18051, 18053, 18053, 18054, 18055, 18055, 18055,
18055, 18055, 18055, 18055, 18062, 18062, 18062, 18062, 18062,
18066, 18068, 18068, 18068, 18069, 18069, 18069, 18069, 18069,
18069, 18069, 18069, 18069, 18069, 18069, 18069, 18069, 18069,
18069, 18069, 18069, 18069, 18069, 18069, 18069, 18069, 18069,
18069, 18069, 18069, 18069, 18069, 18069, 18069, 18069, 18069,
18069, 18069, 18069, 18069, 18069, 18069, 18069, 18069, 18072,
18072, 18072, 18073, 18073, 18073, 18073, 18073, 18073, 18075,
18075, 18076, 18076, 18076, 18076, 18076, 18077, 18077, 18082,
18082, 18083, 18083, 18084, 18084, 18084, 18084, 18084, 18084,
18084, 18084, 18084, 18084, 18085, 18086, 18087, 18097, 18098,
18104, 18104, 18104, 18104, 18104, 18104, 18104, 18104, 18104,
18104, 18104, 18104, 18104, 18106, 18107, 18107, 18108, 18109,
18110, 18110, 18110, 18110, 18110, 18110, 18110, 18111, 18111,
18112, 18115, 18118, 18122, 18123, 18123, 18123, 18123, 18123,
18126, 18129, 18130, 18131, 18132, 18132, 18132, 18132, 18132,
18132, 18132, 18135, 18140, 18140, 18143, 18143, 18143, 18144,
18146, 18146, 18146, 18146, 18146, 18146, 18146, 18147, 18148,
18148, 18152, 18153, 18154, 18154, 18156, 18157, 18158, 18158,
18158, 18158, 18158, 18158, 18158, 18158, 18158, 18159, 18159,
18160, 18160, 18160, 18160, 18160, 18160, 18162, 18162, 18166,
18166, 18166, 18166, 18167, 18167, 18167, 18168, 18170, 18171,
18173, 18173, 18173, 18174, 18174, 18174, 18174, 18174, 18174,
18176, 18022, 18023, 18164, 18164, 18164, 18164, 18164, 18164,
18164, 18164, 18164, 18164, 18164, 18164, 18164, 18072, 18073,
18162, 18162, 18162, 18162, 18162, 18162, 18162, 18162, 18162,
18162, 18162, 18043, 18043, 18151, 18151, 18151, 18151, 18151,
18151, 18151, 18151, 18151, 18151, 18151, 18151, 18151, 18151,
18151, 18034, 18034, 18034, 18034, 18034, 18034, 18034, 18034,
18034, 18034, 18034, 18034, 18034, 18034, 18034, 18034, 18034,
18034, 18034, 18034, 18034, 18034, 18034, 18034, 18034, 18034,
18034, 18034, 18034, 18034, 18034, 18034, 18034, 18034, 18034,
18034, 18034, 18034, 18034, 18034, 18034, 18034, 18034, 18034,
18034, 18034, 18034, 18034, 18034, 18034, 18034, 18034, 18034,
18034, 18034, 18034, 18034, 18034, 18034, 18034, 18034, 18034,
18034, 18034, 18034, 18034, 18034, 18034, 18034, 18034, 18034,
18034, 18034, 18034, 18034, 18034, 18034, 18034, 18034, 18034,
18034, 18034, 18034, 18034, 18034, 18034, 18034, 18034, 18034,
18034, 18034, 18034, 18034, 18034, 18034, 18034, 18034, 18034,
18034, 18034, 18034, 18034, 18034, 18034, 18052, 18052, 18052,
18052, 18052, 18052, 18052, 18052, 18052, 18052, 18052, 18052,
18052, 18052, 18052, 18052, 18052, 18052, 18052, 18052, 18052,
18052, 18052, 18052, 18052, 18052, 18052, 18052, 18052, 18052,
18052, 18060, 18064, 18048, 18069, 18069, 18073, 18073, 18082,
18082, 18084, 18100, 18100, 18111, 18142, 18145, 18146, 18447,
18447, 18447, 18447, 18447, 18447, 18447, 18447, 18447, 18453,
18454, 18454, 18454, 18454, 18454, 18454, 18454, 18454, 18454,
18454, 18454, 18455, 18455, 18456, 18458, 18461, 18461, 18461,
18461, 18461, 18461, 18461, 18461, 18461, 18461, 18461, 18465,
18465, 18467, 18468, 18468, 18468, 18468, 18468, 18468, 18468,
18468, 18468, 18468, 18468, 18468, 18468, 18468, 18469, 18469,
18469, 18475, 18475, 18475, 18475, 18475, 18475, 18475, 18475,
18475, 18475, 18475, 18475, 18475, 18475, 18482, 18482, 18482,
18488, 18493, 18496, 18496, 18496, 18496, 18496, 18497, 18498,
18499, 18499, 18501, 18502, 18502, 18503, 18503, 18503, 18503,
18503, 18503, 18503, 18503, 18503, 18503, 18503, 18503, 18503,
18503, 18503, 18503, 18503, 18503, 18503, 18503, 18503, 18503,
18503, 18503, 18503, 18504, 18504, 18510, 18510, 18510, 18510,
18510, 18510, 18510, 18510, 18510, 18510, 18510, 18510, 18510,
18510, 18510, 18510, 18510, 18510, 18510, 18510, 18510, 18510,
18510, 18510, 18510, 18510, 18510, 18510, 18510, 18510, 18511,
18511, 18512, 18513, 18514, 18515, 18517, 18517, 18517, 18521,
18524, 18524, 18524, 18524, 18524, 18524, 18524, 18524, 18526,
18530, 18528, 18528, 18528, 18528, 18528, 18528, 18528, 18528,
18528, 18528, 18528, 18528, 18528, 18528, 18526, 18526, 18526,
18526, 18526, 18526, 18526, 18526, 18526, 18526, 18526, 18447,
18497, 18501, 18157, 18018, 18021, 18024, 18025, 18029, 18030,
18038, 18038, 18038, 18041, 18041, 18041, 18044, 18046, 18046,
18048, 18050, 18054, 18059, 18060, 18060, 18061, 18061, 18061,
18066, 18067, 18068, 18068, 18068, 18069, 18069, 18069, 18070,
18070, 18070, 18070, 18070, 18070, 18074, 18075, 18075, 18075,
18075, 18076, 18076, 18076, 18076, 18076, 18080, 18080, 18080,
18082, 18082, 18082, 18084, 18084, 18084, 18084, 18084, 18084,
18084, 18084, 18084, 18084, 18084, 18086, 18089, 18089, 18089,
18090, 18092, 18092, 18094, 18094, 18095, 18097, 18097, 18097,
18107, 18107, 18109, 18109, 18110, 18110, 18110, 18111, 18113,
18113, 18117, 18117, 18122, 18122, 18122, 18122, 18128, 18129,
18130, 18130, 18131, 18131, 18131, 18131, 18131, 18131, 18131,
18133, 18133, 18134, 18135, 18137, 18138, 18138, 18139, 18139,
18141, 18141, 18142, 18142, 18144, 18145, 18145, 18145, 18145,
18145, 18145, 18145, 18145, 18145, 18145, 18145, 18145, 18145,
18146, 18148, 18148, 18149, 18149, 18151, 18152, 18152, 18153,
18153, 18155, 18157, 18159, 18159, 18159, 18160, 18160, 18160,
18162, 18165, 18166, 18166, 18166, 18166, 18166, 18166, 18166,
18166, 18167, 18167, 18168, 18169, 18170, 18170, 18170, 18170,
18171, 18171, 18171, 18173, 18173, 18173, 18173, 18173, 18173,
18021, 18022, 18022, 18022, 18022, 18022, 18022, 18022, 18022,
18022, 18022, 18022, 18022, 18022, 18022, 18022, 18022, 18022,
18022, 18022, 18022, 18022, 18022, 18022, 18022, 18022, 18022,
18022, 18022, 18022, 18022, 18048, 18048, 18048, 18048, 18048,
18048, 18048, 18048, 18048, 18048, 18048, 18048, 18048, 18048,
18048, 18048, 18048, 18048, 18048, 18072, 18066, 18066, 18066,
18042, 18042, 18043, 18039, 18039, 18039, 18039, 18039, 18039,
18039, 18039, 18039, 18039, 18039, 18039, 18039, 18039, 18039,
18039, 18039, 18025, 18025, 18025, 18025, 18025, 18025, 18025,
18025, 18025, 18025, 18025, 18025, 18025, 18050, 18050, 18050,
18051, 18152, 18163, 18163, 18163, 18163, 18163, 18163, 18163,
18163, 18163, 18163, 18163, 18163, 18163, 18163, 18163, 18163,
18163, 18163, 18163, 18048, 18048, 18048, 18048, 18048, 18048,
18048, 18048, 18048, 18048, 18048, 18048, 18048, 18177, 18177,
18177, 18177, 18177, 18177, 18177, 18177, 18177, 18177, 18177,
18177, 18177, 18177, 18177, 18177, 18177, 18177, 18177, 18051,
18051, 18051, 18051, 18051, 18051, 18039, 18039, 18039, 18039,
18039, 18039, 18039, 18039, 18045, 18045, 18046, 18046, 18046,
18046, 18046, 18046, 18046, 18047, 18052, 18052, 18052, 18052,
18052, 18052, 18063, 18045, 18045, 18045, 18045, 18045, 18045,
18045, 18045, 18033, 18033, 18033, 18033, 18158, 18158, 18158,
18158, 18158, 18158, 18158, 18158, 18158, 18158, 18158, 18158,
18158, 18158, 18026, 18026, 18026, 18026, 18026, 18026, 18026,
18026, 18026, 18026, 18026, 18026, 18026, 18026, 18026, 18026,
18026, 18026, 18026, 18026, 18026, 18026, 18026, 18026, 18026,
18026, 18026, 18051, 18051, 18057, 18067, 18044, 18044, 18044,
18044, 18044, 18044, 18044, 18044, 18044, 18044, 18044, 18044,
18044, 18044, 18044, 18044, 18044, 18044, 18044, 18044, 18044,
18058, 18058, 18058, 18058, 18058, 18058, 18058, 18058, 18058,
18058, 18058, 18058, 18058, 18058, 18058, 18058, 18058, 18058,
18058, 18149, 18149, 18149, 18149, 18149, 18149, 18149, 18149,
18149, 18149, 18149, 18149, 18149, 18149, 18149, 18149, 18149,
18149, 18149, 18149, 18149, 18149, 18149, 18156, 18156, 18156,
18156, 18156, 18156, 18156, 18156, 18156, 18156, 18156, 18156,
18156, 18156, 18156, 18156, 18156, 18156, 18156, 18156, 18170,
18170, 18170, 18170, 18170, 18170, 18170, 18170, 18170, 18170,
18170, 18170, 18170, 18170, 18170, 18170, 18170, 18170, 18170,
18170, 18036, 18036, 18036, 18036, 18036, 18036, 18036, 18036,
18036, 18036, 18036, 18036, 18159, 18159, 18159, 18159, 18159,
18159, 18159, 18159, 18159, 18159, 18159, 18159, 18159, 18159,
18159, 18159, 18054, 18056, 18056, 18076, 18078, 18083, 18091,
18091, 18091, 18092, 18107, 18108, 18116, 18117, 18118, 18123,
18124, 18125, 18138, 18447, 18447, 18450, 18453, 18453, 18453,
18453, 18453, 18453, 18453, 18454, 18454, 18454, 18454, 18455,
18455, 18455, 18455, 18456, 18457, 18457, 18459, 18460, 18460,
18460, 18460, 18460, 18460, 18461, 18461, 18461, 18463, 18463,
18463, 18467, 18467, 18467, 18467, 18467, 18467, 18468, 18468,
18468, 18468, 18469, 18471, 18473, 18473, 18474, 18474, 18474,
18475, 18477, 18478, 18479, 18481, 18481, 18482, 18482, 18483,
18484, 18484, 18485, 18485, 18485, 18485, 18488, 18488, 18488,
18491, 18492, 18492, 18492, 18493, 18493, 18494, 18496, 18496,
18496, 18499, 18501, 18501, 18501, 18502, 18502, 18502, 18503,
18503, 18503, 18503, 18506, 18506, 18506, 18506, 18509, 18509,
18509, 18509, 18509, 18510, 18510, 18511, 18511, 18511, 18512,
18512, 18516, 18520, 18523, 18524, 18524, 18527, 18527, 18527,
18527, 18527, 18527, 18527, 18527, 18527, 18527, 18527, 18527,
18527, 18527, 18527, 18527, 18504, 18059, 18144, 18119, 18122,
18018, 18018, 18024, 18026, 18027, 18038, 18038, 18040, 18040,
18040, 18040, 18040, 18046, 18046, 18046, 18046, 18046, 18046,
18046, 18047, 18053, 18054, 18054, 18054, 18054, 18056, 18057,
18057, 18057, 18060, 18060, 18060, 18061, 18063, 18064, 18064,
18065, 18065, 18065, 18066, 18066, 18067, 18068, 18068, 18070,
18075, 18075, 18075, 18080, 18082, 18083, 18083, 18084, 18085,
18085, 18085, 18086, 18088, 18089, 18089, 18094, 18095, 18095,
18095, 18095, 18095, 18095, 18096, 18096, 18097, 18097, 18097,
18098, 18098, 18100, 18102, 18102, 18107, 18107, 18108, 18108,
18108, 18108, 18109, 18110, 18110, 18110, 18110, 18111, 18112,
18113, 18113, 18113, 18114, 18114, 18116, 18116, 18116, 18117,
18117, 18118, 18118, 18120, 18120, 18120, 18121, 18121, 18122,
18122, 18123, 18123, 18123, 18123, 18126, 18126, 18127, 18127,
18130, 18130, 18130, 18131, 18131, 18131, 18131, 18131, 18131,
18131, 18131, 18132, 18132, 18132, 18136, 18136, 18136, 18136,
18137, 18137, 18139, 18139, 18140, 18142, 18143, 18144, 18144,
18144, 18144, 18145, 18145, 18145, 18146, 18147, 18148, 18148,
18148, 18149, 18149, 18151, 18152, 18153, 18154, 18154, 18154,
18154, 18155, 18155, 18155, 18155, 18155, 18155, 18155, 18155,
18155, 18155, 18155, 18155, 18155, 18155, 18155, 18155, 18155,
18155, 18155, 18156, 18158, 18159, 18159, 18160, 18160, 18160,
18162, 18162, 18163, 18165, 18165, 18165, 18165, 18165, 18165,
18165, 18165, 18166, 18166, 18166, 18167, 18168, 18169, 18170,
18170, 18170, 18170, 18170, 18170, 18171, 18171, 18173, 18160,
18160, 18160, 18160, 18160, 18021, 18021, 18021, 18021, 18021,
18022, 18022, 18022, 18071, 18065, 18065, 18065, 18041, 18042,
18042, 18042, 18042, 18042, 18042, 18042, 18042, 18042, 18042,
18042, 18042, 18042, 18042, 18042, 18043, 18043, 18043, 18049,
18049, 18051, 18051, 18151, 18029, 18029, 18029, 18029, 18029,
18029, 18029, 18029, 18029, 18024, 18024, 18024, 18024, 18024,
18024, 18024, 18024, 18024, 18024, 18024, 18024, 18175, 18175,
18175, 18175, 18175, 18049, 18049, 18049, 18049, 18049, 18049,
18049, 18049, 18045, 18045, 18045, 18045, 18045, 18045, 18045,
18045, 18046, 18063, 18063, 18063, 18063, 18063, 18063, 18032,
18061, 18066, 18074, 18076, 18078, 18090, 18091, 18091, 18091,
18095, 18095, 18095, 18096, 18101, 18104, 18116, 18119, 18121,
18121, 18122, 18125, 18126, 18134, 18137, 18139, 18140, 18143,
18145, 18447, 18451, 18453, 18453, 18456, 18462, 18463, 18464,
18465, 18465, 18466, 18466, 18466, 18466, 18467, 18468, 18468,
18468, 18470, 18470, 18472, 18472, 18473, 18474, 18475, 18477,
18478, 18480, 18480, 18480, 18481, 18484, 18484, 18484, 18484,
18484, 18484, 18484, 18487, 18487, 18487, 18487, 18488, 18488,
18488, 18488, 18488, 18488, 18488, 18489, 18489, 18490, 18490,
18492, 18493, 18493, 18493, 18493, 18493, 18494, 18494, 18495,
18496, 18496, 18496, 18496, 18497, 18498, 18500, 18500, 18503,
18504, 18504, 18505, 18505, 18506, 18506, 18508, 18509, 18509,
18509, 18509, 18510, 18511, 18514, 18514, 18515, 18515, 18515,
18515, 18516, 18518, 18522, 18526, 18527, 18527, 18018, 18027,
18031, 18031, 18038, 18038, 18038, 18038, 18039, 18042, 18042,
18043, 18043, 18043, 18043, 18045, 18045, 18045, 18048, 18048,
18050, 18051, 18053, 18053, 18054, 18055, 18058, 18058, 18059,
18060, 18061, 18062, 18062, 18062, 18064, 18064, 18064, 18064,
18064, 18064, 18064, 18064, 18064, 18064, 18064, 18064, 18064,
18064, 18064, 18064, 18064, 18064, 18064, 18064, 18065, 18065,
18065, 18067, 18068, 18069, 18072, 18074, 18075, 18075, 18076,
18076, 18077, 18077, 18077, 18078, 18078, 18079, 18080, 18081,
18082, 18084, 18084, 18085, 18085, 18086, 18086, 18088, 18089,
18089, 18089, 18089, 18092, 18092, 18092, 18095, 18096, 18096,
18098, 18098, 18098, 18099, 18100, 18102, 18104, 18104, 18105,
18106, 18106, 18106, 18107, 18108, 18108, 18110, 18111, 18111,
18113, 18113, 18114, 18115, 18116, 18119, 18119, 18120, 18121,
18121, 18122, 18122, 18126, 18127, 18128, 18128, 18128, 18129,
18129, 18130, 18130, 18131, 18131, 18131, 18131, 18133, 18133,
18133, 18134, 18136, 18136, 18138, 18139, 18140, 18144, 18144,
18147, 18148, 18149, 18149, 18149, 18151, 18152, 18152, 18152,
18154, 18155, 18155, 18155, 18155, 18155, 18155, 18157, 18157,
18157, 18157, 18161, 18161, 18162, 18162, 18162, 18163, 18163,
18164, 18164, 18168, 18168, 18169, 18169, 18170, 18170, 18170,
18171, 18171, 18172, 18020, 18020, 18021, 18021, 18021, 18021,
18021, 18064, 18064, 18064, 18064, 18064, 18064, 18064, 18065,
18065, 18042, 18042, 18042, 18042, 18042, 18042, 18042, 18042,
18042, 18042, 18042, 18042, 18042, 18042, 18042, 18042, 18042,
18042, 18042, 18042, 18042, 18042, 18042, 18042, 18042, 18042,
18042, 18042, 18042, 18042, 18042, 18042, 18042, 18042, 18042,
18042, 18042, 18042, 18050, 18050, 18050, 18050, 18050, 18050,
18050, 18050, 18050, 18050, 18050, 18050, 18050, 18050, 18050,
18050, 18050, 18050, 18050, 18050, 18050, 18050, 18050, 18050,
18050, 18050, 18050, 18050, 18050, 18050, 18050, 18050, 18050,
18050, 18050, 18050, 18050, 18050, 18050, 18150, 18150, 18045,
18045, 18045, 18045, 18045, 18045, 18045, 18045, 18045, 18173,
18173, 18173, 18173, 18173, 18173, 18173, 18173, 18173, 18173,
18173, 18173, 18173, 18173, 18173, 18056, 18057, 18074, 18078,
18078, 18079, 18088, 18091, 18094, 18094, 18100, 18100, 18103,
18108, 18113, 18114, 18117, 18120, 18121, 18122, 18125, 18127,
18136, 18137, 18138, 18140, 18161, 18448, 18449, 18450, 18450,
18452, 18454, 18455, 18456, 18459, 18460, 18460, 18462, 18463,
18467, 18467, 18468, 18470, 18470, 18470, 18471, 18474, 18477,
18479, 18479, 18480, 18482, 18482, 18485, 18486, 18487, 18487,
18487, 18487, 18487, 18487, 18488, 18490, 18490, 18490, 18491,
18492, 18492, 18492, 18493, 18493, 18493, 18494, 18494, 18494,
18494, 18494, 18494, 18495, 18496, 18496, 18500, 18500, 18500,
18502, 18504, 18504, 18504, 18506, 18507, 18507, 18507, 18508,
18508, 18511, 18514, 18517, 18517, 18519, 18522, 18525, 18526,
18526, 18018, 18029, 18029, 18030, 18034, 18042, 18042, 18043,
18050, 18053, 18054, 18055, 18055, 18055, 18056, 18056, 18056,
18056, 18056, 18056, 18056, 18056, 18056, 18056, 18056, 18056,
18056, 18056, 18056, 18056, 18056, 18056, 18056, 18056, 18056,
18056, 18059, 18060, 18062, 18062, 18063, 18063, 18064, 18064,
18064, 18064, 18064, 18067, 18068, 18069, 18069, 18070, 18070,
18071, 18073, 18075, 18077, 18077, 18077, 18077, 18077, 18077,
18077, 18077, 18077, 18077, 18077, 18077, 18077, 18077, 18077,
18077, 18077, 18077, 18077, 18077, 18077, 18077, 18077, 18078,
18080, 18080, 18081, 18082, 18082, 18084, 18085, 18086, 18087,
18087, 18088, 18089, 18089, 18089, 18091, 18093, 18094, 18094,
18095, 18095, 18096, 18097, 18098, 18099, 18100, 18102, 18102,
18104, 18105, 18106, 18109, 18110, 18113, 18114, 18114, 18114,
18114, 18115, 18116, 18116, 18117, 18118, 18118, 18118, 18119,
18120, 18121, 18122, 18124, 18125, 18125, 18126, 18128, 18129,
18129, 18130, 18130, 18131, 18131, 18134, 18134, 18136, 18137,
18139, 18140, 18140, 18140, 18140, 18140, 18140, 18140, 18140,
18140, 18141, 18141, 18143, 18150, 18152, 18152, 18154, 18154,
18154, 18154, 18154, 18154, 18157, 18158, 18158, 18159, 18159,
18159, 18159, 18159, 18161, 18163, 18163, 18166, 18166, 18169,
18169, 18170, 18019, 18022, 18063, 18063, 18063, 18063, 18063,
18063, 18063, 18063, 18064, 18064, 18041, 18041, 18041, 18042,
18042, 18042, 18042, 18042, 18049, 18049, 18049, 18050, 18050,
18050, 18050, 18173, 18064, 18071, 18071, 18078, 18084, 18084,
18085, 18092, 18093, 18094, 18094, 18094, 18099, 18104, 18104,
18104, 18104, 18104, 18113, 18113, 18114, 18114, 18116, 18117,
18118, 18118, 18120, 18120, 18122, 18125, 18127, 18127, 18129,
18130, 18132, 18135, 18448, 18449, 18451, 18453, 18462, 18462,
18463, 18463, 18468, 18470, 18470, 18474, 18477, 18479, 18479,
18479, 18480, 18480, 18480, 18482, 18483, 18483, 18484, 18485,
18486, 18486, 18487, 18487, 18488, 18488, 18488, 18488, 18488,
18488, 18489, 18489, 18489, 18490, 18490, 18490, 18491, 18491,
18492, 18492, 18492, 18492, 18493, 18493, 18494, 18494, 18496,
18497, 18498, 18498, 18499, 18503, 18503, 18503, 18504, 18504,
18505, 18505, 18507, 18507, 18509, 18510, 18511, 18512, 18512,
18512, 18513, 18522, 18102, 18041, 18041, 18041, 18041, 18042,
18045, 18046, 18049, 18049, 18054, 18055, 18055, 18056, 18057,
18063, 18063, 18063, 18063, 18063, 18063, 18064, 18064, 18064,
18068, 18069, 18070, 18070, 18070, 18070, 18070, 18072, 18072,
18074, 18077, 18077, 18079, 18085, 18085, 18085, 18085, 18088,
18088, 18088, 18091, 18093, 18093, 18094, 18096, 18098, 18099,
18100, 18100, 18101, 18103, 18105, 18105, 18105, 18106, 18107,
18107, 18108, 18108, 18108, 18112, 18113, 18113, 18113, 18113,
18114, 18116, 18116, 18117, 18117, 18118, 18118, 18119, 18124,
18125, 18125, 18125, 18126, 18126, 18126, 18127, 18130, 18132,
18132, 18132, 18134, 18134, 18134, 18135, 18135, 18139, 18139,
18139, 18139, 18140, 18140, 18140, 18140, 18143, 18145, 18145,
18147, 18147, 18149, 18150, 18153, 18154, 18158, 18158, 18158,
18159, 18159, 18159, 18159, 18168, 18168, 18170, 18170, 18021,
18063, 18063, 18063, 18063, 18063, 18063, 18049, 18049, 18049,
18049, 18050, 18063, 18052, 18071, 18074, 18083, 18083, 18091,
18091, 18093, 18093, 18095, 18106, 18109, 18119, 18124, 18125,
18125, 18132, 18134, 18136, 18447, 18454, 18455, 18460, 18460,
18461, 18469, 18476, 18477, 18477, 18478, 18479, 18480, 18481,
18481, 18481, 18483, 18483, 18483, 18484, 18484, 18485, 18486,
18486, 18487, 18488, 18488, 18489, 18489, 18490, 18490, 18490,
18491, 18491, 18491, 18491, 18492, 18492, 18492, 18494, 18496,
18496, 18496, 18497, 18497, 18497, 18497, 18498, 18498, 18499,
18502, 18502, 18502, 18503, 18504, 18504, 18504, 18505, 18505,
18505, 18505, 18506, 18506, 18507, 18507, 18508, 18510, 18510,
18510, 18512, 18512, 18512, 18512, 18515, 18518, 18518, 18518,
18518, 18519, 18519, 18524, 18062, 18018, 18018, 18020, 18021,
18023, 18023, 18026, 18026, 18027, 18027, 18027, 18027, 18029,
18031, 18032, 18034, 18034, 18034, 18035, 18035, 18037, 18038,
18038, 18038, 18039, 18039, 18039, 18040, 18041, 18041, 18041,
18041, 18041, 18041, 18041, 18041, 18041, 18041, 18041, 18041,
18042, 18042, 18042, 18042, 18045, 18045, 18045, 18045, 18046,
18046, 18047, 18048, 18048, 18048, 18048, 18048, 18048, 18048,
18048, 18048, 18048, 18048, 18048, 18048, 18048, 18048, 18048,
18048, 18048, 18048, 18048, 18048, 18048, 18048, 18048, 18048,
18049, 18049, 18049, 18051, 18051, 18052, 18052, 18052, 18053,
18053, 18054, 18054, 18054, 18054, 18054, 18054, 18054, 18054,
18054, 18054, 18054, 18054, 18054, 18054, 18054, 18054, 18054,
18054, 18054, 18054, 18054, 18055, 18055, 18055, 18055, 18055,
18055, 18055, 18055, 18055, 18055, 18055, 18055, 18055, 18055,
18056, 18056, 18056, 18056, 18056, 18056, 18056, 18056, 18056,
18057, 18057, 18057, 18057, 18058, 18058, 18058, 18058, 18059,
18059, 18059, 18059, 18059, 18059, 18062, 18062, 18062, 18062,
18062, 18062, 18062, 18062, 18062, 18062, 18062, 18062, 18062,
18062, 18062, 18062, 18062, 18062, 18062, 18062, 18062, 18063,
18063, 18063, 18063, 18063, 18063, 18064, 18064, 18065, 18065,
18066, 18066, 18066, 18068, 18068, 18068, 18068, 18068, 18069,
18069, 18069, 18069, 18069, 18069, 18069, 18069, 18069, 18070,
18071, 18071, 18071, 18071, 18072, 18072, 18072, 18074, 18075,
18076, 18076, 18076, 18076, 18076, 18076, 18076, 18076, 18076,
18076, 18076, 18077, 18077, 18077, 18077, 18078, 18078, 18079,
18079, 18079, 18079, 18079, 18079, 18079, 18080, 18080, 18081,
18081, 18081, 18082, 18082, 18083, 18083, 18083, 18083, 18084,
18084, 18084, 18084, 18084, 18084, 18084, 18084, 18085, 18085,
18085, 18085, 18085, 18086, 18086, 18086, 18087, 18087, 18088,
18088, 18088, 18088, 18088, 18089, 18089, 18090, 18090, 18090,
18090, 18090, 18090, 18090, 18090, 18090, 18090, 18090, 18090,
18090, 18091, 18091, 18091, 18091, 18093, 18093, 18093, 18093,
18094, 18094, 18094, 18094, 18094, 18095, 18095, 18095, 18095,
18096, 18096, 18096, 18096, 18097, 18097, 18097, 18097, 18097,
18097, 18097, 18097, 18097, 18097, 18098, 18098, 18098, 18098,
18099, 18100, 18100, 18100, 18100, 18100, 18100, 18100, 18100,
18100, 18100, 18100, 18100, 18100, 18100, 18100, 18100, 18100,
18100, 18101, 18101, 18101, 18101, 18101, 18102, 18102, 18102,
18103, 18103, 18103, 18103, 18104, 18104, 18104, 18104, 18104,
18104, 18104, 18104, 18104, 18104, 18104, 18104, 18104, 18104,
18104, 18104, 18104, 18106, 18106, 18106, 18106, 18107, 18107,
18107, 18107, 18107, 18107, 18108, 18109, 18110, 18110, 18110,
18110, 18111, 18111, 18111, 18111, 18111, 18111, 18111, 18111,
18111, 18111, 18111, 18111, 18111, 18111, 18111, 18112, 18112,
18112, 18112, 18112, 18112, 18112, 18112, 18112, 18112, 18113,
18113, 18113, 18114, 18114, 18115, 18115, 18115, 18116, 18116,
18116, 18116, 18117, 18117, 18117, 18117, 18117, 18118, 18118,
18118, 18118, 18118, 18119, 18119, 18119, 18119, 18120, 18120,
18120, 18122, 18122, 18122, 18122, 18122, 18122, 18123, 18123,
18123, 18123, 18123, 18124, 18124, 18124, 18124, 18124, 18125,
18125, 18125, 18125, 18125, 18125, 18125, 18125, 18125, 18125,
18125, 18125, 18126, 18126, 18126, 18127, 18127, 18127, 18127,
18127, 18127, 18128, 18128, 18129, 18129, 18129, 18129, 18130,
18130, 18131, 18131, 18131, 18132, 18132, 18132, 18132, 18132,
18132, 18132, 18132, 18132, 18132, 18132, 18133, 18133, 18133,
18134, 18134, 18134, 18135, 18135, 18135, 18136, 18136, 18136,
18136, 18137, 18137, 18137, 18137, 18137, 18138, 18138, 18139,
18139, 18139, 18139, 18139, 18139, 18139, 18139, 18139, 18139,
18139, 18139, 18139, 18139, 18139, 18139, 18139, 18139, 18139,
18139, 18139, 18140, 18140, 18140, 18140, 18141, 18141, 18142,
18142, 18143, 18143, 18143, 18143, 18144, 18144, 18145, 18146,
18146, 18146, 18146, 18146, 18146, 18146, 18146, 18146, 18146,
18146, 18146, 18146, 18146, 18146, 18146, 18146, 18146, 18147,
18147, 18147, 18147, 18147, 18147, 18147, 18147, 18147, 18147,
18147, 18147, 18147, 18148, 18148, 18149, 18150, 18150, 18150,
18150, 18150, 18151, 18152, 18152, 18153, 18153, 18153, 18153,
18153, 18153, 18153, 18153, 18153, 18153, 18153, 18153, 18153,
18153, 18153, 18153, 18153, 18153, 18153, 18153, 18153, 18153,
18153, 18153, 18153, 18153, 18154, 18154, 18154, 18154, 18154,
18155, 18155, 18156, 18156, 18156, 18157, 18157, 18157, 18157,
18157, 18157, 18158, 18158, 18158, 18159, 18159, 18159, 18160,
18160, 18160, 18160, 18160, 18160, 18160, 18160, 18160, 18160,
18160, 18160, 18160, 18160, 18160, 18160, 18161, 18161, 18161,
18161, 18161, 18161, 18161, 18161, 18161, 18161, 18161, 18161,
18161, 18161, 18163, 18163, 18163, 18163, 18163, 18163, 18164,
18164, 18164, 18164, 18164, 18164, 18164, 18164, 18164, 18165,
18165, 18165, 18167, 18167, 18167, 18167, 18167, 18168, 18168,
18168, 18168, 18168, 18168, 18025, 18025, 18025, 18025, 18025,
18025, 18025, 18025, 18025, 18025, 18025, 18025, 18025, 18025,
18025, 18025, 18025, 18025, 18025, 18025, 18025, 18063, 18040,
18041, 18042, 18062, 18062, 18062, 18062, 18062, 18062, 18062,
18062, 18062, 18062, 18062, 18062, 18048, 18048, 18153, 18153,
18153, 18153, 18153, 18153, 18153, 18153, 18153, 18153, 18153,
18153, 18056, 18056, 18020, 18020, 18020, 18020, 18020, 18020,
18020, 18020, 18020, 18020, 18020, 18020, 18034, 18034, 18034,
18034, 18034, 18034, 18034, 18034, 18034, 18034, 18034, 18034,
18034, 18146, 18146, 18146, 18146, 18146, 18146, 18146, 18146,
18146, 18146, 18146, 18146, 18146, 18153, 18153, 18153, 18153,
18153, 18153, 18153, 18153, 18153, 18153, 18153, 18153, 18153,
18160, 18160, 18160, 18160, 18160, 18160, 18160, 18160, 18160,
18160, 18160, 18160, 18160, 18160, 18160, 18160, 18167, 18167,
18167, 18167, 18167, 18167, 18167, 18167, 18167, 18167, 18167,
18167, 18167, 18167, 18048, 18048, 18048, 18048, 18048, 18048,
18048, 18048, 18048, 18048, 18048, 18048, 18048, 18048, 18069,
18069, 18070, 18071, 18071, 18071, 18071, 18076, 18076, 18076,
18076, 18076, 18076, 18078, 18078, 18080, 18081, 18083, 18083,
18083, 18083, 18083, 18083, 18084, 18084, 18087, 18087, 18089,
18090, 18090, 18090, 18091, 18092, 18092, 18093, 18094, 18094,
18094, 18094, 18095, 18095, 18095, 18095, 18096, 18097, 18097,
18097, 18097, 18097, 18097, 18097, 18097, 18097, 18097, 18097,
18097, 18098, 18098, 18100, 18100, 18100, 18101, 18102, 18102,
18102, 18103, 18103, 18104, 18104, 18104, 18104, 18104, 18105,
18105, 18107, 18109, 18109, 18111, 18111, 18111, 18112, 18112,
18114, 18114, 18114, 18116, 18118, 18118, 18118, 18118, 18119,
18119, 18119, 18119, 18120, 18120, 18121, 18121, 18124, 18124,
18124, 18125, 18125, 18125, 18125, 18127, 18127, 18128, 18128,
18130, 18131, 18135, 18135, 18138, 18139, 18139, 18139, 18139,
18139, 18139, 18139, 18139, 18139, 18139, 18140, 18140, 18140,
18141, 18141, 18141, 18142, 18146, 18146, 18146, 18146, 18146,
18146, 18146, 18146, 18146, 18146, 18146, 18147, 18147, 18148,
18150, 18151, 18448, 18448, 18448, 18448, 18449, 18455, 18455,
18455, 18456, 18457, 18457, 18459, 18459, 18460, 18460, 18460,
18460, 18461, 18461, 18461, 18461, 18461, 18461, 18462, 18462,
18463, 18463, 18463, 18463, 18464, 18464, 18465, 18467, 18467,
18467, 18468, 18468, 18468, 18468, 18468, 18469, 18469, 18469,
18469, 18470, 18471, 18471, 18472, 18472, 18472, 18473, 18473,
18473, 18475, 18475, 18475, 18475, 18475, 18476, 18476, 18476,
18476, 18476, 18478, 18479, 18480, 18481, 18481, 18481, 18481,
18482, 18482, 18482, 18482, 18482, 18482, 18483, 18483, 18483,
18483, 18483, 18483, 18483, 18483, 18484, 18484, 18484, 18484,
18484, 18484, 18485, 18485, 18485, 18485, 18486, 18486, 18486,
18486, 18486, 18486, 18489, 18489, 18489, 18489, 18489, 18490,
18490, 18491, 18491, 18491, 18491, 18491, 18491, 18491, 18491,
18492, 18492, 18492, 18493, 18494, 18494, 18494, 18494, 18494,
18495, 18495, 18495, 18495, 18495, 18496, 18496, 18496, 18496,
18496, 18496, 18497, 18497, 18497, 18497, 18497, 18497, 18497,
18497, 18497, 18497, 18497, 18498, 18498, 18498, 18498, 18498,
18498, 18499, 18499, 18499, 18500, 18501, 18501, 18502, 18502,
18502, 18502, 18503, 18503, 18503, 18503, 18503, 18503, 18503,
18503, 18503, 18504, 18504, 18504, 18504, 18504, 18504, 18504,
18505, 18505, 18505, 18506, 18506, 18507, 18507, 18507, 18507,
18507, 18507, 18508, 18508, 18508, 18509, 18509, 18510, 18510,
18510, 18510, 18510, 18510, 18510, 18510, 18511, 18511, 18511,
18511, 18511, 18511, 18512, 18512, 18512, 18515, 18515, 18517,
18517, 18518, 18518, 18518, 18519, 18519, 18520, 18521, 18521,
18521, 18521, 18522, 18522, 18522, 18522, 18524, 18524, 18524,
18517, 18517, 18517, 18517, 18517, 18517, 18517, 18517, 18517,
18517, 18517, 18517, 18517, 18504, 18027, 18034, 18041, 18041,
18048, 18053, 18056, 18062, 18062, 18064, 18066, 18066, 18066,
18066, 18066, 18069, 18070, 18070, 18070, 18070, 18070, 18070,
18070, 18075, 18075, 18075, 18076, 18076, 18078, 18083, 18083,
18083, 18083, 18083, 18083, 18083, 18083, 18083, 18083, 18083,
18083, 18083, 18083, 18083, 18083, 18083, 18083, 18083, 18083,
18083, 18083, 18083, 18083, 18083, 18083, 18083, 18083, 18083,
18083, 18083, 18083, 18083, 18083, 18083, 18083, 18084, 18086,
18086, 18092, 18094, 18094, 18096, 18097, 18098, 18099, 18099,
18103, 18112, 18118, 18119, 18119, 18119, 18120, 18120, 18123,
18124, 18125, 18126, 18129, 18133, 18133, 18133, 18135, 18137,
18139, 18139, 18139, 18141, 18141, 18148, 18149, 18149, 18150,
18156, 18158, 18160, 18161, 18162, 18163, 18163, 18062, 18056,
18072, 18076, 18083, 18083, 18083, 18083, 18083, 18090, 18094,
18104, 18107, 18107, 18117, 18118, 18118, 18119, 18125, 18125,
18125, 18125, 18126, 18134, 18136, 18137, 18141, 18141, 18141,
18141, 18454, 18454, 18455, 18482, 18482, 18486, 18487, 18487,
18489, 18489, 18493, 18493, 18493, 18493, 18496, 18496, 18496,
18496, 18497, 18500, 18500, 18506, 18515, 18517, 18518, 18026,
18034, 18040, 18041, 18043, 18043, 18044, 18044, 18046, 18048,
18049, 18050, 18051, 18052, 18053, 18061, 18065, 18070, 18074,
18076, 18076, 18078, 18079, 18080, 18081, 18085, 18085, 18086,
18086, 18087, 18087, 18090, 18090, 18090, 18091, 18094, 18094,
18095, 18096, 18096, 18101, 18101, 18101, 18101, 18102, 18102,
18103, 18104, 18104, 18106, 18106, 18107, 18107, 18107, 18108,
18109, 18110, 18112, 18113, 18115, 18115, 18118, 18118, 18118,
18118, 18120, 18120, 18120, 18123, 18123, 18124, 18127, 18131,
18131, 18131, 18132, 18134, 18134, 18135, 18137, 18138, 18138,
18138, 18138, 18141, 18141, 18141, 18141, 18142, 18147, 18147,
18148, 18151, 18156, 18157, 18158, 18159, 18162, 18162, 18164,
18165, 18165, 18168, 18068, 18069, 18074, 18082, 18083, 18087,
18091, 18092, 18095, 18095, 18098, 18100, 18101, 18102, 18105,
18118, 18120, 18122, 18126, 18127, 18127, 18130, 18132, 18133,
18133, 18136, 18139, 18143, 18143, 18143, 18143, 18147, 18460,
18460, 18461, 18461, 18464, 18469, 18471, 18476, 18479, 18482,
18482, 18484, 18487, 18490, 18493, 18494, 18496, 18498, 18498,
18501, 18501, 18505, 18506, 18508, 18510, 18510, 18511, 18511,
18517, 18139, 18020, 18022, 18028, 18035, 18035, 18038, 18038,
18038, 18055, 18055, 18055, 18055, 18056, 18056, 18058, 18059,
18059, 18059, 18059, 18059, 18063, 18064, 18064, 18066, 18066,
18066, 18067, 18068, 18068, 18069, 18070, 18070, 18070, 18071,
18071, 18073, 18075, 18076, 18082, 18083, 18083, 18085, 18087,
18088, 18089, 18090, 18090, 18091, 18091, 18092, 18092, 18093,
18095, 18097, 18099, 18099, 18100, 18102, 18103, 18106, 18110,
18112, 18112, 18112, 18112, 18112, 18113, 18113, 18114, 18114,
18115, 18116, 18116, 18118, 18118, 18119, 18121, 18121, 18121,
18122, 18122, 18123, 18123, 18123, 18124, 18125, 18126, 18126,
18127, 18127, 18130, 18131, 18132, 18133, 18135, 18137, 18138,
18139, 18140, 18142, 18143, 18143, 18145, 18145, 18146, 18147,
18147, 18150, 18150, 18153, 18153, 18153, 18153, 18154, 18156,
18159, 18161, 18163, 18056, 18056, 18068, 18071, 18072, 18081,
18087, 18093, 18093, 18094, 18094, 18097, 18100, 18100, 18102,
18105, 18107, 18107, 18109, 18113, 18121, 18121, 18123, 18123,
18126, 18127, 18127, 18127, 18127, 18136, 18138, 18149, 18159,
18447, 18461, 18463, 18467, 18467, 18467, 18467, 18467, 18469,
18469, 18469, 18469, 18473, 18476, 18480, 18481, 18481, 18482,
18483, 18485, 18485, 18485, 18486, 18490, 18491, 18491, 18491,
18492, 18493, 18497, 18497, 18500, 18504, 18504, 18504, 18505,
18507, 18508, 18509, 18509, 18509, 18509, 18509, 18513, 18513,
18513, 18518, 18036, 18051, 18052, 18054, 18056, 18056, 18058,
18063, 18069, 18069, 18071, 18071, 18072, 18074, 18075, 18075,
18080, 18084, 18088, 18092, 18097, 18098, 18106, 18110, 18111,
18113, 18114, 18121, 18124, 18132, 18135, 18139, 18141, 18143,
18144, 18146, 18149, 18152, 18153, 18158, 18158, 18160, 18160,
18160, 18160, 18160, 18160, 18160, 18160, 18160, 18095, 18129,
18136, 18138, 18148, 18447, 18448, 18451, 18474, 18475, 18479,
18480, 18481, 18484, 18486, 18492, 18493, 18495, 18497, 18497,
18504, 18512, 18517, 18517, 18517, 18477, 18061, 18062, 18062,
18062, 18063, 18064, 18070, 18070, 18074, 18077, 18080, 18082,
18085, 18087, 18088, 18091, 18095, 18098, 18106, 18108, 18110,
18111, 18120, 18120, 18129, 18136, 18136, 18137, 18141, 18141,
18143, 18143, 18147, 18149, 18161, 18057, 18057, 18057, 18102,
18104, 18113, 18118, 18127, 18451, 18456, 18462, 18463, 18474,
18483, 18487, 18487, 18488, 18491, 18491, 18497, 18497, 18504,
18505, 18506, 18509, 18514, 18056, 18058, 18063, 18075, 18077,
18080, 18088, 18092, 18095, 18107, 18107, 18108, 18109, 18109,
18113, 18117, 18119, 18119, 18125, 18133, 18137, 18162, 18056,
18056, 18056, 18081, 18097, 18117, 18125, 18134, 18461, 18477,
18483, 18490, 18491, 18518, 18020, 18020, 18021, 18027, 18030,
18034, 18034, 18035, 18035, 18038, 18038, 18041, 18048, 18048,
18048, 18049, 18051, 18055, 18055, 18055, 18056, 18057, 18062,
18070, 18076, 18081, 18084, 18084, 18089, 18090, 18091, 18091,
18091, 18091, 18091, 18092, 18094, 18094, 18097, 18098, 18100,
18101, 18104, 18110, 18112, 18112, 18112, 18112, 18114, 18118,
18118, 18118, 18118, 18118, 18123, 18125, 18125, 18125, 18125,
18126, 18128, 18129, 18131, 18132, 18132, 18132, 18132, 18134,
18138, 18139, 18139, 18139, 18139, 18140, 18140, 18141, 18145,
18146, 18147, 18147, 18150, 18153, 18153, 18153, 18155, 18157,
18157, 18158, 18160, 18160, 18160, 18161, 18161, 18161, 18161,
18161, 18076, 18077, 18078, 18078, 18083, 18083, 18090, 18090,
18090, 18094, 18103, 18103, 18111, 18111, 18118, 18118, 18122,
18125, 18132, 18139, 18140, 18147, 18150, 18447, 18448, 18449,
18449, 18451, 18453, 18453, 18453, 18455, 18471, 18473, 18475,
18477, 18477, 18477, 18480, 18480, 18481, 18481, 18482, 18482,
18482, 18484, 18487, 18489, 18490, 18490, 18491, 18491, 18491,
18494, 18495, 18495, 18496, 18496, 18498, 18501, 18501, 18503,
18506, 18507, 18507, 18509, 18509, 18510, 18511, 18514, 18033,
18065, 18069, 18076, 18103, 18112, 18126, 18132, 18140, 18076,
18097, 18130, 18461, 18475, 18479, 18483, 18483, 18483, 18483,
18496, 18497, 18499, 18501, 18076, 18124, 18076, 18111, 18481,
18488, 18495, 18035, 18075, 18101, 18103, 18109, 18109, 18109,
18110, 18480, 18484, 18119, 18119, 18454, 18063, 18074, 18475,
18141, 18111, 18474, 18495, 18028, 18124, 18076, 18447, 18503,
18510, 18070, 18084, 18070, 18084, 18160, 18083, 18076), class = "Date"),
depdate = structure(c(18144, 18157, 18161, 18019, 18021,
18024, 18024, 18025, 18029, 18032, 18035, 18035, 18035, 18035,
18039, 18039, 18040, 18041, 18042, 18043, 18043, 18046, 18046,
18047, 18047, 18048, 18048, 18049, 18049, 18049, 18049, 18050,
18052, 18054, 18054, 18055, 18056, 18056, 18056, 18056, 18056,
18056, 18056, 18063, 18063, 18063, 18063, 18063, 18067, 18069,
18069, 18069, 18070, 18070, 18070, 18070, 18070, 18070, 18070,
18070, 18070, 18070, 18070, 18070, 18070, 18070, 18070, 18070,
18070, 18070, 18070, 18070, 18070, 18070, 18070, 18070, 18070,
18070, 18070, 18070, 18070, 18070, 18070, 18070, 18070, 18070,
18070, 18070, 18070, 18070, 18070, 18070, 18073, 18073, 18073,
18074, 18074, 18074, 18074, 18074, 18074, 18076, 18076, 18077,
18077, 18077, 18077, 18077, 18078, 18078, 18083, 18083, 18084,
18084, 18085, 18085, 18085, 18085, 18085, 18085, 18085, 18085,
18085, 18085, 18086, 18087, 18088, 18098, 18099, 18105, 18105,
18105, 18105, 18105, 18105, 18105, 18105, 18105, 18105, 18105,
18105, 18105, 18107, 18108, 18108, 18109, 18110, 18111, 18111,
18111, 18111, 18111, 18111, 18111, 18112, 18112, 18113, 18116,
18119, 18123, 18124, 18124, 18124, 18124, 18124, 18127, 18130,
18131, 18132, 18133, 18133, 18133, 18133, 18133, 18133, 18133,
18136, 18141, 18141, 18144, 18144, 18144, 18145, 18147, 18147,
18147, 18147, 18147, 18147, 18147, 18148, 18149, 18149, 18153,
18154, 18155, 18155, 18157, 18158, 18159, 18159, 18159, 18159,
18159, 18159, 18159, 18159, 18159, 18160, 18160, 18161, 18161,
18161, 18161, 18161, 18161, 18163, 18163, 18167, 18167, 18167,
18167, 18168, 18168, 18168, 18169, 18171, 18172, 18174, 18174,
18174, 18175, 18175, 18175, 18175, 18175, 18175, 18177, 18023,
18024, 18165, 18165, 18165, 18165, 18165, 18165, 18165, 18165,
18165, 18165, 18165, 18165, 18165, 18073, 18074, 18163, 18163,
18163, 18163, 18163, 18163, 18163, 18163, 18163, 18163, 18163,
18044, 18044, 18152, 18152, 18152, 18152, 18152, 18152, 18152,
18152, 18152, 18152, 18152, 18152, 18152, 18152, 18152, 18035,
18035, 18035, 18035, 18035, 18035, 18035, 18035, 18035, 18035,
18035, 18035, 18035, 18035, 18035, 18035, 18035, 18035, 18035,
18035, 18035, 18035, 18035, 18035, 18035, 18035, 18035, 18035,
18035, 18035, 18035, 18035, 18035, 18035, 18035, 18035, 18035,
18035, 18035, 18035, 18035, 18035, 18035, 18035, 18035, 18035,
18035, 18035, 18035, 18035, 18035, 18035, 18035, 18035, 18035,
18035, 18035, 18035, 18035, 18035, 18035, 18035, 18035, 18035,
18035, 18035, 18035, 18035, 18035, 18035, 18035, 18035, 18035,
18035, 18035, 18035, 18035, 18035, 18035, 18035, 18035, 18035,
18035, 18035, 18035, 18035, 18035, 18035, 18035, 18035, 18035,
18035, 18035, 18035, 18035, 18035, 18035, 18035, 18035, 18035,
18035, 18035, 18035, 18035, 18053, 18053, 18053, 18053, 18053,
18053, 18053, 18053, 18053, 18053, 18053, 18053, 18053, 18053,
18053, 18053, 18053, 18053, 18053, 18053, 18053, 18053, 18053,
18053, 18053, 18053, 18053, 18053, 18053, 18053, 18053, 18061,
18065, 18049, 18070, 18070, 18074, 18074, 18083, 18083, 18085,
18101, 18101, 18112, 18143, 18146, 18147, 18448, 18448, 18448,
18448, 18448, 18448, 18448, 18448, 18448, 18454, 18455, 18455,
18455, 18455, 18455, 18455, 18455, 18455, 18455, 18455, 18455,
18456, 18456, 18457, 18459, 18462, 18462, 18462, 18462, 18462,
18462, 18462, 18462, 18462, 18462, 18462, 18466, 18466, 18468,
18469, 18469, 18469, 18469, 18469, 18469, 18469, 18469, 18469,
18469, 18469, 18469, 18469, 18469, 18470, 18470, 18470, 18476,
18476, 18476, 18476, 18476, 18476, 18476, 18476, 18476, 18476,
18476, 18476, 18476, 18476, 18483, 18483, 18483, 18489, 18494,
18497, 18497, 18497, 18497, 18497, 18498, 18499, 18500, 18500,
18502, 18503, 18503, 18504, 18504, 18504, 18504, 18504, 18504,
18504, 18504, 18504, 18504, 18504, 18504, 18504, 18504, 18504,
18504, 18504, 18504, 18504, 18504, 18504, 18504, 18504, 18504,
18504, 18505, 18505, 18511, 18511, 18511, 18511, 18511, 18511,
18511, 18511, 18511, 18511, 18511, 18511, 18511, 18511, 18511,
18511, 18511, 18511, 18511, 18511, 18511, 18511, 18511, 18511,
18511, 18511, 18511, 18511, 18511, 18511, 18512, 18512, 18513,
18514, 18515, 18516, 18518, 18518, 18518, 18522, 18525, 18525,
18525, 18525, 18525, 18525, 18525, 18525, 18527, 18531, 18529,
18529, 18529, 18529, 18529, 18529, 18529, 18529, 18529, 18529,
18529, 18529, 18529, 18529, 18527, 18527, 18527, 18527, 18527,
18527, 18527, 18527, 18527, 18527, 18527, 18448, 18498, 18502,
18159, 18020, 18023, 18026, 18027, 18031, 18032, 18040, 18040,
18040, 18043, 18043, 18043, 18046, 18048, 18048, 18050, 18052,
18056, 18061, 18062, 18062, 18063, 18063, 18063, 18068, 18069,
18070, 18070, 18070, 18071, 18071, 18071, 18072, 18072, 18072,
18072, 18072, 18072, 18076, 18077, 18077, 18077, 18077, 18078,
18078, 18078, 18078, 18078, 18082, 18082, 18082, 18084, 18084,
18084, 18086, 18086, 18086, 18086, 18086, 18086, 18086, 18086,
18086, 18086, 18086, 18088, 18091, 18091, 18091, 18092, 18094,
18094, 18096, 18096, 18097, 18099, 18099, 18099, 18109, 18109,
18111, 18111, 18112, 18112, 18112, 18113, 18115, 18115, 18119,
18119, 18124, 18124, 18124, 18124, 18130, 18131, 18132, 18132,
18133, 18133, 18133, 18133, 18133, 18133, 18133, 18135, 18135,
18136, 18137, 18139, 18140, 18140, 18141, 18141, 18143, 18143,
18144, 18144, 18146, 18147, 18147, 18147, 18147, 18147, 18147,
18147, 18147, 18147, 18147, 18147, 18147, 18147, 18148, 18150,
18150, 18151, 18151, 18153, 18154, 18154, 18155, 18155, 18157,
18159, 18161, 18161, 18161, 18162, 18162, 18162, 18164, 18167,
18168, 18168, 18168, 18168, 18168, 18168, 18168, 18168, 18169,
18169, 18170, 18171, 18172, 18172, 18172, 18172, 18173, 18173,
18173, 18175, 18175, 18175, 18175, 18175, 18175, 18023, 18024,
18024, 18024, 18024, 18024, 18024, 18024, 18024, 18024, 18024,
18024, 18024, 18024, 18024, 18024, 18024, 18024, 18024, 18024,
18024, 18024, 18024, 18024, 18024, 18024, 18024, 18024, 18024,
18024, 18024, 18050, 18050, 18050, 18050, 18050, 18050, 18050,
18050, 18050, 18050, 18050, 18050, 18050, 18050, 18050, 18050,
18050, 18050, 18050, 18074, 18068, 18068, 18068, 18044, 18044,
18045, 18041, 18041, 18041, 18041, 18041, 18041, 18041, 18041,
18041, 18041, 18041, 18041, 18041, 18041, 18041, 18041, 18041,
18027, 18027, 18027, 18027, 18027, 18027, 18027, 18027, 18027,
18027, 18027, 18027, 18027, 18052, 18052, 18052, 18053, 18154,
18165, 18165, 18165, 18165, 18165, 18165, 18165, 18165, 18165,
18165, 18165, 18165, 18165, 18165, 18165, 18165, 18165, 18165,
18165, 18050, 18050, 18050, 18050, 18050, 18050, 18050, 18050,
18050, 18050, 18050, 18050, 18050, 18179, 18179, 18179, 18179,
18179, 18179, 18179, 18179, 18179, 18179, 18179, 18179, 18179,
18179, 18179, 18179, 18179, 18179, 18179, 18053, 18053, 18053,
18053, 18053, 18053, 18041, 18041, 18041, 18041, 18041, 18041,
18041, 18041, 18047, 18047, 18048, 18048, 18048, 18048, 18048,
18048, 18048, 18049, 18054, 18054, 18054, 18054, 18054, 18054,
18065, 18047, 18047, 18047, 18047, 18047, 18047, 18047, 18047,
18035, 18035, 18035, 18035, 18160, 18160, 18160, 18160, 18160,
18160, 18160, 18160, 18160, 18160, 18160, 18160, 18160, 18160,
18028, 18028, 18028, 18028, 18028, 18028, 18028, 18028, 18028,
18028, 18028, 18028, 18028, 18028, 18028, 18028, 18028, 18028,
18028, 18028, 18028, 18028, 18028, 18028, 18028, 18028, 18028,
18053, 18053, 18059, 18069, 18046, 18046, 18046, 18046, 18046,
18046, 18046, 18046, 18046, 18046, 18046, 18046, 18046, 18046,
18046, 18046, 18046, 18046, 18046, 18046, 18046, 18060, 18060,
18060, 18060, 18060, 18060, 18060, 18060, 18060, 18060, 18060,
18060, 18060, 18060, 18060, 18060, 18060, 18060, 18060, 18151,
18151, 18151, 18151, 18151, 18151, 18151, 18151, 18151, 18151,
18151, 18151, 18151, 18151, 18151, 18151, 18151, 18151, 18151,
18151, 18151, 18151, 18151, 18158, 18158, 18158, 18158, 18158,
18158, 18158, 18158, 18158, 18158, 18158, 18158, 18158, 18158,
18158, 18158, 18158, 18158, 18158, 18158, 18172, 18172, 18172,
18172, 18172, 18172, 18172, 18172, 18172, 18172, 18172, 18172,
18172, 18172, 18172, 18172, 18172, 18172, 18172, 18172, 18038,
18038, 18038, 18038, 18038, 18038, 18038, 18038, 18038, 18038,
18038, 18038, 18161, 18161, 18161, 18161, 18161, 18161, 18161,
18161, 18161, 18161, 18161, 18161, 18161, 18161, 18161, 18161,
18056, 18058, 18058, 18078, 18080, 18085, 18093, 18093, 18093,
18094, 18109, 18110, 18118, 18119, 18120, 18125, 18126, 18127,
18140, 18449, 18449, 18452, 18455, 18455, 18455, 18455, 18455,
18455, 18455, 18456, 18456, 18456, 18456, 18457, 18457, 18457,
18457, 18458, 18459, 18459, 18461, 18462, 18462, 18462, 18462,
18462, 18462, 18463, 18463, 18463, 18465, 18465, 18465, 18469,
18469, 18469, 18469, 18469, 18469, 18470, 18470, 18470, 18470,
18471, 18473, 18475, 18475, 18476, 18476, 18476, 18477, 18479,
18480, 18481, 18483, 18483, 18484, 18484, 18485, 18486, 18486,
18487, 18487, 18487, 18487, 18490, 18490, 18490, 18493, 18494,
18494, 18494, 18495, 18495, 18496, 18498, 18498, 18498, 18501,
18503, 18503, 18503, 18504, 18504, 18504, 18505, 18505, 18505,
18505, 18508, 18508, 18508, 18508, 18511, 18511, 18511, 18511,
18511, 18512, 18512, 18513, 18513, 18513, 18514, 18514, 18518,
18522, 18525, 18526, 18526, 18529, 18529, 18529, 18529, 18529,
18529, 18529, 18529, 18529, 18529, 18529, 18529, 18529, 18529,
18529, 18529, 18506, 18062, 18147, 18122, 18125, 18021, 18021,
18027, 18029, 18030, 18041, 18041, 18043, 18043, 18043, 18043,
18043, 18049, 18049, 18049, 18049, 18049, 18049, 18049, 18050,
18056, 18057, 18057, 18057, 18057, 18059, 18060, 18060, 18060,
18063, 18063, 18063, 18064, 18066, 18067, 18067, 18068, 18068,
18068, 18069, 18069, 18070, 18071, 18071, 18073, 18078, 18078,
18078, 18083, 18085, 18086, 18086, 18087, 18088, 18088, 18088,
18089, 18091, 18092, 18092, 18097, 18098, 18098, 18098, 18098,
18098, 18098, 18099, 18099, 18100, 18100, 18100, 18101, 18101,
18103, 18105, 18105, 18110, 18110, 18111, 18111, 18111, 18111,
18112, 18113, 18113, 18113, 18113, 18114, 18115, 18116, 18116,
18116, 18117, 18117, 18119, 18119, 18119, 18120, 18120, 18121,
18121, 18123, 18123, 18123, 18124, 18124, 18125, 18125, 18126,
18126, 18126, 18126, 18129, 18129, 18130, 18130, 18133, 18133,
18133, 18134, 18134, 18134, 18134, 18134, 18134, 18134, 18134,
18135, 18135, 18135, 18139, 18139, 18139, 18139, 18140, 18140,
18142, 18142, 18143, 18145, 18146, 18147, 18147, 18147, 18147,
18148, 18148, 18148, 18149, 18150, 18151, 18151, 18151, 18152,
18152, 18154, 18155, 18156, 18157, 18157, 18157, 18157, 18158,
18158, 18158, 18158, 18158, 18158, 18158, 18158, 18158, 18158,
18158, 18158, 18158, 18158, 18158, 18158, 18158, 18158, 18158,
18159, 18161, 18162, 18162, 18163, 18163, 18163, 18165, 18165,
18166, 18168, 18168, 18168, 18168, 18168, 18168, 18168, 18168,
18169, 18169, 18169, 18170, 18171, 18172, 18173, 18173, 18173,
18173, 18173, 18173, 18174, 18174, 18176, 18163, 18163, 18163,
18163, 18163, 18024, 18024, 18024, 18024, 18024, 18025, 18025,
18025, 18074, 18068, 18068, 18068, 18044, 18045, 18045, 18045,
18045, 18045, 18045, 18045, 18045, 18045, 18045, 18045, 18045,
18045, 18045, 18045, 18046, 18046, 18046, 18052, 18052, 18054,
18054, 18154, 18032, 18032, 18032, 18032, 18032, 18032, 18032,
18032, 18032, 18027, 18027, 18027, 18027, 18027, 18027, 18027,
18027, 18027, 18027, 18027, 18027, 18178, 18178, 18178, 18178,
18178, 18052, 18052, 18052, 18052, 18052, 18052, 18052, 18052,
18048, 18048, 18048, 18048, 18048, 18048, 18048, 18048, 18049,
18066, 18066, 18066, 18066, 18066, 18066, 18035, 18064, 18069,
18077, 18079, 18081, 18093, 18094, 18094, 18094, 18098, 18098,
18098, 18099, 18104, 18107, 18119, 18122, 18124, 18124, 18125,
18128, 18129, 18137, 18140, 18142, 18143, 18146, 18148, 18450,
18454, 18456, 18456, 18459, 18465, 18466, 18467, 18468, 18468,
18469, 18469, 18469, 18469, 18470, 18471, 18471, 18471, 18473,
18473, 18475, 18475, 18476, 18477, 18478, 18480, 18481, 18483,
18483, 18483, 18484, 18487, 18487, 18487, 18487, 18487, 18487,
18487, 18490, 18490, 18490, 18490, 18491, 18491, 18491, 18491,
18491, 18491, 18491, 18492, 18492, 18493, 18493, 18495, 18496,
18496, 18496, 18496, 18496, 18497, 18497, 18498, 18499, 18499,
18499, 18499, 18500, 18501, 18503, 18503, 18506, 18507, 18507,
18508, 18508, 18509, 18509, 18511, 18512, 18512, 18512, 18512,
18513, 18514, 18517, 18517, 18518, 18518, 18518, 18518, 18519,
18521, 18525, 18529, 18530, 18530, 18022, 18031, 18035, 18035,
18042, 18042, 18042, 18042, 18043, 18046, 18046, 18047, 18047,
18047, 18047, 18049, 18049, 18049, 18052, 18052, 18054, 18055,
18057, 18057, 18058, 18059, 18062, 18062, 18063, 18064, 18065,
18066, 18066, 18066, 18068, 18068, 18068, 18068, 18068, 18068,
18068, 18068, 18068, 18068, 18068, 18068, 18068, 18068, 18068,
18068, 18068, 18068, 18068, 18068, 18069, 18069, 18069, 18071,
18072, 18073, 18076, 18078, 18079, 18079, 18080, 18080, 18081,
18081, 18081, 18082, 18082, 18083, 18084, 18085, 18086, 18088,
18088, 18089, 18089, 18090, 18090, 18092, 18093, 18093, 18093,
18093, 18096, 18096, 18096, 18099, 18100, 18100, 18102, 18102,
18102, 18103, 18104, 18106, 18108, 18108, 18109, 18110, 18110,
18110, 18111, 18112, 18112, 18114, 18115, 18115, 18117, 18117,
18118, 18119, 18120, 18123, 18123, 18124, 18125, 18125, 18126,
18126, 18130, 18131, 18132, 18132, 18132, 18133, 18133, 18134,
18134, 18135, 18135, 18135, 18135, 18137, 18137, 18137, 18138,
18140, 18140, 18142, 18143, 18144, 18148, 18148, 18151, 18152,
18153, 18153, 18153, 18155, 18156, 18156, 18156, 18158, 18159,
18159, 18159, 18159, 18159, 18159, 18161, 18161, 18161, 18161,
18165, 18165, 18166, 18166, 18166, 18167, 18167, 18168, 18168,
18172, 18172, 18173, 18173, 18174, 18174, 18174, 18175, 18175,
18176, 18024, 18024, 18025, 18025, 18025, 18025, 18025, 18068,
18068, 18068, 18068, 18068, 18068, 18068, 18069, 18069, 18046,
18046, 18046, 18046, 18046, 18046, 18046, 18046, 18046, 18046,
18046, 18046, 18046, 18046, 18046, 18046, 18046, 18046, 18046,
18046, 18046, 18046, 18046, 18046, 18046, 18046, 18046, 18046,
18046, 18046, 18046, 18046, 18046, 18046, 18046, 18046, 18046,
18046, 18054, 18054, 18054, 18054, 18054, 18054, 18054, 18054,
18054, 18054, 18054, 18054, 18054, 18054, 18054, 18054, 18054,
18054, 18054, 18054, 18054, 18054, 18054, 18054, 18054, 18054,
18054, 18054, 18054, 18054, 18054, 18054, 18054, 18054, 18054,
18054, 18054, 18054, 18054, 18154, 18154, 18049, 18049, 18049,
18049, 18049, 18049, 18049, 18049, 18049, 18177, 18177, 18177,
18177, 18177, 18177, 18177, 18177, 18177, 18177, 18177, 18177,
18177, 18177, 18177, 18060, 18061, 18078, 18082, 18082, 18083,
18092, 18095, 18098, 18098, 18104, 18104, 18107, 18112, 18117,
18118, 18121, 18124, 18125, 18126, 18129, 18131, 18140, 18141,
18142, 18144, 18165, 18452, 18453, 18454, 18454, 18456, 18458,
18459, 18460, 18463, 18464, 18464, 18466, 18467, 18471, 18471,
18472, 18474, 18474, 18474, 18475, 18478, 18481, 18483, 18483,
18484, 18486, 18486, 18489, 18490, 18491, 18491, 18491, 18491,
18491, 18491, 18492, 18494, 18494, 18494, 18495, 18496, 18496,
18496, 18497, 18497, 18497, 18498, 18498, 18498, 18498, 18498,
18498, 18499, 18500, 18500, 18504, 18504, 18504, 18506, 18508,
18508, 18508, 18510, 18511, 18511, 18511, 18512, 18512, 18515,
18518, 18521, 18521, 18523, 18526, 18529, 18530, 18530, 18023,
18034, 18034, 18035, 18039, 18047, 18047, 18048, 18055, 18058,
18059, 18060, 18060, 18060, 18061, 18061, 18061, 18061, 18061,
18061, 18061, 18061, 18061, 18061, 18061, 18061, 18061, 18061,
18061, 18061, 18061, 18061, 18061, 18061, 18061, 18061, 18064,
18065, 18067, 18067, 18068, 18068, 18069, 18069, 18069, 18069,
18069, 18072, 18073, 18074, 18074, 18075, 18075, 18076, 18078,
18080, 18082, 18082, 18082, 18082, 18082, 18082, 18082, 18082,
18082, 18082, 18082, 18082, 18082, 18082, 18082, 18082, 18082,
18082, 18082, 18082, 18082, 18082, 18082, 18083, 18085, 18085,
18086, 18087, 18087, 18089, 18090, 18091, 18092, 18092, 18093,
18094, 18094, 18094, 18096, 18098, 18099, 18099, 18100, 18100,
18101, 18102, 18103, 18104, 18105, 18107, 18107, 18109, 18110,
18111, 18114, 18115, 18118, 18119, 18119, 18119, 18119, 18120,
18121, 18121, 18122, 18123, 18123, 18123, 18124, 18125, 18126,
18127, 18129, 18130, 18130, 18131, 18133, 18134, 18134, 18135,
18135, 18136, 18136, 18139, 18139, 18141, 18142, 18144, 18145,
18145, 18145, 18145, 18145, 18145, 18145, 18145, 18145, 18146,
18146, 18148, 18155, 18157, 18157, 18159, 18159, 18159, 18159,
18159, 18159, 18162, 18163, 18163, 18164, 18164, 18164, 18164,
18164, 18166, 18168, 18168, 18171, 18171, 18174, 18174, 18175,
18024, 18027, 18068, 18068, 18068, 18068, 18068, 18068, 18068,
18068, 18069, 18069, 18046, 18046, 18046, 18047, 18047, 18047,
18047, 18047, 18054, 18054, 18054, 18055, 18055, 18055, 18055,
18178, 18069, 18076, 18076, 18083, 18089, 18089, 18090, 18097,
18098, 18099, 18099, 18099, 18104, 18109, 18109, 18109, 18109,
18109, 18118, 18118, 18119, 18119, 18121, 18122, 18123, 18123,
18125, 18125, 18127, 18130, 18132, 18132, 18134, 18135, 18137,
18140, 18453, 18454, 18456, 18458, 18467, 18467, 18468, 18468,
18473, 18475, 18475, 18479, 18482, 18484, 18484, 18484, 18485,
18485, 18485, 18487, 18488, 18488, 18489, 18490, 18491, 18491,
18492, 18492, 18493, 18493, 18493, 18493, 18493, 18493, 18494,
