code
stringlengths 1
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findend<-function(inde,left,right,low,upp,N){
lenn<-length(inde)
current<-1
dep<-1
if ((left[current]==0) && (right[current]==0)){
exists<-FALSE
}
else{
exists<-TRUE
}
while ((exists) && ((left[current]>0) || (right[current]>0))){
mid<-(low[current]+upp[current])/2
direc<-depth2com(dep,N)$direc
if (inde[direc]<=mid){
if (left[current]>0){
current<-left[current]
dep<-dep+1
}
else{
exists<-FALSE
}
}
else{
if (right[current]>0){
current<-right[current]
dep<-dep+1
}
else{
exists<-FALSE
}
}
}
return(list(exists=exists,location=current,dep=dep))
}
|
NULL
IM4E <- function(xx,yy,epsilon=0.01,sig=1,lambda=1,max_iter=10,removesmall=FALSE) {
suppressWarnings(
res<-(IM4ECpp(oneIM4E = one.IM4E,xx,yy,epsilon=0.01,
sig=1, lambda=1,max_iter=10,removesmall=FALSE)))
class(res)<-"IM4E"
return(res)
}
|
efs_eval <- function(data, efs_table, file_name,
classnumber, NA_threshold,
logreg = TRUE,
rf = TRUE,
permutation = TRUE, p_num = 100,
variances = TRUE, jaccard = TRUE,
bs_num =100, bs_percentage = 0.9){
if(missing(efs_table))
stop("efs_table argument missing")
if(missing(file_name))
stop("file_name argument missing")
if(missing(classnumber))
stop("classnumber argument missing")
if(missing(NA_threshold)){
NA_threshold=0.2
print("default value for NA_threshold = 0.2")}
if(!is.numeric(NA_threshold) | NA_threshold > 1 |
NA_threshold < 0)
stop("invalid argument:
NA_threshold is required to be in [0,1]")
if(missing(p_num)){
p_num=100
print("default value for p_num = 100")}
if(missing(bs_num)){
bs_num = 100
print("default value for bs_num = 100")}
if(missing(bs_percentage)){
bs_percentage=0.9
print("default value for bs_percentage = 0.9")}
classname = colnames(data)[classnumber]
NrNA= c()
for(i in 1:length(data[1,])){
NrNA[i] = length(which(is.na(data[,i])))
}
NmNA = which(NrNA/length(data[,1])>0.2)
if(length(NmNA) != 0) data=data[,-NmNA]
data = na.omit(data, row.names=F)
data=data[,which(colSums(data,na.rm=F)!=0 & apply(data, 2, var)!=0)]
clnr= which(colnames(data)==classname)
logreg_test <- function(data, efs_table, file_name, classnumber){
e.features = which(colSums(efs_table)>
mean(colSums(efs_table)))
klasse = data[,classname]
clnr= which(colnames(data)==classname)
data = data[,-clnr]
k=length(data[,1])
prob1=c()
prob2=c()
prob3=c()
for(i in 1:k){
Train = seq(i,to=nrow(data),by=k)
training <- data[-Train, ]
training.cl <- klasse[-Train]
testing <- data[ Train, ]
testing.cl <- klasse[Train]
logreg1 = glm(as.factor(training.cl)~.,
data = training, family = binomial,control = list(maxit = 50))
logreg3 = glm(as.factor(training.cl)~.,
data = training[,e.features],
family = binomial,control = list(maxit = 50))
prob1= (c(prob1,predict(logreg1, newdata=testing)))
prob3= (c(prob3,predict(logreg3, newdata=testing)))
}
prob1=prob1[order(as.numeric(names(prob1)))]
roc1=roc(klasse, prob1, ci=T)
ci1=roc1$ci
ci1= gsub('95% CI:', '',ci1)
ci1=round(as.numeric(ci1),1)*100
pred1 <- prediction(prob1, klasse)
perf1 <- performance(pred1, measure = "tpr",
x.measure = "fpr")
auc1 <- performance(pred1, measure = "auc")
auc1 <- [email protected][[1]]
prob3=prob3[order(as.numeric(names(prob3)))]
roc3=roc(klasse, prob3, ci=T)
ci3=roc3$ci
ci3= gsub('95% CI:', '',ci3)
ci3=round(as.numeric(ci3),3)*100
pred3 <- prediction(prob3, klasse)
perf3 <- performance(pred3, measure = "tpr",
x.measure = "fpr")
auc3 <- performance(pred3, measure = "auc")
auc3 <- [email protected][[1]]
r=roc.test(roc1,roc3)
p = as.numeric(r$p.value)
P = paste("p = ",round(p,3),sep ="")
if(p<0.001){P = "p < 0.001"}
d=c(0,1)
pdf(paste(file_name,"_LG_ROC.pdf", sep=""))
plot(perf1, avg="vertical", spread.estimate="boxplot",
main="")
plot(perf3, avg="vertical", spread.estimate="boxplot",
main="",add=TRUE, col = 'blue')
abline(d, lty=2)
text(0.15, 0.9, cex=1, paste("All: ",
round(mean(auc1),3)*100,
"% (", format(ci1[1], nsmall=1),
'...',format(ci1[3], nsmall=1),
")", sep=""))
text(0.15, 1, cex=1, paste("EFS: ",round(mean(auc3),3)*100,
"% (",format(ci3[1], nsmall=1),
'...',format(ci3[3], nsmall=1),
")", sep=""), col='blue')
text(0.8, 0.1, cex=1, paste("ROC-test: ",P,sep=""))
dev.off()
return(rbind(round(mean(auc1),3)*100,round(mean(auc3),3)*100,p))
}
rf_test <- function(data, efs_table, file_name, classnumber){
e.features = which(colSums(efs_table)>
mean(colSums(efs_table)))
klasse = data[,classname]
clnr= which(colnames(data)==classname)
data = data[,-clnr]
votes1 = c()
real1 = c()
votes3 = c()
real3 = c()
for(i in 1:1000)
{
rf1 = randomForest(as.factor(klasse)~., data= data)
votes1 = cbind(votes1, rf1$votes[,2])
real1 = cbind(real1, klasse)
rf3 = randomForest(as.factor(klasse)~., data = data[,e.features])
votes3 = cbind(votes3, rf3$votes[,2])
real3 = cbind(real3, klasse)
}
pred1 = prediction(votes1, real1)
perf1 = performance(pred1, "auc")
roc1=roc(klasse,rf1$votes[,2] , ci=T)
pred3 = prediction(votes3, real3)
perf3 = performance(pred3, "auc")
roc3=roc(klasse, rf3$votes[,2], ci=T)
ci1=roc1$ci
ci1= gsub('95% CI:', '',ci1)
ci1=round(as.numeric(ci1),1)*100
pred1 <- prediction(rf1$votes[,2], klasse)
perf1 <- performance(pred1, measure = "tpr",
x.measure = "fpr")
auc1 <- performance(pred1, measure = "auc")
auc1 <- [email protected][[1]]
ci3=roc3$ci
ci3= gsub('95% CI:', '',ci3)
ci3=round(as.numeric(ci3),3)*100
pred3 <- prediction(rf3$votes[,2], klasse)
perf3 <- performance(pred3, measure = "tpr",
x.measure = "fpr")
auc3 <- performance(pred3, measure = "auc")
auc3 <- [email protected][[1]]
r=roc.test(roc1,roc3)
p = as.numeric(r$p.value