18494, 18494, 18495, 18495, 18495, 18496, 18496, 18497, 18497,
18497, 18497, 18498, 18498, 18499, 18499, 18501, 18502, 18503,
18503, 18504, 18508, 18508, 18508, 18509, 18509, 18510, 18510,
18512, 18512, 18514, 18515, 18516, 18517, 18517, 18517, 18518,
18527, 18108, 18047, 18047, 18047, 18047, 18048, 18051, 18052,
18055, 18055, 18060, 18061, 18061, 18062, 18063, 18069, 18069,
18069, 18069, 18069, 18069, 18070, 18070, 18070, 18074, 18075,
18076, 18076, 18076, 18076, 18076, 18078, 18078, 18080, 18083,
18083, 18085, 18091, 18091, 18091, 18091, 18094, 18094, 18094,
18097, 18099, 18099, 18100, 18102, 18104, 18105, 18106, 18106,
18107, 18109, 18111, 18111, 18111, 18112, 18113, 18113, 18114,
18114, 18114, 18118, 18119, 18119, 18119, 18119, 18120, 18122,
18122, 18123, 18123, 18124, 18124, 18125, 18130, 18131, 18131,
18131, 18132, 18132, 18132, 18133, 18136, 18138, 18138, 18138,
18140, 18140, 18140, 18141, 18141, 18145, 18145, 18145, 18145,
18146, 18146, 18146, 18146, 18149, 18151, 18151, 18153, 18153,
18155, 18156, 18159, 18160, 18164, 18164, 18164, 18165, 18165,
18165, 18165, 18174, 18174, 18176, 18176, 18027, 18069, 18069,
18069, 18069, 18069, 18069, 18055, 18055, 18055, 18055, 18056,
18069, 18058, 18077, 18080, 18089, 18089, 18097, 18097, 18099,
18099, 18101, 18112, 18115, 18125, 18130, 18131, 18131, 18138,
18140, 18142, 18453, 18460, 18461, 18466, 18466, 18467, 18475,
18482, 18483, 18483, 18484, 18485, 18486, 18487, 18487, 18487,
18489, 18489, 18489, 18490, 18490, 18491, 18492, 18492, 18493,
18494, 18494, 18495, 18495, 18496, 18496, 18496, 18497, 18497,
18497, 18497, 18498, 18498, 18498, 18500, 18502, 18502, 18502,
18503, 18503, 18503, 18503, 18504, 18504, 18505, 18508, 18508,
18508, 18509, 18510, 18510, 18510, 18511, 18511, 18511, 18511,
18512, 18512, 18513, 18513, 18514, 18516, 18516, 18516, 18518,
18518, 18518, 18518, 18521, 18524, 18524, 18524, 18524, 18525,
18525, 18530, 18069, 18025, 18025, 18027, 18028, 18030, 18030,
18033, 18033, 18034, 18034, 18034, 18034, 18036, 18038, 18039,
18041, 18041, 18041, 18042, 18042, 18044, 18045, 18045, 18045,
18046, 18046, 18046, 18047, 18048, 18048, 18048, 18048, 18048,
18048, 18048, 18048, 18048, 18048, 18048, 18048, 18049, 18049,
18049, 18049, 18052, 18052, 18052, 18052, 18053, 18053, 18054,
18055, 18055, 18055, 18055, 18055, 18055, 18055, 18055, 18055,
18055, 18055, 18055, 18055, 18055, 18055, 18055, 18055, 18055,
18055, 18055, 18055, 18055, 18055, 18055, 18055, 18056, 18056,
18056, 18058, 18058, 18059, 18059, 18059, 18060, 18060, 18061,
18061, 18061, 18061, 18061, 18061, 18061, 18061, 18061, 18061,
18061, 18061, 18061, 18061, 18061, 18061, 18061, 18061, 18061,
18061, 18061, 18062, 18062, 18062, 18062, 18062, 18062, 18062,
18062, 18062, 18062, 18062, 18062, 18062, 18062, 18063, 18063,
18063, 18063, 18063, 18063, 18063, 18063, 18063, 18064, 18064,
18064, 18064, 18065, 18065, 18065, 18065, 18066, 18066, 18066,
18066, 18066, 18066, 18069, 18069, 18069, 18069, 18069, 18069,
18069, 18069, 18069, 18069, 18069, 18069, 18069, 18069, 18069,
18069, 18069, 18069, 18069, 18069, 18069, 18070, 18070, 18070,
18070, 18070, 18070, 18071, 18071, 18072, 18072, 18073, 18073,
18073, 18075, 18075, 18075, 18075, 18075, 18076, 18076, 18076,
18076, 18076, 18076, 18076, 18076, 18076, 18077, 18078, 18078,
18078, 18078, 18079, 18079, 18079, 18081, 18082, 18083, 18083,
18083, 18083, 18083, 18083, 18083, 18083, 18083, 18083, 18083,
18084, 18084, 18084, 18084, 18085, 18085, 18086, 18086, 18086,
18086, 18086, 18086, 18086, 18087, 18087, 18088, 18088, 18088,
18089, 18089, 18090, 18090, 18090, 18090, 18091, 18091, 18091,
18091, 18091, 18091, 18091, 18091, 18092, 18092, 18092, 18092,
18092, 18093, 18093, 18093, 18094, 18094, 18095, 18095, 18095,
18095, 18095, 18096, 18096, 18097, 18097, 18097, 18097, 18097,
18097, 18097, 18097, 18097, 18097, 18097, 18097, 18097, 18098,
18098, 18098, 18098, 18100, 18100, 18100, 18100, 18101, 18101,
18101, 18101, 18101, 18102, 18102, 18102, 18102, 18103, 18103,
18103, 18103, 18104, 18104, 18104, 18104, 18104, 18104, 18104,
18104, 18104, 18104, 18105, 18105, 18105, 18105, 18106, 18107,
18107, 18107, 18107, 18107, 18107, 18107, 18107, 18107, 18107,
18107, 18107, 18107, 18107, 18107, 18107, 18107, 18107, 18108,
18108, 18108, 18108, 18108, 18109, 18109, 18109, 18110, 18110,
18110, 18110, 18111, 18111, 18111, 18111, 18111, 18111, 18111,
18111, 18111, 18111, 18111, 18111, 18111, 18111, 18111, 18111,
18111, 18113, 18113, 18113, 18113, 18114, 18114, 18114, 18114,
18114, 18114, 18115, 18116, 18117, 18117, 18117, 18117, 18118,
18118, 18118, 18118, 18118, 18118, 18118, 18118, 18118, 18118,
18118, 18118, 18118, 18118, 18118, 18119, 18119, 18119, 18119,
18119, 18119, 18119, 18119, 18119, 18119, 18120, 18120, 18120,
18121, 18121, 18122, 18122, 18122, 18123, 18123, 18123, 18123,
18124, 18124, 18124, 18124, 18124, 18125, 18125, 18125, 18125,
18125, 18126, 18126, 18126, 18126, 18127, 18127, 18127, 18129,
18129, 18129, 18129, 18129, 18129, 18130, 18130, 18130, 18130,
18130, 18131, 18131, 18131, 18131, 18131, 18132, 18132, 18132,
18132, 18132, 18132, 18132, 18132, 18132, 18132, 18132, 18132,
18133, 18133, 18133, 18134, 18134, 18134, 18134, 18134, 18134,
18135, 18135, 18136, 18136, 18136, 18136, 18137, 18137, 18138,
18138, 18138, 18139, 18139, 18139, 18139, 18139, 18139, 18139,
18139, 18139, 18139, 18139, 18140, 18140, 18140, 18141, 18141,
18141, 18142, 18142, 18142, 18143, 18143, 18143, 18143, 18144,
18144, 18144, 18144, 18144, 18145, 18145, 18146, 18146, 18146,
18146, 18146, 18146, 18146, 18146, 18146, 18146, 18146, 18146,
18146, 18146, 18146, 18146, 18146, 18146, 18146, 18146, 18146,
18147, 18147, 18147, 18147, 18148, 18148, 18149, 18149, 18150,
18150, 18150, 18150, 18151, 18151, 18152, 18153, 18153, 18153,
18153, 18153, 18153, 18153, 18153, 18153, 18153, 18153, 18153,
18153, 18153, 18153, 18153, 18153, 18153, 18154, 18154, 18154,
18154, 18154, 18154, 18154, 18154, 18154, 18154, 18154, 18154,
18154, 18155, 18155, 18156, 18157, 18157, 18157, 18157, 18157,
18158, 18159, 18159, 18160, 18160, 18160, 18160, 18160, 18160,
18160, 18160, 18160, 18160, 18160, 18160, 18160, 18160, 18160,
18160, 18160, 18160, 18160, 18160, 18160, 18160, 18160, 18160,
18160, 18160, 18161, 18161, 18161, 18161, 18161, 18162, 18162,
18163, 18163, 18163, 18164, 18164, 18164, 18164, 18164, 18164,
18165, 18165, 18165, 18166, 18166, 18166, 18167, 18167, 18167,
18167, 18167, 18167, 18167, 18167, 18167, 18167, 18167, 18167,
18167, 18167, 18167, 18167, 18168, 18168, 18168, 18168, 18168,
18168, 18168, 18168, 18168, 18168, 18168, 18168, 18168, 18168,
18170, 18170, 18170, 18170, 18170, 18170, 18171, 18171, 18171,
18171, 18171, 18171, 18171, 18171, 18171, 18172, 18172, 18172,
18174, 18174, 18174, 18174, 18174, 18175, 18175, 18175, 18175,
18175, 18175, 18032, 18032, 18032, 18032, 18032, 18032, 18032,
18032, 18032, 18032, 18032, 18032, 18032, 18032, 18032, 18032,
18032, 18032, 18032, 18032, 18032, 18070, 18047, 18048, 18049,
18069, 18069, 18069, 18069, 18069, 18069, 18069, 18069, 18069,
18069, 18069, 18069, 18055, 18055, 18160, 18160, 18160, 18160,
18160, 18160, 18160, 18160, 18160, 18160, 18160, 18160, 18063,
18063, 18027, 18027, 18027, 18027, 18027, 18027, 18027, 18027,
18027, 18027, 18027, 18027, 18041, 18041, 18041, 18041, 18041,
18041, 18041, 18041, 18041, 18041, 18041, 18041, 18041, 18153,
18153, 18153, 18153, 18153, 18153, 18153, 18153, 18153, 18153,
18153, 18153, 18153, 18160, 18160, 18160, 18160, 18160, 18160,
18160, 18160, 18160, 18160, 18160, 18160, 18160, 18167, 18167,
18167, 18167, 18167, 18167, 18167, 18167, 18167, 18167, 18167,
18167, 18167, 18167, 18167, 18167, 18174, 18174, 18174, 18174,
18174, 18174, 18174, 18174, 18174, 18174, 18174, 18174, 18174,
18174, 18055, 18055, 18055, 18055, 18055, 18055, 18055, 18055,
18055, 18055, 18055, 18055, 18055, 18055, 18076, 18076, 18077,
18078, 18078, 18078, 18078, 18083, 18083, 18083, 18083, 18083,
18083, 18085, 18085, 18087, 18088, 18090, 18090, 18090, 18090,
18090, 18090, 18091, 18091, 18094, 18094, 18096, 18097, 18097,
18097, 18098, 18099, 18099, 18100, 18101, 18101, 18101, 18101,
18102, 18102, 18102, 18102, 18103, 18104, 18104, 18104, 18104,
18104, 18104, 18104, 18104, 18104, 18104, 18104, 18104, 18105,
18105, 18107, 18107, 18107, 18108, 18109, 18109, 18109, 18110,
18110, 18111, 18111, 18111, 18111, 18111, 18112, 18112, 18114,
18116, 18116, 18118, 18118, 18118, 18119, 18119, 18121, 18121,
18121, 18123, 18125, 18125, 18125, 18125, 18126, 18126, 18126,
18126, 18127, 18127, 18128, 18128, 18131, 18131, 18131, 18132,
18132, 18132, 18132, 18134, 18134, 18135, 18135, 18137, 18138,
18142, 18142, 18145, 18146, 18146, 18146, 18146, 18146, 18146,
18146, 18146, 18146, 18146, 18147, 18147, 18147, 18148, 18148,
18148, 18149, 18153, 18153, 18153, 18153, 18153, 18153, 18153,
18153, 18153, 18153, 18153, 18154, 18154, 18155, 18157, 18158,
18455, 18455, 18455, 18455, 18456, 18462, 18462, 18462, 18463,
18464, 18464, 18466, 18466, 18467, 18467, 18467, 18467, 18468,
18468, 18468, 18468, 18468, 18468, 18469, 18469, 18470, 18470,
18470, 18470, 18471, 18471, 18472, 18474, 18474, 18474, 18475,
18475, 18475, 18475, 18475, 18476, 18476, 18476, 18476, 18477,
18478, 18478, 18479, 18479, 18479, 18480, 18480, 18480, 18482,
18482, 18482, 18482, 18482, 18483, 18483, 18483, 18483, 18483,
18485, 18486, 18487, 18488, 18488, 18488, 18488, 18489, 18489,
18489, 18489, 18489, 18489, 18490, 18490, 18490, 18490, 18490,
18490, 18490, 18490, 18491, 18491, 18491, 18491, 18491, 18491,
18492, 18492, 18492, 18492, 18493, 18493, 18493, 18493, 18493,
18493, 18496, 18496, 18496, 18496, 18496, 18497, 18497, 18498,
18498, 18498, 18498, 18498, 18498, 18498, 18498, 18499, 18499,
18499, 18500, 18501, 18501, 18501, 18501, 18501, 18502, 18502,
18502, 18502, 18502, 18503, 18503, 18503, 18503, 18503, 18503,
18504, 18504, 18504, 18504, 18504, 18504, 18504, 18504, 18504,
18504, 18504, 18505, 18505, 18505, 18505, 18505, 18505, 18506,
18506, 18506, 18507, 18508, 18508, 18509, 18509, 18509, 18509,
18510, 18510, 18510, 18510, 18510, 18510, 18510, 18510, 18510,
18511, 18511, 18511, 18511, 18511, 18511, 18511, 18512, 18512,
18512, 18513, 18513, 18514, 18514, 18514, 18514, 18514, 18514,
18515, 18515, 18515, 18516, 18516, 18517, 18517, 18517, 18517,
18517, 18517, 18517, 18517, 18518, 18518, 18518, 18518, 18518,
18518, 18519, 18519, 18519, 18522, 18522, 18524, 18524, 18525,
18525, 18525, 18526, 18526, 18527, 18528, 18528, 18528, 18528,
18529, 18529, 18529, 18529, 18531, 18531, 18531, 18524, 18524,
18524, 18524, 18524, 18524, 18524, 18524, 18524, 18524, 18524,
18524, 18524, 18511, 18035, 18042, 18049, 18049, 18056, 18061,
18064, 18070, 18070, 18072, 18074, 18074, 18074, 18074, 18074,
18077, 18078, 18078, 18078, 18078, 18078, 18078, 18078, 18083,
18083, 18083, 18084, 18084, 18086, 18091, 18091, 18091, 18091,
18091, 18091, 18091, 18091, 18091, 18091, 18091, 18091, 18091,
18091, 18091, 18091, 18091, 18091, 18091, 18091, 18091, 18091,
18091, 18091, 18091, 18091, 18091, 18091, 18091, 18091, 18091,
18091, 18091, 18091, 18091, 18091, 18092, 18094, 18094, 18100,
18102, 18102, 18104, 18105, 18106, 18107, 18107, 18111, 18120,
18126, 18127, 18127, 18127, 18128, 18128, 18131, 18132, 18133,
18134, 18137, 18141, 18141, 18141, 18143, 18145, 18147, 18147,
18147, 18149, 18149, 18156, 18157, 18157, 18158, 18164, 18166,
18168, 18169, 18170, 18171, 18171, 18070, 18064, 18080, 18084,
18091, 18091, 18091, 18091, 18091, 18098, 18102, 18112, 18115,
18115, 18125, 18126, 18126, 18127, 18133, 18133, 18133, 18133,
18134, 18142, 18144, 18145, 18149, 18149, 18149, 18149, 18462,
18462, 18463, 18490, 18490, 18494, 18495, 18495, 18497, 18497,
18501, 18501, 18501, 18501, 18504, 18504, 18504, 18504, 18505,
18508, 18508, 18514, 18523, 18525, 18526, 18035, 18043, 18049,
18050, 18052, 18052, 18053, 18053, 18055, 18057, 18058, 18059,
18060, 18061, 18062, 18070, 18074, 18079, 18083, 18085, 18085,
18087, 18088, 18089, 18090, 18094, 18094, 18095, 18095, 18096,
18096, 18099, 18099, 18099, 18100, 18103, 18103, 18104, 18105,
18105, 18110, 18110, 18110, 18110, 18111, 18111, 18112, 18113,
18113, 18115, 18115, 18116, 18116, 18116, 18117, 18118, 18119,
18121, 18122, 18124, 18124, 18127, 18127, 18127, 18127, 18129,
18129, 18129, 18132, 18132, 18133, 18136, 18140, 18140, 18140,
18141, 18143, 18143, 18144, 18146, 18147, 18147, 18147, 18147,
18150, 18150, 18150, 18150, 18151, 18156, 18156, 18157, 18160,
18165, 18166, 18167, 18168, 18171, 18171, 18173, 18174, 18174,
18177, 18077, 18078, 18083, 18091, 18092, 18096, 18100, 18101,
18104, 18104, 18107, 18109, 18110, 18111, 18114, 18127, 18129,
18131, 18135, 18136, 18136, 18139, 18141, 18142, 18142, 18145,
18148, 18152, 18152, 18152, 18152, 18156, 18469, 18469, 18470,
18470, 18473, 18478, 18480, 18485, 18488, 18491, 18491, 18493,
18496, 18499, 18502, 18503, 18505, 18507, 18507, 18510, 18510,
18514, 18515, 18517, 18519, 18519, 18520, 18520, 18526, 18149,
18030, 18032, 18038, 18045, 18045, 18048, 18048, 18048, 18065,
18065, 18065, 18065, 18066, 18066, 18068, 18069, 18069, 18069,
18069, 18069, 18073, 18074, 18074, 18076, 18076, 18076, 18077,
18078, 18078, 18079, 18080, 18080, 18080, 18081, 18081, 18083,
18085, 18086, 18092, 18093, 18093, 18095, 18097, 18098, 18099,
18100, 18100, 18101, 18101, 18102, 18102, 18103, 18105, 18107,
18109, 18109, 18110, 18112, 18113, 18116, 18120, 18122, 18122,
18122, 18122, 18122, 18123, 18123, 18124, 18124, 18125, 18126,
18126, 18128, 18128, 18129, 18131, 18131, 18131, 18132, 18132,
18133, 18133, 18133, 18134, 18135, 18136, 18136, 18137, 18137,
18140, 18141, 18142, 18143, 18145, 18147, 18148, 18149, 18150,
18152, 18153, 18153, 18155, 18155, 18156, 18157, 18157, 18160,
18160, 18163, 18163, 18163, 18163, 18164, 18166, 18169, 18171,
18173, 18066, 18066, 18078, 18081, 18082, 18091, 18097, 18103,
18103, 18104, 18104, 18107, 18110, 18110, 18112, 18115, 18117,
18117, 18119, 18123, 18131, 18131, 18133, 18133, 18136, 18137,
18137, 18137, 18137, 18146, 18148, 18159, 18169, 18457, 18471,
18473, 18477, 18477, 18477, 18477, 18477, 18479, 18479, 18479,
18479, 18483, 18486, 18490, 18491, 18491, 18492, 18493, 18495,
18495, 18495, 18496, 18500, 18501, 18501, 18501, 18502, 18503,
18507, 18507, 18510, 18514, 18514, 18514, 18515, 18517, 18518,
18519, 18519, 18519, 18519, 18519, 18523, 18523, 18523, 18528,
18047, 18062, 18063, 18065, 18067, 18067, 18069, 18074, 18080,
18080, 18082, 18082, 18083, 18085, 18086, 18086, 18091, 18095,
18099, 18103, 18108, 18109, 18117, 18121, 18122, 18124, 18125,
18132, 18135, 18143, 18146, 18150, 18152, 18154, 18155, 18157,
18160, 18163, 18164, 18169, 18169, 18171, 18171, 18171, 18171,
18171, 18171, 18171, 18171, 18171, 18106, 18140, 18147, 18149,
18159, 18458, 18459, 18462, 18485, 18486, 18490, 18491, 18492,
18495, 18497, 18503, 18504, 18506, 18508, 18508, 18515, 18523,
18528, 18528, 18528, 18488, 18073, 18074, 18074, 18074, 18075,
18076, 18082, 18082, 18086, 18089, 18092, 18094, 18097, 18099,
18100, 18103, 18107, 18110, 18118, 18120, 18122, 18123, 18132,
18132, 18141, 18148, 18148, 18149, 18153, 18153, 18155, 18155,
18159, 18161, 18173, 18069, 18069, 18069, 18114, 18116, 18125,
18130, 18139, 18463, 18468, 18474, 18475, 18486, 18495, 18499,
18499, 18500, 18503, 18503, 18509, 18509, 18516, 18517, 18518,
18521, 18526, 18069, 18071, 18076, 18088, 18090, 18093, 18101,
18105, 18108, 18120, 18120, 18121, 18122, 18122, 18126, 18130,
18132, 18132, 18138, 18146, 18150, 18175, 18069, 18069, 18069,
18094, 18110, 18130, 18138, 18147, 18474, 18490, 18496, 18503,
18504, 18531, 18034, 18034, 18035, 18041, 18044, 18048, 18048,
18049, 18049, 18052, 18052, 18055, 18062, 18062, 18062, 18063,
18065, 18069, 18069, 18069, 18070, 18071, 18076, 18084, 18090,
18095, 18098, 18098, 18103, 18104, 18105, 18105, 18105, 18105,
18105, 18106, 18108, 18108, 18111, 18112, 18114, 18115, 18118,
18124, 18126, 18126, 18126, 18126, 18128, 18132, 18132, 18132,
18132, 18132, 18137, 18139, 18139, 18139, 18139, 18140, 18142,
18143, 18145, 18146, 18146, 18146, 18146, 18148, 18152, 18153,
18153, 18153, 18153, 18154, 18154, 18155, 18159, 18160, 18161,
18161, 18164, 18167, 18167, 18167, 18169, 18171, 18171, 18172,
18174, 18174, 18174, 18175, 18175, 18175, 18175, 18175, 18090,
18091, 18092, 18092, 18097, 18097, 18104, 18104, 18104, 18108,
18117, 18117, 18125, 18125, 18132, 18132, 18136, 18139, 18146,
18153, 18154, 18161, 18164, 18461, 18462, 18463, 18463, 18465,
18467, 18467, 18467, 18469, 18485, 18487, 18489, 18491, 18491,
18491, 18494, 18494, 18495, 18495, 18496, 18496, 18496, 18498,
18501, 18503, 18504, 18504, 18505, 18505, 18505, 18508, 18509,
18509, 18510, 18510, 18512, 18515, 18515, 18517, 18520, 18521,
18521, 18523, 18523, 18524, 18525, 18528, 18048, 18080, 18084,
18091, 18118, 18127, 18141, 18147, 18155, 18091, 18112, 18145,
18476, 18490, 18494, 18498, 18498, 18498, 18498, 18511, 18512,
18514, 18516, 18092, 18140, 18092, 18127, 18497, 18504, 18511,
18052, 18092, 18118, 18120, 18126, 18126, 18126, 18127, 18497,
18501, 18137, 18137, 18472, 18082, 18093, 18494, 18161, 18131,
18494, 18515, 18049, 18145, 18097, 18468, 18524, 18531, 18092,
18106, 18093, 18107, 18183, 18111, 18106), class = "Date"),
seasonindex = c(0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.59750000000000003, 0.59750000000000003,
0.59750000000000003, 0.59750000000000003, 0.59750000000000003,
0.59750000000000003, 0.59750000000000003, 0.59750000000000003,
0.59750000000000003, 0.59750000000000003, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.83089999999999997, 0.83089999999999997,
0.83089999999999997, 0.83089999999999997, 0.83089999999999997,
0.83089999999999997, 0.83089999999999997, 0.83089999999999997,
0.83089999999999997, 0.83089999999999997, 0.83089999999999997,
0.83089999999999997, 0.83089999999999997, 0.83089999999999997,
0.83089999999999997, 0.83089999999999997, 0.87039999999999995,
0.87039999999999995, 0.87039999999999995, 0.87039999999999995,
0.87039999999999995, 0.87039999999999995, 0.87039999999999995,
0.87039999999999995, 0.87039999999999995, 0.87039999999999995,
0.87039999999999995, 0.87039999999999995, 1, 1, 1, 1, 1,
1, 1, 1, 1, 0.84260000000000002, 0.84260000000000002, 0.84260000000000002,
0.84260000000000002, 0.84260000000000002, 0.84260000000000002,
0.84260000000000002, 0.84260000000000002, 0.84260000000000002,
0.84260000000000002, 0.84260000000000002, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.57589999999999997, 0.57589999999999997, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.69140000000000001, 0.77780000000000005, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.57589999999999997, 0.57589999999999997,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.59750000000000003,
0.59750000000000003, 0.59750000000000003, 0.59750000000000003,
0.59750000000000003, 0.59750000000000003, 0.59750000000000003,
0.59750000000000003, 0.59750000000000003, 0.59750000000000003,
0.59750000000000003, 0.59750000000000003, 0.59750000000000003,
0.59750000000000003, 0.59750000000000003, 0.59750000000000003,
0.59750000000000003, 0.59750000000000003, 0.59750000000000003,
0.59750000000000003, 0.59750000000000003, 0.59750000000000003,
0.59750000000000003, 0.59750000000000003, 0.59750000000000003,
0.59750000000000003, 0.59750000000000003, 0.59750000000000003,
0.59750000000000003, 0.59750000000000003, 0.59750000000000003,
0.69140000000000001, 0.69140000000000001, 0.57589999999999997,
0.69140000000000001, 0.69140000000000001, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.81169999999999998, 0.81169999999999998,
0.87039999999999995, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.83089999999999997, 0.83089999999999997, 0.83089999999999997,
0.83089999999999997, 0.83089999999999997, 0.83089999999999997,
0.83089999999999997, 0.83089999999999997, 0.83089999999999997,
0.83089999999999997, 0.83089999999999997, 0.83089999999999997,
0.83089999999999997, 0.83089999999999997, 0.83089999999999997,
0.83089999999999997, 0.83089999999999997, 0.83089999999999997,
0.87039999999999995, 0.87039999999999995, 0.87039999999999995,
0.87039999999999995, 0.87039999999999995, 0.87039999999999995,
0.87039999999999995, 0.87039999999999995, 0.87039999999999995,
0.87039999999999995, 0.87039999999999995, 0.87039999999999995,
0.87039999999999995, 0.87039999999999995, 1, 1, 1, 1, 1,
0.84260000000000002, 0.84260000000000002, 0.84260000000000002,
0.84260000000000002, 0.84260000000000002, 0.84260000000000002,
0.84260000000000002, 0.84260000000000002, 0.84260000000000002,
0.84260000000000002, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.77780000000000005, 0.84260000000000002, 0.84260000000000002,
0.65429999999999999, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.59750000000000003, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.79475000000000007, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.85064999999999991, 0.85064999999999991,
0.87039999999999995, 0.87039999999999995, 0.87039999999999995,
0.87039999999999995, 0.87039999999999995, 0.87039999999999995,
0.87039999999999995, 0.87039999999999995, 1, 1, 1, 1, 1,
1, 0.92130000000000001, 0.84260000000000002, 0.84260000000000002,
0.84260000000000002, 0.84260000000000002, 0.84260000000000002,
0.84260000000000002, 0.84260000000000002, 0.84260000000000002,
0.84260000000000002, 0.84260000000000002, 0.84260000000000002,
0.84260000000000002, 0.84260000000000002, 0.74845000000000006,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.73460000000000003, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.5867, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.5867, 0.5867, 0.5867, 0.5867, 0.5867, 0.5867, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.59750000000000003,
0.59750000000000003, 0.59750000000000003, 0.59750000000000003,
0.59750000000000003, 0.59750000000000003, 0.69140000000000001,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.5867, 0.5867,
0.59750000000000003, 0.69140000000000001, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.64444999999999997,
0.64444999999999997, 0.64444999999999997, 0.64444999999999997,
0.64444999999999997, 0.64444999999999997, 0.64444999999999997,
0.64444999999999997, 0.64444999999999997, 0.64444999999999997,
0.64444999999999997, 0.64444999999999997, 0.64444999999999997,
0.64444999999999997, 0.64444999999999997, 0.64444999999999997,
0.64444999999999997, 0.64444999999999997, 0.64444999999999997,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.59750000000000003, 0.59750000000000003,
0.59750000000000003, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.85064999999999991,
0.87039999999999995, 1, 1, 1, 1, 1, 1, 0.65429999999999999,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.83089999999999997, 0.83089999999999997,
0.83089999999999997, 0.83089999999999997, 0.83089999999999997,
0.83089999999999997, 0.83089999999999997, 0.83089999999999997,
0.83089999999999997, 0.83089999999999997, 0.83089999999999997,
0.83089999999999997, 0.85064999999999991, 0.85064999999999991,
0.87039999999999995, 0.87039999999999995, 0.87039999999999995,
0.87039999999999995, 0.87039999999999995, 0.87039999999999995,
0.87039999999999995, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 0.92130000000000001, 0.84260000000000002,
0.84260000000000002, 0.84260000000000002, 0.84260000000000002,
0.74845000000000006, 0.74845000000000006, 0.74845000000000006,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.69140000000000001, 0.65429999999999999, 1, 1, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.59750000000000003, 0.59750000000000003,
0.59750000000000003, 0.59750000000000003, 0.59750000000000003,
0.59750000000000003, 0.62880000000000003, 0.62880000000000003,
0.62880000000000003, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.78910000000000002, 0.78910000000000002,
0.78910000000000002, 0.8004, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.82450000000000001, 0.83089999999999997, 0.83089999999999997,
0.85723333333333329, 0.85723333333333329, 0.87039999999999995,
0.87039999999999995, 0.87039999999999995, 0.87039999999999995,
0.87039999999999995, 0.87039999999999995, 0.87039999999999995,
0.87039999999999995, 0.87039999999999995, 0.87039999999999995,
0.87039999999999995, 0.91359999999999997, 0.91359999999999997,
0.91359999999999997, 0.95679999999999998, 0.95679999999999998,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 0.94753333333333334, 0.94753333333333334, 0.84260000000000002,
0.84260000000000002, 0.84260000000000002, 0.84260000000000002,
0.84260000000000002, 0.84260000000000002, 0.84260000000000002,
0.84260000000000002, 0.84260000000000002, 0.84260000000000002,
0.84260000000000002, 0.84260000000000002, 0.84260000000000002,
0.84260000000000002, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.72020000000000006,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.59030000000000005, 0.59030000000000005, 0.65429999999999999,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.57589999999999997, 0.69140000000000001,
0.69140000000000001, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.83089999999999997, 0.83089999999999997, 1, 1, 1, 1, 1,
1, 1, 0.77983333333333338, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.77780000000000005, 0.78910000000000002, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81809999999999994,
0.81809999999999994, 0.82450000000000001, 0.82450000000000001,
0.82450000000000001, 0.82450000000000001, 0.83089999999999997,
0.83089999999999997, 0.83089999999999997, 0.83089999999999997,
0.83089999999999997, 0.83089999999999997, 0.84406666666666663,
0.84406666666666663, 0.85723333333333329, 0.87039999999999995,
0.87039999999999995, 0.87039999999999995, 0.87039999999999995,
0.95679999999999998, 0.95679999999999998, 0.95679999999999998,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0.94753333333333334, 0.94753333333333334,
0.94753333333333334, 0.94753333333333334, 0.94753333333333334,
0.89506666666666668, 0.89506666666666668, 0.84260000000000002,
0.84260000000000002, 0.84260000000000002, 0.84260000000000002,
0.84260000000000002, 0.84260000000000002, 0.84260000000000002,
0.77983333333333338, 0.77983333333333338, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.5867, 0.59210000000000007, 0.59750000000000003, 0.59750000000000003,
0.59750000000000003, 0.59750000000000003, 0.66792499999999999,
0.66792499999999999, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.75619999999999998, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.78627500000000006, 0.78627500000000006, 0.79475000000000007,
0.79475000000000007, 0.80322499999999997, 0.80322499999999997,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.8165, 0.8165,
0.8165, 0.82129999999999992, 0.82609999999999995, 0.83089999999999997,
0.83089999999999997, 0.83089999999999997, 0.84077499999999994,
0.85064999999999991, 0.85064999999999991, 0.85064999999999991,
0.86052499999999998, 0.87039999999999995, 0.87039999999999995,
0.87039999999999995, 0.87039999999999995, 0.87039999999999995,
0.93520000000000003, 0.93520000000000003, 0.96760000000000002,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0.96065, 0.92130000000000001,
0.88195000000000001, 0.88195000000000001, 0.88195000000000001,
0.84260000000000002, 0.84260000000000002, 0.84260000000000002,
0.84260000000000002, 0.84260000000000002, 0.84260000000000002,
0.84260000000000002, 0.84260000000000002, 0.79552500000000004,
0.79552500000000004, 0.79552500000000004, 0.74845000000000006,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.5867, 0.5867, 0.5867, 0.5867, 0.5867,
0.5867, 0.5867, 0.5867, 0.5867, 0.5867, 0.5867, 0.5867, 0.5867,
0.5867, 0.5867, 0.5867, 0.5867, 0.5867, 0.5867, 0.5867, 0.5867,
0.5867, 0.5867, 0.5867, 0.5867, 0.5867, 0.5867, 0.5867, 0.5867,
0.5867, 0.5867, 0.5867, 0.5867, 0.5867, 0.5867, 0.5867, 0.5867,
0.5867, 0.5867, 0.65429999999999999, 0.65429999999999999,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.62097500000000005, 0.64444999999999997, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.82609999999999995, 0.82609999999999995,
0.83089999999999997, 0.87039999999999995, 0.93520000000000003,
0.96760000000000002, 1, 1, 1, 1, 1, 0.92130000000000001,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.77780000000000005,
0.77780000000000005, 0.78627500000000006, 0.78627500000000006,
0.80322499999999997, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.83089999999999997, 0.83089999999999997, 0.83089999999999997,
0.83089999999999997, 0.83089999999999997, 0.83089999999999997,
0.84077499999999994, 0.87039999999999995, 0.87039999999999995,
0.93520000000000003, 0.93520000000000003, 0.96760000000000002,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.96065, 0.96065,
0.96065, 0.92130000000000001, 0.92130000000000001, 0.92130000000000001,
0.88195000000000001, 0.88195000000000001, 0.88195000000000001,
0.88195000000000001, 0.88195000000000001, 0.88195000000000001,
0.84260000000000002, 0.84260000000000002, 0.84260000000000002,
0.74845000000000006, 0.74845000000000006, 0.74845000000000006,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.58886000000000005, 0.59750000000000003, 0.59750000000000003,
0.61628000000000005, 0.61628000000000005, 0.61628000000000005,
0.63506000000000007, 0.63506000000000007, 0.63506000000000007,
0.63506000000000007, 0.63506000000000007, 0.63506000000000007,
0.63506000000000007, 0.63506000000000007, 0.63506000000000007,
0.63506000000000007, 0.63506000000000007, 0.63506000000000007,
0.63506000000000007, 0.63506000000000007, 0.63506000000000007,
0.63506000000000007, 0.63506000000000007, 0.63506000000000007,
0.63506000000000007, 0.63506000000000007, 0.63506000000000007,
0.63506000000000007, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.70867999999999998,
0.70867999999999998, 0.72596000000000005, 0.72596000000000005,
0.74324000000000001, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.79136000000000006,
0.79813999999999996, 0.80491999999999997, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81553999999999993, 0.81938, 0.82321999999999995, 0.82706000000000002,
0.83089999999999997, 0.83089999999999997, 0.83879999999999999,
0.84670000000000001, 0.85459999999999992, 0.87039999999999995,
0.87039999999999995, 0.94816, 0.97407999999999995, 0.97407999999999995,
0.97407999999999995, 0.97407999999999995, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0.96852000000000005, 0.96852000000000005,
0.93703999999999998, 0.87407999999999997, 0.84260000000000002,
0.84260000000000002, 0.84260000000000002, 0.84260000000000002,
0.84260000000000002, 0.84260000000000002, 0.72962000000000005,
0.72962000000000005, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.57589999999999997,
0.57589999999999997, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.58453999999999995, 0.58453999999999995,
0.58453999999999995, 0.58886000000000005, 0.58886000000000005,
0.58886000000000005, 0.58886000000000005, 0.65429999999999999,
0.69140000000000001, 0.74324000000000001, 0.74324000000000001,
0.77780000000000005, 0.79136000000000006, 0.79136000000000006,
0.79813999999999996, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.82321999999999995, 0.83879999999999999, 0.83879999999999999,
0.83879999999999999, 0.83879999999999999, 0.83879999999999999,
0.94816, 0.94816, 0.97407999999999995, 0.97407999999999995,
1, 1, 1, 1, 1, 1, 1, 0.96852000000000005, 0.90556000000000003,
0.90556000000000003, 0.84260000000000002, 0.84260000000000002,
0.80493999999999999, 0.69196000000000002, 0.77780000000000005,
0.78458000000000006, 0.79813999999999996, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81553999999999993,
0.81553999999999993, 0.83089999999999997, 0.83879999999999999,
0.83879999999999999, 0.87039999999999995, 0.89632000000000001,
0.94816, 0.94816, 0.94816, 0.97407999999999995, 0.97407999999999995,
0.97407999999999995, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0.96852000000000005, 0.96852000000000005,
0.93703999999999998, 0.93703999999999998, 0.93703999999999998,
0.93703999999999998, 0.90556000000000003, 0.90556000000000003,
0.87407999999999997, 0.87407999999999997, 0.84260000000000002,
0.84260000000000002, 0.80493999999999999, 0.80493999999999999,
0.76727999999999996, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.83089999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.5867, 0.5867,
0.61315000000000008, 0.62880000000000003, 0.62880000000000003,
0.64444999999999997, 0.66010000000000002, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.70579999999999998,
0.72020000000000006, 0.73460000000000003, 0.73460000000000003,
0.73460000000000003, 0.73460000000000003, 0.73460000000000003,
0.76340000000000008, 0.76340000000000008, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.8004, 0.8004, 0.8004, 0.8004, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81489999999999996,
0.82129999999999992, 0.82450000000000001, 0.82769999999999999,
0.82769999999999999, 0.83089999999999997, 0.83748333333333336,
0.85064999999999991, 0.85064999999999991, 0.85064999999999991,
0.85723333333333329, 0.86381666666666668, 0.86381666666666668,
0.87039999999999995, 0.87039999999999995, 0.87039999999999995,
0.93520000000000003, 0.95679999999999998, 0.95679999999999998,
0.95679999999999998, 0.95679999999999998, 0.97839999999999994,
1, 1, 1, 1, 1, 1, 1, 0.97376666666666667, 0.94753333333333334,
0.94753333333333334, 0.94753333333333334, 0.92130000000000001,
0.92130000000000001, 0.92130000000000001, 0.89506666666666668,
0.84260000000000002, 0.77983333333333338, 0.77983333333333338,
0.77983333333333338, 0.71706666666666663, 0.71706666666666663,
0.71706666666666663, 0.68568333333333331, 0.68568333333333331,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.57589999999999997, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.5867, 0.5867,
0.5867, 0.5867, 0.59030000000000005, 0.69140000000000001,
0.59750000000000003, 0.749, 0.77780000000000005, 0.78910000000000002,
0.78910000000000002, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.85723333333333329, 0.87039999999999995, 1, 0.97376666666666667,
0.94753333333333334, 0.94753333333333334, 0.77983333333333338,
0.71706666666666663, 0.65429999999999999, 0.77780000000000005,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.83748333333333336,
0.8919999999999999, 0.91359999999999997, 0.91359999999999997,
0.93520000000000003, 0.95679999999999998, 0.97839999999999994,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.97376666666666667,
0.97376666666666667, 0.97376666666666667, 0.94753333333333334,
0.94753333333333334, 0.94753333333333334, 0.94753333333333334,
0.92130000000000001, 0.92130000000000001, 0.92130000000000001,
0.86883333333333335, 0.84260000000000002, 0.84260000000000002,
0.84260000000000002, 0.8112166666666667, 0.8112166666666667,
0.8112166666666667, 0.8112166666666667, 0.77983333333333338,
0.77983333333333338, 0.74845000000000006, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.69140000000000001, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57898571428571421, 0.57898571428571421,
0.58207142857142857, 0.58515714285714282, 0.58515714285714282,
0.58515714285714282, 0.58515714285714282, 0.58515714285714282,
0.58515714285714282, 0.58515714285714282, 0.58515714285714282,
0.58515714285714282, 0.58515714285714282, 0.58515714285714282,
0.58515714285714282, 0.58515714285714282, 0.58515714285714282,
0.58515714285714282, 0.58515714285714282, 0.58515714285714282,
0.58515714285714282, 0.58515714285714282, 0.58515714285714282,
0.58515714285714282, 0.58515714285714282, 0.58515714285714282,
0.58515714285714282, 0.58515714285714282, 0.58824285714285718,
0.58824285714285718, 0.58824285714285718, 0.59441428571428578,
0.59441428571428578, 0.59750000000000003, 0.59750000000000003,
0.59750000000000003, 0.61091428571428574, 0.61091428571428574,
0.62432857142857145, 0.62432857142857145, 0.62432857142857145,
0.62432857142857145, 0.62432857142857145, 0.62432857142857145,
0.62432857142857145, 0.62432857142857145, 0.62432857142857145,
0.62432857142857145, 0.62432857142857145, 0.62432857142857145,
0.62432857142857145, 0.62432857142857145, 0.62432857142857145,
0.62432857142857145, 0.62432857142857145, 0.62432857142857145,
0.62432857142857145, 0.62432857142857145, 0.62432857142857145,
0.63774285714285717, 0.63774285714285717, 0.63774285714285717,
0.63774285714285717, 0.63774285714285717, 0.63774285714285717,
0.63774285714285717, 0.63774285714285717, 0.63774285714285717,
0.63774285714285717, 0.63774285714285717, 0.63774285714285717,
0.63774285714285717, 0.63774285714285717, 0.65115714285714288,
0.65115714285714288, 0.65115714285714288, 0.65115714285714288,
0.65115714285714288, 0.65115714285714288, 0.65115714285714288,
0.65115714285714288, 0.65115714285714288, 0.66457142857142859,
0.66457142857142859, 0.66457142857142859, 0.66457142857142859,
0.6779857142857143, 0.6779857142857143, 0.6779857142857143,
0.6779857142857143, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.71608571428571433,
0.71608571428571433, 0.71608571428571433, 0.71608571428571433,
0.71608571428571433, 0.72842857142857143, 0.72842857142857143,
0.72842857142857143, 0.72842857142857143, 0.72842857142857143,
0.72842857142857143, 0.72842857142857143, 0.72842857142857143,
0.72842857142857143, 0.74077142857142864, 0.75311428571428574,
0.75311428571428574, 0.75311428571428574, 0.75311428571428574,
0.76545714285714295, 0.76545714285714295, 0.76545714285714295,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.7826428571428572, 0.7826428571428572,
0.7826428571428572, 0.78748571428571434, 0.78748571428571434,
0.79232857142857149, 0.79232857142857149, 0.79232857142857149,
0.79232857142857149, 0.79717142857142853, 0.79717142857142853,
0.79717142857142853, 0.79717142857142853, 0.79717142857142853,
0.79717142857142853, 0.79717142857142853, 0.79717142857142853,
0.80201428571428568, 0.80201428571428568, 0.80201428571428568,
0.80201428571428568, 0.80201428571428568, 0.80685714285714283,
0.80685714285714283, 0.80685714285714283, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81444285714285714, 0.81444285714285714,
0.81444285714285714, 0.81444285714285714, 0.81718571428571429,
0.81718571428571429, 0.81718571428571429, 0.81718571428571429,
0.81992857142857145, 0.81992857142857145, 0.81992857142857145,
0.81992857142857145, 0.81992857142857145, 0.81992857142857145,
0.81992857142857145, 0.81992857142857145, 0.81992857142857145,
0.81992857142857145, 0.8226714285714285, 0.8226714285714285,
0.8226714285714285, 0.8226714285714285, 0.82541428571428566,
0.82815714285714281, 0.82815714285714281, 0.82815714285714281,
0.82815714285714281, 0.82815714285714281, 0.82815714285714281,
0.82815714285714281, 0.82815714285714281, 0.82815714285714281,
0.82815714285714281, 0.82815714285714281, 0.82815714285714281,
0.82815714285714281, 0.82815714285714281, 0.82815714285714281,
0.82815714285714281, 0.82815714285714281, 0.82815714285714281,
0.83089999999999997, 0.83089999999999997, 0.83089999999999997,
0.83089999999999997, 0.83089999999999997, 0.83654285714285714,
0.83654285714285714, 0.83654285714285714, 0.8421857142857142,
0.8421857142857142, 0.8421857142857142, 0.8421857142857142,
0.84782857142857138, 0.84782857142857138, 0.84782857142857138,
0.84782857142857138, 0.84782857142857138, 0.84782857142857138,
0.84782857142857138, 0.84782857142857138, 0.84782857142857138,
0.84782857142857138, 0.84782857142857138, 0.84782857142857138,
0.84782857142857138, 0.84782857142857138, 0.84782857142857138,
0.84782857142857138, 0.84782857142857138, 0.85911428571428572,
0.85911428571428572, 0.85911428571428572, 0.85911428571428572,
0.86475714285714278, 0.86475714285714278, 0.86475714285714278,
0.86475714285714278, 0.86475714285714278, 0.86475714285714278,
0.87039999999999995, 0.88891428571428566, 0.90742857142857136,
0.90742857142857136, 0.90742857142857136, 0.90742857142857136,
0.92594285714285707, 0.92594285714285707, 0.92594285714285707,
0.92594285714285707, 0.92594285714285707, 0.92594285714285707,
0.92594285714285707, 0.92594285714285707, 0.92594285714285707,
0.92594285714285707, 0.92594285714285707, 0.92594285714285707,
0.92594285714285707, 0.92594285714285707, 0.92594285714285707,
0.94445714285714288, 0.94445714285714288, 0.94445714285714288,
0.94445714285714288, 0.94445714285714288, 0.94445714285714288,
0.94445714285714288, 0.94445714285714288, 0.94445714285714288,
0.94445714285714288, 0.96297142857142859, 0.96297142857142859,
0.96297142857142859, 0.98148571428571429, 0.98148571428571429,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.97751428571428567, 0.97751428571428567,
0.97751428571428567, 0.97751428571428567, 0.97751428571428567,
0.95502857142857145, 0.95502857142857145, 0.95502857142857145,
0.95502857142857145, 0.95502857142857145, 0.93254285714285712,
0.93254285714285712, 0.93254285714285712, 0.93254285714285712,
0.93254285714285712, 0.93254285714285712, 0.93254285714285712,
0.93254285714285712, 0.93254285714285712, 0.93254285714285712,
0.93254285714285712, 0.93254285714285712, 0.9100571428571429,
0.9100571428571429, 0.9100571428571429, 0.88757142857142857,
0.88757142857142857, 0.88757142857142857, 0.88757142857142857,
0.88757142857142857, 0.88757142857142857, 0.86508571428571435,
0.86508571428571435, 0.84260000000000002, 0.84260000000000002,
0.84260000000000002, 0.84260000000000002, 0.81569999999999998,
0.81569999999999998, 0.78880000000000006, 0.78880000000000006,
0.78880000000000006, 0.76190000000000002, 0.76190000000000002,
0.76190000000000002, 0.76190000000000002, 0.76190000000000002,
0.76190000000000002, 0.76190000000000002, 0.76190000000000002,
0.76190000000000002, 0.76190000000000002, 0.76190000000000002,
0.73499999999999999, 0.73499999999999999, 0.73499999999999999,
0.70809999999999995, 0.70809999999999995, 0.70809999999999995,
0.68120000000000003, 0.68120000000000003, 0.68120000000000003,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.69140000000000001, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.58515714285714282,
0.58515714285714282, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65115714285714288, 0.65115714285714288,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.58515714285714282, 0.58515714285714282, 0.58515714285714282,
0.58515714285714282, 0.58515714285714282, 0.58515714285714282,
0.58515714285714282, 0.58515714285714282, 0.58515714285714282,
0.58515714285714282, 0.58515714285714282, 0.58515714285714282,
0.58515714285714282, 0.58515714285714282, 0.72842857142857143,
0.72842857142857143, 0.74077142857142864, 0.75311428571428574,
0.75311428571428574, 0.75311428571428574, 0.75311428571428574,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.7826428571428572, 0.79232857142857149, 0.79232857142857149,
0.79232857142857149, 0.79232857142857149, 0.79232857142857149,
0.79232857142857149, 0.79717142857142853, 0.79717142857142853,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81444285714285714,
0.81444285714285714, 0.81444285714285714, 0.81444285714285714,
0.81718571428571429, 0.81992857142857145, 0.81992857142857145,
0.81992857142857145, 0.81992857142857145, 0.81992857142857145,
0.81992857142857145, 0.81992857142857145, 0.81992857142857145,
0.81992857142857145, 0.81992857142857145, 0.81992857142857145,
0.81992857142857145, 0.8226714285714285, 0.8226714285714285,
0.82815714285714281, 0.82815714285714281, 0.82815714285714281,
0.83089999999999997, 0.83654285714285714, 0.83654285714285714,
0.83654285714285714, 0.8421857142857142, 0.8421857142857142,
0.84782857142857138, 0.84782857142857138, 0.84782857142857138,
0.84782857142857138, 0.84782857142857138, 0.85347142857142855,
0.85347142857142855, 0.86475714285714278, 0.88891428571428566,
0.88891428571428566, 0.92594285714285707, 0.92594285714285707,
0.92594285714285707, 0.94445714285714288, 0.94445714285714288,
0.98148571428571429, 0.98148571428571429, 0.98148571428571429,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.95502857142857145,
0.95502857142857145, 0.95502857142857145, 0.93254285714285712,
0.93254285714285712, 0.93254285714285712, 0.93254285714285712,
0.88757142857142857, 0.88757142857142857, 0.86508571428571435,
0.86508571428571435, 0.81569999999999998, 0.78880000000000006,
0.68120000000000003, 0.68120000000000003, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.78748571428571434, 0.78748571428571434, 0.78748571428571434,
0.78748571428571434, 0.79232857142857149, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81444285714285714,
0.81444285714285714, 0.81444285714285714, 0.81444285714285714,
0.81444285714285714, 0.81444285714285714, 0.81718571428571429,
0.81718571428571429, 0.81992857142857145, 0.81992857142857145,
0.81992857142857145, 0.81992857142857145, 0.8226714285714285,
0.8226714285714285, 0.82541428571428566, 0.83089999999999997,
0.83089999999999997, 0.83089999999999997, 0.83654285714285714,
0.83654285714285714, 0.83654285714285714, 0.83654285714285714,
0.83654285714285714, 0.8421857142857142, 0.8421857142857142,
0.8421857142857142, 0.8421857142857142, 0.84782857142857138,
0.85347142857142855, 0.85347142857142855, 0.85911428571428572,
0.85911428571428572, 0.85911428571428572, 0.86475714285714278,
0.86475714285714278, 0.86475714285714278, 0.88891428571428566,
0.88891428571428566, 0.88891428571428566, 0.88891428571428566,
0.88891428571428566, 0.90742857142857136, 0.90742857142857136,
0.90742857142857136, 0.90742857142857136, 0.90742857142857136,
0.94445714285714288, 0.96297142857142859, 0.98148571428571429,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.97751428571428567,
0.97751428571428567, 0.97751428571428567, 0.97751428571428567,
0.97751428571428567, 0.95502857142857145, 0.95502857142857145,
0.93254285714285712, 0.93254285714285712, 0.93254285714285712,
0.93254285714285712, 0.93254285714285712, 0.93254285714285712,
0.93254285714285712, 0.93254285714285712, 0.9100571428571429,
0.9100571428571429, 0.9100571428571429, 0.88757142857142857,
0.86508571428571435, 0.86508571428571435, 0.86508571428571435,
0.86508571428571435, 0.86508571428571435, 0.84260000000000002,
0.84260000000000002, 0.84260000000000002, 0.84260000000000002,
0.84260000000000002, 0.81569999999999998, 0.81569999999999998,
0.81569999999999998, 0.81569999999999998, 0.81569999999999998,
0.81569999999999998, 0.78880000000000006, 0.78880000000000006,
0.78880000000000006, 0.78880000000000006, 0.78880000000000006,
0.78880000000000006, 0.78880000000000006, 0.78880000000000006,
0.78880000000000006, 0.78880000000000006, 0.78880000000000006,
0.76190000000000002, 0.76190000000000002, 0.76190000000000002,
0.76190000000000002, 0.76190000000000002, 0.76190000000000002,
0.73499999999999999, 0.73499999999999999, 0.73499999999999999,
0.70809999999999995, 0.68120000000000003, 0.68120000000000003,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.5867, 0.62097500000000005,
0.65618750000000003, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.70220000000000005, 0.70220000000000005,
0.70220000000000005, 0.70220000000000005, 0.70220000000000005,
0.73460000000000003, 0.74540000000000006, 0.74540000000000006,
0.74540000000000006, 0.74540000000000006, 0.74540000000000006,
0.74540000000000006, 0.74540000000000006, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.77780000000000005, 0.79475000000000007,
0.79475000000000007, 0.79475000000000007, 0.79475000000000007,
0.79475000000000007, 0.79475000000000007, 0.79475000000000007,
0.79475000000000007, 0.79475000000000007, 0.79475000000000007,
0.79475000000000007, 0.79475000000000007, 0.79475000000000007,
0.79475000000000007, 0.79475000000000007, 0.79475000000000007,
0.79475000000000007, 0.79475000000000007, 0.79475000000000007,
0.79475000000000007, 0.79475000000000007, 0.79475000000000007,
0.79475000000000007, 0.79475000000000007, 0.79475000000000007,
0.79475000000000007, 0.79475000000000007, 0.79475000000000007,
0.79475000000000007, 0.79475000000000007, 0.79475000000000007,
0.79475000000000007, 0.79475000000000007, 0.79475000000000007,
0.79475000000000007, 0.79475000000000007, 0.79898749999999996,
0.80746249999999997, 0.80746249999999997, 0.81169999999999998,
0.81409999999999993, 0.81409999999999993, 0.81889999999999996,
0.82129999999999992, 0.82369999999999999, 0.82609999999999995,
0.82609999999999995, 0.84571249999999998, 0.95140000000000002,
1, 1, 1, 1, 1, 1, 0.96065, 0.94097500000000001, 0.92130000000000001,
0.90162500000000001, 0.81906250000000003, 0.72491249999999996,
0.72491249999999996, 0.72491249999999996, 0.67783749999999998,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.69140000000000001,
0.65618750000000003, 0.76700000000000002, 0.77780000000000005,
0.79475000000000007, 0.79475000000000007, 0.79475000000000007,
0.79475000000000007, 0.79475000000000007, 0.81169999999999998,
0.81409999999999993, 0.85064999999999991, 0.86546249999999991,
0.86546249999999991, 1, 1, 1, 1, 0.92130000000000001, 0.92130000000000001,
0.92130000000000001, 0.92130000000000001, 0.90162500000000001,
0.70137499999999997, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 1, 1, 1, 1, 1, 0.96065, 0.96065, 0.88195000000000001,
0.88195000000000001, 0.88195000000000001, 0.88195000000000001,
0.79552500000000004, 0.79552500000000004, 0.79552500000000004,
0.79552500000000004, 0.77198750000000005, 0.70137499999999997,
0.70137499999999997, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57829999999999993,
0.57829999999999993, 0.58309999999999995, 0.58789999999999998,
0.59030000000000005, 0.5927, 0.60553333333333337, 0.61836666666666673,
0.62880000000000003, 0.69140000000000001, 0.70100000000000007,
0.749, 0.77780000000000005, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.78156666666666674, 0.78533333333333333,
0.78910000000000002, 0.8041666666666667, 0.8041666666666667,
0.80793333333333328, 0.80793333333333328, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81596666666666662,
0.81596666666666662, 0.81809999999999994, 0.82023333333333326,
0.82023333333333326, 0.83967777777777775, 0.83967777777777775,
0.83967777777777775, 0.83967777777777775, 0.84406666666666663,
0.84406666666666663, 0.84845555555555552, 0.85284444444444441,
0.85284444444444441, 0.86162222222222218, 0.86162222222222218,
0.88041111111111103, 0.88041111111111103, 0.88041111111111103,
0.8992, 0.91359999999999997, 0.92799999999999994, 0.95679999999999998,
0.97119999999999995, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.94753333333333334,
0.94753333333333334, 0.93004444444444445, 0.87757777777777779,
0.75891111111111109, 0.75891111111111109, 0.75891111111111109,
0.73798888888888892, 0.69614444444444445, 0.69614444444444445,
0.67522222222222217, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.7298, 0.73940000000000006, 0.77780000000000005,
0.79286666666666672, 0.7966333333333333, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81809999999999994,
0.81809999999999994, 0.82450000000000001, 0.83315555555555554,
0.83967777777777775, 0.84406666666666663, 0.85723333333333329,
1, 1, 0.96502222222222223, 0.89506666666666668, 0.87757777777777779,
0.87757777777777779, 0.77983333333333338, 0.73798888888888892,
0.71706666666666663, 0.71706666666666663, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.81596666666666662, 0.81596666666666662, 0.81809999999999994,
0.81809999999999994, 0.82450000000000001, 0.84845555555555552,
0.85723333333333329, 0.92799999999999994, 0.97119999999999995,
1, 1, 1, 0.98251111111111111, 0.93004444444444445, 0.87757777777777779,
0.83916666666666673, 0.77983333333333338, 0.73798888888888892,
0.73798888888888892, 0.67522222222222217, 0.67522222222222217,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.65383999999999998,
0.65383999999999998, 0.65383999999999998, 0.65383999999999998,
0.66322999999999999, 0.66322999999999999, 0.68201000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.69140000000000001, 0.69140000000000001, 0.69140000000000001,
0.70004, 0.70004, 0.71732000000000007, 0.71732000000000007,
0.71732000000000007, 0.72596000000000005, 0.73460000000000003,
0.73460000000000003, 0.74324000000000001, 0.75187999999999999,
0.75187999999999999, 0.75187999999999999, 0.76052000000000008,
0.76052000000000008, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.79475000000000007, 0.79813999999999996,
0.79813999999999996, 0.80491999999999997, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81169999999999998, 0.81169999999999998,
0.81362000000000001, 0.81362000000000001, 0.81553999999999993,
0.81938, 0.82321999999999995, 0.83101000000000003, 0.83101000000000003,
0.83687999999999996, 0.84670000000000001, 0.85064999999999991,
0.8754599999999999, 0.93520000000000003, 0.96111999999999997,
0.96111999999999997, 0.96111999999999997, 0.96111999999999997,
0.96111999999999997, 0.97407999999999995, 0.97407999999999995,
0.98704000000000003, 0.98704000000000003, 1, 1, 1, 1, 1,
1, 0.96852000000000005, 0.96852000000000005, 0.96852000000000005,
0.95277999999999996, 0.95277999999999996, 0.93703999999999998,
0.93703999999999998, 0.93703999999999998, 0.92130000000000001,
0.90556000000000003, 0.88982000000000006, 0.88982000000000006,
0.85525000000000007, 0.85525000000000007, 0.76727999999999996,
0.74845000000000006, 0.72962000000000005, 0.71079000000000003,
0.67313000000000001, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.66322999999999999, 0.66322999999999999, 0.73460000000000003,
0.76052000000000008, 0.76916000000000007, 0.79136000000000006,
0.81169999999999998, 0.81553999999999993, 0.81553999999999993,
0.81745999999999996, 0.81745999999999996, 0.82321999999999995,
0.83687999999999996, 0.83687999999999996, 0.84670000000000001,
0.85854999999999992, 0.89237, 0.89237, 0.92223999999999995,
0.97407999999999995, 0.96852000000000005, 0.96852000000000005,
0.93703999999999998, 0.93703999999999998, 0.88982000000000006,
0.85525000000000007, 0.85525000000000007, 0.85525000000000007,
0.85525000000000007, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.79136000000000006,
0.81938, 0.82321999999999995, 0.84275, 0.84275, 0.84275,
0.84275, 0.84275, 0.85064999999999991, 0.85064999999999991,
0.85064999999999991, 0.85064999999999991, 0.89237, 0.93520000000000003,
0.98704000000000003, 1, 1, 1, 1, 1, 1, 1, 0.98426000000000002,
0.92130000000000001, 0.90556000000000003, 0.90556000000000003,
0.90556000000000003, 0.88982000000000006, 0.85525000000000007,
0.74845000000000006, 0.74845000000000006, 0.69196000000000002,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.57589999999999997, 0.62114545454545456, 0.63164545454545462,
0.64871818181818186, 0.6657909090909091, 0.6657909090909091,
0.68286363636363634, 0.69925454545454546, 0.74638181818181826,
0.74638181818181826, 0.76209090909090915, 0.76209090909090915,
0.7699454545454546, 0.77780000000000005, 0.77780000000000005,
0.77780000000000005, 0.79012727272727279, 0.80245454545454542,
0.81169999999999998, 0.81519090909090908, 0.82391818181818177,
0.82925454545454547, 0.88678181818181812, 0.94109090909090909,
0.9528727272727272, 0.97643636363636366, 0.98821818181818177,
0.9570727272727273, 0.91414545454545459, 0.72277272727272723,
0.6714181818181818, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.82042727272727267,
0.77412727272727277, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.79320909090909097, 0.79629090909090905,
0.80553636363636361, 0.91752727272727275, 0.92930909090909086,
0.97643636363636366, 0.98821818181818177, 1, 1, 0.97138181818181824,
0.86840909090909091, 0.83698181818181816, 0.77412727272727277,
0.73989090909090904, 0.73989090909090904, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.9528727272727272, 0.69140000000000001,
0.6986, 0.6986, 0.6986, 0.70579999999999998, 0.71300000000000008,
0.75619999999999998, 0.75619999999999998, 0.77780000000000005,
0.78344999999999998, 0.79192499999999999, 0.79757500000000003,
0.80604999999999993, 0.81169999999999998, 0.81169999999999998,
0.81489999999999996, 0.82129999999999992, 0.83268333333333333,
0.89621666666666666, 0.9244, 0.94599999999999995, 0.95679999999999998,
0.96065, 0.96065, 0.76414166666666672, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.67575000000000007, 0.67575000000000007, 0.67575000000000007,
0.85064999999999991, 0.86803333333333332, 0.97839999999999994,
0.98688333333333333, 0.82175833333333337, 0.80604999999999993,
0.81330000000000002, 0.82289999999999996, 0.82779166666666659,
0.9244, 1, 0.94753333333333334, 0.94753333333333334, 0.93441666666666667,
0.87937500000000002, 0.87937500000000002, 0.73275833333333329,
0.73275833333333329, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.66973076923076924, 0.68417692307692313, 0.71133846153846159,
0.78040769230769236, 0.78562307692307698, 0.79344615384615391,
0.81169999999999998, 0.81760769230769226, 0.8220384615384615,
0.91720769230769228, 0.91720769230769228, 0.93021538461538456,
0.9401846153846154, 0.9401846153846154, 0.98006153846153843,
0.98789230769230774, 0.9636769230769231, 0.9636769230769231,
0.86206153846153843, 0.69775384615384617, 0.65429999999999999,
0.65429999999999999, 0.66973076923076924, 0.66973076923076924,
0.66973076923076924, 0.79605384615384611, 0.83106923076923078,
0.98789230769230774, 0.86206153846153843, 0.68326923076923074,
0.8220384615384615, 0.96012307692307686, 0.98789230769230774,
0.88865384615384613, 0.86206153846153843, 0.65429999999999999,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.57589999999999997,
0.57589999999999997, 0.57589999999999997, 0.58052857142857139,
0.61145000000000005, 0.61145000000000005, 0.61145000000000005,
0.61970000000000003, 0.63619999999999999, 0.66457142857142859,
0.66457142857142859, 0.66457142857142859, 0.67127857142857139,
0.6779857142857143, 0.70991428571428572, 0.75928571428571434,
0.78506428571428577, 0.79717142857142853, 0.80443571428571425,
0.80443571428571425, 0.81444285714285714, 0.81581428571428571,
0.81718571428571429, 0.81718571428571429, 0.81718571428571429,
0.81718571428571429, 0.81718571428571429, 0.81855714285714287,
0.82129999999999992, 0.82129999999999992, 0.83387857142857136,
0.83807142857142858, 0.8464571428571428, 0.85064999999999991,
0.88688571428571428, 0.95371428571428574, 0.97222857142857144,
0.97222857142857144, 0.97222857142857144, 0.97222857142857144,
0.99074285714285715, 0.96627142857142856, 0.96627142857142856,
0.96627142857142856, 0.96627142857142856, 0.96627142857142856,
0.89660714285714282, 0.84722142857142857, 0.84722142857142857,
0.84722142857142857, 0.84722142857142857, 0.82252857142857139,
0.77314285714285713, 0.74845000000000006, 0.72155000000000002,
0.70809999999999995, 0.70809999999999995, 0.70809999999999995,
0.70809999999999995, 0.68120000000000003, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.78506428571428577, 0.78748571428571434, 0.78990714285714292,
0.78990714285714292, 0.80201428571428568, 0.80201428571428568,
0.81581428571428571, 0.81581428571428571, 0.81581428571428571,
0.82129999999999992, 0.87480714285714278, 0.87480714285714278,
0.96297142857142859, 0.96297142857142859, 0.96627142857142856,
0.96627142857142856, 0.92130000000000001, 0.84722142857142857,
0.70809999999999995, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.79717142857142853,
0.79959285714285711, 0.80201428571428568, 0.80201428571428568,
0.80685714285714283, 0.81169999999999998, 0.81169999999999998,
0.81169999999999998, 0.81444285714285714, 0.89896428571428566,
0.92312142857142854, 0.94445714285714288, 0.96297142857142859,
0.96297142857142859, 0.96297142857142859, 0.99074285714285715,
0.99074285714285715, 1, 1, 0.98875714285714289, 0.98875714285714289,
0.98875714285714289, 0.96627142857142856, 0.93254285714285712,
0.89660714285714282, 0.87191428571428575, 0.87191428571428575,
0.84722142857142857, 0.84722142857142857, 0.84722142857142857,
0.77314285714285713, 0.74845000000000006, 0.74845000000000006,
0.73499999999999999, 0.73499999999999999, 0.70809999999999995,
0.66774999999999995, 0.66774999999999995, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.65429999999999999,
0.65429999999999999, 0.65429999999999999, 0.57589999999999997,
0.73172000000000004, 0.75475999999999999, 0.78683999999999998,
0.88315333333333335, 0.97407999999999995, 0.81131333333333333,
0.70451333333333332, 0.65429999999999999, 0.78683999999999998,
0.83631333333333335, 0.72962000000000005, 0.82848666666666659,
0.94816, 0.98272000000000004, 0.96852000000000005, 0.96852000000000005,
0.96852000000000005, 0.96852000000000005, 0.72962000000000005,
0.71706666666666663, 0.69196000000000002, 0.6668533333333333,
0.78839375, 0.84471249999999998, 0.78839375, 0.96760000000000002,
0.980325, 0.88792499999999996, 0.73668124999999995, 0.57589999999999997,
0.78777058823529411, 0.87700588235294119, 0.89689999999999992,
0.95425882352941171, 0.95425882352941171, 0.95425882352941171,
0.96188235294117641, 0.97385882352941178, 0.94444705882352942,
0.91958333333333331, 0.91958333333333331, 0.81703333333333328,
0.73232631578947371, 0.78850526315789471, 0.9590736842105263,
0.65429999999999999, 0.95833999999999997, 0.95463999999999993,
0.72020499999999998, 0.57589999999999997, 0.79937619047619046,
0.79394285714285717, 0.80292857142857144, 0.65429999999999999,
0.65429999999999999, 0.77372272727272728, 0.81144090909090905,
0.77537391304347825, 0.8122869565217391, 0.65429999999999999,
0.81794642857142852, 0.80247000000000002), booking = c("internet",
"direct", "direct", "agency", "agency", "direct", "direct",
"internet", "agency", "direct", "agency", "internet", "internet",
"internet", "direct", "direct", "direct", "agency", "direct",
"agency", "agency", "agency", "agency", "agency", "direct",
"internet", "direct", "direct", "direct", "internet", "agency",
"direct", "agency", "internet", "internet", "direct", "internet",
"internet", "direct", "agency", "internet", "internet", "agency",
"internet", "agency", "internet", "agency", "agency", "direct",
"direct", "internet", "agency", "direct", "direct", "direct",
"direct", "direct", "internet", "direct", "direct", "direct",
"direct", "internet", "internet", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "agency",
"direct", "internet", "internet", "agency", "internet", "direct",
"direct", "internet", "direct", "internet", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"internet", "agency", "agency", "agency", "direct", "internet",
"direct", "direct", "internet", "direct", "internet", "direct",
"agency", "direct", "internet", "agency", "internet", "agency",
"direct", "direct", "agency", "agency", "agency", "agency",
"direct", "direct", "direct", "agency", "direct", "direct",
"agency", "agency", "agency", "agency", "direct", "agency",
"internet", "internet", "direct", "agency", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "agency", "internet",
"internet", "agency", "internet", "internet", "internet",
"internet", "internet", "internet", "internet", "internet",
"direct", "agency", "internet", "direct", "internet", "agency",
"agency", "agency", "agency", "internet", "internet", "agency",
"direct", "agency", "direct", "direct", "direct", "direct",
"agency", "direct", "direct", "direct", "agency", "internet",
"direct", "agency", "agency", "agency", "agency", "direct",
"direct", "direct", "direct", "direct", "direct", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "direct", "agency", "agency", "agency", "agency",
"agency", "agency", "direct", "agency", "agency", "direct",
"agency", "direct", "direct", "direct", "direct", "direct",
"direct", "internet", "agency", "direct", "direct", "direct",
"direct", "agency", "agency", "agency", "direct", "agency",
"internet", "internet", "agency", "agency", "agency", "agency",
"internet", "internet", "internet", "agency", "direct", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "direct", "direct", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "direct",
"direct", "internet", "internet", "agency", "internet", "internet",
"agency", "agency", "agency", "agency", "agency", "direct",
"agency", "internet", "agency", "internet", "internet", "internet",
"agency", "direct", "agency", "direct", "direct", "agency",
"internet", "agency", "internet", "internet", "internet",
"internet", "internet", "internet", "direct", "internet",
"internet", "internet", "internet", "internet", "internet",
"agency", "internet", "agency", "internet", "internet", "internet",
"internet", "internet", "internet", "internet", "internet",
"internet", "agency", "internet", "internet", "internet",
"internet", "direct", "internet", "internet", "internet",
"agency", "internet", "internet", "internet", "internet",
"internet", "internet", "internet", "internet", "direct",
"internet", "internet", "internet", "direct", "internet",
"internet", "internet", "internet", "internet", "internet",
"internet", "agency", "internet", "direct", "direct", "internet",
"direct", "internet", "direct", "internet", "direct", "internet",
"direct", "direct", "direct", "agency", "internet", "agency",
"agency", "agency", "internet", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "internet", "internet",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "direct", "direct", "agency", "agency", "agency",
"direct", "agency", "agency", "direct", "agency", "direct",
"direct", "direct", "direct", "agency", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "agency", "agency", "direct", "direct",
"direct", "internet", "direct", "agency", "internet", "agency",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "direct", "direct", "internet", "internet", "internet",
"agency", "agency", "direct", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "internet", "direct",
"agency", "direct", "agency", "internet", "internet", "agency",
"agency", "agency", "internet", "internet", "agency", "internet",
"agency", "agency", "agency", "internet", "agency", "internet",
"agency", "internet", "agency", "internet", "internet", "agency",
"internet", "agency", "internet", "internet", "internet",
"agency", "direct", "agency", "internet", "agency", "internet",
"internet", "agency", "agency", "agency", "agency", "agency",
"internet", "internet", "agency", "internet", "internet",
"agency", "agency", "agency", "agency", "agency", "direct",
"direct", "internet", "agency", "internet", "direct", "direct",
"agency", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "internet", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "internet", "internet", "internet", "internet",
"internet", "internet", "agency", "internet", "agency", "agency",
"agency", "agency", "internet", "internet", "internet", "direct",
"direct", "internet", "internet", "internet", "internet",
"internet", "direct", "direct", "agency", "direct", "direct",
"direct", "direct", "agency", "agency", "internet", "agency",
"direct", "direct", "direct", "agency", "agency", "agency",
"agency", "direct", "agency", "internet", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "agency", "internet",
"direct", "internet", "internet", "agency", "agency", "agency",
"agency", "agency", "direct", "agency", "agency", "agency",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "agency", "direct", "direct", "internet",
"agency", "agency", "internet", "internet", "internet", "direct",
"internet", "direct", "agency", "agency", "internet", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "direct",
"direct", "direct", "direct", "direct", "direct", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "internet", "internet", "internet", "internet",
"internet", "internet", "internet", "internet", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"direct", "direct", "direct", "direct", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "direct",
"direct", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"internet", "internet", "internet", "agency", "internet",
"agency", "internet", "agency", "agency", "agency", "internet",
"agency", "agency", "internet", "agency", "internet", "agency",
"agency", "agency", "internet", "internet", "internet", "agency",
"internet", "internet", "internet", "internet", "internet",
"internet", "internet", "internet", "internet", "internet",
"internet", "internet", "internet", "internet", "agency",
"internet", "internet", "agency", "agency", "agency", "agency",
"agency", "internet", "agency", "agency", "internet", "internet",
"agency", "internet", "agency", "agency", "internet", "agency",
"internet", "internet", "internet", "internet", "internet",
"internet", "internet", "internet", "direct", "internet",
"internet", "internet", "internet", "agency", "internet",
"internet", "direct", "agency", "internet", "agency", "direct",
"internet", "internet", "internet", "agency", "internet",
"internet", "agency", "internet", "internet", "internet",
"internet", "agency", "internet", "agency", "agency", "agency",
"direct", "internet", "internet", "internet", "agency", "internet",
"internet", "internet", "agency", "internet", "agency", "agency",
"agency", "internet", "agency", "internet", "agency", "agency",
"agency", "agency", "internet", "internet", "agency", "internet",
"internet", "agency", "internet", "agency", "agency", "internet",
"internet", "agency", "agency", "agency", "internet", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"internet", "internet", "internet", "internet", "internet",
"internet", "agency", "agency", "agency", "agency", "agency",
"internet", "agency", "internet", "internet", "internet",
"direct", "agency", "agency", "internet", "internet", "agency",
"internet", "internet", "agency", "agency", "internet", "agency",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "direct", "direct",
"direct", "agency", "direct", "agency", "agency", "agency",
"internet", "internet", "agency", "agency", "agency", "internet",
"internet", "agency", "agency", "agency", "direct", "direct",
"direct", "agency", "agency", "internet", "agency", "agency",
"internet", "agency", "agency", "agency", "agency", "agency",
"agency", "internet", "internet", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "agency", "agency", "internet", "agency", "agency",
"agency", "agency", "agency", "internet", "internet", "agency",
"direct", "agency", "agency", "internet", "internet", "internet",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "internet", "agency", "agency", "agency", "internet",
"internet", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "direct", "internet", "agency",
"direct", "direct", "agency", "agency", "direct", "agency",
"internet", "agency", "direct", "internet", "agency", "direct",
"agency", "internet", "direct", "agency", "agency", "agency",
"agency", "agency", "agency", "internet", "agency", "agency",
"agency", "agency", "agency", "agency", "direct", "internet",
"agency", "agency", "agency", "internet", "agency", "agency",
"internet", "agency", "agency", "agency", "agency", "direct",
"agency", "direct", "direct", "agency", "agency", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "agency", "agency", "direct", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"direct", "direct", "direct", "direct", "direct", "direct",
"agency", "direct", "internet", "agency", "agency", "agency",
"agency", "agency", "direct", "direct", "direct", "direct",
"direct", "agency", "agency", "agency", "internet", "direct",
"direct", "direct", "direct", "direct", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "direct",
"direct", "direct", "agency", "internet", "agency", "agency",
"internet", "internet", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "internet",
"internet", "internet", "agency", "internet", "agency", "agency",
"agency", "agency", "agency", "agency", "internet", "internet",
"agency", "internet", "internet", "agency", "agency", "agency",
"internet", "internet", "agency", "agency", "agency", "agency",
"direct", "agency", "internet", "internet", "internet", "internet",
"internet", "internet", "internet", "internet", "internet",
"direct", "internet", "internet", "internet", "internet",
"agency", "internet", "agency", "internet", "internet", "agency",
"agency", "internet", "agency", "internet", "agency", "agency",
"internet", "direct", "internet", "internet", "internet",
"direct", "agency", "agency", "agency", "agency", "agency",
"internet", "agency", "internet", "internet", "internet",
"agency", "internet", "internet", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "internet", "internet",
"agency", "internet", "agency", "agency", "agency", "agency",
"internet", "internet", "direct", "agency", "agency", "agency",
"direct", "agency", "agency", "agency", "agency", "agency",
"internet", "agency", "internet", "agency", "agency", "agency",
"direct", "agency", "agency", "agency", "internet", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "internet", "agency", "agency", "internet", "agency",
"agency", "internet", "agency", "agency", "agency", "agency",
"agency", "agency", "direct", "internet", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "agency", "direct", "agency", "agency",
"internet", "agency", "agency", "agency", "agency", "agency",
"direct", "direct", "agency", "agency", "agency", "direct",
"internet", "internet", "internet", "agency", "agency", "direct",
"agency", "internet", "internet", "agency", "agency", "agency",
"agency", "internet", "agency", "internet", "agency", "agency",
"agency", "internet", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "internet", "internet", "internet", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"direct", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "internet", "agency", "agency",
"agency", "direct", "internet", "internet", "agency", "internet",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "direct", "direct", "direct", "agency", "agency",
"direct", "agency", "agency", "agency", "direct", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "direct", "direct", "agency", "internet", "agency",
"internet", "internet", "internet", "internet", "agency",
"direct", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "direct", "direct", "internet", "internet", "internet",
"internet", "internet", "agency", "internet", "agency", "agency",
"agency", "internet", "internet", "agency", "internet", "internet",
"agency", "agency", "agency", "agency", "agency", "internet",
"direct", "agency", "agency", "agency", "agency", "agency",
"agency", "internet", "agency", "agency", "internet", "internet",
"internet", "internet", "direct", "internet", "internet",
"agency", "agency", "agency", "agency", "agency", "internet",
"internet", "agency", "internet", "internet", "agency", "agency",
"internet", "internet", "agency", "internet", "agency", "internet",
"internet", "internet", "agency", "internet", "internet",
"internet", "agency", "agency", "internet", "internet", "internet",
"internet", "agency", "agency", "internet", "internet", "agency",
"agency", "agency", "agency", "agency", "agency", "direct",
"agency", "internet", "agency", "internet", "internet", "agency",
"agency", "agency", "agency", "agency", "agency", "internet",
"internet", "agency", "agency", "agency", "internet", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "internet", "internet", "internet",
"agency", "agency", "agency", "internet", "internet", "internet",
"internet", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "internet",
"internet", "agency", "internet", "direct", "direct", "direct",
"direct", "direct", "direct", "agency", "internet", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"internet", "direct", "direct", "direct", "direct", "direct",
"direct", "agency", "direct", "agency", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "internet", "agency", "agency", "agency", "agency",
"internet", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "internet", "agency", "agency",
"internet", "agency", "agency", "agency", "agency", "direct",
"agency", "agency", "internet", "internet", "agency", "agency",
"internet", "agency", "internet", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "direct", "internet", "agency", "internet", "agency",
"direct", "agency", "direct", "direct", "direct", "direct",
"direct", "direct", "agency", "direct", "internet", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "direct", "agency", "agency", "agency",
"agency", "agency", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "direct", "agency",
"internet", "agency", "agency", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "agency", "agency",
"internet", "internet", "agency", "agency", "internet", "internet",
"internet", "internet", "internet", "internet", "internet",
"agency", "internet", "agency", "agency", "internet", "agency",
"internet", "internet", "agency", "internet", "internet",
"internet", "internet", "internet", "internet", "agency",
"agency", "internet", "internet", "agency", "agency", "agency",
"agency", "agency", "internet", "agency", "agency", "internet",
"agency", "internet", "agency", "agency", "agency", "internet",
"agency", "internet", "agency", "internet", "internet", "internet",
"internet", "internet", "agency", "internet", "agency", "agency",
"agency", "agency", "agency", "internet", "agency", "agency",
"agency", "agency", "internet", "internet", "agency", "internet",
"internet", "agency", "agency", "agency", "internet", "agency",
"agency", "internet", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "agency", "internet", "agency", "agency", "agency",
"direct", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "direct", "direct",
"direct", "agency", "direct", "direct", "agency", "direct",
"direct", "internet", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "direct", "agency", "direct", "direct",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "direct", "agency", "agency", "agency", "agency",
"agency", "internet", "agency", "agency", "agency", "internet",
"internet", "internet", "agency", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "internet", "internet",
"agency", "internet", "internet", "agency", "internet", "internet",
"internet", "agency", "agency", "agency", "direct", "internet",
"agency", "agency", "agency", "agency", "internet", "agency",
"agency", "agency", "agency", "internet", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"internet", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "direct", "direct",
"direct", "direct", "direct", "direct", "agency", "agency",
"agency", "agency", "agency", "direct", "agency", "agency",
"agency", "agency", "internet", "agency", "internet", "agency",
"agency", "agency", "internet", "agency", "internet", "internet",
"internet", "internet", "internet", "internet", "agency",
"internet", "agency", "internet", "agency", "internet", "agency",
"internet", "internet", "agency", "internet", "agency", "internet",
"agency", "agency", "internet", "internet", "agency", "agency",
"agency", "internet", "agency", "agency", "internet", "agency",
"agency", "internet", "internet", "internet", "internet",
"internet", "agency", "agency", "internet", "internet", "agency",
"internet", "agency", "agency", "agency", "agency", "agency",
"internet", "internet", "agency", "agency", "agency", "agency",
"agency", "agency", "internet", "agency", "agency", "agency",
"agency", "internet", "internet", "agency", "agency", "internet",
"internet", "internet", "direct", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "internet", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "internet", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "internet", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "internet", "internet",
"agency", "agency", "internet", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "internet", "internet", "agency", "agency", "agency",
"internet", "agency", "agency", "agency", "agency", "agency",
"agency", "internet", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"internet", "agency", "agency", "internet", "internet", "internet",
"internet", "internet", "internet", "agency", "agency", "internet",
"internet", "agency", "internet", "internet", "agency", "agency",
"agency", "internet", "agency", "agency", "agency", "internet",
"agency", "internet", "agency", "agency", "internet", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"internet", "internet", "agency", "agency", "agency", "internet",
"agency", "agency", "internet", "agency", "agency", "agency",
"internet", "agency", "direct", "direct", "internet", "agency",
"direct", "agency", "internet", "internet", "agency", "agency",
"internet", "agency", "agency", "agency", "agency", "agency",
"internet", "agency", "agency", "direct", "agency", "agency",
"agency", "agency", "agency", "agency", "internet", "agency",
"agency", "internet", "agency", "internet", "agency", "internet",
"agency", "agency", "agency", "internet", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"internet", "agency", "agency", "agency", "agency", "agency",
"internet", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "internet",
"internet", "internet", "agency", "internet", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "internet", "agency", "agency", "agency",
"internet", "agency", "internet", "internet", "agency", "agency",
"agency", "agency", "agency", "internet", "agency", "internet",
"agency", "agency", "agency", "agency", "internet", "internet",
"agency", "agency", "agency", "agency", "agency", "internet",
"internet", "agency", "internet", "agency", "internet", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"internet", "internet", "agency", "agency", "agency", "agency",
"agency", "agency", "internet", "agency", "agency", "agency",
"agency", "agency", "agency", "internet", "internet", "internet",
"agency", "agency", "internet", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "internet", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"internet", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "internet", "agency", "agency", "agency",
"agency", "agency", "agency", "internet", "agency", "agency",
"agency", "agency", "internet", "agency", "agency", "agency",
"agency", "agency", "agency", "internet", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "internet", "agency",
"agency", "agency", "agency", "internet", "agency", "agency",
"agency", "internet", "internet", "agency", "agency", "agency",
"agency", "internet", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "internet", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "internet", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "internet", "internet", "agency",
"agency", "agency", "agency", "agency", "internet", "agency",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "internet", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "internet", "agency", "agency", "agency", "agency",
"agency", "internet", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "internet", "agency", "agency",
"internet", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"internet", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "direct", "direct",
"direct", "direct", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "direct", "direct",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "internet", "agency", "internet", "internet", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "internet", "internet", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "internet", "agency", "agency", "agency", "agency",
"internet", "agency", "agency", "agency", "internet", "internet",
"internet", "internet", "internet", "agency", "internet",
"internet", "agency", "internet", "agency", "agency", "agency",
"agency", "agency", "internet", "internet", "internet", "agency",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "agency", "internet", "internet", "internet", "agency",
"agency", "agency", "internet", "internet", "agency", "agency",
"internet", "internet", "agency", "internet", "internet",
"agency", "agency", "agency", "agency", "internet", "internet",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "internet", "internet", "internet", "internet",
"internet", "internet", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "internet", "internet", "internet", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"internet", "internet", "agency", "internet", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "agency", "agency", "internet", "agency", "agency",
"agency", "agency", "agency", "agency", "internet", "agency",
"internet", "internet", "internet", "direct", "agency", "internet",
"agency", "internet", "internet", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "internet",
"internet", "agency", "agency", "agency", "agency", "internet",
"internet", "agency", "agency", "agency", "internet", "direct",
"internet", "agency", "agency", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "internet", "agency", "internet", "agency", "internet",
"internet", "agency", "agency", "agency", "internet", "agency",
"internet", "agency", "agency", "internet", "agency", "internet",
"internet", "internet", "agency", "internet", "internet",
"internet", "agency", "internet", "agency", "internet", "agency",
"internet", "agency", "agency", "internet", "agency", "agency",
"internet", "internet", "internet", "agency", "internet",
"internet", "agency", "agency", "internet", "agency", "agency",
"internet", "internet", "agency", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "agency", "agency", "internet", "agency", "agency",
"agency", "internet", "agency", "agency", "internet", "internet",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "agency", "agency", "internet", "direct", "agency",
"agency", "agency", "agency", "agency", "agency", "internet",
"internet", "agency", "agency", "agency", "agency", "agency",
"agency", "internet", "agency", "agency", "agency", "agency",
"agency", "agency", "internet", "agency", "internet", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "internet", "agency", "agency",
"internet", "agency", "agency", "internet", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "internet", "agency", "agency", "agency",
"internet", "internet", "agency", "internet", "internet",
"agency", "agency", "direct", "internet", "agency", "internet",
"internet", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "internet", "internet", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "direct", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "internet", "agency", "agency", "agency", "agency",
"internet", "agency", "agency", "internet", "agency", "agency",
"internet", "agency", "agency", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "internet", "agency", "agency", "agency", "internet",
"internet", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "direct", "direct", "direct", "direct", "direct",
"direct", "agency", "agency", "internet", "agency", "agency",
"internet", "internet", "agency", "agency", "internet", "agency",
"internet", "internet", "agency", "agency", "agency", "internet",
"internet", "internet", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"internet", "internet", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "direct", "direct",
"internet", "internet", "internet", "agency", "agency", "agency",
"agency", "agency", "internet", "internet", "internet", "internet",
"agency", "agency", "internet", "internet", "agency", "agency",
"agency", "agency", "internet", "internet", "internet", "internet",
"agency", "internet", "internet", "agency", "internet", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"direct", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "agency", "agency", "internet", "agency", "internet",
"internet", "agency", "agency", "agency", "agency", "agency",
"internet", "agency", "internet", "internet", "internet",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "internet", "internet",
"agency", "internet", "agency", "agency", "internet", "internet",
"internet", "internet", "agency", "agency", "agency", "agency",
"agency", "internet", "internet", "agency", "agency", "internet",
"agency", "agency", "internet", "agency", "agency", "agency",
"internet", "internet", "internet", "internet", "internet",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "internet", "internet", "agency", "agency",
"agency", "agency", "internet", "internet", "agency", "internet",
"agency", "agency", "agency", "agency", "internet", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "internet", "agency", "agency", "internet",
"agency", "agency", "agency", "internet", "internet", "internet",
"agency", "internet", "agency", "internet", "internet", "internet",
"internet", "internet", "agency", "agency", "agency", "internet",
"agency", "agency", "internet", "internet", "agency", "internet",
"internet", "internet", "internet", "agency", "agency", "agency",
"internet", "agency", "internet", "internet", "internet",
"internet", "agency", "agency", "agency", "internet", "internet",
"internet", "agency", "agency", "internet", "agency", "agency",
"agency", "agency", "internet", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "internet", "agency",
"agency", "internet", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "internet", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "internet", "agency", "agency",
"agency", "agency", "agency", "agency", "internet", "agency",
"agency", "agency", "agency", "agency", "direct", "agency",
"internet", "agency", "agency", "agency", "agency", "internet",
"internet", "internet", "internet", "agency", "internet",
"internet", "internet", "internet", "internet", "agency",
"agency", "agency", "agency", "internet", "internet", "agency",
"agency", "agency", "internet", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"internet", "agency", "agency", "agency", "internet", "internet",
"internet", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "internet", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "internet", "agency", "agency", "direct",
"direct", "internet", "agency", "agency", "agency", "internet",
"internet", "agency", "internet", "internet", "internet",
"agency", "agency", "agency", "agency", "agency", "agency",
"internet", "agency", "internet", "internet", "agency", "internet",
"agency", "internet", "internet", "agency", "internet", "agency",
"agency", "agency", "agency", "internet", "internet", "internet",
"agency", "internet", "internet", "agency", "agency", "agency",
"internet", "internet", "internet", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "internet", "internet",
"internet", "internet", "internet", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"internet", "internet", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "internet", "agency",
"direct", "direct", "internet", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "internet", "internet",
"agency", "agency", "agency", "agency", "internet", "agency",
"internet", "internet", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "internet", "agency", "internet", "internet", "internet",
"internet", "agency", "agency", "agency", "agency", "internet",
"agency", "internet", "internet", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "agency", "direct",
"internet", "agency", "agency", "agency", "agency", "agency",
"internet", "internet", "direct", "agency", "internet", "agency",
"agency", "internet", "agency", "internet", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "internet", "agency", "internet", "internet",
"agency", "agency", "internet", "agency", "agency", "direct",
"direct", "direct", "internet", "agency", "internet", "agency",
"internet", "internet", "internet", "internet", "agency",
"direct", "agency", "agency", "agency", "internet", "agency",
"agency", "agency", "agency", "agency", "agency", "internet",
"internet", "agency", "agency", "agency", "agency", "internet",
"agency", "agency", "internet", "agency", "internet", "agency",
"agency", "agency", "agency", "agency", "internet", "internet",
"internet", "internet", "agency", "agency", "agency", "agency",
"direct", "direct", "direct", "internet", "internet", "internet",
"internet", "agency", "internet", "internet", "agency", "agency",
"internet", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "internet", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "internet",
"agency", "agency", "agency", "agency", "internet", "internet",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "internet", "internet", "internet", "agency", "agency",
"agency", "agency", "agency", "agency", "internet", "internet",
"internet", "internet", "agency", "agency", "agency", "agency",
"agency", "internet", "internet", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "internet", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "internet",
"internet", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "internet", "agency", "agency", "agency",
"internet", "internet", "internet", "internet", "agency",
"agency", "internet", "internet", "agency", "agency", "internet",
"internet", "internet", "agency", "internet", "agency", "agency",
"agency", "agency", "agency", "internet", "agency", "internet",
"agency", "internet", "internet", "internet", "agency", "internet",
"internet", "agency", "agency", "internet", "agency", "internet",
"agency", "internet", "internet", "agency", "internet", "agency",
"internet", "internet", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"agency", "agency", "agency", "agency", "agency", "agency",
"internet", "agency", "internet", "agency", "agency", "agency",
"agency", "agency", "agency", "internet", "internet", "agency",
"internet", "internet", "internet", "internet", "internet",
"internet", "internet", "internet", "agency", "internet",
"internet", "internet", "internet", "internet", "agency",
"internet", "agency", "internet", "agency", "agency", "internet",
"internet", "internet", "internet", "internet", "agency",
"direct", "internet", "agency", "agency", "agency", "agency",
"internet", "internet", "internet", "internet", "internet",
"internet", "internet", "agency", "internet", "internet",
"internet", "internet", "agency", "agency", "agency", "internet",
"internet", "agency", "internet", "internet", "internet",
"agency", "internet", "agency", "internet", "agency"), roomtype = c("DC",
"DC", "DC", "DC", "DC", "NT", "ND", "S", "DS", "DC", "DC",
"DC", "DC", "DC", "DS", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DEX", "DEX", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "ND", "DCM",
"DC", "DC", "DC", "DEX", "DC", "FC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DS", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DEX", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DS", "DS", "DS", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "FC", "DC", "DC", "DC", "FC", "DC", "DC", "DC",
"DEX", "DC", "DC", "DC", "DC", "DS", "NSU", "DC", "DC", "DC",
"DC", "DEX", "DC", "DC", "DC", "DC", "DC", "DS", "DC", "FC",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DEXF", "DC", "DC", "FC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DEX", "DC", "DS", "DEXF", "DS", "DC",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DEXF", "DC",
"DC", "ND", "ND", "DC", "ND", "DC", "DC", "DEXF", "DC", "DC",
"DC", "DS", "DC", "DEX", "DC", "DC", "DC", "DC", "NSU", "DC",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "NSU", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DCM", "DC",
"DC", "DC", "DC", "DEXF", "DC", "DS", "DC", "DC", "S", "S",
"DC", "DC", "S", "DC", "DC", "DC", "DC", "DC", "DC", "S",
"DC", "DC", "DC", "S", "S", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "S", "DC", "DC", "S", "S", "S", "S", "S",
"S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S",
"S", "DEXF", "S", "DC", "S", "S", "DC", "DEXF", "DC", "NSU",
"DC", "S", "S", "S", "DEXF", "DC", "S", "DC", "DC", "DC",
"S", "DC", "DC", "DC", "S", "DC", "S", "S", "DC", "S", "S",
"S", "DEXF", "DEXF", "DC", "S", "DC", "S", "S", "DC", "DC",
"DC", "DC", "S", "DC", "S", "DEXF", "S", "DEXF", "DC", "S",
"S", "S", "DC", "DC", "S", "S", "S", "DC", "DC", "DC", "S",
"DC", "DC", "DC", "DC", "S", "S", "DC", "DC", "NSU", "S",
"DC", "S", "S", "DC", "S", "DC", "S", "DC", "DC", "NSU",
"NSU", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DEXF", "DC", "DC", "DC", "DC", "DC", "S", "NSU", "S", "S",
"DC", "DEXF", "DC", "DC", "S", "DC", "S", "S", "S", "S",
"S", "S", "DC", "DC", "S", "DC", "S", "S", "S", "S", "S",
"DC", "S", "S", "S", "S", "S", "DC", "DC", "DC", "S", "S",
"DC", "FM", "DC", "NSU", "NSU", "NSU", "ND", "NT", "ND",
"ND", "NSU", "DC", "DC", "DC", "NT", "NT", "NSU", "DC", "FC",
"DS", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "NT",
"NSU", "ND", "DEX", "DEX", "DEX", "DC", "DC", "DEX", "DEX",
"DC", "NSU", "DEX", "DC", "DS", "DC", "DC", "DC", "DC", "DC",
"FC", "FC", "DC", "DC", "DC", "FC", "DC", "DS", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "FC", "DC", "DC", "ND", "DC",
"ND", "DC", "DC", "DC", "DC", "ND", "DC", "DC", "ND", "DC",
"DEX", "DC", "ND", "FC", "ND", "DC", "DC", "DC", "DC", "DC",
"DC", "ND", "DC", "DC", "NSU", "NSU", "DC", "DC", "DC", "DC",
"DCM", "DC", "DC", "DEX", "DC", "DC", "FC", "DC", "DC", "DC",
"FC", "DC", "DC", "NSU", "NTH", "DC", "DC", "DC", "FC", "DC",
"DC", "FC", "FC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "FC", "DS", "DC", "DC", "NDH", "NDH", "DC", "DC", "DC",
"DC", "DC", "NDH", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "NDH", "NSU", "NDH", "NDH",
"NDH", "NT", "DC", "DC", "DEX", "DS", "NSU", "NSU", "DC",
"DC", "FC", "NDH", "DC", "DS", "S", "DC", "S", "DC", "DC",
"DC", "S", "DC", "S", "S", "DC", "DC", "DC", "DC", "S", "S",
"S", "S", "DC", "S", "S", "DC", "S", "S", "S", "NSU", "ND",
"NSU", "DC", "DC", "DC", "DS", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "S", "DC", "DC", "DC", "FC",
"DC", "DC", "FC", "DC", "DEXF", "DC", "DCM", "DC", "DEX",
"DC", "DC", "DC", "DC", "DC", "DC", "DS", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DS", "DEXF", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DEXF", "DEX", "DC", "DC", "DEX",
"DC", "DS", "DC", "DC", "ND", "DC", "DC", "DC", "DC", "DC",
"DC", "DEXF", "DC", "DC", "DC", "DC", "DS", "DS", "DC", "DC",
"FC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DEXF", "DC",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "ND", "FC", "DC", "NT", "ND", "ND", "ND", "DC",
"DC", "DC", "DEX", "DC", "S", "DC", "DC", "DEX", "DC", "DC",
"DC", "DC", "DC", "DEX", "DEX", "DEX", "DEX", "DEX", "DEX",
"DEX", "DC", "DEX", "DEX", "DEX", "DEX", "DEX", "DCM", "DC",
"DC", "DEX", "DC", "DEX", "DC", "DC", "ND", "ND", "DC", "DC",
"DC", "DEX", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "S", "S", "S", "S", "S", "DC", "DC", "S",
"S", "DC", "S", "S", "S", "DC", "S", "S", "S", "S", "S",
"S", "S", "S", "S", "S", "S", "S", "DC", "S", "S", "S", "S",
"S", "S", "DC", "DC", "S", "DC", "DC", "DC", "DC", "DC",
"DC", "S", "DC", "DC", "S", "DC", "DC", "S", "DC", "S", "ND",
"ND", "S", "DEX", "ND", "NT", "DC", "DC", "S", "S", "DC",
"DC", "S", "S", "DC", "DC", "DC", "DC", "DC", "DC", "S",
"S", "DC", "DC", "DC", "DC", "S", "DC", "DC", "S", "S", "DC",
"S", "DC", "DC", "S", "S", "S", "S", "S", "DC", "S", "DC",
"S", "DC", "S", "DC", "DC", "S", "S", "DC", "DC", "DC", "DC",
"DC", "S", "DC", "S", "DC", "DC", "S", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "S", "S", "DC", "DC", "S", "DC",
"DC", "S", "DC", "DC", "S", "DC", "DC", "DC", "DC", "S",
"S", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "FM", "DC", "S", "DC", "DC", "DC", "DC",
"DC", "S", "S", "S", "DEX", "S", "S", "S", "ND", "S", "S",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "S", "DC", "DC",
"S", "DC", "S", "S", "DC", "S", "DC", "S", "S", "S", "S",
"DC", "DC", "DC", "DC", "S", "DC", "DC", "S", "DC", "DC",
"S", "DC", "S", "DC", "DC", "DC", "DC", "S", "DC", "S", "DC",
"DC", "DC", "S", "DC", "DC", "DC", "DC", "DC", "S", "S",
"DC", "DC", "S", "S", "DC", "S", "DC", "DC", "DC", "S", "S",
"NT", "S", "S", "DC", "DC", "S", "S", "DC", "DC", "DC", "S",
"S", "DC", "DC", "DC", "DC", "DC", "S", "DC", "DC", "DC",
"DC", "DC", "S", "DC", "DC", "S", "DC", "S", "DC", "DC",
"DC", "S", "DC", "S", "DC", "DC", "DC", "DC", "DC", "S",
"S", "DC", "S", "DC", "DC", "DC", "S", "S", "DC", "S", "DC",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "S", "S",
"S", "DC", "DC", "DC", "S", "DC", "DC", "DC", "DC", "DC",
"S", "S", "DC", "DC", "DC", "DC", "S", "DC", "DC", "S", "DC",
"DC", "DC", "S", "DC", "DC", "DC", "DC", "S", "DC", "DC",
"S", "S", "DC", "S", "DC", "S", "S", "DC", "DC", "S", "S",
"S", "S", "S", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "S", "DC", "DC", "DC", "DC", "S", "S",
"DC", "S", "DC", "DC", "DC", "S", "S", "DC", "S", "NSU",
"NT", "NT", "NSU", "DC", "DC", "DC", "DC", "NSU", "DEXF",
"NSU", "ND", "DC", "DC", "DC", "NSU", "DEXF", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "DC", "FC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "DS", "DEX", "FC", "DC", "DEX",
"DEX", "DC", "DC", "DC", "NDH", "DS", "DC", "DC", "NTH",
"ND", "FC", "NDH", "DC", "NDH", "NTH", "NT", "NTH", "DC",
"DC", "DC", "DC", "DC", "FC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "ND", "FC", "DEX", "DC", "DC", "DC", "DC",
"FC", "DEX", "DC", "FC", "ND", "DC", "DC", "DC", "NSU", "DC",
"DC", "DC", "DC", "DC", "DC", "NSU", "DCM", "DC", "DEXF",
"DC", "DC", "NSU", "DC", "DC", "DC", "NSU", "NSUH", "DC",
"NDH", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"NDH", "ND", "ND", "DC", "DC", "NDH", "DC", "DC", "NSU",
"NDH", "DC", "DC", "DEX", "S", "DC", "S", "DC", "DC", "DC",
"DC", "DC", "S", "DC", "DC", "S", "S", "DC", "DC", "S", "S",
"S", "ND", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DEX", "FC", "DS", "DC", "DC", "DC", "DC", "DC", "S", "DC",
"DC", "S", "ND", "DC", "DEXF", "DC", "DC", "FC", "NSU", "FC",
"DC", "DCM", "NSU", "NSU", "DS", "FC", "DC", "FC", "FC",
"ND", "DC", "DC", "S", "DC", "S", "DEX", "DC", "NSU", "DC",
"DS", "DC", "DC", "DC", "DC", "DCM", "DC", "DC", "DCM", "NSU",
"DEXF", "DEX", "DEX", "DC", "DEXF", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DS", "DC",
"DC", "FC", "FC", "DC", "DC", "DEXF", "FC", "FC", "FC", "DS",
"DEXF", "DC", "DC", "DC", "DC", "SUM", "FC", "DC", "S", "FC",
"DC", "FC", "FC", "DC", "DC", "DC", "DS", "FC", "DC", "DC",
"DC", "DS", "DC", "DEX", "DEX", "DC", "DC", "DC", "DS", "DS",
"DS", "DC", "DC", "DC", "DC", "DEXF", "FC", "DEX", "ND",
"DEXF", "DC", "DC", "ND", "DC", "DS", "DCM", "NSU", "FC",
"DC", "DC", "DC", "FC", "DC", "DC", "DC", "DC", "DEXF", "DC",
"DC", "DC", "DEX", "DEX", "DC", "DEX", "DEX", "DEX", "DS",
"DEXF", "DS", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "ND", "DC",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "ND", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "DEX", "DC", "NSU", "DC", "DC",
"DC", "DC", "S", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "ND", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DCM", "DC", "S", "S", "DC", "DC",
"DC", "S", "S", "S", "DC", "S", "S", "DC", "S", "DC", "ND",
"ND", "ND", "ND", "DEXF", "DEX", "S", "S", "DEX", "S", "DEX",
"S", "NT", "DEX", "S", "S", "NT", "DC", "S", "DEX", "S",
"ND", "S", "DEX", "DEX", "DEXF", "DC", "S", "S", "S", "DC",
"DC", "S", "S", "S", "S", "DC", "DC", "S", "DC", "S", "S",
"S", "DC", "S", "DC", "DC", "S", "S", "DC", "DC", "DC", "DC",
"DEX", "S", "FC", "DEX", "DEX", "S", "S", "DEX", "S", "S",
"S", "S", "S", "ND", "DC", "S", "S", "S", "ND", "ND", "S",
"ND", "ND", "DC", "DEX", "DC", "DEX", "NSU", "DC", "FC",
"NSU", "DC", "ND", "ND", "ND", "NT", "ND", "DS", "DC", "FC",
"DEX", "DC", "DC", "DC", "DEXF", "NSU", "DEXF", "DC", "NSU",
"DC", "DCM", "NSU", "DC", "NSU", "FC", "FS", "DC", "DC",
"NDH", "DC", "DC", "FC", "DC", "NDH", "DS", "NDH", "DC",
"DEXF", "DS", "DC", "DC", "DC", "DEXF", "NSU", "NSU", "DC",
"DC", "DC", "DC", "FC", "DC", "DC", "NDH", "NSU", "NDH",
"DC", "NSU", "DEXF", "NDH", "DC", "NSU", "DC", "DC", "NDH",
"FC", "DC", "DC", "FC", "DC", "DC", "DC", "NDH", "DCM", "NDH",
"NDH", "ND", "DC", "NSU", "ND", "ND", "NDH", "DC", "DC",
"DCM", "DCM", "DCM", "ND", "DC", "ND", "NDH", "FC", "DEXF",
"NDH", "DC", "DC", "DC", "DC", "DC", "FC", "DC", "DEX", "DC",
"DC", "NDH", "DC", "DCM", "DC", "DC", "NSU", "NSU", "DC",
"DEX", "ND", "DCM", "NTH", "ND", "DC", "DC", "DC", "DC",
"ND", "DC", "DC", "DC", "DC", "DC", "DS", "DC", "DC", "DC",
"DC", "DC", "DC", "NSUH", "DEXF", "DS", "DS", "DEX", "S",
"DC", "DEX", "DC", "DEX", "DC", "DC", "DC", "DC", "DC", "NT",
"ND", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "S", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "S", "DC", "ND", "FM", "DC", "DEX",
"DC", "DC", "DC", "NTH", "NSU", "DEX", "DEXF", "DC", "DC",
"DC", "DCM", "DEXF", "DS", "DC", "NSU", "DC", "DC", "FC",
"DEX", "DC", "ND", "DC", "FC", "DC", "DC", "DC", "DC", "DC",
"DCM", "DC", "DC", "DEX", "DC", "DEX", "DCM", "DC", "FC",
"DC", "DC", "DC", "DC", "FC", "DC", "DC", "DC", "DC", "DC",
"DC", "DS", "DS", "DC", "DC", "FC", "DC", "DC", "DC", "DS",
"DC", "DC", "DS", "DC", "DEX", "DC", "FC", "DC", "DEX", "DEX",
"DC", "DC", "DC", "DC", "DC", "DC", "DEX", "DC", "DCM", "DC",
"DC", "DS", "DC", "DS", "DS", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "DEX", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "FC", "DC", "DS",
"DS", "DEX", "DEX", "DC", "DS", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DS", "DC", "DC", "DS", "S", "S",
"S", "DC", "S", "DC", "S", "S", "DC", "S", "ND", "S", "S",
"ND", "DC", "DC", "DEX", "DEX", "S", "NT", "DEX", "DC", "DEXF",
"S", "DEXF", "DC", "S", "NT", "S", "S", "NT", "S", "S", "DEX",
"S", "DEX", "DEXF", "S", "S", "S", "S", "S", "S", "NT", "DEXF",
"S", "DC", "S", "DC", "DC", "NT", "S", "S", "DEXF", "S",
"S", "DC", "DC", "S", "DC", "DEX", "DC", "DC", "DC", "S",
"DEXF", "DEX", "S", "S", "DC", "S", "S", "DC", "S", "DEX",
"DEX", "DC", "S", "S", "DEXF", "S", "DC", "DC", "DEX", "S",
"S", "DEXF", "DEX", "S", "S", "DC", "DC", "DC", "S", "DEXF",
"S", "S", "S", "ND", "S", "S", "S", "S", "DC", "DS", "DC",
"DS", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "S",
"DC", "DC", "ND", "S", "DEXF", "DEXF", "DEX", "NSU", "ND",
"NSU", "DC", "DC", "DC", "DC", "DEX", "NSU", "NSU", "NSU",
"DEX", "FC", "FC", "ND", "ND", "DEXF", "ND", "FC", "DC",
"DC", "DEX", "DS", "DC", "DC", "DC", "DC", "DC", "DC", "DEX",
"DS", "NSU", "DEXF", "DC", "DCM", "NDH", "DC", "FC", "DC",
"DC", "DEX", "DC", "DS", "ND", "DC", "DC", "NSUH", "DC",
"FC", "DC", "DC", "NDH", "DC", "DC", "DC", "NDH", "DEX",
"FC", "DC", "NSU", "DC", "DC", "DC", "DC", "DCM", "NDH",
"NDH", "DC", "DC", "ND", "ND", "NTH", "NTH", "NDH", "NDH",
"NDH", "DC", "DC", "NDH", "DS", "DC", "NDH", "ND", "ND",
"DC", "DS", "NTH", "DC", "DS", "DEXF", "DC", "NT", "DC",
"ND", "DC", "NDH", "DC", "DCM", "NDH", "ND", "DCM", "DC",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "NT", "DS", "DEX",
"DEX", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "ND", "DC", "DC", "DS", "DC", "DC", "DC",
"DC", "DC", "DC", "FC", "DEXF", "ND", "DC", "DC", "DC", "DC",
"DC", "ND", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "DEX", "DS", "DC",
"DC", "DC", "ND", "DC", "DEX", "DC", "DC", "ND", "DC", "DEX",
"FC", "DC", "DC", "DEX", "DC", "DC", "DEX", "DC", "ND", "DC",
"FC", "FC", "DCM", "DC", "DS", "DC", "DC", "DC", "DC", "DS",
"DEX", "DEX", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "FC", "FM", "DC", "DC", "FC", "DEXF",
"DC", "DC", "NSU", "DC", "DC", "DC", "DC", "DC", "DC", "ND",
"DEXF", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DS", "DC", "DC", "DC", "DS", "DC", "DC",
"DC", "DC", "DC", "FC", "DC", "DC", "ND", "DC", "ND", "DEX",
"DCM", "DC", "DC", "DCM", "DC", "DC", "DC", "DC", "DS", "S",
"S", "ND", "S", "ND", "S", "S", "S", "ND", "NT", "S", "NT",
"DEX", "S", "NT", "DC", "S", "S", "S", "S", "S", "S", "S",
"DEXF", "DEX", "S", "S", "S", "ND", "NSU", "DC", "DEX", "DC",
"NT", "DEX", "NT", "DEXF", "NSU", "DC", "NSU", "DC", "DEXF",
"DC", "DCM", "FC", "ND", "NSU", "NSU", "NSU", "NSU", "ND",
"ND", "DCM", "DC", "DC", "DC", "DC", "DC", "DC", "NSU", "NT",
"NSU", "DEX", "DC", "DEX", "DC", "DC", "NDH", "NDH", "DEX",
"DEX", "DEXF", "FC", "DC", "DC", "NDH", "DC", "NDH", "NDH",
"DS", "NDH", "DC", "NDH", "NDH", "ND", "DS", "NTH", "DC",
"DC", "FC", "DEXF", "DC", "DC", "NSU", "NTH", "DC", "NSUH",
"NDH", "NDH", "NTH", "NDH", "DC", "NDH", "NDH", "DEXF", "DEX",
"DC", "FC", "NTH", "FC", "DC", "NDH", "NDH", "NDH", "NSUH",
"NDH", "NSU", "DS", "DC", "DC", "NDH", "ND", "ND", "NDH",
"NDH", "DC", "DC", "DC", "NTH", "ND", "NDH", "NTH", "NDH",
"DS", "NDH", "DC", "DCM", "ND", "ND", "ND", "S", "NDH", "DS",
"DC", "DEX", "DC", "DS", "DC", "NSU", "DC", "NT", "DC", "DC",
"FC", "S", "ND", "DC", "DC", "DC", "DCM", "DC", "DCM", "DC",
"DC", "DC", "DC", "DCM", "DS", "DEX", "DC", "DS", "DCM",
"NDH", "DEXF", "DC", "DC", "DEXF", "ND", "DC", "ND", "DC",
"DC", "DC", "DEX", "DC", "DC", "DC", "DC", "FC", "DCM", "DC",
"DCM", "DCM", "DEXF", "DC", "DS", "DEX", "DC", "DC", "DS",
"DC", "DCM", "DC", "DEXF", "DC", "DC", "DC", "DC", "DC",
"DC", "DCM", "DC", "FC", "DC", "DC", "DC", "DC", "FC", "DS",
"DC", "DC", "DEX", "DEX", "FC", "NT", "NT", "DCM", "DCM",
"DC", "DC", "FC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DCM", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "DS", "DC", "DC", "DC", "S",
"NT", "S", "ND", "NT", "S", "DC", "DEX", "DC", "S", "S",
"DC", "DC", "NSU", "NSU", "DC", "DC", "FC", "ND", "NSU",
"ND", "NSU", "ND", "FC", "NT", "DC", "FC", "NSU", "NSU",
"NSU", "ND", "DC", "DS", "NTH", "DC", "DC", "NSUH", "NTH",
"DEXF", "FC", "NDH", "DC", "NDH", "DCM", "DS", "DC", "DCM",
"DS", "ND", "DC", "DC", "DC", "NDH", "NTH", "DC", "NTH",
"DS", "DC", "NT", "NT", "DS", "NSU", "NTH", "DC", "DC", "DC",
"NTH", "FC", "DC", "DC", "FC", "NDH", "DC", "DC", "FC", "DS",
"DC", "NDH", "DS", "DS", "NDH", "DCM", "ND", "DC", "NDH",
"NDH", "DEX", "DEX", "DC", "NDH", "ND", "DC", "DC", "DC",
"DC", "DC", "DC", "NDH", "NDH", "NDH", "NDH", "DC", "ND",
"DC", "NDH", "NDH", "ND", "ND", "DC", "DC", "DC", "DC", "NDH",
"DC", "DC", "DC", "DC", "DC", "DC", "NSU", "DS", "DC", "DC",
"DEX", "DC", "DC", "DC", "DEX", "DC", "DC", "DC", "DS", "DS",
"DC", "DC", "DC", "DC", "NDH", "FC", "DS", "FC", "FC", "DCM",
"DCM", "DCM", "DC", "DC", "DCM", "DCM", "DCM", "NSUH", "DC",
"DC", "FC", "DC", "DCM", "DC", "DC", "DEX", "NSUH", "DC",
"DC", "DEX", "DS", "DC", "DC", "DC", "DC", "DC", "ND", "DC",
"DC", "DC", "DC", "ND", "DC", "DC", "DC", "NSU", "DC", "DC",
"DC", "NDH", "DC", "DC", "DC", "DC", "NSU", "DC", "DC", "DC",
"DC", "DS", "DCM", "ND", "DC", "DCM", "DCM", "ND", "DCM",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DEX",
"DC", "DC", "DS", "DS", "DC", "NT", "DC", "FC", "DC", "DCM",
"DC", "DCM", "DC", "DC", "DCM", "DC", "DC", "DC", "NSU",
"FC", "DCM", "DC", "NT", "FC", "FC", "DC", "DC", "DC", "DC",
"ND", "DS", "DEX", "DS", "DS", "DS", "DEX", "NSU", "DC",
"DC", "DCM", "DC", "DC", "DS", "DEX", "FC", "S", "NSU", "FC",
"DC", "DEX", "DEX", "DC", "DEX", "DEXF", "DC", "DC", "DC",
"DC", "FC", "DC", "DEXF", "DEXF", "DC", "ND", "DC", "DC",
"DC", "DEX", "DC", "DC", "DC", "DC", "DS", "DC", "DC", "DC",
"DEXF", "DC", "DS", "FC", "DEX", "DC", "ND", "DC", "DC",
"DC", "ND", "DC", "DS", "DC", "DC", "DC", "DC", "DC", "DC",
"DEXF", "DC", "DS", "DC", "ND", "DCM", "DC", "NT", "DC",
"DC", "DCM", "DCM", "DC", "ND", "DC", "DEX", "FM", "ND",
"DS", "DC", "DC", "DC", "DC", "DC", "DCM", "ND", "DCM", "DC",
"DC", "DC", "FC", "DC", "ND", "DC", "FC", "DC", "DC", "DCM",
"DCM", "DS", "DC", "DEX", "DC", "DC", "FC", "DS", "DC", "DS",
"FC", "DCM", "ND", "ND", "DS", "FC", "ND", "ND", "DC", "DEX",
"DEX", "DS", "FC", "DEX", "NT", "DCM", "DC", "DC", "DEX",
"DC", "ND", "ND", "FC", "DEXF", "NT", "DC", "DC", "NT", "DC",
"FC", "DCM", "DC", "DC", "DC", "DCM", "DS", "DC", "DC", "ND",
"DC", "DC", "FC", "DC", "DC", "ND", "DS", "DS", "DS", "DC",
"DC", "DEX", "DEXF", "DC", "DC", "DC", "DC", "DC", "DCM",
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DEX", "DC",
"DC", "DC", "DC", "FC", "FC", "DCM", "FC", "FC", "DEX", "DEXF",
"NSU", "FC", "DEXF", "DC", "DC", "DS", "DC", "DC", "DC",
"DC", "DC", "DCM", "DEX", "DEX", "DEX", "DEX", "DC", "DEX",
"DC", "DC", "DS", "DC", "DCM", "ND", "ND", "DC", "DC", "DC",
"DC", "DC", "DEXF", "DC", "DCM", "DEX", "DC", "DC", "DC",
"FC", "DC", "DEXF", "DC", "FM", "DC", "DC", "DC", "FC", "DS",
"DC", "DC", "DC", "DC", "FC", "DC", "ND", "DC", "DC", "DEX",
"DC", "DC", "DC", "DC", "DS", "DC", "DC", "DC", "DEX", "DC",
"DEXF", "DCM", "DCM", "DC", "DC", "DC", "DC", "DS", "DC",
"DS", "DCM", "DCM", "DC", "DC", "FC", "FC", "FC", "DC", "DS",
"DC", "DC", "DC", "DC", "ND", "DC", "ND", "ND", "ND", "ND",
"DC", "DC", "DS", "DS", "DS", "DC", "DEXF", "FC", "DC", "DC",
"DC", "DC", "DC", "DC", "FC", "DC", "DC", "DC", "FM", "DS",
"DEX", "ND", "DC", "ND", "ND", "DC", "FC", "DS", "DC", "DC",
"DS", "DC", "DC", "DC", "DC", "DEXF", "DCM", "DC", "DCM",
"DC", "DCM", "FC", "DC", "DC", "DC", "DEX", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "DEX", "DC", "DC", "ND", "DS",
"DEX", "DC", "DC", "ND", "FC", "DC", "DC", "DC", "FM", "FM",
"DC", "DC", "DC", "ND", "DC", "DEX", "DS", "DC", "DC", "DC",
"DC", "DC", "DCM", "DS", "DS", "DC", "DC", "DC", "DCM", "DC",
"DS", "DC", "DC", "DC", "FC", "DCM", "DC", "DC", "DC", "DCM",
"DC", "DC", "DS", "DS", "DC", "DC", "DC", "DC", "ND", "FC",
"DC", "DC", "DC", "NSU", "DC", "DC", "DEX", "DS", "DC", "DCM",
"DCM", "DC", "DC", "DCM", "DS", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DCM", "DC", "DC", "DC", "DS", "DC",
"DCM", "DC", "DC", "DS", "DCM", "DC", "DC", "DEX", "DC",
"FC", "DC", "DC", "DC", "DEX", "DEX", "DC", "DC", "DEXF",
"DC", "DC", "DC", "DC", "DC", "DC", "ND", "DC", "DC", "DC",
"DC", "DC", "DS", "DC", "DC", "DCM", "ND", "DCM", "DC", "ND",
"ND", "DCM", "ND", "DC", "DC", "DEX", "DC", "DC", "NSU",
"DC", "NSU", "DC", "DC", "DC", "DEX", "DC", "DS", "DC", "DC",
"DC", "DC", "DC", "NSU", "DC", "DC", "DS", "DC", "ND", "DC",
"DC", "DCM", "DS", "FM", "DC", "DC", "DS", "DC", "DCM", "DC",
"DCM", "DC", "NSU", "DC", "DC", "DC", "FM", "DCM", "DCM",
"DC", "DCM", "DCM", "DC", "DC", "ND", "DCM", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "S", "DC", "DC", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "DEX", "DS", "DCM", "DS", "DS",
"DCM", "DC", "DC", "DCM", "DCM", "S", "DC", "S", "DC", "DC",
"DC", "S", "DC", "DC", "DC", "DC", "DC", "S", "S", "S", "DC",
"S", "DC", "DC", "DC", "S", "NT", "DEXF", "S", "NT", "S",
"S", "S", "S", "DC", "S", "S", "S", "S", "S", "S", "S", "DEX",
"DC", "S", "S", "S", "S", "S", "S", "S", "S", "S", "DC",
"S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "DS", "S",
"DC", "DC", "DC", "DC", "S", "DC", "S", "DC", "S", "DC",
"S", "S", "S", "DC", "S", "S", "DC", "S", "DC", "S", "S",
"S", "S", "DC", "DC", "DC", "DC", "DC", "S", "DC", "S", "DC",
"DC", "S", "S", "DC", "S", "DC", "DC", "S", "S", "DC", "DC",
"DC", "S", "S", "DC", "S", "S", "S", "S", "DC", "S", "DC",
"S", "S", "S", "S", "DC", "DC", "S", "S", "S", "S", "S",
"DC", "S", "S", "S", "S", "S", "S", "S", "DC", "S", "S",
"DC", "DC", "S", "S", "DC", "DC", "S", "S", "S", "S", "DC",
"ND", "NT", "NSU", "ND", "DEXF", "DC", "DC", "NSU", "ND",
"ND", "ND", "NT", "ND", "DC", "DEX", "NSU", "NSU", "DC",
"FC", "DC", "DC", "ND", "DC", "DC", "DC", "NSU", "NSU", "NT",
"ND", "ND", "ND", "DEX", "DC", "DC", "DC", "DS", "DS", "ND",
"ND", "NSU", "NSU", "DEX", "DEX", "NSU", "NSU", "DS", "FC",
"ND", "DC", "ND", "DC", "DC", "DC", "ND", "NSU", "DCM", "NT",
"DC", "DEX", "DS", "DC", "DEX", "NSU", "NSU", "ND", "FC",
"ND", "ND", "DC", "DC", "FC", "ND", "NSU", "NSU", "NSU",
"NSU", "NSU", "ND", "ND", "ND", "NSU", "ND", "NSU", "ND",
"ND", "NT", "DEX", "DEXF", "ND", "NSU", "DC", "DC", "DC",
"DC", "DEX", "DEX", "DC", "DC", "FC", "DC", "DEX", "ND",
"NT", "DC", "DC", "DCM", "DC", "DEX", "DC", "NT", "NSU",
"DEX", "DEX", "DC", "DC", "DC", "NSU", "DC", "ND", "ND",
"ND", "NSU", "NSU", "NSU", "DC", "DC", "DC", "DC", "ND",
"NSU", "DEX", "NSU", "NSU", "NSU", "ND", "NSU", "DC", "ND",
"ND", "ND", "NT", "ND", "ND", "ND", "NSU", "NSU", "ND", "DS",
"DS", "DC", "DC", "DC", "DCM", "NDH", "DC", "DC", "DC", "DC",
"DC", "DCM", "DEX", "DEX", "DEX", "NSU", "DC", "NSUH", "DC",
"NSUH", "DCM", "FC", "FC", "FC", "NDH", "NDH", "NT", "NDH",
"DC", "DC", "DC", "NDH", "NDH", "DC", "DC", "DEXF", "DC",
"NSUH", "NTH", "DCM", "FM", "DS", "DC", "NDH", "DC", "DC",
"DC", "DCM", "NT", "DCM", "NDH", "FC", "NDH", "DC", "FC",
"NDH", "DC", "NDH", "NDH", "DEXF", "DC", "DEX", "DCM", "DC",
"ND", "FC", "NSU", "DC", "NSU", "NDH", "DC", "NTH", "DS",
"NDH", "NTH", "FM", "DC", "DC", "DC", "DC", "DEXF", "DEX",
"DC", "ND", "SUM", "NSU", "DC", "DS", "DC", "DC", "ND", "DC",
"FM", "NDH", "NDH", "NSU", "ND", "ND", "NDH", "DEXF", "DS",
"DC", "NDH", "DS", "DC", "DS", "DC", "DC", "DC", "NDH", "DCM",
"DC", "DS", "NTH", "DC", "DC", "FC", "DC", "NTH", "DC", "DC",
"DC", "DC", "DC", "DC", "ND", "NDH", "DC", "NSU", "DCM",
"ND", "DC", "DC", "DC", "DC", "FC", "NDH", "DC", "NSU", "DEX",
"DEX", "DC", "NDH", "NDH", "NDH", "DS", "NDH", "DCM", "DC",
"NDH", "DC", "DC", "NTH", "NDH", "NTH", "DC", "DC", "NDH",
"DC", "DCM", "FC", "NTH", "DS", "DS", "FC", "DC", "NDH",
"NDH", "NDH", "NDH", "DC", "DC", "DC", "DC", "DC", "DS",
"DC", "DC", "DC", "NDH", "NDH", "DC", "DC", "NDH", "DC",
"DC", "DC", "DC", "DC", "DC", "DC", "NDH", "DC", "DS", "DC",
"DC", "NSU", "DC", "DCM", "DS", "DC", "DC", "DC", "DS", "DC",
"ND", "FC", "DC", "NDH", "NDH", "DC", "DEX", "DC", "ND",
"NDH", "ND", "FM", "DCM", "NSU", "DC", "ND", "DC", "ND",
"DC", "DC", "NDH", "DS", "DC", "DCM", "FM", "DCM", "S", "S",
"S", "S", "S", "S", "S", "S", "DC", "S", "S", "DC", "S",
"ND", "DC", "DC", "DS", "DC", "NDH", "DC", "NSU", "NSU",
"ND", "NT", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "ND",
"ND", "DC", "DEX", "DS", "DC", "DEXF", "DC", "DC", "ND",
"DCM", "DCM", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DS", "DC", "DC", "S", "DC", "DC", "DC", "DC", "ND", "DC",
"DC", "DC", "DC", "DC", "S", "DC", "DC", "ND", "DC", "DC",
"S", "DC", "DC", "DC", "DC", "DC", "S", "DC", "DS", "FC",
"DEX", "DC", "DS", "DEX", "DCM", "DC", "DEX", "DEXF", "DC",
"DCM", "DEX", "DC", "DEX", "DEX", "DC", "DC", "FC", "DC",
"DC", "DS", "ND", "FC", "DEX", "DC", "DEX", "DS", "FC", "DC",
"DC", "DC", "DC", "DCM", "DS", "DEX", "DEX", "DC", "DC",
"DC", "DS", "NSU", "DC", "DC", "DC", "NT", "S", "NSU", "NSU",
"DS", "NT", "DC", "DC", "DC", "DEXF", "DC", "DCM", "ND",
"ND", "DEXF", "DEX", "DC", "NSU", "ND", "ND", "ND", "ND",
"NSU", "DEX", "DC", "DCM", "NT", "DEX", "NSU", "ND", "DC",
"DS", "DC", "ND", "NTH", "DS", "NDH", "DC", "NDH", "NTH",
"NDH", "DS", "DC", "DC", "DEXF", "NDH", "NDH", "NDH", "DC",
"DS", "DC", "FM", "NDH", "NSUH", "DC", "DC", "DCM", "DC",
"DC", "DC", "DC", "DS", "DC", "FC", "NDH", "FS", "DCM", "DEX",
"FM", "DC", "DC", "DC", "DCM", "FC", "DS", "DCM", "FC", "DEX",
"DS", "DC", "ND", "DEX", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "NSU", "NSU", "DC", "DCM", "DS", "DEXF", "DC", "DC",
"DS", "DS", "DEX", "DEX", "DC", "DC", "DC", "DS", "DCM",
"DC", "DEX", "DC", "DC", "DS", "FC", "DC", "DC", "DC", "DS",
"DC", "DC", "DC", "DC", "DEX", "DEX", "DC", "DEX", "FC",
"DC", "FC", "DC", "DC", "DC", "DC", "DC", "DS", "FC", "DS",
"DS", "DC", "DC", "DC", "DC", "DEX", "DEX", "DC", "DC", "DC",
"DC", "FC", "NSU", "DS", "DC", "NSU", "DC", "DCM", "DC",
"DC", "DC", "DC", "DC", "NSU", "NT", "NSU", "ND", "NSU",
"NT", "FC", "DC", "NSU", "NSU", "NT", "DEX", "DS", "NT",
"DCM", "DC", "DEX", "NT", "NSU", "DEX", "DEX", "FC", "ND",
"NSU", "NSU", "DC", "ND", "DC", "DC", "DC", "ND", "DEX",
"DC", "DC", "DS", "DC", "DC", "FC", "NT", "NT", "DC", "DEXF",
"ND", "NSU", "DEX", "DC", "DC", "DC", "DC", "DC", "NDH",
"NDH", "NDH", "DC", "DC", "DS", "DCM", "DC", "DC", "ND",
"ND", "DS", "DC", "DC", "DS", "DC", "ND", "DC", "DC", "DEX",
"ND", "ND", "DS", "ND", "DC", "NT", "FC", "DS", "NSU", "DC",
"DCM", "DC", "DCM", "DC", "ND", "DC", "DC", "DC", "NT", "DC",
"FC", "DS", "DCM", "DS", "DEX", "DC", "ND", "DC", "DS", "DS",
"DCM", "ND", "ND", "DC", "DS", "DC", "DC", "FC", "FC", "DC",
"DC", "DEXF", "DCM", "DS", "FC", "FC", "ND", "ND", "DS",
"DEXF", "FC", "DC", "FC", "DC", "DC", "DC", "FC", "DC", "FC",
"FC", "DC", "FC", "DC", "DC", "DC", "DCM", "DCM", "DC", "DC",
"FC", "FC", "FC", "DC", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "FC", "DC", "DEXF", "DCM", "DC", "DCM", "DC",
"DCM", "FC", "DC", "DC", "DC", "DC", "ND", "DC", "DC", "FC",
"DC", "FC", "DC", "ND", "DC", "DC", "DEXF", "ND", "DC", "DC",
"FC", "DEX", "S", "S", "NT", "DEX", "NT", "NSU", "NSU", "NSU",
"DC", "DEXF", "DEXF", "ND", "ND", "DEX", "NSU", "DC", "NSU",
"NSU", "NSU", "NSU", "DEX", "DEXF", "DEX", "DEX", "ND", "NSU",
"DEXF", "DC", "DEXF", "DC", "DCM", "NSU", "ND", "DC", "DEXF",
"DS", "DC", "NT", "ND", "NDH", "DC", "DEXF", "DEXF", "DS",
"DEXF", "DC", "NDH", "DC", "DC", "DC", "DC", "NSU", "DEX",
"DS", "DEX", "NSU", "DEX", "NDH", "NTH", "DC", "DC", "FC",
"DC", "DC", "DC", "DCM", "DC", "DC", "DC", "DC", "DC", "DC",
"DEX", "DEX", "DC", "DC", "NSU", "ND", "ND", "DC", "DEX",
"ND", "DEX", "DC", "DC", "DC", "NSU", "DCM", "DC", "DC",
"DC", "DC", "DEX", "DC", "DEX", "DEXF", "DC", "DC", "DC",
"ND", "FM", "DS", "DS", "DS", "DC", "DS", "DC", "ND", "DC",
"DC", "DC", "DC", "DS", "DC", "DC", "DC", "DC", "DC", "DC",
"DC", "DC", "DEX", "NSU", "NSU", "DC", "DEX", "DEX", "DEX",
"DEX", "DEX", "NSU", "NSU", "DC", "NSU", "NSU", "DEX", "DC",
"DC", "FC", "DC", "DC", "NDH", "DC", "ND", "NSU", "DC", "DC",
"DC", "DC", "DC", "DC", "DC", "DEX", "DEX", "DEX", "NSU",
"DCM", "DC", "DCM", "DCM", "DEX", "NT", "S", "DC", "NSU",
"ND", "DEX", "DC", "FC", "DS", "DC", "DC", "DC", "ND", "DEX",
"DC", "DC", "FC", "ND", "FM", "DC", "DC", "DC", "NT", "DC",
"DCM", "DC", "DC", "DS", "DCM", "DC", "S", "S", "S", "NSU",
"DC", "NSU", "DC", "FM", "DC", "DC", "NSU", "DC", "DC", "NTH",
"DC", "DC", "DS", "NDH", "NDH", "NDH", "NTH", "NTH", "DCM",
"DC", "DC", "DS", "DC", "DS", "DC", "DC", "DC", "DC", "DC",
"FC", "NSU", "DC", "DC", "FM", "FC", "DEX", "DC", "DEX",
"DC", "DC", "DC", "DC", "DS", "DC", "S", "S", "S", "NSU",
"DEX", "NSU", "ND", "ND", "DC", "FC", "DC", "DC", "DEX",
"DC", "DS", "DC", "DC", "DS", "FC", "DC", "DC", "DC", "DC",
"DC", "DC", "DEX", "DS", "NSU", "DEX", "DC", "DCM", "DC",
"DC", "DEX", "DCM", "NSU", "DS", "DS", "DEX", "FC", "DCM",
"DC", "NT", "DC", "DC", "DCM", "DEX", "DEX", "DEXF", "DC",
"DC", "DC", "DEXF", "DC", "DC", "DS", "DC", "DC", "ND", "ND",
"ND", "ND", "DC", "FC", "NT", "NSU", "ND", "DEX", "DS", "DC",
"DC", "DS", "FC", "SUM", "FC", "DS", "DC", "DCM", "DC", "DC",
"DC", "DS", "DC", "DS", "DEX", "DC", "DC", "DS", "DS", "DC",
"DEX", "DCM", "DCM", "DS", "DS", "DS", "DS", "DCM", "DCM",
"DS", "DC", "DC", "DS", "DC", "DC", "DC", "DEX", "DC", "DCM",
"DC", "NSU", "NSU", "ND", "NSU", "DC", "S", "SUM", "NSU",
"NSU", "NSU", "FC", "DEX", "NSU", "NSU", "DEX", "DC", "DEX",
"DC", "ND", "NSU", "ND", "ND", "NSU", "DC", "DC", "DC", "DC",
"DC", "DCM", "DCM", "DC", "DC", "DEXF", "FC", "DC", "DC",
"ND", "DC", "DEX", "NTH", "NSU", "ND", "NDH", "DEXF", "NDH",
"DC", "DS", "NSUH", "NDH", "DC", "NDH", "NTH", "NTH", "NDH",
"NTH", "DC", "DS", "NTH", "NTH", "DC", "DC", "NDH", "DC",
"DC", "DEX", "DEX", "DC", "DC", "NSU", "DC", "DS", "DC",
"S", "FM", "DC", "ND", "DC", "DC", "DEX", "DS", "DC", "DC",
"DC", "DCM", "DEXF", "ND", "ND", "ND", "ND", "DC", "DC",
"NSU", "DC", "ND", "DC", "NSU", "NSU", "ND", "DC", "NT",
"ND", "DS", "DCM", "DC", "DEX", "DEX", "DEX", "NSU", "NSU",
"DS", "NSU", "NSU", "DC", "DEX", "DC", "NSUH", "DS", "ND",
"ND", "NSU", "DC", "DC", "DEXF", "DC", "DC", "NSU", "DEXF",
"DS", "DC", "DC", "DC", "NT", "DC"), rmtype3 = structure(c(1L,
1L, 1L, 1L, 1L, 3L, 3L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 2L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 3L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 3L, 3L, 1L, 3L, 1L, 1L, 2L, 1L, 1L, 1L,
2L, 1L, 2L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 3L, 3L, 3L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 2L, 2L, 2L,
1L, 1L, 2L, 2L, 1L, 3L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 3L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 3L, 1L, 2L,
1L, 3L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 3L, 3L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L,
1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L,
1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 1L, 3L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 3L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L,
1L, 3L, 3L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L,
3L, 1L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 1L,
1L, 1L, 1L, 3L, 2L, 3L, 3L, 1L, 1L, 1L, 3L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 3L, 2L, 1L, 1L, 3L, 3L, 1L,
3L, 1L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 3L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 2L, 1L,
2L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 3L, 1L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 1L, 1L, 3L, 1L, 1L, 3L, 3L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 2L,
1L, 1L, 1L, 3L, 1L, 1L, 2L, 3L, 3L, 2L, 1L, 1L, 1L, 1L, 3L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 2L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 2L, 3L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 3L, 2L, 1L, 1L,
3L, 1L, 2L, 2L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
3L, 3L, 3L, 3L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 3L, 2L, 1L,
1L, 3L, 1L, 1L, 2L, 1L, 3L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 3L,
3L, 1L, 2L, 1L, 2L, 3L, 1L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 3L,
2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 3L, 2L, 1L, 3L, 1L, 2L, 3L,
1L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 2L, 3L, 1L,
2L, 2L, 1L, 1L, 1L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
3L, 3L, 3L, 1L, 3L, 2L, 3L, 1L, 3L, 1L, 1L, 3L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 1L,
1L, 2L, 2L, 2L, 3L, 1L, 3L, 3L, 1L, 2L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 3L, 1L, 2L, 1L, 1L, 3L, 3L, 1L, 2L,
3L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 3L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 3L, 1L, 1L, 2L, 1L, 1L, 1L, 3L, 3L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 3L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 3L, 1L, 1L, 2L, 2L,
1L, 3L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 3L, 1L, 1L, 3L, 1L, 1L,
2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 2L, 1L, 1L, 1L,
1L, 1L, 3L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 2L, 2L, 2L,
3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 3L, 3L, 3L, 2L, 1L, 1L, 3L,
3L, 2L, 3L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 3L, 2L, 1L, 2L, 3L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 3L, 1L,
1L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 2L, 1L, 1L, 3L,
1L, 1L, 1L, 1L, 2L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 1L, 1L, 3L, 2L, 1L, 3L, 3L, 3L, 1L, 2L, 3L, 1L, 2L, 2L,
1L, 3L, 1L, 3L, 1L, 3L, 1L, 2L, 3L, 3L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
3L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 1L, 1L,
1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 1L, 1L, 1L, 3L, 1L, 2L, 1L, 1L, 3L, 1L, 2L, 1L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L,
1L, 3L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L,
3L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 3L, 1L,
3L, 1L, 1L, 1L, 3L, 3L, 1L, 3L, 2L, 1L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 3L, 3L, 1L, 2L, 1L, 3L,
2L, 3L, 2L, 3L, 1L, 3L, 1L, 2L, 1L, 2L, 1L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 2L, 1L,
2L, 1L, 1L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 3L, 1L, 3L, 3L,
2L, 3L, 1L, 3L, 3L, 3L, 2L, 3L, 1L, 1L, 1L, 2L, 1L, 1L, 3L,
3L, 1L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 2L, 2L, 1L, 1L, 3L,
1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 1L, 1L, 3L, 3L, 3L, 3L,
3L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 2L, 3L, 3L,
3L, 1L, 3L, 2L, 1L, 2L, 1L, 2L, 1L, 3L, 1L, 3L, 1L, 1L, 1L,
1L, 3L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
1L, 2L, 2L, 3L, 2L, 1L, 1L, 2L, 3L, 1L, 3L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 3L, 3L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 3L,
1L, 3L, 3L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 1L,
1L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 1L,
2L, 3L, 1L, 1L, 3L, 3L, 2L, 1L, 3L, 1L, 3L, 2L, 2L, 1L, 2L,
2L, 3L, 1L, 1L, 1L, 3L, 3L, 1L, 3L, 2L, 1L, 3L, 3L, 2L, 3L,
3L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 2L, 1L,
3L, 2L, 2L, 3L, 2L, 3L, 1L, 3L, 3L, 2L, 2L, 1L, 3L, 3L, 1L,
1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 1L, 3L, 3L, 3L,
3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 2L, 1L,
1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
3L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 3L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 2L, 3L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 1L,
3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 3L, 1L, 2L,
2L, 3L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 3L, 1L, 1L, 1L, 2L, 1L, 2L,
1L, 1L, 2L, 1L, 1L, 1L, 3L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 1L, 2L, 1L, 1L,
2L, 2L, 1L, 1L, 3L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 3L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 3L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 3L, 2L, 1L,
3L, 1L, 1L, 2L, 2L, 1L, 3L, 1L, 2L, 1L, 3L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 3L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 3L, 3L,
2L, 1L, 3L, 3L, 1L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 1L, 1L, 2L,
1L, 3L, 3L, 1L, 2L, 3L, 1L, 1L, 3L, 1L, 1L, 2L, 1L, 1L, 1L,
2L, 2L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 2L, 2L, 2L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 3L,
1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
1L, 2L, 1L, 1L, 2L, 1L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 3L,
1L, 3L, 3L, 3L, 3L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 3L, 1L, 3L, 3L,
1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L,
2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 3L, 2L, 2L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 3L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L,
2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 3L, 2L,
1L, 3L, 3L, 2L, 3L, 1L, 1L, 2L, 1L, 1L, 3L, 1L, 3L, 1L, 1L,
1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 1L, 3L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 3L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 3L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
3L, 2L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 2L, 1L, 1L, 3L,
3L, 3L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 3L, 1L,
1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 1L, 1L, 1L, 2L, 2L, 3L,
3L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 1L, 3L, 1L, 3L, 1L, 1L, 1L,
3L, 3L, 2L, 3L, 1L, 2L, 2L, 1L, 2L, 3L, 3L, 3L, 1L, 3L, 3L,
1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 2L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
1L, 1L, 2L, 3L, 3L, 1L, 1L, 2L, 1L, 2L, 1L, 3L, 3L, 2L, 2L,
1L, 1L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L,
3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 2L, 2L, 1L, 1L, 1L, 2L, 3L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 3L, 1L, 3L, 1L, 3L, 2L, 1L, 1L, 1L, 3L,
3L, 3L, 3L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 2L, 1L, 3L, 3L, 2L,
1L, 2L, 1L, 3L, 1L, 1L, 1L, 2L, 3L, 2L, 3L, 1L, 3L, 1L, 1L,
3L, 1L, 3L, 3L, 2L, 1L, 2L, 2L, 1L, 3L, 1L, 3L, 1L, 3L, 3L,
1L, 3L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 3L, 1L,
3L, 1L, 2L, 1L, 1L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 2L,
2L, 1L, 3L, 2L, 1L, 2L, 1L, 1L, 1L, 3L, 2L, 1L, 2L, 3L, 1L,
1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 3L, 2L,
3L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 3L, 2L, 2L, 1L, 3L, 3L, 3L,
2L, 3L, 2L, 1L, 3L, 1L, 1L, 3L, 3L, 3L, 1L, 1L, 3L, 1L, 2L,
1L, 3L, 2L, 2L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 3L, 1L, 2L, 1L, 1L, 3L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L,
3L, 1L, 1L, 3L, 3L, 1L, 2L, 1L, 3L, 3L, 3L, 1L, 2L, 3L, 1L,
3L, 1L, 3L, 1L, 1L, 3L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 1L, 3L,
1L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L,
2L, 2L, 1L, 2L, 1L, 1L, 3L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 1L, 2L, 1L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 3L, 1L, 1L, 1L,
3L, 1L, 3L, 3L, 2L, 3L, 1L, 1L, 1L, 2L, 1L, 2L, 3L, 3L, 2L,
2L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 1L, 2L, 3L, 2L, 3L, 3L,
1L, 2L, 1L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 2L, 1L, 1L, 2L,
3L, 3L, 3L, 1L, 2L, 1L, 1L, 3L, 3L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 3L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
2L, 1L, 2L, 2L, 1L, 3L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L,
3L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 2L, 1L,
3L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 1L,
1L, 3L, 3L, 3L, 2L, 2L, 3L, 2L, 1L, 2L, 3L, 3L, 2L, 2L, 1L,
3L, 3L, 3L, 1L, 3L, 1L, 1L, 1L, 3L, 2L, 1L, 1L, 2L, 1L, 1L,
1L, 3L, 3L, 1L, 2L, 3L, 3L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 3L,
3L, 1L, 1L, 2L, 2L, 1L, 1L, 3L, 3L, 2L, 1L, 1L, 2L, 1L, 3L,
1L, 1L, 2L, 3L, 3L, 2L, 3L, 1L, 3L, 1L, 2L, 3L, 1L, 2L, 1L,
2L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 3L,
1L, 2L, 2L, 2L, 3L, 3L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 3L, 3L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 3L,
1L, 1L, 2L, 3L, 1L, 1L, 1L, 2L, 1L, 1L, 3L, 2L, 3L, 3L, 3L,
3L, 1L, 2L, 2L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 2L, 2L,
2L, 2L, 3L, 3L, 2L, 1L, 2L, 1L, 2L, 3L, 3L, 1L, 2L, 2L, 1L,
3L, 3L, 3L, 1L, 2L, 2L, 2L, 2L, 1L, 3L, 1L, 1L, 1L, 1L, 3L,
2L, 2L, 2L, 3L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 3L, 3L, 3L, 1L, 2L, 3L,
2L, 1L, 1L, 1L, 3L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
1L, 1L, 3L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 3L, 1L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 1L, 3L, 3L, 2L, 1L, 1L, 1L, 1L, 1L, 3L,
1L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 2L,
1L, 2L, 2L, 2L, 3L, 1L, 1L, 3L, 3L, 2L, 1L, 1L, 2L, 1L, 1L,
1L, 3L, 2L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 2L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 1L, 3L, 1L, 1L, 1L, 1L, 3L,
1L, 1L, 3L, 1L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 1L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 2L, 1L,
2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 3L, 2L, 3L, 3L, 3L,
1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 3L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 3L, 2L, 2L,
2L, 1L, 2L, 1L, 3L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
1L, 1L, 2L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 2L,
2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L,
2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 3L, 3L, 3L,
3L, 1L, 1L, 1L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 2L, 1L, 2L, 1L,
3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L,
1L, 1L, 1L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 2L, 3L,
3L, 1L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 1L, 1L, 3L, 1L,
1L, 2L, 2L, 1L, 1L, 3L, 1L, 2L, 1L, 1L, 1L, 1L, 3L, 1L, 1L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 1L,
3L, 1L, 3L, 3L, 3L, 1L, 3L, 3L, 2L, 2L, 1L, 2L, 2L, 2L, 3L,
3L, 2L, 3L, 3L, 1L, 2L, 1L, 3L, 2L, 3L, 3L, 3L, 1L, 1L, 2L,
1L, 1L, 3L, 2L, 2L, 1L, 1L, 1L, 3L, 1L), .Label = c("Classic",
"Deluxe", "Nidi"), class = "factor"), guests = c(3, 1, 2,
2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 1,
1, 3, 1, 3, 2, 2, 3, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,
3, 2, 3, 2, 3, 4, 2, 1, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 1, 2, 2, 3, 2, 2, 3, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1,
2, 2, 2, 3, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 1, 1, 2, 4, 2, 3, 3, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2,
4, 3, 2, 2, 3, 2, 2, 2, 1, 2, 2, 2, 1, 4, 2, 3, 2, 2, 1,
3, 3, 2, 2, 2, 3, 2, 4, 2, 2, 3, 3, 2, 2, 2, 2, 3, 2, 3,
2, 3, 4, 2, 2, 3, 2, 2, 2, 3, 3, 2, 1, 3, 1, 3, 2, 2, 3,
2, 1, 3, 4, 3, 1, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1,
2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 3, 1, 2, 2, 2, 2,
2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 3, 3, 2, 2, 2, 1, 2, 1, 1,
1, 1, 4, 1, 2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1,
2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 2, 1,
1, 2, 3, 2, 4, 2, 1, 1, 1, 3, 2, 1, 3, 2, 2, 1, 2, 2, 2,
1, 2, 1, 1, 2, 1, 1, 1, 3, 3, 2, 1, 2, 1, 1, 1, 2, 2, 2,
1, 2, 1, 3, 1, 3, 3, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2, 1,
2, 2, 2, 2, 1, 1, 2, 2, 4, 1, 2, 1, 1, 2, 1, 2, 1, 2, 2,
4, 4, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 1, 4,
1, 1, 2, 3, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1,
1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 4, 2, 2,
2, 2, 1, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 4, 2, 3, 2, 4,
3, 3, 3, 3, 3, 3, 4, 2, 3, 3, 2, 2, 3, 3, 2, 2, 3, 2, 2,
2, 3, 4, 2, 2, 4, 4, 4, 1, 2, 2, 4, 3, 2, 3, 2, 3, 2, 3,
2, 3, 4, 3, 2, 2, 2, 2, 2, 3, 2, 2, 2, 3, 3, 2, 2, 3, 2,
2, 4, 2, 3, 3, 3, 2, 2, 2, 2, 2, 3, 2, 2, 2, 3, 3, 2, 2,
2, 2, 2, 2, 1, 4, 2, 2, 2, 3, 2, 2, 4, 3, 1, 3, 3, 4, 3,
2, 4, 4, 3, 3, 2, 3, 1, 2, 2, 3, 4, 2, 2, 2, 2, 2, 3, 2,
2, 2, 2, 2, 3, 3, 3, 3, 2, 1, 2, 2, 2, 2, 2, 2, 2, 3, 4,
3, 3, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 3, 2, 3, 2, 2, 3, 2,
2, 2, 4, 2, 2, 2, 1, 2, 1, 2, 2, 2, 1, 2, 1, 1, 2, 2, 2,
2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 2, 2, 1, 1, 2, 2,
1, 2, 2, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 1, 4, 2, 3, 5, 2,
4, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 2, 1, 1, 1, 2, 2, 2, 2,
2, 3, 2, 2, 3, 2, 3, 2, 1, 1, 4, 2, 1, 3, 2, 2, 2, 2, 1,
2, 2, 2, 1, 1, 1, 2, 4, 2, 2, 2, 2, 2, 2, 1, 2, 4, 3, 3,
2, 2, 2, 2, 3, 4, 2, 2, 2, 2, 3, 2, 2, 2, 2, 3, 2, 2, 2,
2, 4, 2, 3, 1, 2, 1, 2, 3, 2, 2, 2, 1, 3, 2, 2, 2, 2, 1,
2, 2, 1, 1, 1, 2, 1, 2, 2, 2, 1, 1, 1, 3, 1, 2, 2, 2, 2,
2, 2, 1, 2, 1, 1, 1, 2, 1, 3, 3, 2, 1, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1,
1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,
1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 1, 1, 1, 1,
1, 1, 2, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1,
2, 2, 2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2,
1, 2, 1, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2,
1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1,
2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3,
2, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,
2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1,
2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2,
1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 1, 2, 1, 2,
2, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 2, 1, 1, 2, 2, 2,
2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 1, 2,
1, 2, 2, 2, 2, 2, 1, 1, 2, 1, 2, 2, 2, 1, 1, 2, 1, 2, 2,
2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2,
1, 1, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1,
2, 2, 1, 1, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2, 2, 2,
1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 3, 3, 2, 4, 4, 2, 3, 2, 2,
4, 4, 2, 2, 2, 3, 3, 2, 2, 2, 4, 3, 2, 2, 2, 3, 2, 2, 2,
2, 3, 4, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 2, 4, 2, 3, 1,
3, 3, 3, 2, 2, 2, 3, 3, 4, 3, 2, 2, 3, 3, 2, 2, 2, 2, 4,
3, 2, 2, 2, 2, 5, 2, 2, 4, 2, 1, 2, 3, 4, 3, 1, 2, 1, 2,
2, 2, 2, 3, 4, 2, 2, 2, 2, 1, 2, 3, 2, 3, 2, 2, 3, 3, 2,
2, 2, 2, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 5, 2, 2, 2, 2, 1,
2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 2, 1,
3, 2, 2, 2, 2, 2, 2, 2, 4, 2, 1, 2, 1, 1, 1, 1, 2, 2, 1,
1, 2, 4, 3, 2, 4, 3, 2, 3, 2, 2, 2, 2, 4, 2, 4, 4, 2, 1,
1, 1, 1, 1, 2, 2, 2, 3, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 3,
2, 2, 2, 4, 3, 3, 2, 2, 3, 1, 3, 2, 1, 3, 3, 2, 2, 2, 2,
3, 4, 2, 2, 4, 4, 4, 4, 2, 3, 2, 2, 2, 2, 4, 4, 2, 1, 4,
2, 5, 5, 2, 2, 3, 2, 4, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 3,
2, 3, 2, 2, 2, 2, 3, 5, 2, 2, 3, 2, 2, 1, 2, 2, 2, 5, 4,
2, 2, 2, 5, 2, 3, 2, 2, 4, 2, 2, 2, 1, 1, 2, 2, 2, 1, 2,
4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 3, 3, 2,
3, 2, 2, 2, 1, 2, 2, 3, 2, 2, 2, 2, 4, 2, 4, 2, 2, 3, 1,
1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 1, 1, 1, 2, 1,
1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1,
1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1, 1,
1, 1, 2, 2, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2,
2, 1, 3, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2,
2, 1, 2, 2, 2, 1, 1, 2, 3, 3, 4, 2, 1, 2, 2, 2, 3, 2, 3,
2, 4, 1, 2, 2, 2, 4, 2, 4, 1, 2, 2, 2, 1, 2, 3, 4, 2, 3,
3, 2, 3, 2, 4, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 2, 2, 4, 2,
2, 2, 2, 5, 1, 2, 2, 2, 2, 3, 4, 4, 2, 1, 3, 2, 2, 2, 4,
1, 2, 3, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 4, 4, 2, 2, 2, 2, 2, 2, 4, 3, 2, 2, 2,
2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 1, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 2, 2, 2, 1, 2, 2,
2, 2, 3, 1, 1, 2, 2, 3, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 4, 2, 2,
2, 3, 3, 2, 2, 2, 4, 2, 1, 2, 2, 4, 3, 2, 4, 2, 2, 4, 2,
2, 2, 2, 4, 2, 3, 3, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2,
2, 2, 2, 4, 2, 2, 2, 3, 2, 1, 1, 2, 2, 2, 4, 2, 2, 2, 2,
2, 3, 2, 2, 2, 2, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 1,
1, 1, 1, 1, 1, 1, 2, 2, 2, 4, 2, 1, 2, 2, 2, 2, 2, 1, 2,
2, 1, 2, 2, 1, 1, 1, 3, 1, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1,
1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 2, 2, 1,
2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1,
2, 1, 2, 2, 2, 1, 1, 2, 1, 1, 2, 3, 1, 3, 1, 2, 2, 2, 1,
2, 1, 1, 1, 2, 1, 1, 3, 1, 1, 1, 2, 1, 1, 2, 1, 3, 2, 1,
1, 1, 2, 1, 1, 1, 2, 2, 2, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1,
2, 1, 2, 1, 1, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 1, 4, 3,
3, 4, 2, 4, 3, 2, 2, 2, 3, 4, 3, 3, 3, 4, 4, 2, 2, 4, 2,
4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 1, 2, 2, 4, 3, 2, 2, 2,
4, 2, 2, 2, 3, 2, 2, 1, 3, 4, 2, 4, 2, 3, 2, 2, 3, 2, 2,
2, 4, 2, 3, 2, 3, 1, 2, 2, 2, 2, 2, 3, 2, 2, 3, 3, 2, 1,
2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2, 3, 2, 1,
2, 2, 2, 3, 2, 2, 2, 1, 2, 1, 2, 2, 1, 2, 2, 3, 3, 1, 1,
1, 2, 2, 2, 2, 2, 3, 1, 1, 3, 2, 1, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 3, 2, 3, 1, 1, 1, 1, 1, 1, 4, 4, 2, 3, 2, 1,
3, 2, 2, 3, 1, 1, 1, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 1, 2,
2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2,
2, 3, 2, 4, 2, 2, 2, 1, 3, 2, 3, 2, 3, 5, 3, 3, 2, 2, 2,
2, 2, 3, 2, 2, 2, 3, 2, 3, 2, 2, 3, 2, 3, 2, 3, 2, 3, 4,
4, 2, 2, 4, 4, 2, 2, 2, 2, 3, 2, 2, 2, 3, 2, 4, 1, 1, 1,
1, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2,
4, 2, 3, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1,
1, 2, 1, 1, 1, 1, 2, 3, 2, 2, 3, 3, 3, 3, 3, 4, 2, 2, 2,
4, 3, 2, 4, 2, 3, 3, 1, 3, 2, 2, 2, 2, 3, 3, 3, 2, 2, 4,
3, 2, 2, 3, 3, 3, 3, 2, 2, 1, 2, 4, 4, 2, 2, 2, 2, 2, 2,
3, 2, 2, 2, 2, 1, 2, 3, 2, 2, 4, 4, 2, 3, 1, 3, 2, 5, 2,
2, 3, 2, 2, 2, 1, 4, 2, 3, 5, 3, 4, 2, 2, 2, 2, 3, 2, 2,
2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2, 2, 2, 3,
2, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 2, 3, 3, 2, 2, 1, 4, 1,
2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 1, 4,
2, 1, 4, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 3, 4, 3, 2, 2, 2,
4, 1, 2, 2, 3, 2, 2, 2, 2, 3, 4, 3, 3, 2, 3, 2, 3, 2, 2,
4, 3, 2, 3, 2, 4, 2, 2, 2, 2, 1, 4, 3, 3, 3, 3, 2, 2, 4,
3, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 3, 2, 1, 1, 2, 1, 2, 1,
2, 1, 3, 1, 1, 2, 2, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2,
2, 2, 2, 2, 2, 4, 2, 3, 2, 2, 2, 4, 3, 2, 4, 5, 4, 3, 2,
3, 3, 3, 1, 3, 4, 3, 4, 5, 1, 1, 2, 3, 2, 2, 2, 2, 2, 2,
2, 2, 2, 3, 1, 3, 2, 2, 3, 3, 2, 4, 3, 1, 3, 3, 3, 4, 3,
2, 4, 2, 3, 3, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1,
1, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 1, 2, 2,
2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 1, 2, 2, 1,
2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 4, 2, 4, 4, 3, 2, 2,
2, 2, 1, 2, 2, 2, 2, 2, 4, 1, 2, 2, 1, 2, 2, 2, 2, 3, 2,
3, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2,
2, 2, 1, 3, 3, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1,
2, 1, 3, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2,
2, 3, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 4, 2, 2,
2, 2, 3, 2, 2, 2, 4, 3, 2, 5, 2, 2, 3, 4, 4, 1, 2, 2, 2,
2, 2, 3, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 4, 1, 2, 4,
2, 2, 2, 2, 2, 4, 3, 2, 2, 2, 4, 3, 3, 4, 3, 2, 2, 1, 2,
2, 3, 2, 2, 1, 3, 2, 2, 2, 5, 2, 2, 3, 2, 2, 2, 3, 2, 2,
2, 2, 2, 2, 3, 2, 1, 3, 2, 4, 2, 2, 2, 2, 3, 2, 3, 2, 2,
2, 3, 2, 2, 3, 2, 3, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 1, 2,
1, 4, 2, 2, 1, 4, 1, 3, 2, 2, 3, 2, 3, 3, 3, 4, 2, 2, 3,
4, 2, 2, 2, 2, 4, 2, 2, 1, 3, 2, 2, 4, 2, 3, 2, 2, 3, 2,
2, 2, 2, 4, 4, 3, 3, 3, 3, 3, 4, 3, 2, 3, 2, 3, 3, 1, 2,
2, 2, 3, 5, 1, 2, 2, 2, 2, 3, 2, 2, 2, 4, 2, 2, 1, 2, 1,
2, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 1, 2, 4, 4, 2, 4, 4,
2, 4, 3, 4, 4, 3, 2, 3, 2, 1, 2, 2, 3, 2, 2, 2, 2, 2, 2,
3, 3, 3, 3, 2, 2, 2, 2, 3, 2, 3, 2, 3, 4, 2, 3, 2, 2, 2,
3, 4, 2, 4, 3, 4, 2, 2, 2, 3, 2, 2, 2, 3, 2, 4, 2, 2, 2,
2, 2, 3, 2, 2, 2, 2, 2, 3, 2, 2, 2, 4, 2, 2, 2, 2, 2, 1,
2, 2, 2, 2, 2, 2, 2, 4, 4, 5, 2, 2, 3, 2, 3, 3, 2, 3, 2,
2, 2, 2, 2, 3, 2, 2, 3, 3, 4, 4, 1, 2, 2, 3, 2, 3, 3, 2,
2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 4, 3, 2, 2, 3, 2, 2, 3, 2,
4, 3, 2, 2, 2, 2, 4, 2, 2, 1, 2, 2, 1, 2, 3, 2, 2, 2, 3,
3, 2, 2, 2, 2, 2, 3, 3, 2, 4, 2, 2, 2, 2, 2, 2, 1, 1, 2,
2, 2, 2, 3, 1, 3, 1, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 3,
2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 4, 2, 2,
3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4,
1, 3, 2, 2, 2, 1, 1, 4, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 1,
1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
3, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
1, 2, 2, 4, 3, 2, 2, 1, 2, 1, 2, 3, 2, 2, 2, 2, 2, 2, 3,
2, 2, 2, 2, 3, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2,
3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1, 2,
2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 2, 2, 2, 1, 1, 2,
1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1,
1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
2, 2, 2, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2,
1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 1, 1, 2, 1, 2,
2, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1,
1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1,
2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 3, 4, 2, 4, 2, 2, 3,
2, 2, 2, 3, 2, 3, 3, 3, 2, 3, 4, 2, 2, 2, 3, 2, 2, 3, 3,
3, 2, 2, 2, 3, 3, 2, 2, 1, 2, 2, 1, 4, 4, 3, 2, 3, 2, 1,
4, 2, 2, 2, 2, 3, 2, 2, 4, 2, 3, 2, 2, 3, 2, 3, 2, 2, 1,
4, 2, 2, 3, 3, 5, 2, 4, 4, 4, 4, 4, 2, 2, 2, 3, 2, 3, 1,
2, 3, 2, 4, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 4, 2, 3, 2, 3,
3, 2, 2, 3, 2, 2, 3, 3, 3, 2, 2, 1, 1, 2, 3, 2, 2, 2, 1,
2, 3, 3, 2, 2, 3, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 4, 2,
2, 2, 2, 1, 2, 4, 2, 2, 3, 4, 4, 4, 2, 2, 3, 2, 2, 2, 2,
2, 2, 3, 2, 4, 2, 2, 3, 3, 3, 3, 2, 2, 3, 2, 3, 2, 3, 1,
2, 4, 2, 1, 4, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 4, 3, 2, 2,
2, 3, 3, 2, 2, 3, 4, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 3, 2,
3, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 4, 2, 1, 2, 2, 1, 2, 3,
2, 3, 2, 2, 2, 2, 3, 3, 2, 4, 2, 3, 2, 2, 1, 2, 2, 2, 2,
2, 1, 2, 2, 2, 2, 1, 2, 2, 4, 2, 1, 2, 3, 3, 2, 2, 2, 2,
2, 2, 2, 1, 2, 2, 2, 2, 2, 3, 3, 2, 2, 3, 2, 4, 3, 3, 2,
4, 2, 2, 2, 2, 1, 1, 2, 2, 3, 1, 2, 2, 2, 2, 2, 2, 2, 2,
2, 1, 1, 3, 3, 2, 2, 2, 2, 2, 2, 3, 3, 2, 2, 2, 2, 1, 2,
2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2,
1, 1, 2, 1, 2, 1, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2, 2, 1, 1,
2, 2, 2, 2, 2, 2, 2, 1, 2, 4, 4, 3, 2, 2, 2, 1, 1, 1, 1,
2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1,
2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 3, 4, 1, 2, 2, 2,
2, 2, 2, 4, 2, 3, 3, 2, 2, 2, 1, 2, 4, 2, 3, 3, 2, 4, 2,
1, 2, 2, 4, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 3,
3, 2, 1, 3, 2, 2, 3, 1, 1, 2, 4, 3, 2, 2, 2, 4, 2, 2, 2,
2, 2, 2, 2, 4, 2, 2, 3, 3, 3, 4, 2, 1, 2, 2, 2, 3, 2, 2,
2, 2, 3, 2, 3, 2, 2, 4, 2, 1, 2, 3, 2, 2, 4, 2, 2, 2, 2,
2, 2, 1, 2, 2, 2, 2, 5, 2, 2, 2, 2, 4, 1, 2, 2, 2, 4, 3,
2, 4, 2, 3, 2, 2, 2, 1, 2, 2, 1, 2, 3, 1, 3, 4, 2, 2, 3,
4, 3, 1, 3, 3, 2, 1, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 4, 2,
2, 3, 2, 2, 2, 2, 3, 2, 2, 3, 3, 4, 2, 4, 2, 2, 1, 1, 1,
3, 4, 2, 2, 2, 2, 3, 2, 1, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 3, 3, 2, 2, 3, 3, 4, 3, 3, 3, 3, 3,
2, 3, 3, 2, 2, 3, 2, 3, 2, 4, 1, 3, 4, 3, 2, 2, 2, 2, 2,
2, 2, 3, 2, 3, 3, 4, 3, 3, 3, 3, 2, 2, 2, 2, 1, 2, 3, 3,
2, 2, 2, 2, 3, 2, 3, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 1, 1,
2, 2, 2, 2, 2, 1, 3, 5, 2, 4, 2, 2, 3, 2, 2, 2, 1, 1, 2,
3, 2, 4, 2, 2, 2, 2, 2, 2, 3, 2, 3, 2, 2, 2, 2, 2, 2, 3,
4, 4, 2, 2, 4, 2, 2, 5, 4, 2, 2, 3, 5, 4, 3, 4, 3, 2, 2,
4, 2, 4, 4, 2, 4, 2, 2, 2, 2, 2, 2, 3, 4, 4, 4, 2, 3, 2,
2, 3, 2, 2, 2, 3, 4, 2, 4, 2, 2, 3, 2, 2, 4, 2, 2, 2, 2,
2, 1, 1, 4, 1, 4, 2, 2, 2, 1, 3, 2, 2, 2, 5, 2, 1, 1, 3,
2, 2, 4, 4, 4, 2, 4, 4, 2, 2, 2, 3, 2, 4, 4, 3, 4, 3, 4,
2, 3, 2, 3, 4, 2, 4, 2, 2, 3, 2, 2, 4, 2, 2, 3, 1, 2, 2,
4, 4, 3, 4, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 4, 3, 2, 3, 2,
2, 4, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2,
2, 2, 2, 1, 2, 1, 1, 5, 2, 2, 1, 1, 1, 2, 2, 2, 4, 3, 2,
2, 2, 4, 2, 3, 2, 2, 2, 2, 2, 3, 3, 2, 2, 3, 2, 2, 2, 2,
3, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 1, 2, 4, 2, 2, 2, 2,
2, 2, 3, 2, 2, 2, 2, 1, 4, 3, 1, 1, 2, 2, 2, 2, 2, 2, 2,
3, 2, 2, 2, 2, 2, 3, 1, 2, 2, 2, 3, 2, 4, 2, 2, 3, 2, 2,
2, 2, 2, 4, 2, 4, 2, 3, 1, 3, 2, 2, 2, 2, 2, 2, 1, 1, 1,
1, 3, 2, 4, 3, 4, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 3,
3, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 3, 4, 3, 1, 1, 4, 5, 2,
2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 4, 3, 3, 2, 2, 1, 4, 2,
2, 2, 1, 2, 1, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 1, 4, 3, 2, 3, 2, 1, 1, 1, 1, 3,
3, 1, 2, 4, 2, 2, 3, 3, 2, 2, 2, 1, 2, 3, 4, 3, 4, 2, 1,
2, 2, 2, 2, 4, 2, 4, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 1, 2, 2, 1, 2, 1,
2, 2, 2, 2, 5, 2, 2, 2, 2, 1, 2, 4, 2, 2, 4, 3, 4, 2, 2,
2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 2, 2, 1, 4, 3,
3, 1, 2, 1, 2, 3, 2, 1, 2, 4, 2, 3, 2, 4, 2, 2, 2, 3, 3,
2, 3, 2, 3, 3, 3, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2,
1, 2, 2, 1, 3, 1, 1, 2, 2, 3, 1, 2, 4, 2, 2, 1, 2, 3, 2,
2, 2, 2, 3, 2, 2, 2, 3, 3, 2, 2, 2, 2, 1, 2, 1, 4, 3, 3,
4, 4, 1, 1, 2, 2, 2, 2, 2, 2, 1, 3, 3, 2, 1, 2, 4, 3, 1,
2, 1, 3, 1), arrangement = structure(c(1L, 3L, 1L, 1L, 2L,
2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 3L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 3L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 3L, 1L, 1L, 3L, 1L, 2L, 2L, 2L, 3L, 3L, 1L, 3L, 1L, 2L,
2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 2L, 2L, 2L, 2L, 1L, 2L,
1L, 1L, 2L, 2L, 3L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
3L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 3L, 1L, 2L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L,
2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 3L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 2L,
1L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 1L, 2L, 2L,
2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 1L, 1L, 3L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 3L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 2L, 3L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 2L,
2L, 2L, 3L, 2L, 3L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L,
2L, 1L, 2L, 1L, 1L, 2L, 2L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
1L, 2L, 1L, 1L, 3L, 2L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 3L, 2L,
2L, 1L, 2L, 1L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 1L,
2L, 2L, 3L, 1L, 1L, 3L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L,
2L, 2L, 3L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L,
1L, 3L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 3L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L,
1L, 2L, 2L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 3L, 3L,
3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 3L, 2L, 2L, 2L,
2L, 3L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L,
3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 2L, 2L,
2L, 2L, 3L, 3L, 1L, 3L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 3L,
3L, 2L, 1L, 3L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 1L, 2L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 2L, 2L, 1L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 3L, 1L, 2L, 3L, 3L, 2L, 1L, 2L, 2L, 2L, 3L,
2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 3L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 2L, 1L, 2L, 2L, 1L, 2L, 3L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L,
2L, 2L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
3L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 3L, 2L, 2L, 1L, 2L,
1L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 1L, 2L, 2L, 2L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 2L, 2L, 2L, 2L, 1L, 3L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L,
2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 3L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L,
1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 1L,
2L, 3L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 3L,
3L, 2L, 2L, 2L, 2L, 1L, 3L, 2L, 2L, 3L, 1L, 2L, 1L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L,
1L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L,
1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 3L, 1L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 1L, 2L, 1L, 1L, 2L,
3L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 1L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L,
3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 3L, 1L, 2L, 2L,
1L, 1L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 2L,
2L, 2L, 2L, 3L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 2L, 2L, 2L,
1L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L,
2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 3L, 2L, 2L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L,
1L, 2L, 3L, 3L, 2L, 2L, 2L, 1L, 2L, 2L, 3L, 3L, 1L, 2L, 3L,
3L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 1L, 1L,
2L, 2L, 3L, 2L, 3L, 1L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
1L, 2L, 2L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 1L, 2L, 2L,
3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 3L,
3L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 3L,
3L, 3L, 1L, 2L, 2L, 2L, 2L, 3L, 2L, 1L, 3L, 2L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 1L, 3L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
2L, 3L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 1L, 3L, 2L, 2L, 2L, 2L, 3L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L,
3L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 2L, 3L,
3L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 3L, 1L, 2L, 2L,
2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L,
1L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L,
2L, 2L, 2L, 2L, 3L, 2L, 2L, 1L, 2L, 2L, 3L, 2L, 2L, 1L, 3L,
3L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 3L, 2L, 2L, 1L, 1L, 1L, 1L,
2L, 3L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 3L, 2L, 3L, 2L, 2L, 3L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 1L, 1L, 1L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L,
3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 3L, 3L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
3L, 2L, 2L, 2L, 3L, 2L, 1L, 2L, 2L, 3L, 1L, 3L, 2L, 2L, 3L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L,
2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 1L, 3L, 2L,
2L, 2L, 2L, 3L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
2L, 3L, 2L, 2L, 3L, 1L, 1L, 2L, 3L, 3L, 2L, 2L, 3L, 2L, 2L,
2L, 3L, 2L, 2L, 2L, 3L, 2L, 3L, 1L, 2L, 2L, 2L, 3L, 2L, 2L,
2L, 2L, 3L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L,
2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 3L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 1L, 3L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 3L, 2L,
2L, 2L, 2L, 3L, 3L, 2L, 1L, 2L, 3L, 2L, 2L, 2L, 2L, 1L, 3L,
2L, 2L, 1L, 3L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 1L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 2L, 2L, 2L, 1L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L,
2L, 3L, 2L, 2L, 1L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L,
2L, 3L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 2L, 3L, 2L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 3L, 2L, 2L, 1L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 3L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 3L, 3L, 3L, 2L, 1L, 2L, 3L,
2L, 2L, 3L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L,
2L, 2L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L,
2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 2L, 2L, 2L, 2L, 3L, 3L,
2L, 2L, 3L, 3L, 1L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 3L, 2L, 2L,
3L, 2L, 2L, 2L, 3L, 3L, 1L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 2L,
3L, 2L, 2L, 2L, 1L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 3L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
2L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 3L, 2L,
2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 3L, 3L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 3L,
2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L,
1L, 2L, 2L, 2L, 3L, 1L, 3L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L,
3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L,
2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L,
3L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
3L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 1L, 3L,
2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 3L,
2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 3L, 2L, 2L, 3L, 2L, 2L, 2L,
3L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 3L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L,
2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L,
3L, 2L, 3L, 1L, 3L, 2L), .Label = c("BB", "HB", "FB"), class = c("ordered",
"factor")), kids02 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,
1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 2,
0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), kids311 = c(1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 2, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 2, 0, 0,
0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1,
0, 0, 0, 1, 1, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,
0, 1, 0, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,
1, 0, 0, 1, 0, 2, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 2, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1,
2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2,
1, 1, 0, 0, 0, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,
0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 0, 0, 0, 0, 3, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 2, 0, 2, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 2, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 2, 1, 0,
0, 1, 0, 3, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0,
3, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,
0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0,
2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0,
1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 2, 1, 2, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 2, 0, 0,
1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 1, 0, 0, 0,
0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 2, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 0,
0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,
0, 2, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0,
1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 2, 1, 0,
0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0,
0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 2, 2, 1,
1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0,
2, 0, 0, 0, 0, 2, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 1, 0, 1, 2, 0,
0, 1, 2, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0,
0, 0, 0, 0, 0, 2, 2, 1, 1, 2, 0, 0, 1, 1, 0, 1, 0, 1, 1,
0, 1, 0, 1, 1, 2, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 2, 1, 2, 3, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0,
0, 0, 1, 1, 0, 2, 0, 1, 0, 0, 0, 2, 0, 1, 0, 1, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 2, 0, 0, 0, 0, 0, 1, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 1, 0, 1,
0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0,
1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,
1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1,
0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 2, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 1, 2, 0, 0, 1, 1, 2, 2, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2,
0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,
1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0,
0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0,
1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0,
0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0,
2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0,
0, 1, 0, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0,
0, 0, 1, 1, 0, 2, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0,
2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,
0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 2, 2, 0, 1, 0,
1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 2, 2, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 1, 0, 0, 0, 0,
0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,
0, 0, 2, 1, 0, 0, 1, 0, 0, 2, 1, 0, 0, 0, 2, 2, 0, 1, 1,
0, 0, 2, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 2, 2, 0,
0, 0, 0, 1, 0, 0, 0, 1, 2, 0, 1, 0, 0, 1, 0, 0, 2, 0, 0,
0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0,
0, 0, 0, 0, 2, 2, 0, 0, 1, 2, 0, 0, 0, 0, 0, 2, 1, 1, 1,
0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0,
0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1,
0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,
2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 2, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 2, 2, 1,
0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0,
2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0,
1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,
1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 2,
0, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2,
1, 0, 0, 0, 0, 0)), class = "data.frame", row.names = c(16L,
17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L,
30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L,
43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L,
56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L,
69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L,
82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L,
95L, 96L, 97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L, 105L,
106L, 107L, 108L, 109L, 110L, 111L, 112L, 113L, 114L, 115L, 116L,
117L, 118L, 119L, 120L, 121L, 122L, 123L, 124L, 125L, 126L, 127L,
128L, 129L, 130L, 131L, 132L, 133L, 134L, 135L, 136L, 137L, 138L,
139L, 140L, 141L, 142L, 143L, 144L, 145L, 146L, 147L, 148L, 149L,
150L, 151L, 152L, 153L, 154L, 155L, 156L, 157L, 158L, 159L, 160L,
161L, 162L, 163L, 164L, 165L, 166L, 167L, 168L, 169L, 170L, 171L,
172L, 173L, 174L, 175L, 176L, 177L, 178L, 179L, 180L, 181L, 182L,
183L, 184L, 185L, 186L, 187L, 188L, 189L, 190L, 191L, 192L, 193L,
194L, 195L, 196L, 197L, 198L, 199L, 200L, 201L, 202L, 203L, 204L,
205L, 206L, 207L, 208L, 209L, 210L, 211L, 212L, 213L, 214L, 215L,
216L, 217L, 218L, 219L, 220L, 221L, 222L, 223L, 224L, 225L, 226L,
227L, 228L, 229L, 230L, 231L, 232L, 233L, 234L, 235L, 236L, 237L,
238L, 239L, 240L, 241L, 242L, 243L, 244L, 245L, 246L, 247L, 248L,
249L, 250L, 251L, 252L, 253L, 254L, 255L, 262L, 263L, 264L, 265L,
266L, 267L, 268L, 269L, 270L, 271L, 272L, 273L, 274L, 275L, 276L,
277L, 278L, 279L, 280L, 281L, 282L, 283L, 284L, 285L, 286L, 287L,
288L, 289L, 290L, 291L, 292L, 293L, 294L, 295L, 296L, 297L, 298L,
299L, 300L, 301L, 302L, 303L, 304L, 305L, 306L, 307L, 308L, 309L,
310L, 311L, 312L, 313L, 314L, 315L, 316L, 317L, 318L, 319L, 320L,
321L, 322L, 323L, 324L, 325L, 326L, 327L, 328L, 329L, 330L, 331L,
332L, 333L, 334L, 335L, 336L, 337L, 338L, 339L, 340L, 341L, 342L,
343L, 344L, 345L, 346L, 347L, 348L, 349L, 350L, 351L, 352L, 353L,
354L, 355L, 356L, 357L, 358L, 359L, 360L, 361L, 362L, 363L, 364L,
365L, 366L, 367L, 368L, 369L, 370L, 371L, 372L, 373L, 374L, 375L,
376L, 377L, 378L, 379L, 380L, 381L, 382L, 383L, 384L, 385L, 386L,
387L, 388L, 389L, 390L, 391L, 392L, 393L, 394L, 395L, 396L, 397L,
398L, 399L, 400L, 401L, 402L, 403L, 404L, 405L, 406L, 407L, 408L,
409L, 410L, 411L, 412L, 413L, 414L, 415L, 416L, 417L, 418L, 419L,
420L, 421L, 422L, 423L, 424L, 425L, 426L, 427L, 428L, 429L, 430L,
431L, 432L, 433L, 434L, 435L, 436L, 437L, 438L, 439L, 440L, 441L,
442L, 443L, 444L, 445L, 446L, 447L, 448L, 449L, 450L, 451L, 452L,
453L, 454L, 455L, 456L, 457L, 458L, 459L, 460L, 461L, 462L, 463L,
464L, 465L, 466L, 467L, 468L, 469L, 470L, 471L, 472L, 473L, 474L,
475L, 476L, 477L, 478L, 479L, 480L, 481L, 482L, 483L, 484L, 485L,
486L, 487L, 488L, 489L, 490L, 491L, 492L, 493L, 494L, 495L, 496L,
497L, 498L, 499L, 500L, 501L, 502L, 503L, 504L, 505L, 506L, 507L,
508L, 509L, 510L, 511L, 512L, 513L, 514L, 515L, 516L, 517L, 518L,
519L, 520L, 521L, 522L, 523L, 524L, 525L, 526L, 527L, 528L, 529L,
530L, 531L, 532L, 533L, 534L, 535L, 536L, 537L, 538L, 539L, 540L,
541L, 542L, 543L, 544L, 545L, 546L, 547L, 548L, 549L, 550L, 551L,
552L, 553L, 554L, 555L, 556L, 557L, 558L, 559L, 560L, 561L, 562L,
563L, 564L, 565L, 566L, 567L, 568L, 569L, 570L, 571L, 572L, 573L,
574L, 575L, 576L, 577L, 578L, 579L, 580L, 581L, 582L, 583L, 584L,
585L, 586L, 587L, 588L, 589L, 590L, 591L, 592L, 593L, 594L, 595L,
596L, 597L, 598L, 599L, 600L, 601L, 602L, 603L, 604L, 605L, 606L,
607L, 608L, 609L, 610L, 611L, 612L, 613L, 614L, 615L, 616L, 617L,
618L, 619L, 623L, 624L, 625L, 626L, 627L, 628L, 629L, 630L, 631L,
632L, 633L, 634L, 635L, 636L, 637L, 638L, 639L, 640L, 641L, 642L,
643L, 644L, 645L, 646L, 647L, 648L, 649L, 650L, 651L, 652L, 653L,
654L, 655L, 656L, 657L, 658L, 659L, 660L, 661L, 662L, 663L, 664L,
665L, 666L, 667L, 668L, 669L, 670L, 671L, 672L, 673L, 674L, 675L,
676L, 677L, 678L, 679L, 680L, 681L, 682L, 683L, 684L, 685L, 686L,
687L, 688L, 689L, 690L, 691L, 692L, 693L, 694L, 695L, 696L, 697L,
698L, 699L, 700L, 701L, 702L, 703L, 704L, 705L, 706L, 707L, 708L,
709L, 710L, 711L, 712L, 713L, 714L, 715L, 716L, 717L, 718L, 719L,
720L, 721L, 722L, 723L, 724L, 725L, 726L, 727L, 728L, 729L, 730L,
731L, 732L, 733L, 734L, 735L, 736L, 737L, 738L, 739L, 740L, 741L,
742L, 743L, 744L, 745L, 746L, 747L, 748L, 749L, 750L, 751L, 752L,
753L, 754L, 755L, 756L, 757L, 758L, 759L, 760L, 761L, 762L, 763L,
764L, 765L, 766L, 767L, 768L, 769L, 770L, 771L, 772L, 773L, 774L,
775L, 776L, 777L, 778L, 779L, 780L, 781L, 782L, 783L, 784L, 785L,
786L, 787L, 788L, 789L, 790L, 791L, 792L, 793L, 794L, 795L, 796L,
797L, 798L, 799L, 800L, 801L, 802L, 803L, 804L, 805L, 806L, 807L,
808L, 809L, 810L, 811L, 812L, 813L, 814L, 815L, 816L, 817L, 818L,
819L, 820L, 821L, 822L, 823L, 824L, 825L, 826L, 827L, 828L, 829L,
830L, 831L, 832L, 833L, 834L, 835L, 836L, 837L, 838L, 839L, 840L,
841L, 842L, 843L, 844L, 845L, 846L, 847L, 848L, 849L, 850L, 851L,
852L, 853L, 854L, 855L, 856L, 857L, 858L, 859L, 875L, 876L, 877L,
878L, 879L, 880L, 881L, 882L, 883L, 884L, 885L, 886L, 887L, 888L,
889L, 890L, 891L, 892L, 893L, 894L, 895L, 896L, 897L, 898L, 899L,
900L, 901L, 902L, 903L, 904L, 905L, 906L, 907L, 908L, 909L, 910L,
911L, 912L, 913L, 914L, 915L, 916L, 917L, 918L, 919L, 920L, 921L,
922L, 923L, 924L, 925L, 926L, 927L, 928L, 929L, 930L, 931L, 932L,
933L, 934L, 935L, 936L, 937L, 938L, 939L, 940L, 941L, 942L, 943L,
944L, 945L, 946L, 947L, 948L, 949L, 950L, 951L, 952L, 953L, 954L,
955L, 956L, 957L, 958L, 959L, 960L, 961L, 962L, 963L, 964L, 965L,
966L, 967L, 968L, 969L, 970L, 971L, 972L, 973L, 974L, 975L, 976L,
977L, 978L, 979L, 980L, 981L, 982L, 983L, 984L, 985L, 986L, 987L,
988L, 989L, 990L, 991L, 992L, 993L, 994L, 995L, 996L, 997L, 998L,
999L, 1000L, 1001L, 1002L, 1003L, 1004L, 1005L, 1006L, 1007L,
1008L, 1009L, 1010L, 1011L, 1012L, 1013L, 1014L, 1015L, 1016L,
1017L, 1018L, 1019L, 1020L, 1021L, 1022L, 1023L, 1024L, 1025L,
1026L, 1027L, 1028L, 1029L, 1030L, 1031L, 1032L, 1033L, 1034L,
1035L, 1036L, 1037L, 1038L, 1039L, 1040L, 1041L, 1042L, 1043L,
1044L, 1045L, 1046L, 1047L, 1048L, 1049L, 1050L, 1051L, 1052L,
1053L, 1054L, 1055L, 1056L, 1057L, 1058L, 1059L, 1060L, 1061L,
1062L, 1063L, 1064L, 1065L, 1066L, 1067L, 1068L, 1069L, 1070L,
1071L, 1072L, 1073L, 1074L, 1075L, 1076L, 1077L, 1078L, 1079L,
1080L, 1081L, 1082L, 1083L, 1084L, 1085L, 1086L, 1087L, 1088L,
1089L, 1090L, 1091L, 1092L, 1093L, 1094L, 1095L, 1096L, 1097L,
1098L, 1099L, 1100L, 1101L, 1102L, 1103L, 1104L, 1105L, 1106L,
1107L, 1108L, 1109L, 1110L, 1111L, 1112L, 1113L, 1114L, 1115L,
1116L, 1117L, 1118L, 1119L, 1120L, 1121L, 1122L, 1123L, 1124L,
1125L, 1126L, 1127L, 1128L, 1129L, 1130L, 1131L, 1132L, 1133L,
1134L, 1135L, 1136L, 1137L, 1138L, 1139L, 1140L, 1141L, 1142L,
1143L, 1144L, 1145L, 1146L, 1147L, 1148L, 1149L, 1150L, 1151L,
1152L, 1153L, 1154L, 1155L, 1156L, 1157L, 1158L, 1159L, 1160L,
1161L, 1162L, 1163L, 1164L, 1165L, 1166L, 1167L, 1168L, 1169L,
1170L, 1171L, 1172L, 1173L, 1174L, 1175L, 1176L, 1177L, 1178L,
1179L, 1180L, 1181L, 1182L, 1183L, 1184L, 1185L, 1186L, 1187L,
1188L, 1189L, 1190L, 1191L, 1192L, 1193L, 1194L, 1195L, 1196L,
1197L, 1198L, 1199L, 1200L, 1201L, 1202L, 1203L, 1204L, 1205L,
1206L, 1207L, 1208L, 1209L, 1210L, 1211L, 1212L, 1213L, 1214L,
1215L, 1216L, 1217L, 1218L, 1219L, 1220L, 1221L, 1222L, 1223L,
1224L, 1233L, 1234L, 1235L, 1236L, 1237L, 1238L, 1239L, 1240L,
1241L, 1242L, 1243L, 1244L, 1245L, 1246L, 1247L, 1248L, 1249L,
1250L, 1251L, 1252L, 1253L, 1254L, 1255L, 1256L, 1257L, 1258L,
1259L, 1260L, 1261L, 1262L, 1263L, 1264L, 1265L, 1266L, 1267L,
1268L, 1269L, 1270L, 1271L, 1272L, 1273L, 1274L, 1275L, 1276L,
1277L, 1278L, 1279L, 1280L, 1281L, 1282L, 1283L, 1284L, 1285L,
1286L, 1287L, 1288L, 1289L, 1290L, 1291L, 1292L, 1293L, 1294L,
1295L, 1296L, 1297L, 1298L, 1299L, 1300L, 1301L, 1302L, 1303L,
1304L, 1305L, 1306L, 1307L, 1308L, 1309L, 1310L, 1311L, 1312L,
1313L, 1314L, 1315L, 1316L, 1317L, 1318L, 1319L, 1320L, 1321L,
1322L, 1323L, 1324L, 1325L, 1326L, 1327L, 1328L, 1329L, 1330L,
1331L, 1332L, 1333L, 1334L, 1335L, 1336L, 1337L, 1338L, 1339L,
1340L, 1341L, 1344L, 1345L, 1346L, 1347L, 1348L, 1349L, 1350L,
1351L, 1352L, 1353L, 1354L, 1355L, 1356L, 1357L, 1358L, 1359L,
1360L, 1361L, 1362L, 1363L, 1364L, 1365L, 1366L, 1367L, 1368L,
1369L, 1370L, 1371L, 1372L, 1373L, 1374L, 1375L, 1376L, 1377L,
1378L, 1380L, 1381L, 1382L, 1383L, 1384L, 1385L, 1386L, 1387L,
1388L, 1389L, 1390L, 1391L, 1392L, 1393L, 1394L, 1395L, 1396L,
1397L, 1398L, 1399L, 1400L, 1401L, 1402L, 1403L, 1404L, 1405L,
1406L, 1407L, 1408L, 1409L, 1410L, 1411L, 1412L, 1413L, 1414L,
1415L, 1416L, 1417L, 1418L, 1419L, 1420L, 1421L, 1422L, 1423L,
1424L, 1425L, 1426L, 1427L, 1428L, 1429L, 1430L, 1431L, 1432L,
1433L, 1434L, 1435L, 1436L, 1437L, 1438L, 1439L, 1440L, 1441L,
1442L, 1443L, 1444L, 1445L, 1446L, 1447L, 1448L, 1449L, 1450L,
1451L, 1452L, 1453L, 1454L, 1455L, 1456L, 1457L, 1458L, 1459L,
1460L, 1461L, 1462L, 1463L, 1464L, 1465L, 1466L, 1467L, 1468L,
1469L, 1470L, 1471L, 1472L, 1473L, 1474L, 1475L, 1476L, 1477L,
1478L, 1479L, 1480L, 1481L, 1482L, 1483L, 1484L, 1485L, 1486L,
1487L, 1488L, 1489L, 1490L, 1491L, 1492L, 1493L, 1494L, 1495L,
1496L, 1497L, 1498L, 1499L, 1500L, 1501L, 1502L, 1503L, 1504L,
1505L, 1506L, 1507L, 1508L, 1509L, 1510L, 1511L, 1512L, 1513L,
1514L, 1515L, 1516L, 1517L, 1518L, 1519L, 1520L, 1521L, 1522L,
1523L, 1524L, 1525L, 1526L, 1527L, 1528L, 1529L, 1530L, 1531L,
1532L, 1533L, 1534L, 1535L, 1536L, 1537L, 1538L, 1539L, 1540L,
1541L, 1542L, 1543L, 1544L, 1545L, 1546L, 1547L, 1548L, 1549L,
1550L, 1551L, 1552L, 1553L, 1554L, 1555L, 1556L, 1557L, 1558L,
1559L, 1560L, 1561L, 1562L, 1563L, 1564L, 1565L, 1566L, 1567L,
1568L, 1569L, 1570L, 1571L, 1572L, 1573L, 1574L, 1575L, 1576L,
1577L, 1578L, 1579L, 1580L, 1581L, 1582L, 1583L, 1584L, 1585L,
1586L, 1587L, 1588L, 1589L, 1590L, 1591L, 1592L, 1593L, 1594L,
1595L, 1604L, 1605L, 1606L, 1607L, 1608L, 1609L, 1610L, 1611L,
1612L, 1613L, 1614L, 1615L, 1616L, 1617L, 1618L, 1619L, 1620L,
1621L, 1622L, 1623L, 1624L, 1625L, 1626L, 1627L, 1628L, 1629L,
1630L, 1631L, 1632L, 1633L, 1634L, 1635L, 1636L, 1637L, 1638L,
1639L, 1640L, 1641L, 1642L, 1643L, 1644L, 1645L, 1646L, 1647L,
1648L, 1649L, 1650L, 1651L, 1652L, 1653L, 1654L, 1655L, 1656L,
1657L, 1658L, 1659L, 1660L, 1661L, 1662L, 1663L, 1664L, 1665L,
1666L, 1667L, 1668L, 1669L, 1670L, 1671L, 1672L, 1673L, 1674L,
1675L, 1676L, 1677L, 1678L, 1679L, 1680L, 1681L, 1682L, 1683L,
1684L, 1685L, 1686L, 1687L, 1688L, 1689L, 1690L, 1691L, 1692L,
1693L, 1694L, 1695L, 1696L, 1697L, 1698L, 1699L, 1700L, 1701L,
1702L, 1703L, 1704L, 1705L, 1706L, 1707L, 1708L, 1709L, 1710L,
1711L, 1712L, 1713L, 1714L, 1715L, 1716L, 1717L, 1718L, 1719L,
1720L, 1721L, 1722L, 1723L, 1724L, 1725L, 1726L, 1727L, 1728L,
1729L, 1730L, 1731L, 1732L, 1733L, 1734L, 1735L, 1736L, 1737L,
1738L, 1739L, 1740L, 1741L, 1742L, 1743L, 1744L, 1745L, 1746L,
1747L, 1748L, 1749L, 1750L, 1751L, 1752L, 1753L, 1754L, 1755L,
1756L, 1757L, 1758L, 1759L, 1760L, 1761L, 1762L, 1763L, 1764L,
1765L, 1766L, 1767L, 1768L, 1769L, 1770L, 1771L, 1772L, 1773L,
1774L, 1775L, 1776L, 1777L, 1778L, 1779L, 1780L, 1781L, 1782L,
1783L, 1784L, 1785L, 1786L, 1787L, 1788L, 1789L, 1790L, 1791L,
1792L, 1793L, 1794L, 1795L, 1796L, 1797L, 1798L, 1799L, 1800L,
1801L, 1802L, 1803L, 1804L, 1805L, 1808L, 1809L, 1810L, 1811L,
1812L, 1813L, 1814L, 1815L, 1816L, 1817L, 1818L, 1819L, 1820L,
1821L, 1822L, 1823L, 1824L, 1825L, 1826L, 1827L, 1828L, 1829L,
1830L, 1831L, 1832L, 1833L, 1834L, 1835L, 1836L, 1837L, 1838L,
1839L, 1840L, 1841L, 1842L, 1843L, 1844L, 1845L, 1846L, 1847L,
1848L, 1849L, 1850L, 1851L, 1852L, 1853L, 1854L, 1855L, 1856L,
1857L, 1858L, 1859L, 1860L, 1861L, 1862L, 1863L, 1864L, 1865L,
1866L, 1867L, 1868L, 1869L, 1870L, 1871L, 1872L, 1873L, 1874L,
1875L, 1876L, 1877L, 1878L, 1879L, 1880L, 1881L, 1882L, 1883L,
1884L, 1885L, 1886L, 1887L, 1888L, 1889L, 1890L, 1891L, 1892L,
1893L, 1894L, 1895L, 1896L, 1897L, 1898L, 1899L, 1900L, 1901L,
1902L, 1903L, 1904L, 1905L, 1906L, 1907L, 1908L, 1909L, 1910L,
1911L, 1912L, 1913L, 1914L, 1915L, 1916L, 1917L, 1918L, 1919L,
1920L, 1921L, 1922L, 1923L, 1924L, 1925L, 1926L, 1927L, 1928L,
1929L, 1930L, 1931L, 1932L, 1933L, 1934L, 1935L, 1936L, 1937L,
1938L, 1939L, 1940L, 1941L, 1942L, 1943L, 1944L, 1945L, 1946L,
1947L, 1948L, 1949L, 1950L, 1951L, 1952L, 1953L, 1954L, 1955L,
1956L, 1957L, 1958L, 1959L, 1960L, 1961L, 1962L, 1963L, 1964L,
1965L, 1966L, 1967L, 1968L, 1969L, 1970L, 1971L, 1972L, 1973L,
1974L, 1975L, 1976L, 1977L, 1978L, 1979L, 1980L, 1981L, 1982L,
1983L, 1984L, 1985L, 1986L, 1987L, 1988L, 1989L, 1990L, 1991L,
1992L, 1993L, 1994L, 1995L, 1996L, 1997L, 1998L, 1999L, 2004L,
2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 2013L,
2014L, 2015L, 2016L, 2017L, 2018L, 2019L, 2020L, 2021L, 2022L,
2023L, 2024L, 2025L, 2026L, 2027L, 2028L, 2029L, 2030L, 2031L,
2032L, 2033L, 2034L, 2035L, 2036L, 2037L, 2038L, 2039L, 2040L,
2041L, 2042L, 2043L, 2044L, 2045L, 2046L, 2047L, 2048L, 2049L,
2050L, 2051L, 2052L, 2053L, 2054L, 2055L, 2056L, 2057L, 2058L,
2059L, 2060L, 2061L, 2062L, 2063L, 2064L, 2065L, 2066L, 2067L,
2068L, 2069L, 2070L, 2071L, 2072L, 2073L, 2074L, 2075L, 2076L,
2077L, 2078L, 2079L, 2080L, 2081L, 2082L, 2083L, 2084L, 2085L,
2086L, 2087L, 2088L, 2089L, 2090L, 2091L, 2092L, 2093L, 2094L,
2095L, 2096L, 2097L, 2098L, 2099L, 2100L, 2101L, 2102L, 2103L,
2104L, 2105L, 2106L, 2107L, 2108L, 2109L, 2110L, 2111L, 2112L,
2113L, 2114L, 2115L, 2116L, 2117L, 2118L, 2119L, 2120L, 2121L,
2122L, 2123L, 2124L, 2125L, 2126L, 2127L, 2128L, 2129L, 2130L,
2131L, 2132L, 2133L, 2134L, 2135L, 2136L, 2137L, 2138L, 2139L,
2140L, 2141L, 2142L, 2143L, 2144L, 2145L, 2146L, 2147L, 2148L,
2149L, 2150L, 2151L, 2152L, 2153L, 2154L, 2155L, 2156L, 2157L,
2158L, 2159L, 2160L, 2161L, 2162L, 2163L, 2164L, 2165L, 2166L,
2167L, 2168L, 2169L, 2170L, 2171L, 2172L, 2173L, 2174L, 2175L,
2176L, 2177L, 2178L, 2179L, 2180L, 2181L, 2182L, 2183L, 2184L,
2185L, 2186L, 2187L, 2188L, 2189L, 2190L, 2191L, 2192L, 2193L,
2194L, 2195L, 2196L, 2197L, 2198L, 2199L, 2200L, 2201L, 2202L,
2203L, 2204L, 2205L, 2206L, 2207L, 2208L, 2209L, 2210L, 2211L,
2212L, 2213L, 2214L, 2215L, 2216L, 2217L, 2218L, 2219L, 2220L,
2221L, 2222L, 2223L, 2224L, 2225L, 2226L, 2227L, 2228L, 2229L,
2230L, 2231L, 2232L, 2233L, 2234L, 2235L, 2236L, 2237L, 2238L,
2239L, 2240L, 2241L, 2242L, 2243L, 2244L, 2245L, 2246L, 2247L,
2248L, 2249L, 2250L, 2251L, 2252L, 2253L, 2254L, 2255L, 2256L,
2257L, 2258L, 2259L, 2260L, 2261L, 2262L, 2263L, 2264L, 2265L,
2266L, 2267L, 2268L, 2269L, 2270L, 2271L, 2272L, 2273L, 2274L,
2275L, 2276L, 2277L, 2278L, 2279L, 2280L, 2281L, 2282L, 2283L,
2284L, 2285L, 2286L, 2287L, 2288L, 2289L, 2290L, 2291L, 2292L,
2293L, 2294L, 2295L, 2296L, 2297L, 2298L, 2299L, 2300L, 2301L,
2302L, 2303L, 2304L, 2305L, 2306L, 2307L, 2308L, 2309L, 2310L,
2311L, 2312L, 2313L, 2314L, 2315L, 2316L, 2317L, 2318L, 2319L,
2320L, 2321L, 2322L, 2323L, 2324L, 2325L, 2326L, 2327L, 2328L,
2329L, 2330L, 2331L, 2332L, 2333L, 2334L, 2335L, 2336L, 2337L,
2338L, 2339L, 2340L, 2341L, 2342L, 2343L, 2344L, 2345L, 2346L,
2347L, 2348L, 2349L, 2350L, 2351L, 2352L, 2353L, 2354L, 2355L,
2356L, 2357L, 2358L, 2359L, 2360L, 2361L, 2362L, 2363L, 2364L,
2365L, 2366L, 2367L, 2368L, 2369L, 2370L, 2371L, 2372L, 2373L,
2374L, 2375L, 2376L, 2377L, 2378L, 2379L, 2380L, 2381L, 2382L,
2383L, 2384L, 2385L, 2386L, 2387L, 2388L, 2389L, 2390L, 2391L,
2392L, 2393L, 2394L, 2395L, 2396L, 2397L, 2398L, 2399L, 2400L,
2401L, 2402L, 2404L, 2405L, 2406L, 2407L, 2408L, 2409L, 2410L,
2411L, 2412L, 2413L, 2414L, 2415L, 2416L, 2417L, 2418L, 2419L,
2420L, 2421L, 2422L, 2423L, 2424L, 2425L, 2426L, 2427L, 2428L,
2429L, 2430L, 2431L, 2432L, 2433L, 2434L, 2435L, 2436L, 2437L,
2438L, 2439L, 2440L, 2441L, 2442L, 2443L, 2444L, 2445L, 2446L,
2447L, 2448L, 2449L, 2450L, 2451L, 2452L, 2453L, 2454L, 2455L,
2456L, 2457L, 2458L, 2459L, 2460L, 2461L, 2462L, 2463L, 2464L,
2465L, 2466L, 2467L, 2468L, 2469L, 2470L, 2471L, 2472L, 2473L,
2474L, 2475L, 2476L, 2477L, 2478L, 2479L, 2480L, 2481L, 2482L,
2483L, 2484L, 2485L, 2486L, 2487L, 2488L, 2489L, 2490L, 2491L,
2492L, 2493L, 2494L, 2495L, 2496L, 2497L, 2498L, 2499L, 2500L,
2501L, 2502L, 2503L, 2504L, 2505L, 2506L, 2507L, 2508L, 2509L,
2510L, 2511L, 2512L, 2513L, 2514L, 2515L, 2516L, 2517L, 2518L,
2519L, 2520L, 2521L, 2522L, 2523L, 2524L, 2525L, 2526L, 2527L,
2528L, 2529L, 2530L, 2531L, 2532L, 2533L, 2534L, 2535L, 2536L,
2537L, 2539L, 2540L, 2541L, 2542L, 2543L, 2544L, 2545L, 2546L,
2547L, 2548L, 2549L, 2550L, 2551L, 2552L, 2553L, 2554L, 2555L,
2556L, 2557L, 2558L, 2559L, 2560L, 2561L, 2562L, 2563L, 2564L,
2565L, 2566L, 2567L, 2568L, 2569L, 2570L, 2571L, 2572L, 2573L,
2574L, 2575L, 2576L, 2577L, 2578L, 2579L, 2580L, 2581L, 2582L,
2583L, 2584L, 2585L, 2586L, 2587L, 2588L, 2589L, 2590L, 2591L,
2592L, 2593L, 2594L, 2595L, 2596L, 2597L, 2598L, 2599L, 2600L,
2601L, 2602L, 2603L, 2604L, 2605L, 2606L, 2607L, 2608L, 2609L,
2610L, 2611L, 2612L, 2613L, 2614L, 2615L, 2616L, 2617L, 2618L,
2619L, 2620L, 2621L, 2622L, 2623L, 2624L, 2625L, 2626L, 2627L,
2628L, 2629L, 2630L, 2631L, 2632L, 2633L, 2634L, 2635L, 2636L,
2637L, 2638L, 2639L, 2640L, 2641L, 2642L, 2643L, 2644L, 2645L,
2646L, 2647L, 2648L, 2649L, 2650L, 2651L, 2652L, 2653L, 2654L,
2655L, 2656L, 2657L, 2658L, 2659L, 2660L, 2661L, 2710L, 2711L,
2712L, 2713L, 2714L, 2715L, 2716L, 2717L, 2718L, 2719L, 2720L,
2721L, 2722L, 2723L, 2724L, 2725L, 2726L, 2727L, 2728L, 2729L,
2730L, 2731L, 2732L, 2733L, 2734L, 2735L, 2736L, 2737L, 2738L,
2739L, 2740L, 2746L, 2747L, 2748L, 2749L, 2750L, 2751L, 2752L,
2753L, 2754L, 2755L, 2756L, 2757L, 2758L, 2759L, 2760L, 2761L,
2762L, 2763L, 2764L, 2765L, 2766L, 2767L, 2768L, 2769L, 2770L,
2771L, 2772L, 2773L, 2774L, 2775L, 2776L, 2777L, 2778L, 2779L,
2780L, 2781L, 2782L, 2783L, 2784L, 2785L, 2786L, 2787L, 2788L,
2789L, 2790L, 2791L, 2792L, 2793L, 2794L, 2795L, 2796L, 2797L,
2798L, 2799L, 2800L, 2801L, 2802L, 2803L, 2804L, 2805L, 2806L,
2807L, 2808L, 2809L, 2810L, 2811L, 2812L, 2813L, 2814L, 2815L,
2816L, 2817L, 2818L, 2819L, 2820L, 2821L, 2822L, 2823L, 2824L,
2825L, 2826L, 2828L, 2829L, 2830L, 2831L, 2832L, 2833L, 2834L,
2835L, 2836L, 2837L, 2838L, 2839L, 2840L, 2841L, 2842L, 2843L,
2844L, 2845L, 2846L, 2847L, 2848L, 2849L, 2850L, 2851L, 2852L,
2853L, 2854L, 2855L, 2856L, 2857L, 2858L, 2859L, 2860L, 2861L,
2862L, 2863L, 2864L, 2865L, 2866L, 2867L, 2868L, 2869L, 2870L,
2871L, 2872L, 2873L, 2874L, 2875L, 2876L, 2877L, 2878L, 2879L,
2880L, 2881L, 2882L, 2883L, 2884L, 2885L, 2886L, 2887L, 2888L,
2889L, 2890L, 2891L, 2892L, 2893L, 2894L, 2895L, 2896L, 2897L,
2898L, 2899L, 2900L, 2901L, 2902L, 2903L, 2904L, 2905L, 2906L,
2907L, 2908L, 2909L, 2910L, 2911L, 2912L, 2913L, 2914L, 2915L,
2916L, 2917L, 2918L, 2919L, 2920L, 2921L, 2922L, 2923L, 2924L,
2925L, 2926L, 2927L, 2928L, 2929L, 2930L, 2931L, 2932L, 2933L,
2934L, 2935L, 2936L, 2937L, 2938L, 2939L, 2940L, 2941L, 2942L,
2943L, 2944L, 2945L, 2946L, 2947L, 2948L, 2949L, 2950L, 2951L,
2952L, 2953L, 2954L, 2955L, 2956L, 2957L, 2958L, 2959L, 2960L,
2961L, 2962L, 2963L, 2964L, 2965L, 2966L, 2967L, 2968L, 2969L,
2970L, 2971L, 2972L, 2973L, 2974L, 2975L, 2976L, 2977L, 2978L,
2979L, 2980L, 2981L, 2982L, 2983L, 2984L, 2985L, 2986L, 2987L,
2988L, 2989L, 2990L, 2991L, 2992L, 2993L, 2994L, 2995L, 2996L,
2997L, 2998L, 2999L, 3000L, 3001L, 3002L, 3003L, 3004L, 3005L,
3006L, 3007L, 3008L, 3009L, 3010L, 3011L, 3012L, 3013L, 3014L,
3015L, 3016L, 3017L, 3018L, 3019L, 3020L, 3021L, 3022L, 3023L,
3024L, 3025L, 3026L, 3027L, 3028L, 3029L, 3030L, 3031L, 3032L,
3033L, 3034L, 3035L, 3036L, 3037L, 3038L, 3039L, 3040L, 3041L,
3042L, 3043L, 3044L, 3045L, 3046L, 3047L, 3048L, 3049L, 3050L,
3051L, 3052L, 3053L, 3054L, 3055L, 3056L, 3057L, 3058L, 3059L,
3060L, 3061L, 3062L, 3063L, 3064L, 3065L, 3066L, 3067L, 3068L,
3069L, 3070L, 3071L, 3072L, 3073L, 3074L, 3075L, 3076L, 3077L,
3078L, 3079L, 3080L, 3081L, 3082L, 3083L, 3084L, 3085L, 3086L,
3087L, 3088L, 3089L, 3090L, 3091L, 3092L, 3093L, 3094L, 3095L,
3096L, 3097L, 3098L, 3099L, 3100L, 3101L, 3102L, 3103L, 3104L,
3105L, 3106L, 3107L, 3108L, 3109L, 3110L, 3111L, 3112L, 3113L,
3114L, 3115L, 3116L, 3117L, 3118L, 3119L, 3120L, 3121L, 3122L,
3123L, 3124L, 3125L, 3126L, 3127L, 3128L, 3129L, 3130L, 3131L,
3132L, 3133L, 3134L, 3135L, 3136L, 3137L, 3138L, 3139L, 3140L,
3141L, 3142L, 3143L, 3144L, 3145L, 3146L, 3147L, 3148L, 3149L,
3150L, 3151L, 3152L, 3153L, 3154L, 3155L, 3156L, 3157L, 3158L,
3159L, 3160L, 3161L, 3162L, 3163L, 3164L, 3165L, 3166L, 3167L,
3168L, 3169L, 3170L, 3171L, 3172L, 3173L, 3174L, 3175L, 3176L,
3177L, 3178L, 3179L, 3180L, 3181L, 3182L, 3183L, 3184L, 3185L,
3186L, 3187L, 3188L, 3189L, 3190L, 3191L, 3192L, 3193L, 3194L,
3195L, 3196L, 3197L, 3198L, 3199L, 3200L, 3201L, 3202L, 3203L,
3204L, 3205L, 3206L, 3207L, 3208L, 3209L, 3210L, 3211L, 3212L,
3213L, 3214L, 3215L, 3216L, 3217L, 3218L, 3219L, 3220L, 3221L,
3222L, 3223L, 3224L, 3225L, 3226L, 3227L, 3228L, 3229L, 3230L,
3231L, 3232L, 3233L, 3234L, 3235L, 3236L, 3237L, 3238L, 3239L,
3240L, 3241L, 3242L, 3243L, 3244L, 3245L, 3246L, 3247L, 3248L,
3249L, 3250L, 3251L, 3252L, 3253L, 3254L, 3255L, 3256L, 3257L,
3258L, 3259L, 3260L, 3261L, 3262L, 3263L, 3264L, 3265L, 3266L,
3267L, 3268L, 3269L, 3270L, 3271L, 3272L, 3273L, 3274L, 3275L,
3276L, 3277L, 3278L, 3279L, 3280L, 3281L, 3282L, 3283L, 3284L,
3285L, 3286L, 3287L, 3288L, 3289L, 3290L, 3291L, 3292L, 3293L,
3294L, 3295L, 3296L, 3297L, 3298L, 3299L, 3300L, 3301L, 3302L,
3303L, 3304L, 3305L, 3306L, 3307L, 3308L, 3309L, 3310L, 3311L,
3312L, 3313L, 3314L, 3315L, 3316L, 3317L, 3318L, 3319L, 3320L,
3321L, 3322L, 3323L, 3324L, 3325L, 3326L, 3327L, 3328L, 3329L,
3330L, 3331L, 3332L, 3333L, 3334L, 3335L, 3336L, 3337L, 3338L,
3339L, 3340L, 3341L, 3342L, 3343L, 3344L, 3345L, 3346L, 3347L,
3348L, 3349L, 3350L, 3351L, 3352L, 3353L, 3354L, 3355L, 3356L,
3357L, 3358L, 3359L, 3360L, 3361L, 3362L, 3363L, 3364L, 3365L,
3366L, 3367L, 3368L, 3369L, 3370L, 3371L, 3372L, 3373L, 3374L,
3375L, 3376L, 3377L, 3378L, 3379L, 3380L, 3381L, 3382L, 3383L,
3384L, 3385L, 3386L, 3387L, 3388L, 3389L, 3390L, 3391L, 3392L,
3393L, 3394L, 3395L, 3396L, 3397L, 3398L, 3399L, 3400L, 3401L,
3402L, 3403L, 3404L, 3405L, 3406L, 3407L, 3408L, 3409L, 3410L,
3411L, 3412L, 3413L, 3414L, 3415L, 3416L, 3417L, 3418L, 3419L,
3420L, 3421L, 3422L, 3423L, 3424L, 3425L, 3426L, 3427L, 3428L,
3429L, 3430L, 3431L, 3432L, 3433L, 3434L, 3435L, 3436L, 3437L,
3438L, 3439L, 3440L, 3441L, 3442L, 3443L, 3444L, 3445L, 3446L,
3447L, 3448L, 3449L, 3450L, 3451L, 3452L, 3453L, 3454L, 3455L,
3456L, 3457L, 3458L, 3459L, 3460L, 3461L, 3462L, 3463L, 3464L,
3465L, 3466L, 3467L, 3468L, 3469L, 3470L, 3471L, 3472L, 3473L,
3474L, 3475L, 3476L, 3477L, 3478L, 3479L, 3480L, 3481L, 3482L,
3483L, 3484L, 3485L, 3486L, 3487L, 3488L, 3489L, 3490L, 3491L,
3492L, 3493L, 3494L, 3495L, 3496L, 3497L, 3498L, 3499L, 3500L,
3501L, 3502L, 3503L, 3504L, 3505L, 3506L, 3507L, 3508L, 3509L,
3510L, 3511L, 3512L, 3513L, 3514L, 3515L, 3516L, 3517L, 3518L,
3519L, 3520L, 3525L, 3526L, 3527L, 3528L, 3529L, 3530L, 3531L,
3532L, 3533L, 3534L, 3535L, 3536L, 3537L, 3538L, 3539L, 3540L,
3541L, 3542L, 3543L, 3544L, 3545L, 3546L, 3547L, 3548L, 3549L,
3550L, 3551L, 3552L, 3553L, 3554L, 3555L, 3556L, 3557L, 3558L,
3559L, 3560L, 3561L, 3562L, 3563L, 3564L, 3565L, 3566L, 3567L,
3568L, 3569L, 3570L, 3571L, 3572L, 3573L, 3574L, 3575L, 3576L,
3577L, 3578L, 3579L, 3580L, 3581L, 3582L, 3583L, 3584L, 3585L,
3586L, 3587L, 3588L, 3589L, 3590L, 3591L, 3592L, 3593L, 3594L,
3595L, 3596L, 3597L, 3598L, 3599L, 3600L, 3601L, 3602L, 3603L,
3604L, 3605L, 3606L, 3607L, 3608L, 3609L, 3610L, 3611L, 3612L,
3613L, 3614L, 3615L, 3616L, 3617L, 3618L, 3619L, 3620L, 3621L,
3622L, 3623L, 3624L, 3625L, 3626L, 3627L, 3628L, 3629L, 3630L,
3631L, 3632L, 3633L, 3634L, 3635L, 3636L, 3637L, 3638L, 3639L,
3640L, 3641L, 3642L, 3643L, 3644L, 3645L, 3646L, 3647L, 3648L,
3649L, 3650L, 3651L, 3652L, 3653L, 3654L, 3655L, 3656L, 3657L,
3658L, 3659L, 3660L, 3661L, 3662L, 3663L, 3664L, 3665L, 3666L,
3667L, 3668L, 3669L, 3670L, 3671L, 3672L, 3673L, 3674L, 3675L,
3676L, 3677L, 3678L, 3679L, 3680L, 3681L, 3682L, 3683L, 3684L,
3685L, 3686L, 3687L, 3688L, 3689L, 3690L, 3691L, 3692L, 3693L,
3694L, 3695L, 3696L, 3697L, 3698L, 3699L, 3700L, 3701L, 3702L,
3703L, 3704L, 3705L, 3706L, 3707L, 3708L, 3709L, 3710L, 3711L,
3712L, 3713L, 3714L, 3715L, 3716L, 3717L, 3718L, 3719L, 3720L,
3721L, 3722L, 3723L, 3724L, 3725L, 3726L, 3727L, 3728L, 3729L,
3730L, 3731L, 3732L, 3733L, 3734L, 3735L, 3736L, 3737L, 3738L,
3739L, 3740L, 3741L, 3742L, 3743L, 3744L, 3745L, 3746L, 3747L,
3748L, 3749L, 3750L, 3751L, 3752L, 3753L, 3754L, 3755L, 3756L,
3757L, 3758L, 3759L, 3760L, 3761L, 3762L, 3763L, 3764L, 3765L,
3766L, 3767L, 3768L, 3769L, 3770L, 3771L, 3772L, 3773L, 3774L,
3775L, 3776L, 3777L, 3778L, 3779L, 3780L, 3781L, 3782L, 3783L,
3784L, 3785L, 3786L, 3787L, 3788L, 3789L, 3790L, 3791L, 3792L,
3793L, 3794L, 3795L, 3796L, 3797L, 3798L, 3799L, 3800L, 3801L,
3802L, 3803L, 3804L, 3805L, 3806L, 3807L, 3808L, 3809L, 3810L,
3811L, 3812L, 3813L, 3814L, 3815L, 3816L, 3817L, 3818L, 3819L,
3821L, 3822L, 3823L, 3824L, 3825L, 3826L, 3827L, 3828L, 3829L,
3830L, 3831L, 3832L, 3833L, 3834L, 3835L, 3836L, 3837L, 3838L,
3839L, 3840L, 3841L, 3842L, 3843L, 3844L, 3845L, 3846L, 3847L,
3848L, 3849L, 3850L, 3851L, 3852L, 3853L, 3854L, 3855L, 3856L,
3857L, 3858L, 3859L, 3860L, 3861L, 3862L, 3863L, 3864L, 3865L,
3866L, 3867L, 3868L, 3869L, 3870L, 3871L, 3872L, 3873L, 3874L,
3875L, 3876L, 3877L, 3878L, 3879L, 3880L, 3881L, 3882L, 3883L,
3884L, 3885L, 3886L, 3887L, 3888L, 3889L, 3890L, 3891L, 3892L,
3893L, 3894L, 3895L, 3896L, 3897L, 3898L, 3899L, 3900L, 3901L,
3902L, 3903L, 3904L, 3905L, 3906L, 3907L, 3908L, 3909L, 3910L,
3911L, 3912L, 3913L, 3914L, 3915L, 3916L, 3917L, 3918L, 3919L,
3920L, 3921L, 3922L, 3923L, 3924L, 3925L, 3926L, 3927L, 3928L,
3929L, 3930L, 3931L, 3932L, 3933L, 3934L, 3935L, 3936L, 3937L,
3938L, 3939L, 3940L, 3941L, 3942L, 3943L, 3944L, 3945L, 3946L,
3947L, 3948L, 3949L, 3950L, 3951L, 3952L, 3953L, 3954L, 3955L,
3956L, 3957L, 3958L, 3959L, 3960L, 3961L, 3962L, 3963L, 3964L,
3965L, 3966L, 3967L, 3968L, 3969L, 3970L, 3971L, 3972L, 3973L,
3974L, 3975L, 3976L, 3977L, 3978L, 3979L, 3980L, 3981L, 3982L,
3983L, 3984L, 3985L, 3986L, 3987L, 3988L, 3989L, 3990L, 3991L,
3992L, 3993L, 3994L, 3995L, 3996L, 3997L, 3998L, 3999L, 4000L,
4001L, 4002L, 4003L, 4004L, 4005L, 4006L, 4007L, 4008L, 4009L,
4010L, 4011L, 4012L, 4013L, 4014L, 4015L, 4016L, 4017L, 4018L,
4019L, 4020L, 4021L, 4022L, 4023L, 4024L, 4025L, 4026L, 4027L,
4028L, 4029L, 4030L, 4031L, 4032L, 4033L, 4034L, 4035L, 4036L,
4037L, 4038L, 4039L, 4040L, 4041L, 4042L, 4043L, 4044L, 4045L,
4046L, 4047L, 4048L, 4049L, 4050L, 4051L, 4052L, 4055L, 4056L,
4057L, 4058L, 4059L, 4060L, 4061L, 4062L, 4063L, 4064L, 4065L,
4066L, 4067L, 4068L, 4069L, 4070L, 4071L, 4072L, 4073L, 4074L,
4075L, 4076L, 4077L, 4078L, 4079L, 4080L, 4081L, 4082L, 4083L,
4084L, 4085L, 4086L, 4087L, 4088L, 4089L, 4090L, 4091L, 4092L,
4093L, 4094L, 4095L, 4096L, 4097L, 4098L, 4099L, 4100L, 4101L,
4102L, 4103L, 4104L, 4105L, 4106L, 4107L, 4108L, 4109L, 4110L,
4111L, 4112L, 4113L, 4114L, 4115L, 4116L, 4117L, 4118L, 4119L,
4120L, 4121L, 4122L, 4123L, 4124L, 4125L, 4126L, 4127L, 4128L,
4129L, 4130L, 4131L, 4132L, 4133L, 4134L, 4135L, 4136L, 4137L,
4138L, 4139L, 4140L, 4141L, 4142L, 4143L, 4144L, 4145L, 4146L,
4147L, 4148L, 4149L, 4150L, 4151L, 4152L, 4153L, 4154L, 4155L,
4156L, 4157L, 4158L, 4159L, 4160L, 4161L, 4162L, 4163L, 4164L,
4165L, 4166L, 4167L, 4168L, 4169L, 4170L, 4171L, 4172L, 4173L,
4174L, 4175L, 4176L, 4177L, 4178L, 4181L, 4182L, 4183L, 4184L,
4185L, 4186L, 4187L, 4188L, 4189L, 4190L, 4191L, 4192L, 4193L,
4194L, 4195L, 4196L, 4197L, 4198L, 4199L, 4200L, 4201L, 4202L,
4203L, 4204L, 4205L, 4206L, 4207L, 4208L, 4209L, 4210L, 4211L,
4212L, 4213L, 4214L, 4215L, 4216L, 4217L, 4218L, 4219L, 4220L,
4221L, 4222L, 4223L, 4224L, 4225L, 4226L, 4227L, 4228L, 4229L,
4230L, 4231L, 4232L, 4233L, 4234L, 4235L, 4236L, 4237L, 4238L,
4239L, 4240L, 4241L, 4242L, 4243L, 4244L, 4245L, 4246L, 4247L,
4248L, 4249L, 4250L, 4251L, 4252L, 4253L, 4254L, 4255L, 4256L,
4257L, 4258L, 4259L, 4260L, 4261L, 4262L, 4263L, 4264L, 4265L,
4266L, 4267L, 4268L, 4269L, 4270L, 4271L, 4272L, 4273L, 4274L,
4275L, 4276L, 4277L, 4278L, 4279L, 4280L, 4281L, 4282L, 4283L,
4284L, 4285L, 4286L, 4287L, 4288L, 4289L, 4290L, 4291L, 4292L,
4293L, 4294L, 4295L, 4296L, 4297L, 4298L, 4299L, 4300L, 4301L,
4302L, 4303L, 4304L, 4305L, 4306L, 4307L, 4308L, 4309L, 4310L,
4311L, 4312L, 4313L, 4314L, 4315L, 4316L, 4317L, 4318L, 4319L,
4320L, 4321L, 4322L, 4323L, 4324L, 4325L, 4326L, 4327L, 4328L,
4329L, 4330L, 4331L, 4332L, 4333L, 4334L, 4335L, 4336L, 4337L,
4338L, 4343L, 4344L, 4345L, 4346L, 4347L, 4348L, 4349L, 4350L,
4351L, 4352L, 4353L, 4354L, 4355L, 4356L, 4357L, 4358L, 4359L,
4360L, 4361L, 4362L, 4363L, 4364L, 4365L, 4366L, 4367L, 4368L,
4369L, 4370L, 4371L, 4372L, 4373L, 4374L, 4375L, 4376L, 4377L,
4378L, 4379L, 4380L, 4381L, 4382L, 4383L, 4384L, 4385L, 4386L,
4387L, 4388L, 4389L, 4390L, 4391L, 4392L, 4393L, 4394L, 4395L,
4396L, 4397L, 4398L, 4399L, 4400L, 4401L, 4402L, 4403L, 4404L,
4405L, 4406L, 4407L, 4408L, 4409L, 4410L, 4411L, 4412L, 4413L,
4414L, 4415L, 4416L, 4417L, 4418L, 4419L, 4420L, 4421L, 4422L,
4423L, 4424L, 4425L, 4426L, 4427L, 4428L, 4429L, 4430L, 4431L,
4432L, 4433L, 4434L, 4435L, 4436L, 4437L, 4438L, 4439L, 4440L,
4441L, 4442L, 4443L, 4444L, 4445L, 4446L, 4447L, 4448L, 4449L,
4450L, 4451L, 4452L, 4453L, 4454L, 4455L, 4456L, 4457L, 4458L,
4459L, 4460L, 4461L, 4462L, 4463L, 4464L, 4465L, 4466L, 4467L,
4468L, 4469L, 4470L, 4471L, 4472L, 4473L, 4474L, 4475L, 4476L,
4477L, 4478L, 4479L, 4480L, 4481L, 4482L, 4483L, 4484L, 4485L,
4486L, 4487L, 4488L, 4489L, 4490L, 4491L, 4492L, 4493L, 4494L,
4495L, 4496L, 4497L, 4498L, 4499L, 4500L, 4501L, 4502L, 4503L,
4504L, 4505L, 4506L, 4507L, 4508L, 4509L, 4510L, 4511L, 4512L,
4513L, 4514L, 4515L, 4516L, 4517L, 4518L, 4519L, 4520L, 4521L,
4522L, 4523L, 4524L, 4525L, 4526L, 4527L, 4528L, 4529L, 4530L,
4531L, 4532L, 4533L, 4534L, 4535L, 4536L, 4537L, 4538L, 4539L,
4540L, 4541L, 4542L, 4543L, 4544L, 4545L, 4546L, 4547L, 4548L,
4549L, 4550L, 4551L, 4552L, 4553L, 4554L, 4555L, 4556L, 4557L,
4558L, 4559L, 4560L, 4561L, 4562L, 4563L, 4564L, 4565L, 4566L,
4567L, 4568L, 4569L, 4570L, 4571L, 4572L, 4573L, 4574L, 4575L,
4576L, 4577L, 4578L, 4579L, 4580L, 4581L, 4582L, 4583L, 4584L,
4585L, 4586L, 4587L, 4588L, 4589L, 4590L, 4591L, 4592L, 4593L,
4594L, 4595L, 4596L, 4597L, 4598L, 4599L, 4600L, 4601L, 4602L,
4603L, 4604L, 4605L, 4606L, 4607L, 4608L, 4609L, 4610L, 4611L,
4612L, 4613L, 4614L, 4615L, 4616L, 4617L, 4618L, 4619L, 4620L,
4621L, 4622L, 4623L, 4624L, 4625L, 4626L, 4627L, 4628L, 4629L,
4630L, 4631L, 4632L, 4633L, 4634L, 4635L, 4636L, 4637L, 4638L,
4639L, 4640L, 4641L, 4642L, 4643L, 4644L, 4645L, 4646L, 4647L,
4648L, 4649L, 4650L, 4651L, 4652L, 4653L, 4654L, 4655L, 4656L,
4657L, 4658L, 4659L, 4660L, 4661L, 4662L, 4663L, 4664L, 4665L,
4666L, 4667L, 4668L, 4669L, 4670L, 4671L, 4672L, 4673L, 4674L,
4675L, 4676L, 4677L, 4679L, 4680L, 4681L, 4682L, 4683L, 4684L,
4685L, 4686L, 4687L, 4688L, 4689L, 4690L, 4691L, 4692L, 4693L,
4694L, 4695L, 4696L, 4697L, 4698L, 4699L, 4700L, 4701L, 4702L,
4703L, 4704L, 4705L, 4706L, 4707L, 4708L, 4709L, 4710L, 4711L,
4712L, 4713L, 4714L, 4715L, 4716L, 4717L, 4718L, 4719L, 4720L,
4721L, 4722L, 4724L, 4725L, 4726L, 4727L, 4728L, 4729L, 4730L,
4731L, 4732L, 4733L, 4734L, 4735L, 4736L, 4737L, 4738L, 4739L,
4740L, 4741L, 4742L, 4743L, 4744L, 4745L, 4746L, 4747L, 4748L,
4749L, 4750L, 4751L, 4752L, 4753L, 4754L, 4755L, 4756L, 4757L,
4758L, 4759L, 4760L, 4761L, 4762L, 4763L, 4764L, 4765L, 4766L,
4767L, 4768L, 4769L, 4770L, 4771L, 4772L, 4773L, 4774L, 4775L,
4776L, 4777L, 4779L, 4780L, 4781L, 4782L, 4783L, 4784L, 4785L,
4786L, 4787L, 4788L, 4789L, 4790L, 4791L, 4792L, 4793L, 4794L,
4795L, 4796L, 4797L, 4798L, 4799L, 4800L, 4801L, 4802L, 4803L,
4804L, 4805L, 4806L, 4807L, 4808L, 4809L, 4810L, 4811L, 4812L,
4813L, 4814L, 4815L, 4816L, 4817L, 4818L, 4819L, 4820L, 4821L,
4822L, 4823L, 4824L, 4825L, 4826L, 4827L, 4828L, 4829L, 4830L,
4831L, 4832L, 4833L, 4834L, 4835L, 4836L, 4837L, 4838L, 4839L,
4840L, 4841L, 4842L, 4843L, 4844L, 4845L, 4846L, 4847L, 4848L,
4849L, 4850L, 4851L, 4852L, 4853L, 4854L, 4855L, 4856L, 4857L,
4858L, 4859L, 4860L, 4861L, 4862L, 4863L, 4864L, 4865L, 4866L,
4867L, 4868L, 4869L, 4870L, 4871L, 4872L, 4873L, 4874L, 4875L,
4876L, 4877L, 4878L, 4879L, 4880L, 4881L, 4882L, 4883L, 4884L,
4885L, 4886L, 4887L, 4888L, 4889L, 4890L, 4891L, 4892L, 4893L,
4894L, 4895L, 4896L, 4897L, 4898L, 4899L, 4900L, 4901L, 4902L,
4903L, 4904L, 4905L, 4906L, 4907L, 4908L, 4909L, 4910L, 4911L,
4912L, 4913L, 4914L, 4915L, 4916L, 4917L, 4918L, 4919L, 4920L,
4921L, 4922L, 4923L, 4924L, 4925L, 4926L, 4927L, 4928L, 4929L,
4930L, 4931L, 4932L, 4933L, 4934L, 4935L, 4936L, 4937L, 4938L,
4939L, 4940L, 4941L, 4942L, 4943L, 4944L, 4945L, 4946L, 4947L,
4948L, 4949L, 4950L, 4951L, 4952L, 4953L, 4954L, 4955L, 4956L,
4957L, 4958L, 4959L, 4960L, 4961L, 4962L, 4963L, 4964L, 4965L,
4966L, 4967L, 4968L, 4969L, 4970L, 4971L, 4972L, 4973L, 4974L,
4975L, 4976L, 4977L, 4978L, 4979L, 4980L, 4981L, 4982L, 4983L,
4984L, 4985L, 4986L, 4987L, 4988L, 4989L, 4990L, 4991L, 4992L,
4993L, 4994L, 4995L, 4996L, 4997L, 4998L, 4999L, 5000L, 5001L,
5002L, 5003L, 5004L, 5005L, 5006L, 5007L)) |
labkey.webdav.get <- function(baseUrl=NULL, folderPath, remoteFilePath, localFilePath, overwrite=TRUE, fileSet="@files")
{
baseUrl=labkey.getBaseUrl(baseUrl);
if (missing(baseUrl) || is.null(baseUrl) || missing(folderPath) || missing(remoteFilePath) || missing(localFilePath)){
stop (paste("A value must be specified for each of baseUrl, folderPath, fileSet, remoteFilePath, and localFilePath"));
}
if (labkey.webdav.isDirectory(baseUrl = baseUrl, folderPath = folderPath, remoteFilePath = remoteFilePath, fileSet = fileSet, haltOnError = T)){
stop('The requested file is a directory. Please see labkey.webdav.downloadFolder()')
}
folderPath <- encodeFolderPath(folderPath);
remoteFilePath <- encodeRemotePath(remoteFilePath)
url <- paste(baseUrl, "_webdav", folderPath, fileSet, "/", remoteFilePath, sep="");
ret <- labkey.webdav.getByUrl(url, localFilePath, overwrite)
if (!is.null(ret) && !is.na(ret) && ret == FALSE) {
return(FALSE)
}
return(file.exists(localFilePath))
}
labkey.webdav.getByUrl <- function(url, localFilePath, overwrite=TRUE)
{
if (!overwrite & file.exists(localFilePath)) {
return(FALSE)
}
if (dir.exists(localFilePath)) {
stop(paste0("The local filepath exists and is a directory: ", localFilePath))
}
localDownloadDir <- dirname(localFilePath)
if (!file.exists(localDownloadDir)) {
dir.create(localDownloadDir, recursive=TRUE)
}
options <- labkey.getRequestOptions(method="GET")
if (!is.null(.lkdefaults[["debug"]]) && .lkdefaults[["debug"]] == TRUE){
print(paste0("URL: ", url))
response <- GET(url=url, write_disk(localFilePath, overwrite=overwrite), config=options, verbose(data_in=TRUE, info=TRUE, ssl=TRUE))
} else {
response <- GET(url=url, write_disk(localFilePath, overwrite=overwrite), config=options)
}
processResponse(response)
}
labkey.webdav.put <- function(localFile, baseUrl=NULL, folderPath, remoteFilePath, fileSet="@files", description=NULL)
{
if (missing(localFile)) {
stop (paste("A value must be specified for localFile"))
}
if (!file.exists(localFile)){
stop (paste0("File does not exist: ", localFile));
}
url <- labkey.webdav.validateAndBuildRemoteUrl(baseUrl=baseUrl, folderPath=folderPath, fileSet=fileSet, remoteFilePath=remoteFilePath)
options <- labkey.getRequestOptions(method="POST")
pbody <- upload_file(localFile)
if (!is.null(description)) {
url <- paste0(url, "?description=", URLencode(description))
}
if (!is.null(.lkdefaults[["debug"]]) && .lkdefaults[["debug"]] == TRUE) {
print(paste0("URL: ", url))
response <- PUT(url=url, config=options, body=pbody, verbose(data_in=TRUE, info=TRUE, ssl=TRUE))
} else {
response <- PUT(url=url, config=options, body=pbody)
}
processResponse(response, responseType="text/plain; charset=utf-8")
return(TRUE)
}
labkey.webdav.mkDir <- function(baseUrl=NULL, folderPath, remoteFilePath, fileSet="@files")
{
url <- labkey.webdav.validateAndBuildRemoteUrl(baseUrl=baseUrl, folderPath=folderPath, fileSet=fileSet, remoteFilePath=remoteFilePath)
options <- labkey.getRequestOptions(method="POST")
if (!is.null(.lkdefaults[["debug"]]) && .lkdefaults[["debug"]] == TRUE) {
print(paste0("URL: ", url))
response <- VERB("MKCOL", url=url, config=options, verbose(data_in=TRUE, info=TRUE, ssl=TRUE))
} else {
response <- VERB("MKCOL", url=url, config=options)
}
processResponse(response, responseType="text/plain; charset=utf-8")
return(TRUE)
}
labkey.webdav.validateAndBuildRemoteUrl <- function(baseUrl=NULL, folderPath, remoteFilePath, fileSet="@files")
{
baseUrl=labkey.getBaseUrl(baseUrl);
if (missing(baseUrl) || is.null(baseUrl) || missing(folderPath) || missing(fileSet) || missing(remoteFilePath)){
stop (paste("A value must be specified for each of baseUrl, folderPath, fileSet, and remoteFilePath"));
}
folderPath <- encodeFolderPath(folderPath);
remoteFilePath <- encodeRemotePath(remoteFilePath)
return(paste(baseUrl, "_webdav", folderPath, fileSet, "/", remoteFilePath, sep=""))
}
encodeRemotePath <- function(path, splitSlash = TRUE) {
if (splitSlash) {
path <- strsplit(path, "/")[[1]]
}
return(paste0(sapply(path, URLencode, reserved = T), collapse = '/'))
}
labkey.webdav.pathExists <- function(baseUrl=NULL, folderPath, remoteFilePath, fileSet="@files")
{
baseUrl=labkey.getBaseUrl(baseUrl);
if (missing(baseUrl) || is.null(baseUrl) || missing(folderPath) || missing(remoteFilePath)) {
stop (paste("A value must be specified for each of baseUrl, folderPath, fileSet, and remoteFilePath"));
}
ret <- labkey.webdav.listDir(baseUrl=baseUrl, folderPath=folderPath, fileSet=fileSet, remoteFilePath=remoteFilePath, haltOnError=F)
return(is.null(ret$exception))
}
labkey.webdav.isDirectory <- function(baseUrl=NULL, folderPath, remoteFilePath, fileSet="@files", haltOnError = TRUE) {
json <- labkey.webdav.listDir(baseUrl = baseUrl, folderPath = folderPath, remoteFilePath = remoteFilePath, fileSet = fileSet, haltOnError = haltOnError)
return(!is.null(json[['fileCount']]))
}
labkey.webdav.listDir <- function(baseUrl=NULL, folderPath, remoteFilePath, fileSet="@files", haltOnError = TRUE)
{
baseUrl=labkey.getBaseUrl(baseUrl);
url <- labkey.webdav.validateAndBuildRemoteUrl(baseUrl=baseUrl, folderPath=folderPath, fileSet=fileSet, remoteFilePath=remoteFilePath)
url <- paste0(url, "?method=JSON")
logMessage(paste0("URL: ", url))
content <- labkey.post(url, pbody="", responseType="text/plain; charset=utf-8", haltOnError = haltOnError)
ret <- fromJSON(content, simplifyVector=FALSE, simplifyDataFrame=FALSE)
colNames <- c("id", "href", "text", "creationdate", "createdby", "lastmodified", "contentlength", "size", "isdirectory")
ret[["files"]] <- lapply(ret[["files"]], function(l){
idx <- match("collection", names(l))
if (!is.na(idx)){
names(l)[idx] <- "isdirectory"
} else {
l$isdirectory <- FALSE
}
l <- l[colNames]
names(l) <- colNames
return(l)
})
return(ret)
}
labkey.webdav.delete <- function(baseUrl=NULL, folderPath, remoteFilePath, fileSet="@files")
{
baseUrl=labkey.getBaseUrl(baseUrl);
url <- labkey.webdav.validateAndBuildRemoteUrl(baseUrl=baseUrl, folderPath=folderPath, fileSet=fileSet, remoteFilePath=remoteFilePath)
url <- paste0(url, "?method=DELETE")
if (!is.null(.lkdefaults[["debug"]]) && .lkdefaults[["debug"]] == TRUE) {
print(paste0("URL: ", url))
}
labkey.post(url, pbody="", responseType="text/plain; charset=utf-8")
return(T)
}
labkey.webdav.mkDirs <- function(baseUrl=NULL, folderPath, remoteFilePath, fileSet="@files")
{
baseUrl=labkey.getBaseUrl(baseUrl);
if (missing(baseUrl) || is.null(baseUrl) || missing(folderPath) || missing(remoteFilePath)){
stop (paste("A value must be specified for each of baseUrl, folderPath, fileSet, and remoteFilePath"))
}
remoteFilePaths <- strsplit(remoteFilePath, "/")[[1]]
toCreate <- ""
for (folderName in remoteFilePaths) {
toCreate <- paste0(toCreate, folderName, "/")
if (!labkey.webdav.pathExists(baseUrl=baseUrl, folderPath=folderPath, fileSet=fileSet, remoteFilePath=toCreate)) {
if (!labkey.webdav.mkDir(baseUrl=baseUrl, folderPath=folderPath, fileSet=fileSet, remoteFilePath=toCreate)){
stop(paste0("Failed to create folder: ", toCreate))
}
}
}
return(TRUE)
}
labkey.webdav.downloadFolder <- function(localBaseDir, baseUrl=NULL, folderPath, remoteFilePath, overwriteFiles=TRUE, mergeFolders=TRUE, fileSet="@files") {
if (missing(localBaseDir) || missing(baseUrl) || is.null(baseUrl) || missing(folderPath) || missing(remoteFilePath)){
stop (paste("A value must be specified for each of localBaseDir, baseUrl, folderPath, fileSet, and remoteFilePath"))
}
if (file.exists(localBaseDir) && !dir.exists(localBaseDir)) {
stop(paste0("Download folder exists, but is not a directory: ", localBaseDir))
}
if (!dir.exists(localBaseDir)) {
stop(paste0("Download folder does not exist: ", localBaseDir))
}
remoteFilePath <- normalizeSlash(remoteFilePath, leading = F)
subfolder <- basename(remoteFilePath)
if (subfolder != ''){
localBaseDir <- normalizeFolder(localBaseDir)
localBaseDir <- file.path(localBaseDir, subfolder)
logMessage(paste0('target local folder: ', localBaseDir))
}
if (!labkey.webdav.isDirectory(baseUrl = baseUrl, folderPath = folderPath, remoteFilePath = remoteFilePath, fileSet = fileSet, haltOnError = T)){
stop('The requested file is not a directory.')
}
if (!prepareDirectory(localPath = localBaseDir, overwriteFiles = overwriteFiles, mergeFolders = mergeFolders)){
return(F)
}
labkey.webdav.doDownloadFolder(localDir = localBaseDir, baseUrl = baseUrl, folderPath = folderPath, remoteFilePath = remoteFilePath, overwriteFiles = overwriteFiles, mergeFolders = mergeFolders, fileSet = fileSet)
}
normalizeFolder <- function(localDir){
localDir <- gsub("[\\]", "/", localDir)
localDir <- gsub("[/]+", "/", localDir)
if (substr(localDir, nchar(localDir), nchar(localDir))=="/") {
localDir <- substr(localDir,1, nchar(localDir)-1)
}
return(localDir)
}
logMessage <- function(msg) {
if (!is.null(.lkdefaults[["debug"]]) && .lkdefaults[["debug"]] == TRUE) {
print(msg)
}
}
labkey.webdav.doDownloadFolder <- function(localDir, baseUrl=NULL, folderPath, remoteFilePath, depth, overwriteFiles=TRUE, mergeFolders=TRUE, fileSet="@files")
{
baseUrl <- normalizeSlash(baseUrl, leading = F, trailing = F)
folderPath <- normalizeSlash(folderPath, leading = F)
fileSet <- normalizeSlash(fileSet, leading = F, trailing = F)
remoteFilePath <- normalizeSlash(remoteFilePath, leading = F)
localDir <- normalizeFolder(localDir)
prefix <- paste0("/_webdav/", folderPath, fileSet, '/')
files <- labkey.webdav.listDir(baseUrl=baseUrl, folderPath=folderPath, fileSet=fileSet, remoteFilePath=remoteFilePath)
for (file in files[["files"]]) {
relativeToRemoteRoot <- sub(prefix, "", file[["id"]])
relativeToDownloadStart <- sub(paste0(prefix, remoteFilePath), "", file[["id"]])
localPath <- file.path(localDir, relativeToDownloadStart)
if (file[["isdirectory"]]) {
logMessage(paste0("Downloading folder: ", relativeToRemoteRoot))
logMessage(paste0("to: ", localPath))
if (file.exists(localPath) && !dir.exists(localPath)) {
if (overwriteFiles) {
unlink((localPath))
} else {
stop(paste0('Target of folder download already exists, but is a file, not a folder: ', localPath))
}
}
if (!prepareDirectory(localPath, overwriteFiles, mergeFolders)) {
next
}
labkey.webdav.doDownloadFolder(localDir=localPath, baseUrl=baseUrl, folderPath=folderPath, fileSet=fileSet, remoteFilePath=relativeToRemoteRoot, overwriteFiles=overwriteFiles, mergeFolders=mergeFolders)
} else {
url <- paste0(baseUrl, trimLeadingPath(file[["href"]]))
logMessage(paste0("Downloading file: ", relativeToRemoteRoot))
logMessage(paste0("to: ", localPath))
labkey.webdav.getByUrl(url, localPath, overwriteFiles)
}
}
return(TRUE)
}
trimLeadingPath <- function(url){
pos <- regexpr('/_webdav', tolower(url))
if (pos == -1) {
return(url)
}
return(substr(url, pos, nchar(url)))
}
prepareDirectory <- function(localPath, overwriteFiles, mergeFolders) {
if (dir.exists(localPath)) {
logMessage(paste0('existing folder found: ', localPath))
if (!mergeFolders && overwriteFiles) {
logMessage('deleting existing folder')
unlink(localPath, recursive = T)
}
else if (!mergeFolders && !overwriteFiles) {
logMessage('skipping existing folder')
return(F)
}
else if (mergeFolders) {
logMessage('existing folder will be left alone and contents downloaded')
}
} else {
dir.create(localPath, recursive=TRUE)
}
return(T)
} |
test_that("gs4_fodder() works", {
dat <- gs4_fodder(3, 5)
expect_named(dat, LETTERS[1:5])
ltrs <- rep(LETTERS[1:5], each = 3)
nbrs <- rep(1:3, 5) + 1
expect_equal(
as.vector(as.matrix(dat)),
paste0(ltrs, nbrs)
)
}) |
checkPathForOutput = function(x, overwrite = FALSE, extension = NULL) {
if (!qtest(x, "S+"))
return("No path provided")
qassert(overwrite, "B1")
x = normalizePath(x, mustWork = FALSE)
dn = dirname(x)
w = wf(!dir.exists(dn))
if (length(w) > 0L)
return(sprintf("Path to file (dirname) does not exist: '%s' of '%s'", dn[w], x[w]))
w = which(file.exists(x))
if (length(w) > 0L) {
if (overwrite)
return(checkAccess(dn, "w") %and% checkAccess(x[w], "rw"))
return(sprintf("File at path already exists: '%s'", x[w]))
}
if (!is.null(extension)) {
qassert(extension, "S1")
if (!endsWith(x, paste0(".", extension)))
return(sprintf("File must have extension '.%s'", extension))
}
return(checkAccess(dn, "w"))
}
check_path_for_output = checkPathForOutput
assertPathForOutput = makeAssertionFunction(checkPathForOutput, use.namespace = FALSE)
assert_path_for_output = assertPathForOutput
testPathForOutput = makeTestFunction(checkPathForOutput)
test_path_for_output = testPathForOutput
expect_path_for_output = makeExpectationFunction(checkPathForOutput, use.namespace = FALSE) |
data(met)
Obs = Obs[order(Obs$ID),]
Gen = data.matrix(Gen)
chr = data.frame(table(as.numeric(substring(colnames(Gen),3,4))))[,2]
fam = as.numeric(substring(rownames(Gen),6,7))
Y = matrix(NA,nrow(Gen),3)
rownames(Y) = rownames(Gen)
colnames(Y) = 2013:2015
y13 = Obs[which(Obs$Year==2013),'DTM']
names(y13) = as.character(Obs[which(Obs$Year==2013),'ID'])
Y[,1] = y13[rownames(Y)]
y14 = Obs[which(Obs$Year==2014),'DTM']
names(y14) = as.character(Obs[which(Obs$Year==2014),'ID'])
Y[,2] = y14[rownames(Y)]
y15 = Obs[which(Obs$Year==2015),'DTM']
names(y15) = as.character(Obs[which(Obs$Year==2015),'ID'])
Y[,3] = y15[rownames(Y)]
chr = round(chr/5)
subsample = seq(1,nrow(Y),5)
Gen = Gen[subsample,seq(1,ncol(Gen),5)]
Y = Y[subsample,]; fam = fam[subsample]
GxE_GWA = gwasGE(Y,Gen,fam,chr)
par(mfrow=c(1,1))
plot(GxE_GWA,main="GWAS",pch=20)
par(mfrow=c(2,2))
plot(GxE_GWA$PolyTest$vi,main="Allele Effect",pch=20,ylab="Mu")
plot(GxE_GWA$PolyTest$vi,main="GxE variance",pch=20,ylab="Var (GE)")
plot(GxE_GWA$PolyTest$vg,main="Genetic variance",pch=20,ylab="Var (G)")
plot(GxE_GWA$PolyTest$vg,main="Environmental variance",pch=20,ylab="Var (E)") |
backprop_r = function(model,a,c,j,...){
if(model$network_type == "rnn"){
backprop_rnn(model,a,c,j,...)
} else if (model$network_type == "lstm"){
backprop_lstm(model,a,c,j,...)
} else if (model$network_type == "gru"){
backprop_gru(model,a,c,j,...)
}else{
stop("network_type_unknown for the backprop")
}
}
backprop_rnn = function(model,a,c,j,...){
model$last_layer_error[j,,] = c - model$store[[length(model$store)]][j,,,drop=F]
model$last_layer_delta[j,,] = model$last_layer_error[j,,,drop = F] * sigmoid_output_to_derivative(model$store[[length(model$store)]][j,,,drop=F])
if(model$seq_to_seq_unsync){
model$last_layer_error[j,1:(model$time_dim_input - 1),] = 0
model$last_layer_delta[j,1:(model$time_dim_input - 1),] = 0
}
model$error[j,model$current_epoch] <- apply(model$last_layer_error[j,,,drop=F],1,function(x){sum(abs(x))})
future_layer_delta = list()
for(i in seq(length(model$hidden_dim))){
future_layer_delta[[i]] <- matrix(0,nrow=length(j), ncol = model$hidden_dim[i])
}
for (position in model$time_dim:1) {
x = array(a[,position,],dim=c(length(j),model$input_dim))
layer_up_delta = array(model$last_layer_delta[j,position,],dim=c(length(j),model$output_dim))
for(i in (length(model$store)):1){
if(i != 1){
layer_current = array(model$store[[i-1]][j,position,],dim=c(length(j),model$hidden_dim[i-1]))
if(position != 1){
prev_layer_current = array(model$store[[i-1]][j,position - 1,],dim=c(length(j),model$hidden_dim[i-1]))
}else{
prev_layer_current = array(0,dim=c(length(j),model$hidden_dim[i-1]))
}
layer_current_delta = (future_layer_delta[[i-1]] %*% t(model$recurrent_synapse[[i-1]]) + layer_up_delta %*% t(model$time_synapse[[i]])) *
sigmoid_output_to_derivative(layer_current)
model$time_synapse_update[[i]] = model$time_synapse_update[[i]] + t(layer_current) %*% layer_up_delta
model$bias_synapse_update[[i]] = model$bias_synapse_update[[i]] + colMeans(layer_up_delta)
model$recurrent_synapse_update[[i-1]] = model$recurrent_synapse_update[[i-1]] + t(prev_layer_current) %*% layer_current_delta
layer_up_delta = layer_current_delta
future_layer_delta[[i-1]] = layer_current_delta
}else{
model$time_synapse_update[[i]] = model$time_synapse_update[[i]] + t(x) %*% layer_up_delta
}
}
}
return(model)
}
backprop_lstm = function(model,a,c,j,...){
model$last_layer_error[j,,] = c - model$store[[length(model$store)]][j,,,drop=F]
model$last_layer_delta[j,,] = model$last_layer_error[j,,,drop = F] * sigmoid_output_to_derivative(model$store[[length(model$store)]][j,,,drop=F])
if(model$seq_to_seq_unsync){
model$last_layer_error[j,1:(model$time_dim_input - 1),] = 0
model$last_layer_delta[j,1:(model$time_dim_input - 1),] = 0
}
model$error[j,model$current_epoch] <- apply(model$last_layer_error[j,,,drop=F],1,function(x){sum(abs(x))})
future_layer_cell_delta = list()
future_layer_hidden_delta = list()
for(i in seq(length(model$hidden_dim))){
future_layer_cell_delta[[i]] = matrix(0, nrow = length(j), ncol = model$hidden_dim[i])
future_layer_hidden_delta[[i]] = matrix(0, nrow = length(j), ncol = model$hidden_dim[i])
}
for (position in model$time_dim:1) {
layer_up_delta = array(model$last_layer_delta[j,position,],dim=c(length(j),model$output_dim))
i = length(model$hidden_dim)
layer_hidden = array(model$store[[i]][j,position,,1],dim=c(length(j),model$hidden_dim[i]))
model$time_synapse_update[[i+1]] = model$time_synapse_update[[i+1]] + (t(layer_hidden) %*% layer_up_delta)
model$bias_synapse_update[[i+1]] = model$bias_synapse_update[[i+1]] + colMeans(layer_up_delta)
layer_up_delta = (layer_up_delta %*% t(model$time_synapse_update[[i+1]])) * sigmoid_output_to_derivative(layer_hidden) + future_layer_hidden_delta[[i]]
for(i in length(model$hidden_dim):1){
if(i == 1){
x = array(a[,position,],dim=c(length(j),model$input_dim))
}else{
x = array(model$store[[i - 1]][j,position,,1],dim=c(length(j),model$synapse_dim[i]))
}
layer_hidden = array(model$store[[i]][j,position,,1],dim=c(length(j),model$hidden_dim[i]))
layer_cell = array(model$store[[i]][j,position,,2],dim=c(length(j), model$hidden_dim[i]))
if(position != 1){
prev_layer_hidden =array(model$store[[i]][j,position-1,,1],dim=c(length(j),model$hidden_dim[i]))
preview_layer_cell = array(model$store[[i]][j,position-1,,2],dim=c(length(j), model$hidden_dim[i]))
}else{
prev_layer_hidden =array(0,dim=c(length(j),model$hidden_dim[i]))
preview_layer_cell = array(0,dim=c(length(j), model$hidden_dim[i]))
}
layer_f = array(model$store[[i]][j,position,,3],dim=c(length(j), model$hidden_dim[i]))
layer_i = array(model$store[[i]][j,position,,4],dim=c(length(j), model$hidden_dim[i]))
layer_c = array(model$store[[i]][j,position,,5],dim=c(length(j), model$hidden_dim[i]))
layer_o = array(model$store[[i]][j,position,,6],dim=c(length(j), model$hidden_dim[i]))
layer_cell_delta = (layer_up_delta * layer_o)* tanh_output_to_derivative(layer_cell) + future_layer_cell_delta[[i]]
layer_o_delta_post_activation = layer_up_delta * tanh(layer_cell)
layer_c_delta_post_activation = layer_cell_delta * layer_i
layer_i_delta_post_activation = layer_cell_delta * layer_c
layer_f_delta_post_activation = layer_cell_delta * preview_layer_cell
future_layer_cell_delta[[i]] = layer_cell_delta * layer_f
layer_o_delta_pre_activation = layer_o_delta_post_activation * sigmoid_output_to_derivative(layer_o)
layer_c_delta_pre_activation = layer_c_delta_post_activation * tanh_output_to_derivative(layer_c)
layer_i_delta_pre_activation = layer_i_delta_post_activation * sigmoid_output_to_derivative(layer_i)
layer_f_delta_pre_activation = layer_f_delta_post_activation * sigmoid_output_to_derivative(layer_f)
model$recurrent_synapse_update[[i]][,,1] = model$recurrent_synapse_update[[i]][,,1] + t(prev_layer_hidden) %*% layer_f_delta_post_activation
model$recurrent_synapse_update[[i]][,,2] = model$recurrent_synapse_update[[i]][,,2] + t(prev_layer_hidden) %*% layer_i_delta_post_activation
model$recurrent_synapse_update[[i]][,,3] = model$recurrent_synapse_update[[i]][,,3] + t(prev_layer_hidden) %*% layer_c_delta_post_activation
model$recurrent_synapse_update[[i]][,,4] = model$recurrent_synapse_update[[i]][,,4] + t(prev_layer_hidden) %*% layer_o_delta_post_activation
model$time_synapse_update[[i]][,,1] = model$time_synapse_update[[i]][,,1] + t(x) %*% layer_f_delta_post_activation
model$time_synapse_update[[i]][,,2] = model$time_synapse_update[[i]][,,2] + t(x) %*% layer_i_delta_post_activation
model$time_synapse_update[[i]][,,3] = model$time_synapse_update[[i]][,,3] + t(x) %*% layer_c_delta_post_activation
model$time_synapse_update[[i]][,,4] = model$time_synapse_update[[i]][,,4] + t(x) %*% layer_o_delta_post_activation
model$bias_synapse_update[[i]][,1] = model$bias_synapse_update[[i]][,1] + colMeans(layer_f_delta_post_activation)
model$bias_synapse_update[[i]][,2] = model$bias_synapse_update[[i]][,2] + colMeans(layer_i_delta_post_activation)
model$bias_synapse_update[[i]][,3] = model$bias_synapse_update[[i]][,3] + colMeans(layer_c_delta_post_activation)
model$bias_synapse_update[[i]][,4] = model$bias_synapse_update[[i]][,4] + colMeans(layer_o_delta_post_activation)
layer_f_delta_pre_weight = layer_f_delta_pre_activation %*% t(array(model$recurrent_synapse[[i]][,,1],dim=c(dim(model$recurrent_synapse[[i]])[1:2])))
layer_i_delta_pre_weight = layer_i_delta_pre_activation %*% t(array(model$recurrent_synapse[[i]][,,2],dim=c(dim(model$recurrent_synapse[[i]])[1:2])))
layer_c_delta_pre_weight = layer_c_delta_pre_activation %*% t(array(model$recurrent_synapse[[i]][,,3],dim=c(dim(model$recurrent_synapse[[i]])[1:2])))
layer_o_delta_pre_weight = layer_o_delta_pre_activation %*% t(array(model$recurrent_synapse[[i]][,,4],dim=c(dim(model$recurrent_synapse[[i]])[1:2])))
future_layer_hidden_delta[[i]] = layer_o_delta_pre_weight + layer_c_delta_pre_weight + layer_i_delta_pre_weight + layer_f_delta_pre_weight
layer_f_delta_pre_weight = layer_f_delta_pre_activation %*% t(array(model$time_synapse[[i]][,,1],dim=c(dim(model$time_synapse[[i]])[1:2])))
layer_i_delta_pre_weight = layer_i_delta_pre_activation %*% t(array(model$time_synapse[[i]][,,2],dim=c(dim(model$time_synapse[[i]])[1:2])))
layer_c_delta_pre_weight = layer_c_delta_pre_activation %*% t(array(model$time_synapse[[i]][,,3],dim=c(dim(model$time_synapse[[i]])[1:2])))
layer_o_delta_pre_weight = layer_o_delta_pre_activation %*% t(array(model$time_synapse[[i]][,,4],dim=c(dim(model$time_synapse[[i]])[1:2])))
layer_up_delta = layer_o_delta_pre_weight + layer_c_delta_pre_weight + layer_i_delta_pre_weight + layer_f_delta_pre_weight
}
}
return(model)
}
backprop_gru = function(model,a,c,j,...){
model$last_layer_error[j,,] = c - model$store[[length(model$store)]][j,,,drop=F]
model$last_layer_delta[j,,] = model$last_layer_error[j,,,drop = F] * sigmoid_output_to_derivative(model$store[[length(model$store)]][j,,,drop=F])
if(model$seq_to_seq_unsync){
model$last_layer_error[j,1:(model$time_dim_input - 1),] = 0
model$last_layer_delta[j,1:(model$time_dim_input - 1),] = 0
}
model$error[j,model$current_epoch] <- apply(model$last_layer_error[j,,,drop=F],1,function(x){sum(abs(x))})
future_layer_hidden_delta = list()
for(i in seq(length(model$hidden_dim))){
future_layer_hidden_delta[[i]] = matrix(0, nrow = length(j), ncol = model$hidden_dim[i])
}
for (position in model$time_dim:1) {
layer_up_delta = array(model$last_layer_delta[j,position,],dim=c(length(j),model$output_dim))
i = length(model$hidden_dim)
layer_hidden = array(model$store[[i]][j,position,,1],dim=c(length(j),model$hidden_dim[i]))
model$time_synapse_update[[i+1]] = model$time_synapse_update[[i+1]] + (t(layer_hidden) %*% layer_up_delta)
model$bias_synapse_update[[i+1]] = model$bias_synapse_update[[i+1]] + colMeans(layer_up_delta)
layer_up_delta = (layer_up_delta %*% t(model$time_synapse_update[[i+1]])) * sigmoid_output_to_derivative(layer_hidden) + future_layer_hidden_delta[[i]]
for(i in length(model$hidden_dim):1){
if(i == 1){
x = array(a[,position,],dim=c(length(j),model$input_dim))
}else{
x = array(model$store[[i - 1]][j,position,,1],dim=c(length(j),model$synapse_dim[i]))
}
layer_hidden = array(model$store[[i]][j,position,,1],dim=c(length(j),model$hidden_dim[i]))
if(position != 1){
prev_layer_hidden =array(model$store[[i]][j,position-1,,1],dim=c(length(j),model$hidden_dim[i]))
}else{
prev_layer_hidden =array(0,dim=c(length(j),model$hidden_dim[i]))
}
layer_z = array(model$store[[i]][j,position,,2],dim=c(length(j), model$hidden_dim[i]))
layer_r = array(model$store[[i]][j,position,,3],dim=c(length(j), model$hidden_dim[i]))
layer_h = array(model$store[[i]][j,position,,4],dim=c(length(j), model$hidden_dim[i]))
layer_hidden_delta = layer_up_delta + future_layer_hidden_delta[[i]]
layer_h_delta_post_activation = layer_hidden_delta * layer_z
layer_h_delta_pre_activation = layer_h_delta_post_activation * tanh_output_to_derivative(layer_h)
layer_z_delta_post_split = layer_hidden_delta * layer_h
layer_z_delta_post_1_minus = layer_hidden_delta * prev_layer_hidden
layer_hidden_delta = layer_hidden_delta * (1 - layer_z)
layer_z_delta_post_activation = (1 - layer_z_delta_post_1_minus)
layer_z_delta_pre_activation = layer_z_delta_post_activation* sigmoid_output_to_derivative(layer_z)
layer_z_delta_pre_weight_h = (layer_z_delta_pre_activation %*% t(model$recurrent_synapse[[i]][,,1]) )
layer_z_delta_pre_weight_x = (layer_z_delta_pre_activation %*% array(t(model$time_synapse[[i]][,,1]),dim = dim(model$time_synapse[[i]])[2:1]))
model$recurrent_synapse_update[[i]][,,1] = model$recurrent_synapse_update[[i]][,,1] + t(prev_layer_hidden) %*% layer_z_delta_post_activation
model$time_synapse_update[[i]][,,1] = model$time_synapse_update[[i]][,,1] + t(x) %*% layer_z_delta_post_activation
model$bias_synapse_update[[i]][,1] = model$bias_synapse_update[[i]][,1] + colMeans(layer_z_delta_post_activation)
layer_h_delta_pre_weight_h = (layer_h_delta_pre_activation %*% t(model$recurrent_synapse[[i]][,,3]))
layer_h_delta_pre_weight_x = ( layer_h_delta_pre_activation %*% array(t(model$time_synapse[[i]][,,3]),dim = dim(model$time_synapse[[i]])[2:1]))
model$recurrent_synapse_update[[i]][,,3] = model$recurrent_synapse_update[[i]][,,3] + t(prev_layer_hidden * layer_r) %*% layer_h_delta_post_activation
model$time_synapse_update[[i]][,,3] = model$time_synapse_update[[i]][,,3] + t(x) %*% layer_h_delta_post_activation
model$bias_synapse_update[[i]][,3] = model$bias_synapse_update[[i]][,3] + colMeans(layer_h_delta_post_activation)
layer_r_delta_post_activation = prev_layer_hidden * layer_h_delta_pre_weight_h
layer_r_delta_pre_activation = layer_r_delta_post_activation * sigmoid_output_to_derivative(layer_r)
layer_hidden_delta = layer_hidden_delta + layer_r * layer_h_delta_pre_weight_h
layer_r_delta_pre_weight_h = (layer_r_delta_pre_activation %*% t(model$recurrent_synapse[[i]][,,2]))
layer_r_delta_pre_weight_x = (layer_r_delta_post_activation %*% array(t(model$time_synapse[[i]][,,2]),dim = dim(model$time_synapse[[i]])[2:1]))
model$recurrent_synapse_update[[i]][,,2] = model$recurrent_synapse_update[[i]][,,2] + t(prev_layer_hidden) %*% layer_r_delta_post_activation
model$time_synapse_update[[i]][,,2] = model$time_synapse_update[[i]][,,2] + t(x) %*% layer_r_delta_post_activation
model$bias_synapse_update[[i]][,2] = model$bias_synapse_update[[i]][,2] + colMeans(layer_r_delta_post_activation)
layer_r_and_z_delta_pre_weight_h = layer_r_delta_pre_weight_h + layer_z_delta_pre_weight_h
layer_r_and_z_delta_pre_weight_x = layer_r_delta_pre_weight_x + layer_z_delta_pre_weight_x
future_layer_hidden_delta[[i]] = layer_hidden_delta + layer_r_and_z_delta_pre_weight_h
layer_up_delta = layer_r_and_z_delta_pre_weight_x + layer_h_delta_pre_weight_x
}
}
return(model)
} |
get_hc_info <- function(varname, resplvl, Mlist, parelmts, lp) {
all_lvls <- Mlist$group_lvls
rel_lvls <- names(all_lvls)[all_lvls > all_lvls[resplvl]]
rel_lvls_surv <- names(all_lvls)[all_lvls >= all_lvls[resplvl]]
newrandom <- check_random_lvls(
Mlist$random[[varname]],
rel_lvls = if (Mlist$models[varname] %in% c("coxph", "survreg", "JM") &
length(rel_lvls_surv) > 1L) {
rel_lvls_surv
} else {
rel_lvls
})
if (length(newrandom) > 0) {
hc_columns <- lapply(newrandom, get_hc_columns, Mlist = Mlist)
structure(
orga_hc_parelmts(
resplvl,
intersect(rel_lvls, names(newrandom)),
all_lvls = all_lvls,
hc_columns = hc_columns,
parelmts = parelmts,
lp = lp
),
warnings = rescale_ranefs_warning(lapply(hc_columns, attr, "incomplete"),
Mlist$scale_pars, varname))
}
}
rescale_ranefs_warning <- function(incompl, scale_pars, varname) {
nlapply(names(incompl), function(lvl) {
if (any(incompl[[lvl]]) &&
any(!is.na(do.call(rbind, unname(
scale_pars
)))[names(incompl[[lvl]]), ])) {
w <- warnmsg(
"There are missing values in a variable for which a random effect
is specified (%s). It will not be possible to re-scale the
random effects %s and their variance covariance matrix %s back
to the original scale of the data. If you are not interested in
the estimated random effects or their (co)variances this is not a
problem. The fixed effects estimates are not affected by this.
If you are interested in the random effects or the (co)variances
you need to specify that %s are not scaled (using the argument %s).",
dQuote(names(incompl[[lvl]])[incompl[[lvl]]]),
dQuote(paste0("b_", varname, "_", lvl)),
dQuote(paste0("D_", varname, "_", lvl)),
paste_and(dQuote(names(incompl[[lvl]]))),
dQuote("scale_params")
)
w
}
})
}
check_random_lvls <- function(random, rel_lvls) {
if (length(rel_lvls) == 0L) {
return(NULL)
}
if (is.null(random)) {
nlapply(rel_lvls, function(x) ~ 1)
} else {
rd <- remove_grouping(random)
if (any(!names(rd) %in% rel_lvls)) {
errormsg("You have specified random effects for levels on which there
should not be random effects (%s).",
dQuote(setdiff(names(rd), rel_lvls)))
} else {
rd
}
}
}
get_hc_columns <- function(rdfmla, Mlist) {
Mlvls <- Mlist$Mlvls
Mnam <- nlapply(Mlist$M, colnames)
z_names <- get_dsgnmat_names(rdfmla, Mlist$data, Mlist$refs)
inters <- Mlist$interactions[!names(Mlist$interactions) %in% z_names]
in_z <- if (length(inters) > 0L) {
nlapply(z_names, function(x)
lapply(lapply(inters, attr, 'elements'), `%in%`, x))
}
structure(
nlapply(z_names, function(x) {
list(
main = if (x %in% names(Mlvls)) {
setNames(match(x, Mnam[[Mlvls[x]]]), Mlvls[x])
},
interact = if (any(unlist(in_z[[x]]))) {
w <- sapply(in_z[[x]], any)
inters[w]
}
)
}),
rd_intercept = attr(terms(rdfmla), 'intercept'),
z_names = z_names,
incomplete = lvapply(Mlist$data[, all_vars(rdfmla), drop = FALSE],
function(x) any(is.na(x)))
)
}
get_dsgnmat_names <- function(formula, data, refs) {
contr_list <- lapply(refs, attr, "contr_matrix")
colnames(
model.matrix(formula, data,
contrasts.arg = contr_list[intersect(all_vars(formula),
names(contr_list))]))
}
hc_rdslope_info <- function(hc_cols, parelmts) {
hc_cols <- hc_cols[names(hc_cols) != "(Intercept)"]
rd_slope_list <- lapply(names(hc_cols), function(var) {
M_lvl <- names(hc_cols[[var]]$main)
elmts <- parelmts[[M_lvl]]
if (is.list(elmts)) {
data.frame(rd_effect = var,
term = var,
matrix = M_lvl,
cols = hc_cols[[var]]$main,
parelmts = NA,
stringsAsFactors = FALSE
)
} else {
data.frame(rd_effect = var,
term = var,
matrix = M_lvl,
cols = hc_cols[[var]]$main,
parelmts = ifelse(is.null(elmts[var]), NA, unname(elmts[var])),
stringsAsFactors = FALSE
)
}
})
do.call(rbind, rd_slope_list)
}
hc_rdslope_interact <- function(hc_cols, parelmts, lvls) {
hc_cols <- hc_cols[names(hc_cols) != "(Intercept)"]
rd_slope_interact_coefs <- lapply(names(hc_cols), function(var) {
if (any(lvapply(parelmts, is.list))) {
do.call(rbind,
lapply(hc_cols[[var]]$interact, function(x) {
data.frame(rd_effect = var,
term = attr(x, 'interaction'),
matrix = names(x$elmts[attr(x, 'elements') != var]),
cols = x$elmts[attr(x, 'elements') != var],
parelmts = NA,
stringsAsFactors = FALSE
)
})
)
} else {
do.call(rbind,
lapply(hc_cols[[var]]$interact, function(x) {
mat <- names(x$elmts)[attr(x, 'elements') != var]
if (length(mat) > 1L) {
elmt <- paste0(attr(x,"elements")[attr(x, 'elements') != var],
collapse = ":")
mat <- unlist(lapply(names(parelmts), function(p) {
if (elmt %in% names(parelmts[[p]])) p
}))
col <- if (!is.null(mat)) {
which(names(parelmts[[mat]]) == elmt)
}
} else {
col <- x$elmts[attr(x, 'elements') != var]
}
if (!is.null(mat)) {
data.frame(rd_effect = var,
term = attr(x, 'interaction'),
matrix = mat,
cols = col,
parelmts = unname(parelmts[[names(x$interterm)]][
attr(x, "interaction")]),
stringsAsFactors = FALSE
)
}
})
)
}
})
rd_slope_interact_coefs <- do.call(rbind, rd_slope_interact_coefs)
if (!is.null(rd_slope_interact_coefs)) {
subset(rd_slope_interact_coefs, matrix %in% paste0('M_', lvls))
}
}
orga_hc_parelmts <- function(resplvl, lvls, all_lvls, hc_columns, parelmts, lp) {
hc_vars <- nlapply(lvls, function(lvl) {
rd_slope_coefs <- hc_rdslope_info(hc_columns[[lvl]], parelmts)
rd_slope_interact_coefs <- hc_rdslope_interact(hc_columns[[lvl]],
parelmts,
lvls)
elmts <- parelmts[[paste0("M_", lvl)]][
!parelmts[[paste0("M_", lvl)]] %in%
rbind(rd_slope_coefs, rd_slope_interact_coefs)$parelmts]
rd_intercept_coefs <- if (!is.null(elmts) &
attr(hc_columns[[lvl]], 'rd_intercept') == 1) {
if (is.list(elmts) | length(elmts) == 0) {
NULL
} else {
data.frame(
rd_effect = "(Intercept)",
term = names(elmts),
matrix = paste0("M_", lvl),
cols = lp[[paste0("M_", lvl)]][names(elmts)],
parelmts = elmts,
stringsAsFactors = FALSE
)
}
}
structure(
list(rd_intercept_coefs = rd_intercept_coefs,
rd_slope_coefs = rd_slope_coefs,
rd_slope_interact_coefs = rd_slope_interact_coefs
),
rd_intercept = "(Intercept)" %in% names(hc_columns[[lvl]]),
incomplete = attr(hc_columns[[lvl]], "incomplete"),
z_names = attr(hc_columns[[lvl]], "z_names")
)
})
used <- lapply(nlapply(hc_vars, do.call, what = rbind), "[[", "parelmts")
othervars <- nlapply(
names(all_lvls)[all_lvls <= min(all_lvls[lvls])], function(lvl) {
other <- get_othervars_mat(lvl, parelmts, lp)
nonprop <- get_othervars_mat(lvl, lapply(parelmts, 'attr', 'nonprop'),
attr(lp, 'nonprop'))
if (!inherits(other, 'list'))
other <- other[!other$parelmts %in% unlist(used), ]
list(
other = if (all(dim(other) > 0))
other,
nonprop = nonprop
)
})
list(hcvars = hc_vars,
othervars = lapply(othervars, "[[", "other"),
nonprop = lapply(othervars, "[[", "nonprop")
)
}
get_othervars_mat <- function(lvl, parelmts, lp) {
pe <- parelmts[[paste0("M_", lvl)]]
linpred <- lp[[paste0("M_", lvl)]]
if (length(pe) == 0) {
NULL
} else if (is.list(pe)) {
lapply(pe, function(p) {
data.frame(term = names(p),
matrix = if (!is.null(linpred)) paste0("M_", lvl),
cols = linpred[names(p)],
parelmts = p,
stringsAsFactors = FALSE)
})
} else {
data.frame(term = names(pe),
matrix = if (!is.null(linpred)) paste0("M_", lvl),
cols = linpred,
parelmts = pe,
stringsAsFactors = FALSE)
}
} |
GetCDLImage <- function(aoi = NULL, year = NULL, type = NULL, format = 'png',
crs = NULL, destfile = NULL, verbose = TRUE, tol_time = 20){
targetCRS <- "+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"
if(is.null(aoi)) stop('aoi must be provided. See details. \n')
if(is.null(year)) stop('year must be provided. See details. \n')
if(is.null(type)) stop('type must be provided. See details. \n')
if(type == 'p') stop('Cannot request statistics for a single point. \n')
if(!type %in% c('f', 'ps', 'b', 's')) stop('Invalid type value. See details. \n')
if(type == 'f'){
aoi <- as.character(aoi)
if ((nchar(aoi) == 1)|(nchar(aoi) == 4)){
aoi <- paste0('0', aoi)
}
GetCDLImageF(fips = aoi, year = year, format = format, verbose = verbose, destfile = destfile, tol_time = tol_time)
}
if(type == 's'){
if(!is.null(crs)) stop('The coordinate system must be the Albers projection system. \n')
GetCDLImageS(poly = aoi, year = year, format = format, verbose = verbose, destfile = destfile, tol_time = tol_time)
}
if(type == 'ps'){
if(length(aoi) < 6) stop('The aoi must be a numerical vector with at least 6 elements. \n')
if(!is.null(crs)){ aoi <- convert_crs(aoi, crs)}
GetCDLImagePs(points = aoi, year = year, format = format, verbose = verbose, destfile = destfile, tol_time = tol_time)
}
if(type == 'b'){
if (!is.numeric(aoi)) {
if (!(class(aoi)[1] == "sf" | class(aoi)[2] == "sfc")) stop('aoi must be a numerical vector or a sf object. \n')
if(is.na(sf::st_crs(aoi))) stop('The sf object for aoi does not contain crs. \n')
aoi_crs <- sf::st_crs(aoi)[[2]]
if(aoi_crs != targetCRS){aoi <- sf::st_transform(aoi, targetCRS)}
GetCDLImageB(box = sf::st_bbox(aoi), year = year, format = format, verbose = verbose, destfile = destfile, tol_time = tol_time)
}else{
if(length(aoi) != 4) stop('The aoi must be a numerical vector with 4 elements. \n')
if(!is.null(crs)){ aoi <- convert_crs(aoi, crs)}
GetCDLImageB(box = aoi, year = year, format = format, verbose = verbose, destfile = destfile, tol_time = tol_time)
}
}
} |
merge_named_lists <- function(list1, list2, fun=NULL) {
if(!is.list(list1) || !is.list(list2))
stop("function merge_named_list requires two lists")
if(is.null(fun) || !is.function(fun))
fun <- sum
res_list <- list1
for(n in names(list2)) {
if(!n %in% names(res_list)) res_list[[n]] <- list2[[n]]
else res_list[[n]] <- fun(res_list[[n]],list2[[n]])
}
return(res_list)
}
c_to_string <- function(var) {
s <- "["
if(length(var)>0)
for(i in 1:length(var)) {
if(i<length(var)) s <- paste0(s,var[[i]],",")
else s <- paste0(s,var[[i]])
}
return(paste0(s,"]"))
} |
test_that("Validate GetVirtualReportSuiteSettings using legacy credentials", {
skip_on_cran()
SCAuth(Sys.getenv("USER", ""), Sys.getenv("SECRET", ""))
gvrss <- GetVirtualReportSuiteSettings("vrs_zwitch0_vrs1")
gvrss2 <- GetVirtualReportSuiteSettings(c("vrs_zwitch0_zwitchdevvrs", "vrs_zwitch0_vrs1"))
expect_is(gvrss, "data.frame")
expect_is(gvrss2, "data.frame")
}) |
context("Testing test_docker_installation")
test_that("Testing that test_docker_installation works", {
skip_on_github_actions()
skip_on_cran()
expect_true(test_docker_installation())
expect_message(test_docker_installation(detailed = TRUE), "\u2714.*")
}) |
library(jvcoords)
set.seed(1)
X1 <- matrix(rnorm(99), nrow = 33, ncol = 3)
X2 <- matrix(rnorm(99), nrow = 3, ncol = 33)
X3 <- matrix(rnorm(99), nrow = 99, ncol = 1)
X4 <- matrix(1:6, 3, 2)
for (x in list(X1, X2, X3, X4)) {
s <- standardize(x)
stopifnot(max(abs(colMeans(s$y))) < 1e-10)
stopifnot(max(abs(apply(s$y, 2, var) - 1)) < 1e-10)
stopifnot(max(abs(toCoords(s, x) - s$y)) < 1e-10)
cat(dim(x), "to mat OK\n")
stopifnot(max(abs(toCoords(s, x[1, ]) - s$y[1, ])) < 1e-10)
cat(dim(x), "to vec OK\n")
stopifnot(max(abs(fromCoords(s, s$y) - x)) < 1e-10)
cat(dim(x), "from mat OK\n")
stopifnot(max(abs(fromCoords(s, s$y[1, ]) - x[1, ])) < 1e-10)
cat(dim(x), "from vec OK\n")
Y <- matrix(rnorm(100 * s$q), 100, s$q)
stopifnot(max(abs(Y - toCoords(s, fromCoords(s, Y)))) < 1e-10)
cat(dim(x), "from&to mat OK\n")
stopifnot(max(abs(Y[1, ] - toCoords(s, fromCoords(s, Y[1, ])))) < 1e-10)
cat(dim(x), "from&to vec OK\n")
} |
data(satsolvers)
vbsp = sum(parscores(satsolvers, vbs))
vbsm = sum(misclassificationPenalties(satsolvers, vbs))
vbss = sum(successes(satsolvers, vbs))
test_that("singleBest and vbs", {
skip.expensive()
vbsse = sum(apply(satsolvers$data[satsolvers$success], 1, max))
expect_equal(vbsse, 2125)
expect_equal(vbss, 2125)
vbsp1 = sum(parscores(satsolvers, vbs, 1))
vbsp1e = sum(apply(satsolvers$data[satsolvers$performance], 1, min))
expect_equal(vbsp1e, 1288664.971)
expect_equal(vbsp1, 1288664.971)
expect_equal(vbsp, 11267864.97)
expect_equal(vbsm, 0)
sbp = sum(parscores(satsolvers, singleBest))
sbm = sum(misclassificationPenalties(satsolvers, singleBest))
sbs = sum(successes(satsolvers, singleBest))
sbse = sum(satsolvers$data[,"clasp_success"])
expect_equal(sbse, 2048)
expect_equal(sbs, 2048)
sbp1 = sum(parscores(satsolvers, singleBest, 1))
sbp1e = sum(satsolvers$data["clasp"])
expect_equal(sbp1e, 1586266.044)
expect_equal(sbp1, 1586266.044)
sbme = sum(apply(satsolvers$data[satsolvers$performance], 1, function(x) { abs(x["clasp"] - min(x)) }))
expect_equal(sbme, 297601.073)
expect_equal(sbm, 297601.073)
expect_equal(sbp, 14060266.04)
})
folds = cvFolds(satsolvers)
test_that("classify", {
skip.expensive()
res = classify(classifier=makeLearner("classif.OneR"), data=folds)
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
res = classify(classifier=list(makeLearner("classif.OneR"),
makeLearner("classif.OneR"),
makeLearner("classif.OneR")),
data=folds)
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
res = classify(classifier=list(makeLearner("classif.OneR"),
makeLearner("classif.OneR"),
makeLearner("classif.OneR"),
.combine=makeLearner("classif.OneR")),
data=folds)
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
})
test_that("classifyPairs", {
skip.expensive()
res = classifyPairs(classifier=makeLearner("classif.OneR"), data=folds)
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
res = classifyPairs(classifier=makeLearner("classif.OneR"),
data=folds,
combine=makeLearner("classif.OneR"))
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
})
test_that("cluster", {
skip.expensive()
res = cluster(clusterer=makeLearner("cluster.SimpleKMeans"), data=folds, pre=normalize)
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
res = cluster(clusterer=makeLearner("cluster.SimpleKMeans"), data=folds,
bestBy="successes", pre=normalize)
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
res = cluster(clusterer=list(makeLearner("cluster.SimpleKMeans"),
makeLearner("cluster.SimpleKMeans"),
makeLearner("cluster.SimpleKMeans")),
data=folds, pre=normalize)
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
res = cluster(clusterer=list(makeLearner("cluster.SimpleKMeans"),
makeLearner("cluster.SimpleKMeans"),
makeLearner("cluster.SimpleKMeans"),
.combine=makeLearner("classif.OneR")), data=folds, pre=normalize)
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
})
test_that("regression", {
skip.expensive()
res = regression(regressor=makeLearner("regr.lm"), data=folds)
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
res = regression(regressor=makeLearner("regr.lm"),
data=folds,
combine=makeLearner("classif.OneR"))
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
res = regression(regressor=makeLearner("regr.lm"),
data=folds,
combine=makeLearner("classif.OneR"),
expand=function(x) { cbind(x, combn(c(1:ncol(x)), 2,
function(y) { abs(x[,y[1]] - x[,y[2]]) })) })
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
})
test_that("regressionPairs", {
skip.expensive()
res = regressionPairs(regressor=makeLearner("regr.lm"), data=folds)
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
res = regressionPairs(regressor=makeLearner("regr.lm"), data=folds,
combine=makeLearner("classif.OneR"))
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
})
test_that("perfScatterPlot", {
skip.expensive()
model = classify(classifier=makeLearner("classif.J48"), data=folds)
library(ggplot2)
p = perfScatterPlot(parscores, model, singleBest, folds, satsolvers) +
scale_x_log10() + scale_y_log10() +
xlab("J48") + ylab("single best")
expect_false(is.null(p))
satsolvers$extra = c("foo")
satsolvers$data$foo = 1:nrow(satsolvers$data)
p = perfScatterPlot(parscores, model, singleBest, folds, satsolvers, pargs=aes(colour = foo)) +
scale_x_log10() + scale_y_log10() +
xlab("J48") + ylab("single best")
expect_false(is.null(p))
})
test_that("tune", {
skip.expensive()
ps = makeParamSet(makeIntegerParam("M", lower = 1, upper = 100))
design = generateRandomDesign(10, ps)
res = tuneModel(folds, classify, makeLearner("classif.J48"), design, parscores, nfolds = 3L, quiet = TRUE)
expect_equal(class(res), "llama.model")
expect_equal(attr(res, "type"), "classify")
expect_true(res$parvals$M >= 1 && res$parvals$M <= 100)
expect_true(all(sapply(res$inner.parvals, function(x) (x$M >= 1 && x$M <= 100))))
}) |
chao.sd <-
function(x) {
f.one <- sum(x[x==1])
f.two <- sum(x[x==2])
gamma <- f.two*(0.25*(f.one/f.two)^4+(f.one/f.two)^3+0.5*(f.one/f.two)^2)
return(sqrt(gamma))
} |
auto_name <- function(x, max_width = 40) {
names(x) <- auto_names(x, max_width = max_width)
x
}
auto_names <- function(x, max_width = 40) {
x <- as.lazy_dots(x)
nms <- names(x) %||% rep("", length(x))
missing <- nms == ""
expr <- lapply(x[missing], `[[`, "expr")
nms[missing] <- vapply(expr, deparse_trunc, width = max_width,
FUN.VALUE = character(1), USE.NAMES = FALSE)
nms
}
deparse_trunc <- function(x, width = getOption("width")) {
if (is.symbol(x)) {
return(as.character(x))
}
text <- deparse(x, width.cutoff = width)
if (length(text) == 1 && nchar(text) < width) return(text)
paste0(substr(text[1], 1, width - 3), "...")
} |
"obounce" <-
function (x, d)
{
ord <- order(x)
xsort <- x[ord]
n <- length(x)
xnew <- xsort
if (n > 1) {
i1 <- 1
while (i1 < n) {
x1 <- xsort[i1]
i2 <- i1 + 1
for (j in i2:n) {
nobounce <- TRUE
jn <- n - j + i2
dj <- xsort[jn] - x1
dsought <- (jn - i1) * d
if (dj < dsought) {
jot <- (dsought - dj)/2
for (k in i1:jn) xnew[k] <- x1 - jot + (k -
i1) * d
i1 <- jn + 1
nobounce <- FALSE
break
}
}
if (nobounce)
i1 <- i1 + 1
}
if (min(diff(xnew)) < d * 0.999) {
n1 <- (1:(n - 1))[diff(xnew) < d]
cat("Error in bounce(). Improperly separated points are:",
fill = TRUE)
cat(paste(n1, ":", n1 + 1, sep = ""), fill = TRUE)
cat(paste(xnew[n1], ":", xnew[n1 + 1], sep = ""),
fill = TRUE)
}
}
x[ord] <- xnew
x
} |
sensIvreg <-
function(ivregfit, coeftestIVres, variable, ...){
UseMethod("sensIvreg")
} |
summary.mslapmeg<- function(object,n=5,...){
dr<-data.frame(object[[1]],object[[2]],object[[3]])
colnames(dr)<-names(object)[-4]
dr<-utils::head(dr[order(dr[,2]),],n)
row.names(dr)<-NULL
print(dr)
} |
test_that("tbl_format_header() results", {
local_unknown_rows()
local_foo_tbl()
expect_snapshot({
tbl_format_header(tbl_format_setup(as_tbl(mtcars), width = 80))
tbl_format_header(tbl_format_setup(as_unknown_rows(trees), width = 30, n = 10))
"Narrow"
tbl_format_header(tbl_format_setup(as_tbl(mtcars), width = 10))
"Custom tbl_sum()"
tbl_format_header(tbl_format_setup(new_foo_tbl(), width = 30))
})
}) |
method_ids <- unique(data$method_id) %>% setdiff("error")
plots <- map(method_ids, function(method_id) {
print(method_id)
data_method <- data %>% filter(method_id == !!method_id)
model_method <- models %>% filter(method_id == !!method_id)
data_noerror <- data_method %>% filter(error_status == "no_error")
data_error <- data_method %>% filter(error_status != "no_error")
pred_method <- data_pred %>% filter(method_id == !!method_id)
data_noerror <- bind_rows(
data_noerror,
data_noerror %>% group_by(method_id, lnrow, lncol) %>% select_if(is.numeric) %>% summarise_if(is.numeric, mean, na.rm = TRUE) %>% mutate(orig_dataset_id = "mean") %>% ungroup()
)
data_error <- bind_rows(
data_error,
data_error %>% group_by(method_id, lnrow, lncol) %>% select_if(is.numeric) %>% summarise_if(is.numeric, mean, na.rm = TRUE) %>% mutate(orig_dataset_id = "mean") %>% ungroup()
)
g1 <-
ggplot(data_method) +
geom_rect(aes(xmin = lnrow - .1, xmax = lnrow + .1, ymin = lncol - .1, ymax = lncol + .1, fill = error_status)) +
scale_fill_manual(values = dynbenchmark::method_status_colours) +
scale_x_nrow + scale_y_ncol +
theme_classic() +
theme(legend.position = "bottom") +
labs(x = "
facet_wrap(~ orig_dataset_id, ncol = 1) +
coord_equal()
if (nrow(data_noerror) > 0) {
g2 <-
ggplot(data_noerror) +
geom_rect(aes(xmin = lnrow - .1, xmax = lnrow + .1, ymin = lncol - .1, ymax = lncol + .1), fill = "darkgray", data_error) +
geom_rect(aes(xmin = lnrow - .1, xmax = lnrow + .1, ymin = lncol - .1, ymax = lncol + .1, fill = log10(time_method))) +
scale_fill_distiller(palette = "RdYlBu") +
scale_x_nrow + scale_y_ncol +
theme_classic() +
theme(legend.position = "bottom") +
labs(x = "
facet_wrap(~ orig_dataset_id, ncol = 1) +
coord_equal()
tmp_error <- bind_rows(data_error, data_noerror %>% filter(log10(max_mem) < 8))
g3 <-
ggplot(data_noerror %>% filter(log10(max_mem) >= 8)) +
geom_rect(aes(xmin = lnrow - .1, xmax = lnrow + .1, ymin = lncol - .1, ymax = lncol + .1), fill = "darkgray", tmp_error) +
geom_rect(aes(xmin = lnrow - .1, xmax = lnrow + .1, ymin = lncol - .1, ymax = lncol + .1, fill = log10(max_mem))) +
scale_fill_distiller(palette = "RdYlBu") +
scale_x_nrow + scale_y_ncol +
theme_classic() +
theme(legend.position = "bottom") +
labs(x = "
facet_wrap(~ orig_dataset_id, ncol = 1) +
coord_equal()
g4a <-
ggplot(pred_method %>% select(lnrow, lncol, time_lpred)) +
geom_rect(aes(xmin = lnrow - .1, xmax = lnrow + .1, ymin = lncol - .1, ymax = lncol + .1, fill = time_lpred)) +
scale_fill_distiller(palette = "RdYlBu") +
scale_x_nrow + scale_y_ncol +
theme_classic() +
theme(legend.position = "bottom") +
labs(x = "
coord_equal()
g4b <-
ggplot(pred_method) +
geom_rect(aes(xmin = lnrow - .1, xmax = lnrow + .1, ymin = lncol - .1, ymax = lncol + .1, fill = mem_lpred)) +
scale_fill_distiller(palette = "RdYlBu") +
scale_x_nrow + scale_y_ncol +
theme_classic() +
theme(legend.position = "bottom") +
labs(x = "
coord_equal()
patchwork::wrap_plots(
patchwork::wrap_plots(
g1 + labs(title = "Execution information"),
g2 + labs(title = "Timings grid"),
g3 + labs(title = "Memory grid"),
nrow = 1
),
patchwork::wrap_plots(
g4a + labs(title = "Predicted timings"),
g4b + labs(title = "Predicted memory"),
ncol = 1
),
nrow = 1,
widths = c(3, 1)
) +
patchwork::plot_annotation(
title = paste0("Scalability results for ", data_method$method_name[[1]]),
theme = theme(title = element_text(size = 20))
)
} else {
patchwork::wrap_plots(
g1 + labs(title = "Execution information")
) +
patchwork::plot_annotation(
title = paste0("Scalability results for ", data_method$method_name[[1]]),
theme = theme(title = element_text(size = 20))
)
}
})
dir.create(result_file("results"), showWarnings = FALSE)
dir.create(result_file("results/error_class_plots"), showWarnings = FALSE)
map(seq_along(method_ids), function(i) {
mid <- method_ids[[i]]
pl <- plots[[i]]
cat("Plotting ", mid, "\n", sep = "")
ggsave(result_file(c("results/", mid, ".png")), pl, width = 12, height = 14, dpi = 100)
}) |
df2m <- function(x)
{
if(!is.null(x)) {
xattr <- attributes(x)
nxa <- names(xattr)
x$intnr <- x$paramnr <- x$varname <- NULL
cn <- colnames(x)
x <- as.matrix(x)
rownames(x) <- 1L:nrow(x)
colnames(x) <- rep(cn, length.out = ncol(x))
for(k in 1L:length(nxa))
if(all(nxa[k] != c("dim", "dimnames", "class", "names", "row.names"))) {
attr(x, nxa[k]) <- xattr[[k]]
}
}
return(x)
} |
library(readr)
library(dplyr)
library(tidyr)
library(magrittr)
library(ggplot2)
CDC_parser <- function(year, url) {
all_deaths_name <- paste0("deaths_", substr(year, 3, 4))
all_deaths_save <- paste0("all_deaths_", substr(year, 3, 4), ".RData")
gun_name <- paste0("guns_", substr(year, 3, 4))
gun_save <- paste0("gun_deaths_", substr(year, 3, 4), ".RData")
suicide_name <- paste0("suicide_", substr(year, 3, 4))
suicide_save <- paste0("suicide_", substr(year, 3, 4), ".RData")
layout <- fwf_widths(c(19,1,40,2,1,1,2,2,1,4,1,2,2,2,2,1,1,1,16,4,1,1,1,1,34,1,1,4,3,1,3,3,2,1,281,1,2,1,1,1,1,33,3,1,1),
col_names = c("drop1", "res_status", "drop2", "education_89", "education_03", "education_flag", "month",
"drop3", "sex", "detail_age", "age_flag", "age_recode", "age_recode2", "age_group",
"age_infant", "death_place", "marital", "day_of_week", "drop4", "data_year", "at_work",
"death_manner", "burial", "autopsy", "drop5", "activity", "injury_place",
"underlying_cause", "cause_recode358", "drop6", "cause_recode113", "cause_recode130",
"cause_recode39", "drop7", "multiple_causes", "drop8", "race", "race_bridged", "race_flag",
"race_recode", "race_recode2", "drop9", "hispanic", "drop10", "hispanic_recode"))
temp <- tempfile()
download.file(url, temp, quiet = T)
raw_file <- read_fwf(unzip(temp), layout)
raw_file <- raw_file %>%
select(-contains("drop"))
assign(eval(all_deaths_name), raw_file)
save(list = all_deaths_name, file = all_deaths_save)
suicide_code <- list()
for (i in 1:24) {
suicide_code[[i]] <- paste0("X", i + 59)
}
suicide_code[length(suicide_code)+1] <- "U03"
suicide_code[length(suicide_code)+1] <- "Y870"
suicide <- raw_file %>%
filter(underlying_cause %in% suicide_code) %>%
mutate(gun = ifelse(underlying_cause %in% c("X72", "X73", "X74"), 1, 0),
year = year)
assign(eval(suicide_name), suicide)
save(list = suicide_name, file = suicide_save)
rm(suicide)
rm(list = suicide_name)
guns <- raw_file %>%
filter(underlying_cause %in% c("W32", "W33", "W34", "X72", "X73", "X74", "U014", "X93", "X94", "X95", "Y22", "Y23", "Y24", "Y350"))
rm(raw_file)
guns <- guns %>%
mutate(intent = ifelse(underlying_cause %in% c("W32", "W33", "W34"), "Accidental",
ifelse(underlying_cause %in% c("X72", "X73", "X74"), "Suicide",
ifelse(underlying_cause %in% c("*U01.4", "X93", "X94", "X95", "Y350"), "Homicide",
ifelse(underlying_cause %in% c("Y22", "Y23", "Y24"), "Undetermined", NA)))),
police = ifelse(underlying_cause == "Y350", 1, 0),
weapon = ifelse(underlying_cause %in% c("W32", "X72", "X93", "Y22"), "Handgun",
ifelse(underlying_cause %in% c("W33", "X73", "X94", "Y23"), "Rifle etc",
"Other/unknown")),
year = year)
guns <- guns %>%
mutate(age = ifelse(substr(detail_age, 1, 1) == "1", as.numeric(substr(detail_age, 2, 4)),
ifelse(detail_age == 9999, NA, 0)),
age = ifelse(age == 999, NA, age))
assign(eval(gun_name), guns)
save(list = gun_name, file = gun_save)
rm(guns)
rm(list = gun_name)
}
year <- 2013
url <- "ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/DVS/mortality/mort2013us.zip"
CDC_parser(year, url)
load("gun_deaths_14.RData")
load("gun_deaths_13.RData")
load("gun_deaths_12.RData")
all_guns <- rbind(guns_12, guns_13, guns_14)
all_guns <- all_guns %>%
filter(res_status != 4)
all_guns <- all_guns %>%
mutate(place = factor(injury_place, labels = c("Home", "Residential institution", "School/instiution", "Sports", "Street",
"Trade/service area", "Industrial/construction", "Farm", "Other specified",
"Other unspecified")),
education = ifelse(education_flag == 1,
cut(as.numeric(education_03), breaks = c(0, 2, 3, 5, 8, 9)),
cut(as.numeric(education_89), breaks = c(0, 11, 12, 15, 17, 99))),
education = factor(education, labels = c("Less than HS", "HS/GED", "Some college", "BA+", NA)),
race = ifelse(hispanic > 199 & hispanic <996, "Hispanic",
ifelse(race == "01", "White",
ifelse(race == "02", "Black",
ifelse(as.numeric(race) >= 4 & as.numeric(race) <= 78, "Asian/Pacific Islander","Native American/Native Alaskan")))),
race = ifelse(is.na(race), "Unknown", race)) %>%
select(year, month, intent, police, sex, age, race, hispanic, place, education)
all_guns %>%
filter(intent == "Suicide") %>%
group_by(year) %>%
summarize(suicides = length(year))
all_guns %>%
filter(intent == "Homicide", age >= 15, age < 35, sex == "M") %>%
group_by(year) %>%
summarize(homicides = length(year))
save(all_guns, file = "all_guns.RData")
write.csv(all_guns, file = "full_data.csv") |
variance_drv = function(data, years = 10) {
n = years
U = 1 + data
u = mean(U)
u2 = moment(U,
central = FALSE,
absolute = FALSE,
order = 2)
app1 = rep(NA, n - 1)
for (i in 1:(n - 1)) app1[i]=moment(U,
central = FALSE,
absolute = FALSE,
order = 2*i)
p1 = sum(app1)
app2 = matrix(NA, n,n)
for (r in 1:nrow(app2)) for (s in 1:ncol(app2)) app2[r,s]=moment(U,
central = FALSE,
absolute = FALSE,
order = sum(r+s))
for (r in 1:nrow(app2)) for (s in 1:ncol(app2)) if (s<=r) app2[r,s]=0
app2[,n]=0
p2 = 2*sum(app2)
app3=rep(NA, n - 1)
for (i in 1:(n - 1)) app3[i]=moment(U,
central = FALSE,
absolute = FALSE,
order = i)
p3 =(sum(app3))^2
p4 =moment(U,
central = FALSE,
absolute = FALSE,
order = 2*n)
un=moment(U,
central = FALSE,
absolute = FALSE,
order = n)
p5=un^2
app6=rep(NA,(2*n)-(n+1))
for (i in 1:length(app6)) app6[i]=moment(U,
central = FALSE,
absolute = FALSE,
order = n+i)
p6=2*sum(app6)
app7=rep(NA,n-1)
for (i in 1:length(app7)) app7[i]=moment(U,
central = FALSE,
absolute = FALSE,
order = i)
p7=2*un*sum(app7)
var=p1+p2-p3+p4-p5+p6-p7
return(var)
} |
getNamedArgs_OdbcDriver <-
function(dsn = NULL,
...,
timezone = "UTC",
driver = NULL,
server = NULL,
database = NULL,
uid = NULL,
pwd = NULL,
.connection_string = NULL) {
list(
dsn = dsn,
...,
timezone = timezone,
driver = driver,
server = server,
database = database,
uid = uid,
.connection_string = .connection_string
)
} |
tLagPropOdds <- function(data,
...,
ti = NULL,
td = NULL,
itmax = 500,
tol = 1e-5) {
inputs <- .verifyInputs(df = data, ti = ti, td = td)
ipwObj <- .ipw(uv = inputs$uv,
cats = inputs$cats,
txOpts = inputs$txOpts,
itmax = itmax,
tol = tol)
res <- .aipw(uv = inputs$uv,
ti = inputs$ti,
td = inputs$td,
ipwObj = ipwObj,
txOpts = inputs$txOpts,
uniqueCensor = inputs$uniqueCensor)
class(x = res) <- 'tLagObj'
attr(x = res, which = "type") <- inputs$type
return( res )
} |
context("EM")
theta0 <- list(A = matrix(0.5),
B = matrix(0.5, 1, 7),
C = matrix(0.5),
D = matrix(0.5, 1, 7),
Q = matrix(1),
R = matrix(1),
mu1 = matrix(1),
V1 = matrix(1))
obs <- log(ldsr:::P1annual$Qa)
mu <- mean(obs)
y <- t(obs - mu)
N <- 85
uInst <- vInst <- t(ldsr:::P1pc[322:406])
u <- v <- t(NPpc[601:813])
foreach::registerDoSEQ()
test_that("Numerical results are correct for the instrumental period in the first two iterations for P1", {
smooth1 <- ldsr:::Kalman_smoother(y, uInst, vInst, theta0)
theta1 <- ldsr:::Mstep(y, uInst, vInst, smooth1)
smooth2 <- ldsr:::Kalman_smoother(y, uInst, vInst, theta1)
theta2 <- ldsr:::Mstep(y, uInst, vInst, smooth2)
expect_equal(smooth1$lik, -11.678657, tolerance = 1e-6)
expect_equal(smooth1$X[c(1, 85)], c(1.293356, -0.987671), tolerance = 1e-6)
expect_equal(theta1$A[1], 0.606066, tolerance = 1e-6)
expect_equal(theta1$C[1], -0.005995, tolerance = 1e-6)
expect_equal(theta1$Q[1], 3.640236, tolerance = 1e-6)
expect_equal(smooth2$lik, -0.114224, tolerance = 1e-6)
expect_equal(theta2$A[1], 0.603945, tolerance = 1e-6)
expect_equal(theta2$C[1], -0.012004, tolerance = 1e-6)
expect_equal(theta2$Q[1], 3.644322, tolerance = 1e-6)
})
test_that("Convergence is correct for the instrumental period", {
fit <- ldsr:::LDS_EM(y, uInst, vInst, theta0, 100, 1e-5)
expect_equal(length(fit$liks), 68)
expect_equal(fit$lik, -0.039093, tolerance = 1e-6)
})
test_that("Fixed restart works", {
fit <- LDS_reconstruction(NPannual, u, v, start.year = 1800, init = make_init(nrow(u), nrow(v), 2))
expect_is(fit, "list")
})
test_that("Randomized restarts works", {
fit <- LDS_reconstruction(NPannual, u, v, start.year = 1800, num.restarts = 2)
expect_is(fit, "list")
})
test_that("Cross validation works with randomized restarts", {
cv <- cvLDS(NPannual, u, v, start.year = 1800, num.restarts = 2, Z = make_Z(NPannual$Qa, 2))
expect_is(cv, "list")
})
test_that("u can be NULL", {
fit <- LDS_reconstruction(NPannual, u = NULL, v, start.year = 1800, num.restarts = 2)
expect_is(fit, "list")
cv <- cvLDS(NPannual, u = NULL, v, start.year = 1800, num.restarts = 2, Z = make_Z(NPannual$Qa, 2))
expect_is(cv, "list")
})
test_that("v can be NULL", {
fit <- LDS_reconstruction(NPannual, u, v = NULL, start.year = 1800, num.restarts = 2)
expect_is(fit, "list")
cv <- cvLDS(NPannual, u, v = NULL, start.year = 1800, num.restarts = 2, Z = make_Z(NPannual$Qa, 2))
expect_is(cv, "list")
})
test_that("u and v can have different nrows", {
fit <- LDS_reconstruction(NPannual, u[1:2, ], v, start.year = 1800, num.restarts = 2)
expect_is(fit, "list")
cv <- cvLDS(NPannual, u[1:2, ], v, start.year = 1800, num.restarts = 2, Z = make_Z(NPannual$Qa, 2))
expect_is(cv, "list")
}) |
context("Haversine distance")
test_that("Bankstown airport to Sydney airport approximately 17628m", {
expect_lt(haversine_distance(-33 - 56/60 - 46/3600, 151 + 10/60 + 38/3600,
-33 - 55/60 - 28/3600, 150 + 59/60+18/3600) / 17.628 - 1,
0.01)
})
test_that("Broken Hill airport to Sydney airport approximately 932158", {
expect_lt(haversine_distance(-33 - 56/60 - 46/3600, 151 + 10/60 + 38/3600,
-32 - 00/60 - 05/3600, 141 + 28/60 + 18/3600) / 932.158 - 1,
0.01)
}) |
library(testthat)
library(PReMiuM)
test_check("PReMiuM") |
sim.morpho <- function(tree, characters, states = 1, model = "ER", rates, substitution = c(stats::runif, 2, 2), invariant = TRUE, verbose = FALSE)
{
check.class(tree, "phylo")
check.class(characters, "numeric")
check.length(characters, 1, " must be a single numeric value.")
check.class(states, "numeric")
if(sum(states) != 1) {
stop.call("", "States argument must sum up to 1.")
}
if(length(states) > 1 && model == "HKY") {
states <- 1
warning("The HKY model only allows the use of two states characters (binary).")
}
if(!is(model, "function")) {
model <- toupper(model)
implemented_models <- c("ER", "HKY", "MIXED")
check.method(model, implemented_models, "The model")
model_name <- model
if(!is(model, "function") && model_name == "ER") {
model <- rTraitDisc.mk
substitution <- c(stats::runif, 1, 1)
}
if(!is(model, "function") && model_name == "HKY") {
model <- gen.seq.HKY.binary
}
if(!is(model, "function") && model_name == "MIXED") {
model <- MIXED.model
}
} else {
stop.call("", "User functions are not supported yet for the model argument.\nTry using \"HKY\", \"ER\" or \"MIXED\".")
}
if(length(rates) == 1) {
check.class(rates, "function")
} else {
check.class(rates[[1]], "function")
test <- NULL ; try(test <- sample.distribution(1, rates), silent = TRUE)
if(length(test) != 1) {
stop.call("", "Error in rates argument format. Should be c(function, arg1, arg2, ...).")
}
}
if(length(substitution) == 1) {
check.class(substitution, "function")
} else {
check.class(substitution[[1]], "function")
test <- NULL ; try(test <- sample.distribution(1, substitution), silent = TRUE)
if(length(test) != 1) {
stop.call("", "Error in substitution argument format. Should be c(function, arg1, arg2, ...).")
}
}
check.class(invariant, "logical")
check.class(verbose, "logical")
if(verbose == TRUE) cat(paste("Generating a matrix of ", characters, " characters for ", Ntip(tree), " taxa:", sep=""))
matrix <- replicate(characters, model(tree = tree, states = states, rates = rates, substitution = substitution, verbose = verbose))
if(verbose == TRUE) cat("Done.\n")
if(invariant == FALSE) {
if(any(apply(matrix, 2, is.invariant))) {
if(verbose == TRUE) cat("Re-simulating ", length(which(apply(matrix, 2, is.invariant)) == TRUE), " invariant characters:", sep="")
while(any(apply(matrix, 2, is.invariant))) {
matrix[, which(apply(matrix, 2, is.invariant) == TRUE)] <- replicate(length(which(apply(matrix, 2, is.invariant) == TRUE)), model(tree = tree, states = states, rates = rates, substitution = substitution, verbose = verbose))
if(verbose == TRUE) cat(".")
}
if(verbose == TRUE) cat("Done.\n")
}
}
if(length(tree$tip.label) != 0) {
rownames(matrix) <- tree$tip.label
}
return(matrix)
} |
NULL
create_ranking <- function(orderings){
if(is.vector(orderings)){
stopifnot(validate_permutation(orderings))
return(order(orderings))
} else if(is.matrix(orderings)){
n_items <- ncol(orderings)
orderings <- split(orderings, f = seq_len(nrow(orderings)))
check <- lapply(orderings, validate_permutation)
if(!Reduce(`&&`, check)){
stop(paste("orderings must contain proper permutations. Problem row(s):", which(!check)))
}
rankings <- lapply(orderings, function(x) {
missing_items <- setdiff(1:n_items, x)
candidates <- matrix(1:n_items, ncol = n_items, nrow = n_items)
candidates[, missing_items] <- NA_real_
inds <- outer(X = x, Y = 1:n_items, FUN = "==")
inds[1, colSums(inds, na.rm = TRUE) == 0] <- TRUE
inds[is.na(inds)] <- FALSE
candidates[inds]
} )
return(t(matrix(unlist(rankings), ncol = length(rankings))))
} else {
stop("orderings must be a vector or matrix")
}
}
create_ordering <- function(rankings){
create_ranking(rankings)
} |
source("ESEUR_config.r")
library("igraph")
unloadNamespace("sna")
vehicles=read.graph(paste0(ESEUR_dir, "ecosystems/Braha/Braha_VehicleNetworkinPajek.net"), format="pajek")
software=read.graph(paste0(ESEUR_dir, "ecosystems/Braha/Braha_SoftwareNetworkinPajek.net"), format="pajek")
pharmaceutical=read.graph(paste0(ESEUR_dir, "ecosystems/Braha/Braha_PharmaceuticaNetwork.txt"), format="pajek")
hospital=read.graph(paste0(ESEUR_dir, "ecosystems/Braha/Braha_SixteenStoryHospital.txt"), format="pajek")
pal_col=rainbow_hcl(3)
graph_prop=function(net_graph)
{
t1=vcount(net_graph)
t2=ecount(net_graph)
t3=average.path.length(net_graph)
t4=transitivity(net_graph)
t6=mean(degree(net_graph, mode="out"))
t7=graph.density(net_graph)
return(cbind(Nodes=t1, Edges=t2, "Average path length"=t3,
"Clustering coeff"=t4, Degree=t6, Density=t7))
}
tab=graph_prop(hospital)
tab=rbind(tab, graph_prop(pharmaceutical))
tab=rbind(tab, graph_prop(software))
tab=rbind(tab, graph_prop(vehicles))
rownames(tab)=c("Hospital", "Pharmaceutical", "Software", "Vehicles")
library("ascii")
print(ascii(tab, include.rownames=TRUE, include.colnames=TRUE)) |
isEuclidean = function(x) {
return(x$edge.weight.type %in% c("EUC_2D", "EUC_3D"))
} |
logspec2cov<-function(ebeta,vbeta,SARIMA=list(sigma2=1),lags,subdivisions=100){
nb<-length(ebeta)
acv<-c()
pb<-txtProgressBar(min=0,max=lags,style= 3)
for(k in 0:lags){
setTxtProgressBar(pb,k)
dumfun<-function(x){
g0<-log(SARIMAspec(SARIMA,freq=x)$spec)
B<-basis(x,nb)
mu<-B%*%ebeta+g0
s2<-rowSums((B%*%vbeta)*B)
exp(mu+0.5*s2)*cos(2*pi*k*x)
}
acv[k+1]<-2*pi*integrate(dumfun,0,0.5,rel.tol=1e-2,subdivisions=subdivisions)$value
}
close(pb)
return(acv)
} |
context("scatter_plot")
test_that("scatter_plot works", {
g = scatter_plot(mtcars, "cyl", "wt")
expect_equal(nrow(g$data), 32)
g = scatter_plot(mtcars, "as.character(cyl)", "wt", "'a'")
expect_equal(nrow(g$data), 32)
g = scatter_plot(mtcars, "as.character(cyl)", "wt", "am")
expect_equal(nrow(g$data), 32)
}) |
msaedbns <- function (formula, vardir, weight,cluster,nonsample, samevar = FALSE, MAXITER = 100, PRECISION = 1e-04,
data) {
result = list(MSAE_Eblup_sampled = NA,MSAE_Eblup_all = NA, MSE_Eblup_sampled = NA, MSE_Eblup_all = NA, randomEffect_sampled = NA, randomEffect_all = NA, Rmatrix_sampled = NA,
fit = list(method = NA, convergence = NA, iterations = NA,
estcoef = NA, refvar = NA, informationFisher = NA),
difference_benchmarking = list(Estimation_sampled = NA,
Estimation_all = NA,
Aggregation_sampled = NA,
Aggregation_all = NA,
MSE_DB_sampled = NA,
MSE_DB_all = NA,
g4.a = NA,
g4.b = NA))
if (!(TRUE %in% data[,"nonsample"])){
stop("this msaedbns() function is used for at least 1 observation has no sample, check your 'nonsample' variable or use msaedb() instead ")
}
if (length(formula)<=1){
stop("this msaedbns() function is used for at least 2 response variables, numbers of your response variables is ",length(formula),". use saedbns() function instead")
}
r <- length(formula)
RIn_function <- function(vardir, n,r){
it <- 0
it2 <- 0
RIn <- list()
rmat2 <- matrix(0,n,n)
for (i in 1:r){
for (j in 1:r){
it <- it +1
if (i>j){
RIn[[it+it2]] <- rmat2
it <- it-1
it2 <- it2+1
}else { RIn[[it+it2]] <- diag(vardir[,it])}
}
}
RIN <- list()
for ( i in 1:r){
if (i == 1){
RIN[[i]] <- RIn[[i]]
for (j in 1:(r-1)){
RIN[[i]] <- cbind(RIN[[i]],RIn[[j+1]])
}
} else {
RIN[[i]] <- RIn[[i*r-r+1]]
for (j in 1:(r-1)){
RIN[[i]] <- cbind(RIN[[i]],RIn[[(i*r-r+1)+j]])
}
}
}
RR <- do.call(rbind,RIN)
RR <- (t(RR)+RR)*(matrix(1,n*r,n*r)-diag(0.5,n*r))
RIn<- return(RR)
}
index <- cbind(rep(1:dim(data)[1]))
data <- cbind(index,data)
data_sampled <- data[which(data$nonsample == FALSE), ]
formuladata <- formula
for(i in 1:r) {formuladata[[i]] <- model.frame(formula[[i]], na.action = na.omit, data_sampled )}
y.vec <- unlist(lapply(formuladata, function(x){x[1][1]}))
x.matrix <- formula
for(i in 1:r) {x.matrix[[i]] <- model.matrix(formula[[i]], na.action = na.omit, data_sampled)}
x.matrix = Reduce(adiag,x.matrix)
w.matrix_temp = as.matrix(data_sampled[,weight])
w.matrix <- matrix(0,dim(w.matrix_temp)[1],r)
for (i in 1:dim(w.matrix)[1]) {
for (j in 1:r){
w.matrix[i,j] <- w.matrix_temp[i,j]/sum(w.matrix_temp[,j])
}
}
n = length(y.vec)/r
dfnonsample <- data[which(data$nonsample == TRUE), ]
anns <- dfnonsample$index
vardirns <- dfnonsample[,vardir]
x.matrixns <- formula
for(i in 1:r) {x.matrixns[[i]] <- model.matrix(formula[[i]], na.action = na.omit, dfnonsample)}
x.matrixns = Reduce(adiag,x.matrixns)
if (any(is.na(data[, weight])))
stop("Object weight contains NA values.")
if (!all(weight %in% names(data)))
stop("Object weight is not appropiate with data")
if (length(weight) != r)
stop("Length of weight is not appropiate, the length must be ",r)
if (any(is.na(data[, vardir])))
stop("Object vardir contains NA values.")
if (!all(vardir %in% names(data)))
stop("Object vardir is not appropiate with data")
if (length(vardir) != sum(1:r))
stop("Length of vardir is not appropiate, the length must be ", sum(1:r))
RIn = RIn_function(data_sampled[, vardir],n,r)
for (i in 1:r) {
if (attr(attributes(formuladata[[i]])$terms, "response") == 1)
textformula = paste(formula[[i]][2], formula[[i]][1],
formula[[i]][3])
else textformula = paste(formula[[i]][1], formula[[i]][2])
if (length(na.action(formuladata[[i]])) > 0) {
stop("Argument formula= ", textformula, " contains NA values.")
}
}
varnames_Y <- lapply(formula, function(x) {x[[2]]})
In = diag(n)
Ir = diag(r)
d.sigma <- lapply(formula, function(x){x=matrix(0,r,r)})
for (i in 1:r) {d.sigma[[i]][i, i] = 1}
d.SIGMA <- lapply(d.sigma, function(x){kronecker(x,In)})
convergence = TRUE
if (samevar) {
Varu <- median(diag(RIn))
k <- 0
diff <- rep(PRECISION + 1, r)
while (any(diff > PRECISION) & (k < MAXITER)) {
Varu1<- Varu
G <- kronecker(Varu, Ir)
GIn <- kronecker(G, In)
SIGMA<- (GIn + RIn)
SIGMA_inv <- solve(SIGMA)
Xt_Si<- t(SIGMA_inv %*% x.matrix)
Q <- solve(Xt_Si %*% x.matrix,tol = 1e-30)
P <- SIGMA_inv - t(Xt_Si) %*% Q %*% Xt_Si
Py <- P %*% y.vec
s <- (-0.5) * sum(diag(P)) + 0.5 * (t(Py) %*% Py)
F <- 0.5 * sum(diag(P %*% P))
Varu <- Varu1 + solve(F) %*% s
diff <- abs((Varu - Varu1)/Varu1)
k <- k + 1
}
Varu = as.vector((rep(max(Varu,0), r)))
names(Varu) = varnames_Y
if (k >= MAXITER && diff >= PRECISION) {
convergence = FALSE
}
GIn <- kronecker(diag(Varu), In)
SIGMA <- (GIn + RIn)
SIGMA_inv <- solve(SIGMA)
Xt_Si <- t(SIGMA_inv %*% x.matrix)
Q <- solve(Xt_Si %*% x.matrix)
P <- SIGMA_inv - t(Xt_Si) %*% Q %*% Xt_Si
Py <- P %*% y.vec
beta.REML <- Q %*% Xt_Si %*% y.vec
resid <- y.vec - x.matrix %*% beta.REML
MSAE_Eblup<- data.frame(matrix(x.matrix %*% beta.REML +GIn %*% SIGMA_inv %*% resid,n, r))
colnames(MSAE_Eblup) = varnames_Y
std.err.beta <- sqrt(diag(Q))
tvalue <- beta.REML/std.err.beta
pvalue <- 2 * pnorm(abs(tvalue), lower.tail = FALSE)
coef <- cbind(beta.REML, std.err.beta, tvalue, pvalue)
colnames(coef) = c("beta", "std.error", "t.statistics","p.value")
Bi <- RIn%*%solve(SIGMA)
m <- dim(x.matrix)[1]
p <- dim(x.matrix)[2]
I <- diag(m)
g1d <- diag(Bi%*%GIn)
g2d <- diag(Bi%*%x.matrix%*%Q%*%t(x.matrix)%*%t(Bi))
dg <- SIGMA_inv - (I-Bi) %*% SIGMA_inv
g3d <- diag(dg %*% SIGMA %*% t(dg))/F
W <- diag(as.vector(w.matrix))
g4.a<- matrix(0,m,m)
for (i in 1:r){
g4.a <- g4.a + d.SIGMA[[i]]*sum(diag(d.SIGMA[[i]]%*%W%*%Bi%*%SIGMA%*%t(W)%*%t(Bi)))
}
g4.a <- diag(g4.a)
g4.b <- matrix(0,r,r)
for (l in 0:(r-1)) {
for (i in ((l*n)+1):((l+1)*n)) {
xdi <- matrix(x.matrix[i, ], nrow = 1, ncol = p)
for (j in ((l*n)+1):((l+1)*n)) {
xdj <- matrix(x.matrix[j, ], nrow = 1, ncol = p)
g4.b<- g4.b + d.sigma[[l+1]]*as.numeric(W[i,i]*W[j,j]*Bi[i,i]*Bi[j,j]*as.numeric(xdi %*% Q %*% t(xdj)))
}
}
}
g4.b <- diag(kronecker(g4.b,In))
g4d <- g4.a - g4.b
g4.a <- as.data.frame(matrix(g4.a,n,r))
g4.a <- apply(g4.a, 2, median)
names(g4.a) <- varnames_Y
g4.b <- as.data.frame(matrix(g4.b,n,r))
g4.b <- apply(g4.b, 2, median)
names(g4.b) <- varnames_Y
MSE_Eblup <- g1d + g2d + 2 * g3d
MSE_DB <- g1d + g2d + 2 * g3d + g4d
MSE_Eblup <- data.frame(matrix(MSE_Eblup, n, r))
MSE_DB <- data.frame(matrix(MSE_DB, n, r))
names(MSE_Eblup) = varnames_Y
names(MSE_DB) = varnames_Y
} else {
Varu <- apply(matrix(diag(RIn), n, r), 2, median)
k <- 0
diff <- rep(PRECISION + 1, r)
while (any(diff > rep(PRECISION, r)) & (k < MAXITER)) {
Varu1 <- Varu
G <- diag(as.vector(Varu1))
GIn <- kronecker(G, In)
SIGMA <- GIn + RIn
SIGMA_inv <- solve(SIGMA)
Xt_Si <- t(SIGMA_inv %*% x.matrix)
Q <- solve(Xt_Si %*% x.matrix)
P <- SIGMA_inv - t(Xt_Si) %*% Q %*% Xt_Si
Py <- P %*% y.vec
s <- vector()
for (i in 1:r){s[i] <- (-0.5) * sum(diag(P %*% d.SIGMA[[i]])) + 0.5 * (t(Py) %*% d.SIGMA[[i]] %*% Py)}
F <- matrix(NA,r,r)
for (i in 1:r){
for (j in 1:r){
F[j,i] <- 0.5*sum(diag(P %*% d.SIGMA[[i]] %*% P %*% d.SIGMA[[j]]))
}
}
Varu <- Varu1 + solve(F) %*% s
diff <- abs((Varu - Varu1)/Varu1)
k <- k + 1
}
Varu <- as.vector(sapply(Varu, max,0))
names(Varu) = varnames_Y
if (k >= MAXITER && diff >= PRECISION) {
convergence = FALSE
}
G <- diag(as.vector(Varu))
GIn <- kronecker(G, In)
SIGMA <- GIn + RIn
SIGMA_inv <- solve(SIGMA)
Xt_Si <- t(SIGMA_inv %*% x.matrix)
Q <- solve(Xt_Si %*% x.matrix)
P <- SIGMA_inv - t(Xt_Si) %*% Q %*% Xt_Si
Py <- P %*% y.vec
beta.REML <- Q %*% Xt_Si %*% y.vec
resid <- y.vec - x.matrix %*% beta.REML
MSAE_Eblup <- data.frame(matrix(x.matrix %*% beta.REML +GIn %*% SIGMA_inv %*% resid,n, r))
colnames(MSAE_Eblup) = varnames_Y
std.err.beta <- sqrt(diag(Q))
tvalue <- beta.REML/std.err.beta
pvalue <- 2 * pnorm(abs(tvalue), lower.tail = FALSE)
coef <- cbind(beta.REML, std.err.beta, tvalue, pvalue)
colnames(coef)= c("beta", "std.error", "t.statistics","p.value")
F_inv <- solve(F)
Bi <- RIn%*%solve(SIGMA)
m <- dim(x.matrix)[1]
p <- dim(x.matrix)[2]
I <- diag(m)
g1d <- diag(Bi%*%GIn)
g2d <- diag(Bi%*%x.matrix%*%Q%*%t(x.matrix)%*%t(Bi))
dg <- lapply(d.SIGMA, function(x) x %*% SIGMA_inv - GIn %*%
SIGMA_inv %*% x %*% SIGMA_inv)
g3d = list()
for (i in 1:r) {
for (j in 1:r) {
g3d[[(i - 1) * r + j]] = F_inv[i, j] * (dg[[i]] %*% SIGMA %*% t(dg[[j]]))
}
}
g3d <- diag(Reduce("+", g3d))
W <- diag(as.vector(w.matrix))
g4.a <- matrix(0,m,m)
for (i in 1:r){
g4.a<- g4.a + d.SIGMA[[i]]*sum(diag(d.SIGMA[[i]]%*%W%*%Bi%*%SIGMA%*%t(W)%*%t(Bi)))
}
g4.a <- diag(g4.a)
g4.b <- matrix(0,r,r)
for (l in 0:(r-1)) {
for (i in ((l*n)+1):((l+1)*n)) {
xdi <- matrix(x.matrix[i, ], nrow = 1, ncol = p)
for (j in ((l*n)+1):((l+1)*n)) {
xdj <- matrix(x.matrix[j, ], nrow = 1, ncol = p)
g4.b<- g4.b + d.sigma[[l+1]]*as.numeric(W[i,i]*W[j,j]*Bi[i,i]*Bi[j,j]*as.numeric(xdi %*% Q %*% t(xdj)))
}
}
}
g4.b <- diag(kronecker(g4.b,In))
g4d <- g4.a - g4.b
g4.a <- as.data.frame(matrix(g4.a,n,r))
g4.a <- apply(g4.a, 2, median)
names(g4.a) <- varnames_Y
g4.b <- as.data.frame(matrix(g4.b,n,r))
g4.b <- apply(g4.b, 2, median)
names(g4.b) <- varnames_Y
MSE_Eblup <- g1d + g2d + 2 * g3d
MSE_DB <- g1d + g2d + 2 * g3d + g4d
MSE_Eblup <- data.frame(matrix(MSE_Eblup, n, r))
MSE_DB <- data.frame(matrix(MSE_DB, n, r))
names(MSE_Eblup) = varnames_Y
names(MSE_DB) = varnames_Y
}
randomEffect <- GIn%*%SIGMA_inv%*%resid
randomEffect <- as.data.frame(matrix(randomEffect, n, r))
names(randomEffect) <- varnames_Y
XBns <- matrix(x.matrixns%*%beta.REML,length(anns),r)
XBns <- cbind(XBns,dfnonsample[,cluster])
estrEff <- cbind(randomEffect,data_sampled[,cluster],nonsample=data_sampled$nonsample,data_sampled$index)
avrEffc <- matrix(0,max(data[,cluster]),r)
for (i in 1:r){
for (j in 1:max(data[,cluster])){
if (!(j %in% estrEff[,i+r])){
newline <- rep(NA,dim(estrEff)[2])
newline[1:r] <-0
newline[i+r] <- j
estrEff <- rbind(estrEff,newline)
}
}
}
for (i in 1:r){
avrEffc[,i] <- sapply(split(estrEff[,i], estrEff[,i+r]), mean)
}
colnames(avrEffc) <- varnames_Y
avrEffc <- cbind(cluster = c(1:max(data[,cluster])), avrEffc)
EBLUPCluster <- matrix(0,length(anns),r)
for (i in 1:length(anns)){
for (j in 1:r){
EBLUPCluster[i,j] <- XBns[i,j]+avrEffc[XBns[i,j+r],j+1]
}
}
colnames(EBLUPCluster) <- varnames_Y
EBLUPCluster <- data.frame(an = anns,EBLUPCluster )
totalArea <- dim(MSAE_Eblup)[1]+dim(EBLUPCluster)[1]
idx <- cbind(index = rep(1:totalArea))
MSAE_Eblup_temp <- cbind(index=data_sampled$index,MSAE_Eblup)
MSAE_Eblup_all <- merge(x=as.matrix(idx), y=MSAE_Eblup_temp, by = "index", all.x=TRUE)
MSAE_Eblup_all <- MSAE_Eblup_all[,-1]
for (i in 1:totalArea){
for (j in 1:length(anns)){
if (i==anns[j]){
MSAE_Eblup_all[i,] <- EBLUPCluster[which(EBLUPCluster$an == i), ][,-1]
}
}
}
y.vec <- matrix(y.vec, n,r)
colnames(y.vec) = varnames_Y
W <- as.matrix(w.matrix)
alfa <- ginv(t(W))%*%(t(W)%*%y.vec-t(W)%*%as.matrix(MSAE_Eblup))
MSAE_DB <- as.data.frame(MSAE_Eblup + alfa)
colnames(MSAE_DB) <- varnames_Y
Aggregation_Direct <- colSums(as.matrix(y.vec)*(W))
Aggregation_DB <- colSums(as.matrix(MSAE_DB)*(W))
Aggregation_EBLUP <- colSums(as.matrix(MSAE_Eblup)*(W))
Aggregation <- matrix(unlist(rbind(Aggregation_Direct,Aggregation_DB,Aggregation_EBLUP)),3,r)
rownames(Aggregation) <- c("Aggregation_Direct","Aggregation_DB","Aggregation_EBLUP")
colnames(Aggregation) <- varnames_Y
y.direct <- y.vec
colnames(y.direct) = varnames_Y
W_direct = as.matrix(w.matrix)
W_all = as.matrix(data[,weight])
alfa_all <- ginv(t(W_all))%*%(t(W_direct)%*%y.direct-t(W_all)%*%as.matrix(MSAE_Eblup_all))
MSAE_DB_all <- as.data.frame(MSAE_Eblup_all + alfa_all)
colnames(MSAE_DB_all) <- varnames_Y
Aggregation_Direct <- colSums(as.matrix(y.direct)*(W_direct))
Aggregation_DB <- colSums(as.matrix(MSAE_DB_all)*(W_all))
Aggregation_EBLUP <- colSums(as.matrix(MSAE_Eblup_all)*(W_all))
Aggregation_all <- matrix(unlist(rbind(Aggregation_Direct,Aggregation_DB,Aggregation_EBLUP)),3,r)
rownames(Aggregation_all) <- c("Aggregation_Direct","Aggregation_DB","Aggregation_EBLUP")
colnames(Aggregation_all) <- varnames_Y
v <- matrix(diag(RIn),n,r)
colnames(v) <- varnames_Y
SampledV <- cbind(v,data_sampled[,cluster],an = data_sampled$index,nonsample=data_sampled$nonsample)
avVardir <- matrix(0,max(data[,cluster]),r)
for (i in 1:r){
for (j in 1:max(data[,cluster])){
if (!(j %in% SampledV[,i+r])){
newline <- rep(NA,dim(SampledV)[2])
newline[1:r] <-0
newline[i+r] <- j
SampledV <- rbind(SampledV,newline)
}
}
}
for (i in 1:r){
avVardir[,i] <- sapply(split(SampledV[,i], SampledV[,r+i]), mean)
}
colnames(avVardir) <- varnames_Y
avVardir <- cbind(cluster = c(1:max(data[,cluster])), avVardir)
vardirns <- cbind(dfnonsample[, vardir][,1:r],dfnonsample[,cluster],an =dfnonsample$index)
vardir.ns <- matrix(0,length(anns),r)
for (i in 1:length(anns)){
for (j in 1:r){
vardir.ns[i,j] <- avVardir[vardirns[i,j+r],j+1]
}
}
RInns <- diag(as.vector(vardir.ns))
GInns <- kronecker(diag(Varu),diag(length(anns)))
Bins <- RInns%*%solve(GInns+RInns)
g1dns <- Bins%*%GInns
g1dns <- matrix(diag(g1dns),length(anns),r)
SIGMAns <- GInns + RInns
SIGMA_invns <- solve(SIGMAns)
Xt_Sins <- t(SIGMA_invns %*% x.matrixns)
Qns <- ginv(Xt_Sins%*%x.matrixns)
xqxns <- (x.matrixns)%*%Qns%*%t(x.matrixns)
g2dns <- (Bins%*%xqxns%*%t(Bins))
g2dns <- matrix(diag(g2dns),length(anns),r)
g3eblup <- matrix(g3d,n,r)
colnames(g3eblup) <- varnames_Y
Sampledg3d <- cbind(g3eblup,data_sampled[,cluster],an = data_sampled$index ,nonsample=data_sampled$nonsample)
avg3d <- matrix(0,max(data[,cluster]),r)
for (i in 1:r){
for (j in 1:max(data[,cluster])){
if (!(j %in% Sampledg3d[,i+r])){
newline <- rep(NA,dim(Sampledg3d)[2])
newline[1:r] <-0
newline[i+r] <- j
Sampledg3d <- rbind(Sampledg3d,newline)
}
}
}
for (i in 1:r){
avg3d[,i] <- sapply(split(Sampledg3d[,i], Sampledg3d[,r+i]), mean)
}
colnames(avg3d) <- varnames_Y
avg3d <- cbind(cluster = c(1:max(data[,cluster])), avg3d)
g3dns <- matrix(0,length(anns),r)
for (i in 1:length(anns)){
for (j in 1:r){
g3dns[i,j] <-avg3d[vardirns[i,j+r],j+1]
}
}
MSE_Eblupns = g1dns+g2dns+2*g3dns
MSE_Eblupns <- data.frame(an=anns,MSE_Eblupns)
MSE_Eblup_temp <- cbind(index=data_sampled$index,MSE_Eblup)
MSE_Eblup_all <- merge(x=as.matrix(idx), y=MSE_Eblup_temp, by = "index", all.x=TRUE)
MSE_Eblup_all <- MSE_Eblup_all[,-1]
for (i in 1:totalArea){
for (j in 1:length(anns)){
if (i==anns[j]){
MSE_Eblup_all[i,] <- MSE_Eblupns[which(MSE_Eblupns$an == i), ][,-1]
}
}
}
g4dns <- colMeans(matrix(g4d,n,r))
g4dns <- matrix(NA,totalArea,r)
for (i in 1:totalArea){
g4dns[i,] <- colMeans(matrix(g4d,n,r))
}
MSE_DB_all <-MSE_Eblup_all+g4dns
randomEffectns <- matrix(0,length(anns),r)
randomEffectns <- cbind(randomEffectns,dfnonsample[,cluster],an =dfnonsample$index)
for (i in 1:length(anns)){
for (j in 1:r){
randomEffectns[i,j] <- randomEffectns[i,j]+avrEffc[XBns[i,j+r],j+1]
}
}
randomEffectns <- randomEffectns[,1:r]
randomEffectns <- data.frame(an=anns,randomEffectns)
randomEffect_temp <- cbind(index=data_sampled$index,randomEffect)
randomEffect_all <- merge(x=as.matrix(idx), y=randomEffect_temp, by = "index", all.x=TRUE)
randomEffect_all <- randomEffect_all[,-1]
for (i in 1:totalArea){
for (j in 1:length(anns)){
if (i==anns[j]){
randomEffect_all[i,] <- randomEffectns[which(randomEffectns$an == i), ][,-1]
}
}
}
result$MSAE_Eblup_sampled = MSAE_Eblup
result$MSAE_Eblup_all = MSAE_Eblup_all
result$MSE_Eblup_sampled = MSE_Eblup
result$MSE_Eblup_all = MSE_Eblup_all
result$randomEffect_sampled = signif(randomEffect, digits = 5)
result$randomEffect_all = signif(randomEffect_all, digits = 5)
result$Rmatrix_sampled = signif(RIn, digits = 5)
result$fit$method = "REML"
result$fit$convergence = convergence
result$fit$iterations = k
result$fit$estcoef = signif(coef, digits = 5)
result$fit$refvar = signif(data.frame(t(Varu)), digits = 5)
result$fit$informationFisher = signif(F, digits = 5)
result$difference_benchmarking$Estimation_sampled = MSAE_DB
result$difference_benchmarking$Estimation_all = MSAE_DB_all
result$difference_benchmarking$Aggregation_sampled = Aggregation
result$difference_benchmarking$Aggregation_all = Aggregation_all
result$difference_benchmarking$MSE_DB_sampled = MSE_DB
result$difference_benchmarking$MSE_DB_all = MSE_DB_all
result$difference_benchmarking$g4.a = g4.a
result$difference_benchmarking$g4.b = g4.b
return(result)
} |
normalize_height = function(las, algorithm, na.rm = FALSE, use_class = c(2L,9L), ..., add_lasattribute = FALSE, Wdegenerated = TRUE)
{
UseMethod("normalize_height", las)
}
normalize_height.LAS = function(las, algorithm, na.rm = FALSE, use_class = c(2L,9L), ..., add_lasattribute = FALSE, Wdegenerated = TRUE)
{
assert_is_a_bool(na.rm)
assert_is_a_bool(add_lasattribute)
assert_is_a_bool(Wdegenerated)
if (is(algorithm, "RasterLayer"))
{
Zground <- raster::extract(algorithm, coordinates(las), ...)
isna <- is.na(Zground)
nnas <- sum(isna)
if (nnas > 0 && na.rm == FALSE)
stop(glue::glue("{nnas} points were not normalizable because the DTM contained NA values. Process aborted."))
}
else if (is.function(algorithm))
{
assert_is_algorithm(algorithm)
assert_is_algorithm_spi(algorithm)
if (any(as.integer(use_class) != use_class))
stop("'add_class' is not a vector of integers'", call. = FALSE)
use_class <- as.integer(use_class)
if (!"Classification" %in% names(las@data)) stop("No field 'Classification' found. This attribute is required to interpolate ground points.", call. = FALSE)
. <- Z <- Zref <- X <- Y <- Classification <- NULL
ground <- las@data[Classification %in% c(use_class), .(X,Y,Z)]
if (nrow(ground) == 0) stop("No ground points found. Impossible to compute a DTM.", call. = FALSE)
ground <- check_degenerated_points(ground, Wdegenerated)
lidR.context <- "normalize_height"
ground <- LAS(ground, las@header, proj4string = las@proj4string, check = FALSE, index = las@index)
Zground <- algorithm(ground, las@data)
isna <- is.na(Zground)
nnas <- sum(isna)
if (nnas > 0 & na.rm == FALSE)
stop(glue::glue("{nnas} points were not normalizable. Process aborted."), call. = FALSE)
}
else
{
stop(glue::glue("Parameter 'algorithm' is a {class(algorithm)}. Expected type is 'RasterLayer' or 'function'"), call. = FALSE)
}
zoffset <- las@header@PHB[["Z offset"]]
zscale <- las@header@PHB[["Z scale factor"]]
if (!"Zref" %in% names(las@data))
las@data[["Zref"]] <- las@data[["Z"]]
las@data[["Z"]] <- las@data[["Z"]] - Zground
if (add_lasattribute && is.null(las@header@VLR$Extra_Bytes[["Extra Bytes Description"]][["Zref"]]))
las <- add_lasattribute_manual(las, name = "Zref", desc = "Elevation above sea level", type = "int", offset = zoffset, scale = zscale)
if (nnas > 0 && na.rm == TRUE)
{
las <- filter_poi(las, !isna)
message(glue::glue("{nnas} points were not normalizable and removed."))
}
fast_quantization(las@data[["Z"]], zscale, zoffset)
las <- lasupdateheader(las)
las@index$sensor <- las@index$sensor + NLAS
return(las)
}
normalize_height.LAScluster = function(las, algorithm, na.rm = FALSE, use_class = c(2L,9L), ..., add_lasattribute = FALSE, Wdegenerated = TRUE)
{
buffer <- NULL
x <- readLAS(las)
if (is.empty(x)) return(NULL)
x <- normalize_height(x, algorithm, na.rm, use_class, ..., add_lasattribute = add_lasattribute, Wdegenerated = Wdegenerated)
x <- filter_poi(x, buffer == 0)
return(x)
}
normalize_height.LAScatalog = function(las, algorithm, na.rm = FALSE, use_class = c(2L,9L), ..., add_lasattribute = FALSE, Wdegenerated = TRUE)
{
opt_select(las) <- "*"
options <- list(need_buffer = TRUE, drop_null = TRUE, need_output_file = TRUE, automerge = TRUE)
output <- catalog_apply(las, normalize_height, algorithm = algorithm, na.rm = na.rm, use_class = use_class, ..., add_lasattribute = add_lasattribute, Wdegenerated = Wdegenerated, .options = options)
return(output)
}
unnormalize_height = function(las)
{
stopifnotlas(las)
if ("Zref" %in% names(las@data))
{
las@data[["Z"]] <- las@data[["Zref"]]
las@data[["Zref"]] <- NULL
las <- lasupdateheader(las)
}
else
message("No attribute 'Zref' found. Un-normalizisation is impossible.")
if (las@index$sensor > NLAS) las@index$sensor <- las@index$sensor - NLAS
return(las)
}
setMethod("-", c("LAS", "RasterLayer"), function(e1, e2)
{
return(normalize_height(e1,e2))
})
setOldClass("lidRAlgorithm")
setMethod("-", c("LAS", "lidRAlgorithm"), function(e1, e2)
{
return(normalize_height(e1,e2))
})
check_degenerated_points = function(points, Wdegenerated = TRUE)
{
. <- X <- Y <- Z <- NULL
dup_xyz = duplicated(points, by = c("X", "Y", "Z"))
dup_xy = duplicated(points, by = c("X", "Y"))
ndup_xyz = sum(dup_xyz)
ndup_xy = sum(dup_xy & !dup_xyz)
if (ndup_xyz > 0 && Wdegenerated)
warning(glue::glue("There were {ndup_xyz} degenerated ground points. Some X Y Z coordinates were repeated. They were removed."), call. = FALSE)
if (ndup_xy > 0 && Wdegenerated)
warning(glue::glue("There were {ndup_xy} degenerated ground points. Some X Y coordinates were repeated but with different Z coordinates. min Z were retained."), call. = FALSE)
if (ndup_xy > 0 | ndup_xyz > 0)
points = points[, .(Z = min(Z)), by = .(X,Y)]
return(points)
} |
getOptimalFeatureSet <- function(filt.data, ordered.genes, elbow.pt = 25, k = 10, num.pcs = 20, error = 0) {
mean_knn_vec <- c()
minNumGenes = ""
numStepsUnchangedMin = 0
message("Determining optimal feature set...")
pb <- utils::txtProgressBar(min = elbow.pt, max = length(ordered.genes), style = 3)
for(num_genes in seq(from = elbow.pt, to = length(ordered.genes), by = 25)) {
neighbour_feature_genes <- ordered.genes[1:num_genes]
log.feature.data <-
filt.data[neighbour_feature_genes, ]
suppressWarnings({
temp.seurat <- Seurat::CreateSeuratObject(counts = log.feature.data)
Seurat::VariableFeatures(temp.seurat) <- neighbour_feature_genes
temp.seurat <-
Seurat::ScaleData(object = temp.seurat,
features = neighbour_feature_genes,
verbose = FALSE)
temp.seurat <-
Seurat::RunPCA(object = temp.seurat,
features = neighbour_feature_genes,
verbose = FALSE)})
pca.data <- as.matrix(temp.seurat@[email protected][, 1:min(num.pcs, ncol(temp.seurat@[email protected]))])
rownames(pca.data) <- colnames(log.feature.data)
system.time(
my.knn <- RANN::nn2(
data = pca.data,
k = (k + 1),
treetype = "kd",
searchtype = "standard",
eps = error
)
)
nn.dists <- my.knn$nn.dists
rownames(nn.dists) <- rownames(pca.data)
nn.dists <- nn.dists[,-1]
sdVec <- stats::na.omit(temp.seurat@reductions$pca@stdev[1:num.pcs])
length_scale <- sqrt(sum(sdVec ^ 2))
mean_nn_dist <- mean(x = nn.dists)
scaled_mean_nn_dist <- mean_nn_dist / length_scale
names(scaled_mean_nn_dist) <- num_genes
mean_knn_vec <-
append(mean_knn_vec, scaled_mean_nn_dist)
if (which.min(mean_knn_vec) != minNumGenes) {
minNumGenes = which.min(mean_knn_vec)
numStepsUnchangedMin = 0
} else {
numStepsUnchangedMin = numStepsUnchangedMin + 1
}
utils::setTxtProgressBar(pb = pb, value = num_genes)
}
optimal_feature_genes <- ordered.genes[1:as.numeric(names(minNumGenes))]
message(" ")
message("Done.")
return(list("optimal.feature.genes" = optimal_feature_genes, "density.index" = mean_knn_vec))
} |
fweibullgpd <- function(x, phiu = TRUE, useq = NULL, fixedu = FALSE, pvector = NULL,
std.err = TRUE, method = "BFGS", control = list(maxit = 10000), finitelik = TRUE, ...) {
call <- match.call()
np = 5
check.quant(x, allowna = TRUE, allowinf = TRUE)
check.logic(phiu)
check.posparam(useq, allowvec = TRUE, allownull = TRUE)
check.logic(fixedu)
check.logic(std.err)
check.optim(method)
check.control(control)
check.logic(finitelik)
if (any(!is.finite(x))) {
warning("non-finite cases have been removed")
x = x[is.finite(x)]
}
if (any(x <= 0)) {
warning("non-positive values have been removed")
x = x[x > 0]
}
check.quant(x)
n = length(x)
if ((method == "L-BFGS-B") | (method == "BFGS")) finitelik = TRUE
if (fixedu & is.null(useq))
stop("for fixed threshold approach, useq must be specified (as scalar or vector)")
if (is.null(useq)) {
check.nparam(pvector, nparam = np, allownull = TRUE)
if (is.null(pvector)) {
initfweibull = fitdistr(x, "weibull", lower = c(1e-8, 1e-8))
pvector[1] = initfweibull$estimate[1]
pvector[2] = initfweibull$estimate[2]
pvector[3] = as.vector(quantile(x, 0.9))
initfgpd = fgpd(x, pvector[3], std.err = FALSE)
pvector[4] = initfgpd$sigmau
pvector[5] = initfgpd$xi
}
} else {
check.nparam(pvector, nparam = np - 1, allownull = TRUE)
if (length(useq) != 1) {
useq = useq[sapply(useq, FUN = function(u, x) sum(x > u) > 5, x = x)]
check.posparam(useq, allowvec = TRUE)
nllhu = sapply(useq, profluweibullgpd, pvector = pvector, x = x, phiu = phiu,
method = method, control = control, finitelik = finitelik, ...)
if (all(!is.finite(nllhu))) stop("thresholds are all invalid")
u = useq[which.min(nllhu)]
} else {
u = useq
}
if (fixedu) {
if (is.null(pvector)) {
initfweibull = fitdistr(x, "weibull", lower = c(1e-8, 1e-8))
pvector[1] = initfweibull$estimate[1]
pvector[2] = initfweibull$estimate[2]
initfgpd = fgpd(x, u, std.err = FALSE)
pvector[3] = initfgpd$sigmau
pvector[4] = initfgpd$xi
}
} else {
if (is.null(pvector)) {
initfweibull = fitdistr(x, "weibull", lower = c(1e-8, 1e-8))
pvector[1] = initfweibull$estimate[1]
pvector[2] = initfweibull$estimate[2]
pvector[3] = u
initfgpd = fgpd(x, pvector[3], std.err = FALSE)
pvector[4] = initfgpd$sigmau
pvector[5] = initfgpd$xi
} else {
pvector[5] = pvector[4]
pvector[4] = pvector[3]
pvector[3] = u
}
}
}
if (fixedu) {
nllh = nluweibullgpd(pvector, u, x, phiu)
if (is.infinite(nllh)) {
pvector[4] = 0.1
nllh = nluweibullgpd(pvector, u, x, phiu)
}
if (is.infinite(nllh)) stop("initial parameter values are invalid")
fit = optim(par = as.vector(pvector), fn = nluweibullgpd, u = u, x = x, phiu = phiu,
finitelik = finitelik, method = method, control = control, hessian = TRUE, ...)
wshape = fit$par[1]
wscale = fit$par[2]
sigmau = fit$par[3]
xi = fit$par[4]
} else {
nllh = nlweibullgpd(pvector, x, phiu)
if (is.infinite(nllh)) {
pvector[5] = 0.1
nllh = nlweibullgpd(pvector, x, phiu)
}
if (is.infinite(nllh)) stop("initial parameter values are invalid")
fit = optim(par = as.vector(pvector), fn = nlweibullgpd, x = x, phiu = phiu,
finitelik = finitelik, method = method, control = control, hessian = TRUE, ...)
wshape = fit$par[1]
wscale = fit$par[2]
u = fit$par[3]
sigmau = fit$par[4]
xi = fit$par[5]
}
conv = TRUE
if ((fit$convergence != 0) | any(fit$par == pvector) | (abs(fit$value) >= 1e6)) {
conv = FALSE
warning("check convergence")
}
pu = pweibull(u, wshape, wscale)
if (phiu) {
phiu = 1 - pu
se.phiu = NA
} else {
phiu = mean(x > u, na.rm = TRUE)
se.phiu = sqrt(phiu * (1 - phiu) / n)
}
if (std.err) {
qrhess = qr(fit$hessian)
if (qrhess$rank != ncol(qrhess$qr)) {
warning("observed information matrix is singular")
se = NULL
invhess = NULL
} else {
invhess = solve(qrhess)
vars = diag(invhess)
if (any(vars <= 0)) {
warning("observed information matrix is singular")
invhess = NULL
se = NULL
} else {
se = sqrt(vars)
}
}
} else {
invhess = NULL
se = NULL
}
if (!exists("nllhu")) nllhu = NULL
list(call = call, x = as.vector(x),
init = as.vector(pvector), fixedu = fixedu, useq = useq, nllhuseq = nllhu,
optim = fit, conv = conv, cov = invhess, mle = fit$par, se = se, rate = phiu,
nllh = fit$value, n = n,
wshape = wshape, wscale = wscale, u = u, sigmau = sigmau, xi = xi, phiu = phiu, se.phiu = se.phiu)
}
lweibullgpd <- function(x, wshape = 1, wscale = 1, u = qweibull(0.9, wshape, wscale),
sigmau = sqrt(wscale^2 * gamma(1 + 2/wshape) - (wscale * gamma(1 + 1/wshape))^2),
xi = 0, phiu = TRUE, log = TRUE) {
check.quant(x, allowna = TRUE, allowinf = TRUE)
check.param(wshape)
check.param(wscale)
check.param(u)
check.param(sigmau)
check.param(xi)
check.phiu(phiu, allowfalse = TRUE)
check.logic(log)
if (any(!is.finite(x))) {
warning("non-finite cases have been removed")
x = x[is.finite(x)]
}
if (any(x <= 0)) {
warning("non-positive values have been removed")
x = x[x > 0]
}
check.quant(x)
n = length(x)
check.inputn(c(length(wshape), length(wscale), length(u), length(sigmau), length(xi), length(phiu)),
allowscalar = TRUE)
xu = x[which(x > u)]
nu = length(xu)
xb = x[which(x <= u)]
nb = length(xb)
if (n != nb + nu) {
stop("total non-finite sample size is not equal to those above threshold and those below or equal to it")
}
if ((wscale <= 0) | (wshape <= 0) | (sigmau <= 0) | (u <= 0) | (u <= min(x)) | (u >= max(x))) {
l = -Inf
} else {
pu = pweibull(u, wshape, wscale)
if (is.logical(phiu)) {
if (phiu) {
phiu = 1 - pu
} else {
phiu = nu / n
}
}
phib = (1 - phiu) / pu
syu = 1 + xi * (xu - u) / sigmau
if ((min(syu) <= 0) | (phiu <= 0) | (phiu >= 1) | (pu < .Machine$double.eps) | (pu <= 0) | (pu >= 1)) {
l = -Inf
} else {
l = lgpd(xu, u, sigmau, xi, phiu)
l = l + (wshape - 1) * sum(log(xb)) - sum(xb^wshape) / wscale^wshape + nb * log(wshape) - nb * wshape * log(wscale) + nb * log(phib)
}
}
if (!log) l = exp(l)
l
}
nlweibullgpd <- function(pvector, x, phiu = TRUE, finitelik = FALSE) {
np = 5
check.nparam(pvector, nparam = np)
check.quant(x, allowna = TRUE, allowinf = TRUE)
check.phiu(phiu, allowfalse = TRUE)
check.logic(finitelik)
wshape = pvector[1]
wscale = pvector[2]
u = pvector[3]
sigmau = pvector[4]
xi = pvector[5]
nllh = -lweibullgpd(x, wshape, wscale, u, sigmau, xi, phiu)
if (finitelik & is.infinite(nllh)) {
nllh = sign(nllh) * 1e6
}
nllh
}
profluweibullgpd <- function(u, pvector, x, phiu = TRUE, method = "BFGS",
control = list(maxit = 10000), finitelik = TRUE, ...) {
np = 5
check.nparam(pvector, nparam = np - 1, allownull = TRUE)
check.posparam(u)
check.quant(x, allowna = TRUE, allowinf = TRUE)
check.phiu(phiu, allowfalse = TRUE)
check.optim(method)
check.control(control)
check.logic(finitelik)
if (any(!is.finite(x))) {
warning("non-finite cases have been removed")
x = x[is.finite(x)]
}
if (any(x <= 0)) {
warning("non-positive values have been removed")
x = x[x > 0]
}
check.quant(x)
if (!is.null(pvector)) {
nllh = nluweibullgpd(pvector, u, x, phiu)
if (is.infinite(nllh)) pvector = NULL
}
if (is.null(pvector)) {
initfweibull = fitdistr(x, "weibull", lower = c(1e-8, 1e-8))
pvector[1] = initfweibull$estimate[1]
pvector[2] = initfweibull$estimate[2]
initfgpd = fgpd(x, u, std.err = FALSE)
pvector[3] = initfgpd$sigmau
pvector[4] = initfgpd$xi
nllh = nluweibullgpd(pvector, u, x, phiu)
}
if (is.infinite(nllh)) {
pvector[4] = 0.1
nllh = nluweibullgpd(pvector, u, x, phiu)
}
if (is.infinite(nllh)) {
warning(paste("initial parameter values for threshold u =", u, "are invalid"))
fit = list(par = rep(NA, np), value = Inf, counts = 0, convergence = NA,
message = "initial values invalid", hessian = rep(NA, np))
} else {
fit = optim(par = as.vector(pvector), fn = nluweibullgpd, u = u, x = x, phiu = phiu,
finitelik = finitelik, method = method, control = control, hessian = TRUE, ...)
}
if (finitelik & is.infinite(fit$value)) {
fit$value = sign(fit$value) * 1e6
}
fit$value
}
nluweibullgpd <- function(pvector, u, x, phiu = TRUE, finitelik = FALSE) {
np = 5
check.nparam(pvector, nparam = np - 1)
check.posparam(u)
check.quant(x, allowna = TRUE, allowinf = TRUE)
check.phiu(phiu, allowfalse = TRUE)
check.logic(finitelik)
wshape = pvector[1]
wscale = pvector[2]
sigmau = pvector[3]
xi = pvector[4]
nllh = -lweibullgpd(x, wshape, wscale, u, sigmau, xi, phiu)
if (finitelik & is.infinite(nllh)) {
nllh = sign(nllh) * 1e6
}
nllh
} |
randomReturns <- function(na, ns, sd, mean = 0, rho = 0) {
ans <- rnorm(ns*na)
dim(ans) <- c(na, ns)
if (!identical(rho, 0)) {
if (length(rho == 1L)) {
C <- array(rho, dim = c(na, na))
diag(C) <- 1
} else
C <- rho
ans <- t(chol(C)) %*% ans
}
ans <- ans*sd
ans <- ans + mean
t(ans)
} |
Lg.pond<-function(liste.mat,ponde){
Lg.pond<-Lg(liste.mat)
Lg.pond<-sweep(Lg.pond,1,STATS=ponde,FUN="*")
Lg.pond<-sweep(Lg.pond,2,STATS=ponde,FUN="*")
return(Lg.pond)
} |
knitr::opts_chunk$set(message=FALSE, error=FALSE, warning=FALSE, comment=NA)
savefigs <- FALSE
library("rprojroot")
root<-has_file(".ROS-Examples-root")$make_fix_file()
library("rstanarm")
library("ggplot2")
theme_set(bayesplot::theme_default(base_family = "sans"))
mile <- read.csv(root("Mile/data","mile.csv"), header=TRUE)
head(mile)
fit <- stan_glm(seconds ~ year, data = mile, refresh = 0)
print(fit, digits=2)
print(1007 -.393*c(1900,2000))
print(coef(fit)[1] + coef(fit)[2]*c(1900,2000), digits=4)
if (savefigs) pdf(root("Mile/figs","aplusbx1a.pdf"), height=3.5, width=5)
a <- 0.15
b <- 0.4
par(mar=c(3,3,1,1), mgp=c(2,.5,0), tck=-.01)
plot(c(0,2.2), c(0,a+2.2*b), pch=20, cex=.5, main="y = a + bx (with b > 0)",
bty="l", type="n", xlab="x", ylab="y", xaxt="n", yaxt="n", xaxs="i", yaxs="i")
axis(1, c(0,1,2))
axis(2, c(a,a+b,a+2*b), c("a","a+b","a+2b"))
abline(a, b)
if (savefigs) dev.off()
if (savefigs) pdf(root("Mile/figs","aplusbx1b.pdf"), height=3.5, width=5)
a <- 0.95
b <- -0.4
par(mar=c(3,3,1,1), mgp=c(2,.5,0), tck=-.01)
plot(c(0,2.2), c(0,a+.2), pch=20, cex=.5, main="y = a + bx (with b < 0)",
bty="l", type="n", xlab="x", ylab="y", xaxt="n", yaxt="n", xaxs="i", yaxs="i")
axis(1, c(0,1,2))
axis(2, c(a,a+b,a+2*b), c("a","a+b","a+2b"))
abline(a, b)
if (savefigs) dev.off()
if (savefigs) pdf(root("Mile/figs","aplusbx2a.pdf"), height=3.5, width=5)
par(mar=c(3,3,1,1), mgp=c(2,.5,0), tck=-.01)
curve(1007 - 0.393*x, from=0, to=2.1, xlab="x", ylab="y", bty="l", xaxs="i",
main="y = 1007 - 0.393x")
if (savefigs) dev.off()
if (savefigs) pdf(root("Mile/figs","aplusbx2b.pdf"), height=3.5, width=5)
par(mar=c(3,3,1,1), mgp=c(2,.5,0), tck=-.01)
curve(1007 - 0.393*x, from=0, to=100, xlab="x", ylab="y", bty="l", xaxs="i",
main="y = 1007 - 0.393x")
if (savefigs) dev.off()
if (savefigs) pdf(root("Mile/figs","aplusbx3.pdf"), height=3.5, width=5)
par(mar=c(3,3,1,1), mgp=c(2,.5,0), tck=-.01)
curve(1007 - 0.393*x, from=1900, to=2000,
xlab="Year", ylab="Time (seconds)", bty="l", xaxs="i",
main="Approx. trend of record times in the mile run",
ylim=c(215, 265))
text(1960, 246, "y = 1007 - 0.393x")
if (savefigs) dev.off()
ggplot(aes(x=year, y=seconds), data=mile) + geom_point(shape=1, size=2) +
geom_abline(intercept=fit$coefficients[1], slope=fit$coefficients[2]) +
labs(x="Year", y="Time (seconds)",
title = "Approx. trend of record times in the mile run")
if (savefigs) pdf(root("Mile/figs","mile1a.pdf"), height=3.5, width=5)
plot(mile$year, mile$seconds,
main="World record times in the mile run")
if (savefigs) dev.off()
if (savefigs) pdf(root("Mile/figs","mile1b.pdf"), height=3.5, width=5)
par(mar=c(3,3,3,1), mgp=c(2,.5,0), tck=-.01)
plot(mile$year, mile$seconds, bty="l",
main="World record times in the mile run", xlab="Year", ylab="Seconds")
if (savefigs) dev.off()
if (savefigs) pdf(root("Mile/figs","mile2.pdf"), height=3.5, width=5)
par(mar=c(3,3,3,1), mgp=c(2,.5,0), tck=-.01)
plot(mile$year, mile$seconds, bty="l",
main="World record times in the mile run", xlab="Year", ylab="Seconds")
curve(coef(fit)[1] + coef(fit)[2]*x, add=TRUE)
if (savefigs) dev.off() |
test_that("expect error when n = 0 or n < 0", {
expect_error(rm_latest_packages(n = 0))
expect_error(rm_latest_packages(n = -1))
expect_error(rm_latest_packages(n = -1))
expect_error(rm_latest_packages(n = -100))
})
test_that("expect error when n not numeric or not length 1", {
expect_error(rm_latest_packages(n = "hello world"))
expect_error(rm_latest_packages(n = c(32, 2323)))
expect_error(rm_latest_packages(n = mtcars))
}) |
options(digits=12)
if(!require("optimx"))stop("this test requires package optimx.")
if(!require("setRNG"))stop("this test requires setRNG.")
test.rng <- list(kind="Wichmann-Hill", normal.kind="Box-Muller", seed=c(979,1479,1542))
old.seed <- setRNG(test.rng)
cat("optimx test chen-x.f ...\n")
chen.f <- function(x) {
v <- log(x) + exp(x)
f <- (v - sqrt(v^2 + 5e-04))/2
sum (f * f)
}
p0 <- rexp(50)
system.time(ans.optx <- optimx(par=p0, fn=chen.f, lower=0, control=list(all.methods=TRUE,save.failures=TRUE,maxit=2500)))[1]
print(ans.optx)
|
test_that("gm_mean returns a numeric", {
expect_identical(gm_mean(numeric(0)), NaN)
expect_identical(gm_mean(1), 1)
expect_identical(gm_mean(-1), NaN)
expect_identical(gm_mean(c(1, -1)), NaN)
expect_identical(gm_mean(NA_real_), NA_real_)
expect_identical(gm_mean(NA_real_, na.rm = TRUE), NaN)
expect_identical(gm_mean(c(1, NA)), NA_real_)
expect_identical(gm_mean(c(1, NA), na.rm = TRUE), 1)
expect_identical(gm_mean(0), 0)
expect_identical(gm_mean(0, zero.propagate = FALSE), NaN)
expect_identical(gm_mean(c(1, 0)), 0)
expect_identical(gm_mean(c(NA_real_, 0)), 0)
expect_identical(gm_mean(c(NA_real_, 0), zero.propagate = FALSE), NA_real_)
expect_identical(gm_mean(c(NA_real_, 0), na.rm = TRUE), 0)
expect_identical(gm_mean(c(NA_real_, 0), na.rm = TRUE, zero.propagate = FALSE), NaN)
expect_identical(gm_mean(c(1, 0), zero.propagate = FALSE), 1)
expect_identical(gm_mean(1L), 1)
expect_error(gm_mean("1"))
expect_identical(round(gm_mean(c(13, 5, 70, 80))), 25)
}) |
draw.selic = function(){
selic = BETSget(4189)
target = BETSget(432)
start = c(2006,1)
if(!is.null(start)){
selic = window(selic, start = start)
}
else{
start = start(selic)
}
inx = grep("-15$",target[,1])
first = target[1,1]
target = ts(target[inx,2], start = as.numeric(c(format(first,"%Y"),format(first,"%m"))), frequency = 12)
target = window(target, start = start, frequency = 12)
lims = chart.add_basic(ts = selic, title = "Base Interest Rate (SELIC)", subtitle = "Accumulated in the Month, in Annual Terms", col = "darkolivegreen", arr.pos = "h", leg.pos = "none")
chart.add_extra(target, ylim = lims[3:4], xlim = lims[1:2], arr.pos = "none", leg.pos = "none", col = "darkgray")
legend("bottomleft", c("SELIC", "Target"), lty=c(1,2), lwd=c(2.5,2.5),col=c("darkolivegreen", "darkgray"), bty = "n", cex = 0.9)
chart.add_notes(selic, ylim = lims[3:4], xlim = lims[1:2])
} |
globalVariables(c('PROVINCE',
'SHARE',
'PNS',
'NATIONAL_SHARE',
'PNS_WEIGHTED'))
psns <- function(tidy_votes, method = 'Jones-Mainwaring') {
if (!ncol(tidy_votes) == 3) {
stop('Data frame must have exactly 3 columns')
}
if (!identical(names(tidy_votes), c('PROVINCE', 'PARTY', 'VOTES'))) {
stop('Data frame names must be PROVINCE, PARTY and VOTES')
}
if (!method %in% c('Jones-Mainwaring', 'Golosov')) {
stop('Not a valid method')
}
tidy_votes %>%
group_by(PROVINCE) %>%
mutate(SHARE = VOTES/sum(VOTES)) %>%
group_by(PARTY) %>%
summarize(PNS = pns(SHARE, method),
VOTES = sum(VOTES)) %>%
mutate(NATIONAL_SHARE = VOTES/sum(VOTES),
PNS_WEIGHTED = PNS*NATIONAL_SHARE) %>%
ungroup() %>%
summarise(pns = sum(PNS_WEIGHTED)) %>%
pull()
} |
ddistr <- function(x, meanvalue, distr=c("poisson", "nbinom"), distrcoefs, ...){
distr <- match.arg(distr)
result <- switch(distr,
"poisson"=dpois(x, lambda=meanvalue, ...),
"nbinom"=dnbinom(x, mu=meanvalue, size=distrcoefs[[1]], ...)
)
return(result)
}
pdistr <- function(q, meanvalue, distr=c("poisson", "nbinom"), distrcoefs, ...){
distr <- match.arg(distr)
result <- switch(distr,
"poisson"=ppois(q, lambda=meanvalue, ...),
"nbinom"=pnbinom(q, mu=meanvalue, size=distrcoefs[[1]], ...)
)
return(result)
}
qdistr <- function(p, meanvalue, distr=c("poisson", "nbinom"), distrcoefs, ...){
distr <- match.arg(distr)
result <- switch(distr,
"poisson"=qpois(p, lambda=meanvalue, ...),
"nbinom"=qnbinom(p, mu=meanvalue, size=distrcoefs[[1]], ...)
)
return(result)
}
rdistr <- function(n, meanvalue, distr=c("poisson", "nbinom"), distrcoefs){
distr <- match.arg(distr)
result <- switch(distr,
"poisson"=rpois(n, lambda=meanvalue),
"nbinom"=rnbinom(n, mu=meanvalue, size=distrcoefs[[1]])
)
return(result)
}
sddistr <- function(meanvalue, distr=c("poisson", "nbinom"), distrcoefs){
distr <- match.arg(distr)
result <- switch(distr,
"poisson"=sqrt(meanvalue),
"nbinom"=sqrt(meanvalue + meanvalue^2/distrcoefs[[1]])
)
return(result)
}
ardistr <- function(response, meanvalue, distr=c("poisson", "nbinom"), distrcoefs){
result <- switch(distr,
"poisson"=3/2*(response^(2/3)-meanvalue^(2/3))/meanvalue^(1/6),
"nbinom"=(3/distrcoefs[["size"]]*((1+response*distrcoefs[[1]])^(2/3) - (1+meanvalue*distrcoefs[[1]])^(2/3)) + 3*(response^(2/3)-meanvalue^(2/3))) / (2*(meanvalue+meanvalue^2*distrcoefs[[1]])^(1/6))
)
return(result)
}
checkdistr <- function(distr=c("poisson", "nbinom"), distrcoefs){
distr <- match.arg(distr)
if(distr=="nbinom"){
if(missing(distrcoefs) || length(distrcoefs)!=1) stop("For the negative binomial parameter (only) the dispersion parameter 'size' has to be provided in argument 'distrcoefs'")
if(distrcoefs[[1]]<=0) stop("The additional dispersion parameter for the negative binomial distribution has to be greater than zero")
}
} |
context("Exponential functions")
test_that("Raw moments for the exponential distribution work correctly", {
expect_equal(pexp(2, rate = 1), mexp(truncation = 2))
x <- rexp(1e5, rate = 1)
expect_equal(mean(x), mexp(r = 1, lower.tail = FALSE), tolerance = 1e-1)
expect_equal(sum(x[x > quantile(x, 0.1)]) / length(x), mexp(r = 1, truncation = quantile(x, 0.1), lower.tail = FALSE), tolerance = 1e-1)
}) |
expected <- eval(parse(text="\"\\\" \\\\t\\\\n\\\\\\\"\\\\\\\\'`><=%;,|&{()}\\\"\""));
test(id=0, code={
argv <- eval(parse(text="list(\" \\t\\n\\\"\\\\'`><=%;,|&{()}\", 0L, \"\\\"\", 0L, FALSE)"));
.Internal(encodeString(argv[[1]], argv[[2]], argv[[3]], argv[[4]], argv[[5]]));
}, o=expected); |
context("Read HMR files")
test_that("test that can read HMR file", {
expect_true(file.exists(system.file("extdata", "nanosims_data", "hmr", package = "lans2r")))
expect_true(is(hmr <- load_HMR(system.file("extdata", "nanosims_data", "hmr", package = "lans2r"),
prefix = "", suffix = ".hmr_txt"), "data.frame"))
expect_equal({
summary <- hmr %>% select(-step, -voltage, -cts) %>% distinct()
summary$ion
}, c("1H", "2H"))
expect_equal(summary$trolley, c("
expect_equal(summary$B, c("998.7", "998.7"))
expect_equal(summary$R, c("130.53", "184.90"))
expect_equal(summary$M, c("1.025", "2.056"))
}) |
setClass("CoImp",
representation(Missing.data.matrix = "matrix"
,Perc.miss = "matrix"
,Estimated.Model = "list"
,Estimation.Method = "character"
,Index.matrix.NA = "matrix"
,Smooth.param = "vector"
,Imputed.data.matrix = "matrix"
,Estimated.Model.Imp = "list"
,Estimation.Method.Imp = "character"
),
prototype = list(Missing.data.matrix = matrix(0,0,0)
,Perc.miss = matrix(0,0,0)
,Estimated.Model = list()
,Estimation.Method = character()
,Index.matrix.NA = matrix(0,0,0)
,Smooth.param = vector()
,Imputed.data.matrix = matrix(0,0,0)
,Estimated.Model.Imp = list()
,Estimation.Method.Imp = character()
)
)
setMethod(
f="plot",
signature(x = "CoImp",y = "missing"),
definition=function(x, y, plot.legend = TRUE, args.legend = list(y = 110, cex = 0.8), plot.name = NULL, ...){
par(mfrow=c(1,1));
bar.plot(tab = [email protected], legend.plot = plot.legend, args.legend = args.legend, ...)
X <- [email protected]
smoothing <- [email protected]
n.marg <- ncol(X)
fit0_nn <- list()
for(i in 1:n.marg){
fit0_nn[[i]] <- locfit::locfit( ~ locfit::lp(X[,i], nn=smoothing[i], deg=1))
}
fit0 <- fit0_nn
alpha <- smoothing
f.x <- list()
for(j in 1:n.marg){
f.x[[j]] <- function(x){predict(fit0[[j]], newdata=x)}
knorm <- try(integrate(f.x[[j]], lower=0, upper=Inf),silent=TRUE)
if(knorm[[1]]==0 | inherits(knorm, "try-error")==TRUE){knorm[[1]] <- 0.001}
f.x[[j]] <- function(x){predict(fit0[[j]], newdata=x)/as.numeric(knorm[[1]])}
}
if(n.marg<=3){
dev.new();
par(mfrow=c(1,ceiling(n.marg)))
}else{
if(n.marg >3 && n.marg<=6){
dev.new();
par(mfrow=c(2,ceiling(n.marg/2)))
}else{
dev.new();
par(mfrow=c(3,ceiling(n.marg/3)))
}
}
par(mai=c(0.5,0.5,0.3,0.5))
for(i in 1:n.marg){
j <- i
minimo <- min(X[complete.cases(X[,i]),i])
massimo <- max(X[complete.cases(X[,i]),i])
opt1 <- f.x[[i]](optimize(f.x[[i]], c(minimo,massimo), maximum=TRUE)$maximum)
his <- hist(X[,i], plot=FALSE)
opt2 <- max(his$density)
opt <- max(opt1,opt2)
plot(his, freq=FALSE, ylim=c(0,opt), main="", xlab="", ylab="")
plot(f.x[[i]], lwd=2, xlim=c(minimo-1, massimo+1),ylim=c(0,opt), col="blue", add=TRUE)
if(is.null(plot.name)){
title(main = paste("Variable X", i,sep=""))
}else{
title(main = plot.name[i])
}
}
}
)
setMethod(
f="show",
signature="CoImp",
definition=function(object){
out <- object
cat (" Main output of the function CoImp \n")
cat (" -------------------------------------------------------------------------- \n")
cat (" Percentage of missing and available data : \n")
print(out@"Perc.miss")
cat (" -------------------------------------------------------------------------- \n")
cat (" Imputed data matrix : \n")
print(out@"Imputed.data.matrix")
cat (" -------------------------------------------------------------------------- \n")
}
)
CoImp <- function(X, n.marg = ncol(X), x.up = NULL, x.lo = NULL, q.up = rep(0.85, n.marg),
q.lo = rep(0.15, n.marg), type.data = "continuous", smoothing =
rep(0.5, n.marg), plot = TRUE, model = list(normalCopula(0.5,
dim=n.marg), claytonCopula(10, dim=n.marg), gumbelCopula(10,
dim=n.marg), frankCopula(10, dim=n.marg), tCopula(0.5,
dim=n.marg,...), rotCopula(claytonCopula(10,dim=n.marg),
flip=rep(TRUE,n.marg)),...), start. = NULL, ...){
if(!is.matrix(X))
stop("Tha data should be a matrix")
if(n.marg<=1)
stop("The data matrix should contain at least two variables")
if(nrow(X)<1)
stop("The data matrix should contain at least one observation")
if(any(is.na(X))==FALSE)
stop("The data matrix should contain at least one missing value")
if(sum(complete.cases(X))==0)
stop("The data matrix should contain at least one complete record")
if(type.data != "discrete" & type.data != "continuous")
stop("The data must be either continuous or discrete")
if(type.data == "discrete")
warning("The variables are treated as continuous and rounded off.")
if(!is.null(x.up) & !is.null(q.up))
stop("Specify either x.up xor q.up")
if(!is.null(x.lo) & !is.null(q.lo))
stop("Specify either x.lo xor q.lo")
if(!is.null(q.up) & (any(q.up<=0) || any(q.up>=1)))
stop("q.up must lie in (0, 1)")
if(!is.null(q.lo) & (any(q.lo<=0) || any(q.lo>=1)))
stop("q.lo must lie in (0, 1)")
if(is.null(x.up) & is.null(q.up))
stop("Specify either x.up xor q.up")
if(is.null(x.lo) & is.null(q.lo))
stop("Specify either x.lo xor q.lo")
fit0_nn <- list()
for(i in 1:n.marg){
fit0_nn[[i]] <- locfit::locfit( ~ locfit::lp(X[,i], nn=smoothing[i], deg=1))
}
fit0 <- fit0_nn
alpha <- smoothing
names(alpha) <- c(paste("x",1:n.marg,sep=""))
f.x <- list()
F.x <- list()
for(j in 1:n.marg){
f.x[[j]] <- function(x){predict(fit0[[j]], newdata=x)}
knorm <- try(integrate(f.x[[j]], lower=0, upper=Inf),silent=TRUE)
if(knorm[[1]]==0 | inherits(knorm, "try-error")==TRUE){knorm[[1]] <- 0.001}
f.x[[j]] <- function(x){predict(fit0[[j]], newdata=x)/as.numeric(knorm[[1]])}
F.x[[j]] <- function(x){ifelse(!is.na(x),integrate(f.x[[j]], lower=0, upper=x)$value,return(NA))}
}
ind.miss <- which(is.na(X), arr.ind = TRUE)
if(nrow(ind.miss)>1){ind.miss <- ind.miss[order(ind.miss[,1]),]}
ind.cols.na <- split(ind.miss[,2],ind.miss[,1])
num.rows.na <- length(ind.cols.na)
n.mod <- length(model)
loglik <- double(length=n.mod)
metodo.fin <- double(length=n.mod)
metodo.optim.fin <- double(length=n.mod)
for(i in 1:n.mod){
metodo <- "ml"
metodo.c <- "BFGS"
udat.na <- fit.margin(dataset=t(X), param=list(dimc=n.marg))
udat <- udat.na[complete.cases(udat.na),]
fitc <- try(fitCopula(data=udat, copula=model[[i]], start=start., method=metodo, optim.method=metodo.c), silent=TRUE)
if(inherits(fitc, "try-error")==TRUE){
metodo <- c("mpl", "itau", "irho")
repeat{
if(length(metodo)==0 || inherits(fitc, "try-error")==FALSE)
break
fitc <- try(fitCopula(data = udat, copula = model[[i]],
start = start., method = metodo[[1]]), silent = TRUE)
metodo <- setdiff(metodo, metodo[[1]])
}
}
if(inherits(fitc, "try-error")==TRUE){
metodo.c <- c("Nelder-Mead", "CG", "L-BFGS-B", "SANN")
repeat{
if(length(metodo.c)==0 || inherits(fitc, "try-error")==FALSE)
break
metodo <- c("ml","mpl","itau","irho")
repeat{
if(length(metodo)==0 || inherits(fitc, "try-error")==FALSE)
break
fitc <- try(fitCopula(data = udat, copula = model[[i]],
start = start., method = metodo[[1]], optim.method=metodo.c[[1]]), silent = TRUE)
metodo <- setdiff(metodo, metodo[[1]])
}
metodo.c <- setdiff(metodo.c, metodo.c[[1]])
}
}
if (inherits(fitc, "try-error") || is.nan(suppressWarnings(loglikCopula(param=fitc@estimate, u=udat, copula=model[[i]])))) {
loglik[i] <- -10000
}else{
loglik[i] <- suppressWarnings(loglikCopula(param=fitc@estimate, u=udat, copula=model[[i]]))
}
if(length(metodo)==0){
metodo.fin[i] <- 0
}else{
metodo.fin[i] <- metodo[[1]]
}
if(length(metodo.c)==0){
metodo.optim.fin[i] <- 0
}else{
metodo.optim.fin[i] <- metodo.c[[1]]
}
}
best <- which(loglik==max(loglik[which(!is.na(loglik))]))[[1]]
mod.fin.base <- model[[best]]
metodo.fin.base <- metodo.fin[[best]]
metodo.optim.fin.base <- metodo.optim.fin[[best]]
if(metodo.fin.base=="ml"){
udat.na <- fit.margin(dataset=t(X), param=list(dimc=n.marg))
udat <- udat.na[complete.cases(udat.na),]
}else{
udat.na <- fit.margin2(dataset=t(X), param=list(dimc=n.marg))
udat <- udat.na[complete.cases(udat.na),]
}
mod.fin <- try(fitCopula(data=udat, copula=mod.fin.base, start=start., method=metodo.fin.base, optim.method=metodo.optim.fin.base),silent=TRUE)
if (inherits(mod.fin, "try-error")) {
stop("Imputation failed")
}else{
if(class(mod.fin.base)=="rotExplicitCopula" | class(mod.fin.base)=="rotCopula"){
mod.fin.base@copula@parameters <- mod.fin@estimate
}else{
mod.fin.base@parameters <- mod.fin@estimate
}
}
dati.fin <- X
x.min <- apply(X, 2, min, na.rm=TRUE)
x.max <- apply(X, 2, max, na.rm=TRUE)
if(is.null(x.up)){
x.up <- diag(apply(X, 2, quantile, p=q.up, na.rm=TRUE))
}
if(is.null(x.lo)){
x.lo <- diag(apply(X, 2, quantile, p=q.lo, na.rm=TRUE))
}
for(i in 1:num.rows.na){
cat("\r Number of imputed rows: ", i, "\n");
cols.na <- ind.cols.na[[i]]
cols.no.na <- seq(1:n.marg)[-cols.na]
rows.na <- as.numeric(names(ind.cols.na))[i]
y <- X[rows.na,]
lcn <- length(cols.na)
meanX <- colMeans(X,na.rm=TRUE)
medianX <- apply(X=X, FUN=median, MARGIN=2, na.rm=TRUE)
zz <- y
fcond <- function(x){
zz[cols.na] <- x
fcond.mod(x, y=zz, ind=cols.na, model=mod.fin.base, distr=F.x, dens=f.x)
}
fcond2 <- Vectorize(fcond)
if(lcn==n.marg)
stop("It cannot impute a full NA record")
a <- x.min[cols.na]
b <- x.max[cols.na]
aa <- x.lo[cols.na]
bb <- x.up[cols.na]
if(lcn==1){
maxf <- suppressWarnings(optimize(fcond2, interval=c(a, b), maximum=TRUE)$max)
if(maxf>=aa & maxf<=bb){
umissM <- hitormiss(FUN=fcond, p=lcn, h=fcond(maxf), a=aa, b=bb)
}else{
umissM <- hitormiss(FUN=fcond, p=lcn, h=fcond(maxf), a=a, b=b)
}
}else{
maxf <- suppressWarnings(try(optim(par=meanX[cols.na], fn=fcond, lower=a, upper=b, control = list(fnscale=-1))$par,silent=TRUE))
if(inherits(maxf, "try-error")==TRUE){
maxf <- suppressWarnings(optim(par=medianX[cols.na], fn=fcond, lower=a, upper=b, control = list(fnscale=-1))$par)
}
if(all(maxf>=aa) & all(maxf<=bb)){
umissM <- suppressWarnings(hitormiss(FUN=fcond,p=lcn,h=fcond(maxf),a=aa,b=bb))
}else{
umissM <- suppressWarnings(hitormiss(FUN=fcond,p=lcn,h=fcond(maxf),a=a,b=b))
}
}
dati.fin[rows.na,cols.na] <- umissM
}
loglik.imp <- double(length=n.mod)
metodo.fin.imp <- double(length=n.mod)
metodo.optim.fin.imp <- double(length=n.mod)
if(type.data=="discrete"){
dati.fin <- round(dati.fin,0)
}
for(i in 1:n.mod){
metodo <- "ml"
metodo.c <- "BFGS"
udat.na.imp <- fit.margin(dataset=t(dati.fin), param=list(dimc=n.marg))
udat.imp <- udat.na.imp[complete.cases(udat.na.imp),]
fitc.imp <- try(fitCopula(data=udat.imp, copula=model[[i]], start=start., method=metodo, optim.method=metodo.c), silent=TRUE)
if(inherits(fitc.imp, "try-error")==TRUE){
metodo <- c("mpl","itau","irho")
repeat{
if(length(metodo)==0 || inherits(fitc.imp, "try-error")==FALSE)
break
fitc.imp <- try(fitCopula(data = udat.imp, copula = model[[i]],
start = start., method = metodo[[1]]), silent = TRUE)
metodo <- setdiff(metodo, metodo[[1]])
}
}
if(inherits(fitc.imp, "try-error")==TRUE){
metodo.c <- c("Nelder-Mead", "CG", "L-BFGS-B", "SANN")
repeat{
if(length(metodo.c)==0 || inherits(fitc.imp, "try-error")==FALSE)
break
metodo <- c("ml","mpl","itau","irho")
repeat{
if(length(metodo)==0 || inherits(fitc.imp, "try-error")==FALSE)
break
fitc.imp <- try(fitCopula(data = udat.imp, copula = model[[i]],
start = start., method = metodo[[1]], optim.method=metodo.c[[1]]), silent = TRUE)
metodo <- setdiff(metodo, metodo[[1]])
}
metodo.c <- setdiff(metodo.c, metodo.c[[1]])
}
}
if (inherits(fitc.imp, "try-error") || is.nan(suppressWarnings(loglikCopula(param=fitc.imp@estimate, u=udat.imp, copula=model[[i]])))) {
loglik.imp[i] <- -10000
}else{
loglik.imp[i] <- suppressWarnings(loglikCopula(param=fitc.imp@estimate, u=udat.imp, copula=model[[i]]))
}
if(length(metodo)==0){
metodo.fin.imp[i] <- 0
}else{
metodo.fin.imp[i] <- metodo[[1]]
}
if(length(metodo.c)==0){
metodo.optim.fin.imp[i] <- 0
}else{
metodo.optim.fin.imp[i] <- metodo.c[[1]]
}
}
best.imp <- which(loglik.imp==max(loglik.imp[which(!is.na(loglik.imp))]))[[1]]
mod.fin.base.imp <- model[[best.imp]]
metodo.fin.base.imp <- metodo.fin.imp[[best.imp]]
metodo.optim.fin.base.imp <- metodo.optim.fin.imp[[best.imp]]
if(metodo.fin.base.imp=="ml"){
udat.na.imp <- fit.margin(dataset=t(dati.fin), param=list(dimc=n.marg))
udat.imp <- udat.na.imp[complete.cases(udat.na.imp),]
}else{
udat.na.imp <- fit.margin2(dataset=t(dati.fin), param=list(dimc=n.marg))
udat.imp <- udat.na.imp[complete.cases(udat.na.imp),]
}
mod.fin.imp <- try(fitCopula(data=udat.imp, copula=mod.fin.base.imp, start=start., method=metodo.fin.base.imp, optim.method=metodo.optim.fin.base.imp),silent=TRUE)
if (inherits(mod.fin.imp, "try-error")) {
stop("Imputation failed")
}else{
if(class(mod.fin.base.imp)=="rotExplicitCopula" | class(mod.fin.base.imp)=="rotCopula"){
mod.fin.base.imp@copula@parameters <- mod.fin.imp@estimate
}else{
mod.fin.base.imp@parameters <- mod.fin.imp@estimate
}
}
perc.miss <- round(colMeans(is.na(X)*100),2)
perc.data <- round(rbind(100-perc.miss, perc.miss),3)
rownames(perc.data) <- c("Data","Missing")
ifelse(is.null(colnames(X)), colnames(perc.data) <- paste("X",c(1:n.marg),sep=""), colnames(perc.data) <- colnames(X))
if(is.null(colnames(X))) colnames(X) <- paste("X",c(1:n.marg),sep="")
ifelse(is.null(colnames(X)), colnames(dati.fin) <- paste("X",c(1:n.marg),sep=""), colnames(dati.fin) <- colnames(X))
if(class(mod.fin.base)=="rotExplicitCopula" | class(mod.fin.base)=="rotCopula"){
mod.fin.base <- mod.fin.base@copula
}
if(class(mod.fin.base.imp)=="rotExplicitCopula" | class(mod.fin.base.imp)=="rotCopula"){
mod.fin.base.imp <- mod.fin.base.imp@copula
}
mod.pre <- list(model = mod.fin.base@class, dimension = mod.fin.base@dimension, parameter = mod.fin.base@parameters, number = best)
mod.post <- list(model = mod.fin.base.imp@class, dimension = mod.fin.base.imp@dimension, parameter = mod.fin.base.imp@parameters, number = best.imp)
out <- new("CoImp")
[email protected] <- X;
[email protected] <- perc.data;
[email protected] <- mod.pre;
[email protected] <- metodo.fin.base;
[email protected] <- ind.miss;
[email protected] <- alpha;
[email protected] <- dati.fin;
[email protected] <- mod.post;
[email protected] <- metodo.fin.base.imp;
if(plot==TRUE) plot(out)
return(out);
} |
\donttest{
data(DengueSimR02)
r.max<-seq(20,1000,20)
r.min<-seq(0,980,20)
type<-2-(DengueSimR02[,"time"]<120)
tmp<-cbind(DengueSimR02,type=type)
typed.pi<-get.pi.typed(tmp,typeA=1,typeB=2,r=r.max,r.low=r.min)
} |
context("Primary Inputs of locFDR BF theoretic function cor")
test_that("Absent of BetaHat and SE will throw error", {
expect_error(analytic_locFDR_BF_cor(1:10), "BetaHat or SE vector is missing!")
expect_error(analytic_locFDR_BF_cor(SpikeVar=0.3), "BetaHat or SE vector is missing!")
expect_error(analytic_locFDR_BF_cor(1:10, 1:10), "Correlation matrix is missing!")
})
test_that("Throws error if BetaHat is not a numeric vector of length more than 1 and no missing value", {
expect_error(analytic_locFDR_BF_cor(1, 1:10, ExampleDataCor$cor), "Number of elements in the BetaHat vector must be more than 1!")
expect_error(analytic_locFDR_BF_cor(NA, 1:10, ExampleDataCor$cor), "BetaHat must be a numeric vector.")
expect_error(analytic_locFDR_BF_cor(c(NA,1), 1:10, ExampleDataCor$cor), "BetaHat for one or more phenotypes are missing!")
expect_error(analytic_locFDR_BF_cor("AB", 1:10, ExampleDataCor$cor), "BetaHat must be a numeric vector.")
expect_error(analytic_locFDR_BF_cor(c("A", 1), 1:10, ExampleDataCor$cor), "BetaHat must be a numeric vector.")
expect_error(analytic_locFDR_BF_cor(matrix(0,2,2), 1:10, ExampleDataCor$cor), "BetaHat must be a vector.")
expect_error(analytic_locFDR_BF_cor(data.frame(rep(1:10)), 1:10, ExampleDataCor$cor), "BetaHat must be a vector.")
expect_warning(analytic_locFDR_BF_cor(matrix(1:10, 10, 1), 1:10, ExampleDataCor$cor), "BetaHat is a matrix!")
})
test_that("Throws error if SE is not a positive numeric vector of length more than 1 and no missing value", {
expect_error(analytic_locFDR_BF_cor(1:10, 1, ExampleDataCor$cor), "Number of elements in the SE vector must be more than 1!")
expect_error(analytic_locFDR_BF_cor(1:10, NA, ExampleDataCor$cor), "SE must be a numeric vector.")
expect_error(analytic_locFDR_BF_cor(1:10, c(NA,1), ExampleDataCor$cor), "SE for one or more phenotypes are missing!")
expect_error(analytic_locFDR_BF_cor(1:10, "AB", ExampleDataCor$cor), "SE must be a numeric vector.")
expect_error(analytic_locFDR_BF_cor(1:10, c("A", 1), ExampleDataCor$cor), "SE must be a numeric vector.")
expect_error(analytic_locFDR_BF_cor(1:10, matrix(0,2,2), ExampleDataCor$cor), "SE must be a vector.")
expect_error(analytic_locFDR_BF_cor(1:10, data.frame(rep(1:10)), ExampleDataCor$cor), "SE must be a vector.")
expect_error(analytic_locFDR_BF_cor(1:10, c(-1, 2,3), ExampleDataCor$cor), "One or more elements in the SE vector are not positive!")
expect_warning(analytic_locFDR_BF_cor(1:10, matrix(1:10, 10, 1), ExampleDataCor$cor), "SE is a matrix!")
})
test_that("Throws error if Beta and SE are not of same length", {
expect_error(analytic_locFDR_BF_cor(1:10, 1:15, ExampleDataCor$cor), "BetaHat and SE vectors must have the same number of elements!")
expect_error(analytic_locFDR_BF_cor(1:100, 1:15, ExampleDataCor$cor), "BetaHat and SE vectors must have the same number of elements!")
}) |
context("Testing anova and haplo.glm")
tmp <- Sys.setlocale("LC_ALL", "C")
tmp <- Sys.getlocale()
options(stringsAsFactor=FALSE)
label <-c("DQB","DRB","B")
data(hla.demo)
y <- hla.demo$resp
y.bin <- 1*(hla.demo$resp.cat=="low")
geno <- as.matrix(hla.demo[,c(17,18,21:24)])
geno <- setupGeno(geno, miss.val=c(0,NA))
my.data <- data.frame(geno=geno, age=hla.demo$age, male=hla.demo$male,
y=y, y.bin=y.bin)
seed <- c(17, 53, 1, 40, 37, 0, 62, 56, 5, 52, 12, 1)
set.seed(seed)
fit.hla.gaus.gender <- haplo.glm(y ~ male + geno, family = gaussian,
na.action="na.geno.keep",
data=my.data, locus.label=label,
control = haplo.glm.control(haplo.min.count=10))
coeff.hla.gender <- summary(fit.hla.gaus.gender)$coefficients
set.seed(seed)
fit.hla.gaus.inter <- haplo.glm(y ~ male * geno, family = gaussian,
na.action="na.geno.keep", data=my.data, locus.label=label,
control = haplo.glm.control(haplo.min.count = 10))
coeff.hla.inter <- summary(fit.hla.gaus.inter)$coefficients
if(0) {
saveRDS(fit.hla.gaus.gender, "fit.gaus.gender.rds")
saveRDS(fit.hla.gaus.inter, "fit.gaus.inter.rds")
}
fit.gender <- readRDS("fit.gaus.gender.rds")
fit.inter <- readRDS("fit.gaus.inter.rds")
coeffgender <- summary(fit.gender)$coefficients
coeffinter <- summary(fit.inter)$coefficients
test_that("Basic haplo.glm anova and glm coefficients", {
expect_equal(coeff.hla.gender, expected=coeffgender, tolerance=1e-3)
expect_equal(coeff.hla.inter, expected=coeffinter, tolerance=1e-3)
}) |
gridfunction<-function(npoints,linf,lsup){
npar=length(linf)
xgrid=matrix(0,npoints,npar)
for(i in 1:npar){
xgrid[,i]=seq(linf[i], lsup[i], length.out = npoints)
}
grid.l=data.frame(xgrid)
gridoutput=make.surface.grid(grid.l)
return(gridoutput[,])
} |
MDCSIS=function(X,Y,nsis=(dim(X)[1])/log(dim(X)[1])){
if (dim(X)[1]!=length(Y)) {
stop("X and Y should have same number of rows!")
}
if (missing(X)|missing(Y)) {
stop("The data is missing!")
}
if (TRUE%in%(is.na(X)|is.na(Y)|is.na(nsis))) {
stop("The input vector or matrix cannot have NA!")
}
if (inherits(Y,"Surv")) {
stop("MDCSIS can not implemented with object of Surv")
}
corr=c();
n=dim(X)[1];
p=dim(X)[2];
Y_ij=Y%*%t(Y)
for (k in 1:p){
X_matrix=matrix(X[,k],n,n)
X_ij=abs(X_matrix-t(X_matrix))
covxy=mean(X_ij*Y_ij)
covx=mean(X_ij*X_ij)
covy=mean(Y_ij*Y_ij)
corr[k]=abs(covxy)/sqrt(covx*covy)
}
A=order(corr,decreasing=TRUE)
return (A[1:nsis])
} |
days_frost <- function(mintemps, fendates, lastday = 181){
mintemps <- mintemps %>% filter(mintemps$DOY<=lastday)
Datescrit_10 <- rep(NA,lastday)
Datescrit_90 <- rep(NA,lastday)
len = length(fendates$DOY)
for (i in 1:len){
Datescrit_10[fendates$DOY[i]]=fendates$LT_10[i]
Datescrit_90[fendates$DOY[i]]=fendates$LT_90[i]
}
Tcritant_LT10 <- rep(fendates$LT_10[1], fendates$DOY[1]-1)
Tcritant_LT90 <- rep(fendates$LT_90[1], fendates$DOY[1]-1)
Tcritpost_LT10 <- rep(fendates$LT_10[len], lastday-fendates$DOY[len])
Tcritpost_LT90 <- rep(fendates$LT_90[len], lastday-fendates$DOY[len])
Tcrit_gap_10 <- zoo(Datescrit_10)
Tcrit_gap_90 <- zoo(Datescrit_90)
Tcrit_fgap_10 <- na.approx(Tcrit_gap_10)
Tcrit_fgap_90 <- na.approx(Tcrit_gap_90)
Tcritcent_10 <- coredata(Tcrit_fgap_10)
Tcritcent_90 <- coredata(Tcrit_fgap_90)
mintemps$LT_10 <- c(Tcritant_LT10,Tcritcent_10,Tcritpost_LT10)
mintemps$LT_90 <- c(Tcritant_LT90,Tcritcent_90,Tcritpost_LT90)
mintemps <- mintemps %>% mutate(LT_0 = (LT_10-LT_90)/8+LT_10,
LT_100 = LT_0-(LT_10-LT_90)*10/8,
Dam = ifelse((LT_0-Tmin)/(LT_0-LT_100)<0,0,
ifelse((LT_0-Tmin)/(LT_0-LT_100)>1,1,
(LT_0-Tmin)/(LT_0-LT_100))) )
return(mintemps)
} |
knitr::opts_chunk$set(
collapse = TRUE,
comment = "
echo = TRUE,
message = FALSE,
warning = FALSE,
fig.align="center",
fig.height= 6,
fig.width = 6
)
library(tci)
data("eleveld_pk")
data("eleveld_pd")
pkpd_eb <- merge(eleveld_pk, eleveld_pd)
prior_pars_id1 <- eleveld_poppk(df = pkpd_eb[1,])
prior_pars_id1
pars_pk_id1 <- unlist(prior_pars_id1[,c("V1","V2","V3","CL","Q2","Q3","KE0")])
pars_pd_id1 <- unlist(prior_pars_id1[,c("CE50","GAMMA","GAMMA2","BIS0","BIS0")])
olc_inf_id1 <- tci_pd(pdresp = c(50,50),
tms = c(0,5),
pkmod = pkmod3cptm,
pdmod = emax_eleveld,
pdinv = inv_emax_eleveld,
pars_pk = pars_pk_id1,
pars_pd = pars_pd_id1)
plot(olc_inf_id1)
eb_pars_pk_id1 <- unlist(pkpd_eb[1,c("CL","Q2","Q3","V1","V2","V3","KE0")])
eb_pars_pd_id1 <- unlist(pkpd_eb[1,c("E50","GAM","GAM1","EMAX","EMAX")])
eb_sigma_id1 <- unlist(pkpd_eb[1,"RESD"])
eb_bd_id1 <- unlist(pkpd_eb[1,"ALAG1"]) * 60
set.seed(1)
datasim_id1 <- gen_data(inf = olc_inf_id1,
pkmod = pkmod3cptm,
pdmod = emax_eleveld,
pars_pk0 = eb_pars_pk_id1,
pars_pd0 = eb_pars_pd_id1,
sigma_add = eb_sigma_id1,
delay = eb_bd_id1)
plot(datasim_id1)
cl_targets <- function(time, target){
data.frame(time = time, target = target)
}
cl_updates <- function(time, full_data = TRUE, plot_progress = FALSE){
data.frame(time = time, full_data = full_data, plot_progress = plot_progress)
}
targets = cl_targets(time = seq(0,10,1/6),
target = 50)
updates <- cl_updates(time = seq(2,10,2))
prior_par_list <- list(
pars_pkpd = prior_pars_id1[c("CL","Q2","Q3","V1","V2","V3",
"KE0","CE50","GAMMA","GAMMA2",
"BIS0","BIS0")],
pk_ix = 1:7,
pd_ix = 8:12,
fixed_ix = 9:12,
err = prior_pars_id1[1,"SIGMA"],
sig = eleveld_vcov(prior_pars_id1,
rates = FALSE)[[1]]
)
true_par_list <- list(pars_pkpd = pkpd_eb[1,c("CL","Q2","Q3","V1",
"V2","V3","KE0",
"E50","GAM","GAM1",
"EMAX","EMAX")],
pk_ix = 1:7,
pd_ix = 8:12,
fixed_ix = 9:12,
err = pkpd_eb[1,"RESD"],
delay = pkpd_eb[1,"ALAG1"])
bayes_sim <- bayes_control(targets = targets,
updates = updates,
prior = prior_par_list,
true_pars = true_par_list)
plot(bayes_sim) |
OuterPara <- function(m, nu, N, eps){
cm <- sqrt(m / nu)
d2 <- as.numeric(compute_zl(nu, eps)[1])
zl <- as.numeric(compute_zl(nu, eps)[2])
zu <- zl + d2
h <- d2/(N - 1)
zvec <- seq(zl, zu, by = h)
wvec <- transf(zvec) / cm
psinu.zvec <- Func_psi_nu(zvec, nu)
cons1 <- sqrt(2/m) * exp(lgamma(nu/2) - lgamma(m/2))
cons2 <- (2/m) * exp(lgamma(nu/2) - lgamma(m/2))
exp.w <- sqrt(2/m) * exp(lgamma((m + 1)/2) - lgamma(m/2))
out <- list(h=h, cons1=cons1, cons2=cons2, exp.w=exp.w,
wvec=wvec, psinu.zvec=psinu.zvec)
} |
if (requiet("testthat") && requiet("performance") && requiet("lme4")) {
data(sleepstudy, package = "lme4")
m1.1 <- glm(Reaction ~ Days, data = sleepstudy, family = gaussian())
m1.2 <- glm(Reaction ~ Days, data = sleepstudy, family = gaussian("log"))
m1.3 <- glm(Reaction ~ Days, data = sleepstudy, family = gaussian("inverse"))
m2.1 <- glm(Reaction ~ Days, data = sleepstudy, family = inverse.gaussian())
m2.2 <- glm(Reaction ~ Days, data = sleepstudy, family = inverse.gaussian("log"))
m2.3 <- glm(Reaction ~ Days, data = sleepstudy, family = inverse.gaussian("inverse"))
cp <- compare_performance(m1.1, m1.2, m1.3, m2.1, m2.2, m2.3)
test_that("rmse", {
expect_equal(cp$RMSE, c(47.4489, 47.39881, 47.38701, 47.41375, 47.39979, 47.38705), tolerance = 1e-3)
})
} |
"seas.sum" <-
function(x, var, width = 11, start.day = 1,
prime, a.cut = 0.3, na.cut = 0.2) {
orig <- as.character(substitute(x))[[1]]
if (missing(var))
var <- c("precip", "rain", "snow", "leak", "evap", "ezwat", "et", "runoff", "air", "soil")
var <- names(x)[names(x) %in% var]
if (missing(prime))
prime <- ifelse("precip" %in% var, "precip", var[1])
sc <- seas.df.check(x, orig, c(prime, var))
if (is.na(a.cut) || a.cut <= 0)
a.cut = FALSE
x$fact <- mkseas(x, width, start.day)
x$ann <- mkann(x, start.day)
bins <- levels(x$fact)
num <- length(bins)
years <- levels(x$ann)
ann <- data.frame(year=years, active=NA, days=NA, na=NA)
seas <- array(dim=c(length(years), num, length(var)),
dimnames=list(years, bins, var))
days <- array(dim=c(length(years), num),
dimnames=list(years, bins))
na <- days
if (is.na(a.cut) || !a.cut) {
a.cut <- FALSE
} else {
active <- seas
is.active <- function(test) {
tot <- numeric(length(test))
tot[is.na(test)] <- NA
tot[test > a.cut] <- 1
na.rm <- ifelse(sum(is.na(tot)) / length(tot) < na.cut[2], TRUE, FALSE)
return(sum(tot, na.rm=na.rm))
}
}
if (length(na.cut) == 1)
na.cut <- rep(na.cut, 2)
sum.is.num <- function(x)sum(is.finite(x))
ann$days <- attr(x$ann, "year.lengths")
if (a.cut)
ann$active <- tapply(x[,prime], x$ann, is.active)
else
ann$active <- NULL
ann$na <- tapply(x[,prime], x$ann, sum.is.num)
for (p in var)
ann[,p] <- tapply(x[,p], x$ann, sum, na.rm=TRUE)
td <- function(y) table(mkseas(width=width, year=y, calendar=sc$calendar))
days[,] <- t(sapply(ann$days, td))
for (y in 1:length(years)) {
s <- x[x$ann == years[y],, drop=FALSE]
if (nrow(s) > 0) {
na[y,] <- tapply(s[,prime], s$fact, sum.is.num)
for (p in var) {
seas[y,,p] <- tapply(s[,p], s$fact, sum, na.rm=TRUE)
if (a.cut)
active[y,,p] <- tapply(s[,p], s$fact, is.active)
}
}
}
ann$na[is.na(ann$na)] <- 0
ann$na <- ann$days - ann$na
na[is.na(na)] <- 0
na <- days - na
ann.na <- ann$na / ann$days > na.cut[1]
seas.na <- na/days > na.cut[2]
ann[ann.na, var] <- NA
seas[,,var][seas.na] <- NA
if (a.cut) {
ann[ann.na, "active"] <- NA
active[,,var][seas.na] <- NA
}
l <- list(ann=ann, seas=seas, active=NA, days=days, na=na)
if (a.cut)
l$active <- active
else
l$active <- NULL
l$start.day <- start.day
l$years <- years
l$var <- var
l$units <- list()
l$long.name <- list()
for (v in var) {
l$units[[v]] <- attr(x[[v]], "units")
l$long.name[[v]] <- attr(x[[v]], "long.name")
if (is.null(l$long.name[[v]]))
l$long.name[[v]] <- v
}
l$prime <- prime
l$width <- width
l$bins <- bins
l$bin.lengths <- attr(x$fact, "bin.lengths")
l$year.range <- attr(x$ann, "year.range")
l$na.cut <- na.cut
l$a.cut <- a.cut
l$id <- sc$id
l$name <- sc$name
attr(l, "class") <- "seas.sum"
return(l)
} |
github.m3it.covid19data <- function(level) {
if(!level %in% 1:2) return(NULL)
url <- "https://raw.githubusercontent.com/M3IT/COVID-19_Data/master/Data/COVID19_Data_Hub.csv"
x <- read.csv(url, na.strings = c("NA",""))
x$date <- as.Date(x$date)
x <- x[x$administrative_area_level == level,]
return(x)
} |
update_SS <- function(z, S, hyperprior=NULL){
S_up <- S
mu0 <- S[["mu"]]
kappa0 <- S[["kappa"]]
nu0 <- S[["nu"]]
lambda0 <- S[["lambda"]]
if(length(dim(z))>1 & dim(z)[2]>1){
n <- ncol(z)
zbar <- apply(X=z, MARGIN=1, FUN=mean)
kappa1 <- kappa0 + n
nu1 <- nu0 + n
mu1 <- n/(kappa0 + n)*zbar + kappa0/(kappa0 + n)*mu0
varz <- tcrossprod(z-zbar)
if(!is.null(hyperprior)){
g0 <- nu0
lambda0 <- wishrnd(n=nu0+g0, Sigma=solve(solve(lambda0)+solve(hyperprior[["Sigma"]])))
}
lambda1 <- (lambda0 + kappa0*n/(kappa0 + n)*tcrossprod(zbar - mu0) + varz)
}else{
kappa1 <- kappa0 + 1
nu1 <- nu0 + 1
mu1 <- (kappa0/(kappa0 + 1)*mu0 + 1/(kappa0 + 1)*z)[,1]
if(!is.null(hyperprior)){
g0 <- nu0
lambda0 <- wishrnd(n=nu0+g0, Sigma=solve(solve(lambda0)+solve(hyperprior[["Sigma"]])))
}
lambda1 <- lambda0 + kappa0/(kappa0 + 1)*tcrossprod(z[,1] - mu0)
}
S_up[["mu"]] <- mu1
S_up[["kappa"]] <- kappa1
S_up[["nu"]] <- nu1
S_up[["lambda"]] <- lambda1
return(S_up)
} |
LassoPath <- function (data, formula) {
X <- model.matrix(formula, data)
y <- model.frame(formula, data)[, 1]
get_lambdas <- cv.glmnet(X,
y,
family="binomial",
type.measure="auc",
nfolds=3)
glmnet.output <- glmnet(X, y, alpha = 1, family = "binomial",
lambda = get_lambdas$lambda,
standardize=TRUE,
intercept = FALSE,
type.logistic = "modified.Newton")
dimension <- glmnet.output$df
coeff <- t(as.matrix(glmnet.output$beta))
lambda <- glmnet.output$lambda
path <- cbind(lambda, dimension, coeff)
class(path) <- "LassoPath"
return(path)
} |
GE_translate_inputs <- function(beta_list, rho_list, prob_G, cov_Z=NULL, cov_W=NULL, corr_G=NULL)
{
if (length(beta_list) != 6 | length(rho_list) != 6 | class(beta_list) != 'list' | class(rho_list) != 'list')
{
stop('Input vectors not the right size!')
}
num_G <- length(beta_list[[2]])
num_I <- length(beta_list[[4]])
num_Z <- length(beta_list[[5]]); if(num_Z == 1 & beta_list[[5]][1] == 0) {num_Z <- 0}
num_W <- length(beta_list[[6]]); if(num_W == 1 & beta_list[[6]][1] == 0) {num_W <- 0}
if (num_G != num_I | num_G != length(prob_G)) {
stop('Discordance between effect sizes for G and probabilities of G')
}
rho_GE <- rho_list[[1]]; rho_GZ <- rho_list[[2]]; rho_EZ <- rho_list[[3]]
rho_GW <- rho_list[[4]]; rho_EW <- rho_list[[5]]; rho_ZW <- rho_list[[6]]
if (length(rho_GE) != num_G) {
stop('Incompatible number of elements in beta/rho_list')
}
if (num_Z > 0) {
if (length(rho_GZ) != num_Z*num_G | length(rho_EZ) != num_Z)
{
stop('Incompatible number of elements in beta/rho_list')
}
if (num_W > 0) {
if (length(rho_ZW) != num_Z*num_W) {
stop('Incompatible number of elements in beta/rho_list')
}
}
}
if (num_W > 0) {
if (length(rho_GW) != num_G*num_W | length(rho_EW) != num_W) {
stop('Incompatible number of elements in beta/rho_list')
}
}
if (num_G > 1) {
if (ncol(corr_G) != num_G | nrow(corr_G) != num_G) {
stop('Incompatible number of elements in beta/rho_list')
}
}
if (num_G > 1) {
G_bin_struct <- corr_G
cprob <- tryCatch(bindata::bincorr2commonprob(margprob = prob_G, bincorr=G_bin_struct),
error=function(e) e)
if ('error' %in% class(cprob)) {
stop ('You specified an invalid corr_G structure')
}
sigma_struct <- tryCatch(bindata::commonprob2sigma(commonprob=cprob),
error=function(e) e)
if ('error' %in% class(sigma_struct)) {
stop ('You specified an invalid corr_G structure')
}
G_segment <- matrix(data=0, nrow=2*num_G, ncol=2*num_G)
for (i in 1:num_G) {
odd_seq <- seq(from=1, to=(2*num_G-1), by=2)
even_seq <- odd_seq + 1
G_segment[i*2-1, odd_seq] <- sigma_struct[i, ]
G_segment[i*2, even_seq] <- sigma_struct[i, ]
}
} else {
G_segment <- diag(x=1, nrow=2, ncol=2)
}
w_vec <- qnorm(1-prob_G)
r_GE <- rho_GE / (2*dnorm(w_vec))
GE_segment <- rep(r_GE, each=2)
if (num_Z != 0) {
GZ_segment <- matrix(data=NA, nrow=2*num_G, ncol=num_Z)
for (i in 1:num_G) {
r_GZ <- rho_GZ[((i-1)*num_Z+1):(i*num_Z)] / (2*dnorm(w_vec[i]))
GZ_segment[i*2-1, ] <- r_GZ
GZ_segment[i*2, ] <- r_GZ
}
}
if (num_W != 0) {
GW_segment <- matrix(data=NA, nrow=2*num_G, ncol=num_W)
for (i in 1:num_G) {
r_GW <- rho_GW[((i-1)*num_W+1):(i*num_W)] / (2*dnorm(w_vec[i]))
GW_segment[i*2-1, ] <- r_GW
GW_segment[i*2, ] <- r_GW
}
}
MVN_sig_tot <- matrix(data=NA, nrow=(2*num_G+num_Z+num_W+1), ncol=(2*num_G+num_Z+num_W+1))
MVN_sig_tot[1:(2*num_G), 1:(2*num_G)] <- G_segment
MVN_sig_tot[(2*num_G+1), 1:(2*num_G)] <- GE_segment
MVN_sig_tot[1:(2*num_G), (2*num_G+1)] <- GE_segment
MVN_sig_tot[(2*num_G+1), (2*num_G+1)] <- 1
if (num_Z > 0 & num_W > 0) {
MVN_sig_tot[(2*num_G+2):(2*num_G+num_Z+1), 1:(2*num_G)] <- t(GZ_segment)
MVN_sig_tot[1:(2*num_G), (2*num_G+2):(2*num_G+num_Z+1)] <- GZ_segment
MVN_sig_tot[(2*num_G+num_Z+2):(2*num_G+num_Z+num_W+1), 1:(2*num_G)] <- t(GW_segment)
MVN_sig_tot[1:(2*num_G), (2*num_G+num_Z+2):(2*num_G+num_Z+num_W+1)] <- GW_segment
MVN_sig_tot[(2*num_G+2):(2*num_G+num_Z+1), (2*num_G+1)] <- rho_EZ
MVN_sig_tot[(2*num_G+1), (2*num_G+2):(2*num_G+num_Z+1)] <- rho_EZ
if (num_Z == 1) {
MVN_sig_tot[(2*num_G+2):(2*num_G+num_Z+1), (2*num_G+2):(2*num_G+num_Z+1)] <- 1
} else {
MVN_sig_tot[(2*num_G+2):(2*num_G+num_Z+1), (2*num_G+2):(2*num_G+num_Z+1)] <- cov_Z
}
MVN_sig_tot[(2*num_G+1), (2*num_G+num_Z+2):(2*num_G+num_Z+num_W+1)] <- rho_EW
MVN_sig_tot[(2*num_G+num_Z+2):(2*num_G+num_Z+num_W+1), (2*num_G+1)] <- rho_EW
ZW_segment <- matrix(data=NA, nrow=num_Z, ncol=num_W)
for (i in 1:num_Z) {
ZW_segment[i, ] <- rho_ZW[((i-1)*num_W+1):(i*num_W)]
}
MVN_sig_tot[(2*num_G+2):(2*num_G+num_Z+1), (2*num_G+num_Z+2):(2*num_G+num_Z+num_W+1)] <- ZW_segment
MVN_sig_tot[(2*num_G+num_Z+2):(2*num_G+num_Z+num_W+1), (2*num_G+2):(2*num_G+num_Z+1)] <- t(ZW_segment)
if (num_W == 1) {
MVN_sig_tot[(2*num_G+num_Z+2):(2*num_G+num_Z+num_W+1), (2*num_G+num_Z+2):(2*num_G+num_Z+num_W+1)] <- 1
} else {
MVN_sig_tot[(2*num_G+num_Z+2):(2*num_G+num_Z+num_W+1), (2*num_G+num_Z+2):(2*num_G+num_Z+num_W+1)] <- cov_W
}
}
if (num_Z > 0 & num_W == 0) {
MVN_sig_tot[(2*num_G+2):(2*num_G+num_Z+1), 1:(2*num_G)] <- t(GZ_segment)
MVN_sig_tot[1:(2*num_G), (2*num_G+2):(2*num_G+num_Z+1)] <- GZ_segment
MVN_sig_tot[(2*num_G+2):(2*num_G+num_Z+1), (2*num_G+1)] <- rho_EZ
MVN_sig_tot[(2*num_G+1), (2*num_G+2):(2*num_G+num_Z+1)] <- rho_EZ
if (num_Z == 1) {
MVN_sig_tot[(2*num_G+2):(2*num_G+num_Z+1), (2*num_G+2):(2*num_G+num_Z+1)] <- 1
} else {
MVN_sig_tot[(2*num_G+2):(2*num_G+num_Z+1), (2*num_G+2):(2*num_G+num_Z+1)] <- cov_Z
}
}
if (num_Z == 0 & num_W > 0) {
MVN_sig_tot[(2*num_G+num_Z+2):(2*num_G+num_Z+num_W+1), 1:(2*num_G)] <- t(GW_segment)
MVN_sig_tot[1:(2*num_G), (2*num_G+num_Z+2):(2*num_G+num_Z+num_W+1)] <- GW_segment
MVN_sig_tot[(2*num_G+1), (2*num_G+num_Z+2):(2*num_G+num_Z+num_W+1)] <- rho_EW
MVN_sig_tot[(2*num_G+num_Z+2):(2*num_G+num_Z+num_W+1), (2*num_G+1)] <- rho_EW
if (num_W == 1) {
MVN_sig_tot[(2*num_G+num_Z+2):(2*num_G+num_Z+num_W+1), (2*num_G+num_Z+2):(2*num_G+num_Z+num_W+1)] <- 1
} else {
MVN_sig_tot[(2*num_G+num_Z+2):(2*num_G+num_Z+num_W+1), (2*num_G+num_Z+2):(2*num_G+num_Z+num_W+1)] <- cov_W
}
}
if (!isSymmetric(MVN_sig_tot)) {stop("Problem building covariance matrix!")}
test_data <- tryCatch(mvtnorm::rmvnorm(n=1, sigma=MVN_sig_tot),
warning=function(w) w, error=function(e) e)
if (class(test_data)[1] != 'matrix') {stop('You specified an impossible covariance matrix!')}
return(list(sig_mat_total=MVN_sig_tot))
} |
SVN <- function(level){
x <- NULL
if(level==1){
x1 <- gov.si(level = level)
x2 <- github.cssegisanddata.covid19(country = "Slovenia") %>%
select(-c("confirmed", "deaths"))
x3 <- ourworldindata.org(id = "SVN") %>%
select(-c("tests", "hosp", "icu"))
x <- x1 %>%
full_join(x2, by = "date") %>%
full_join(x3, by = "date")
}
return(x)
} |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.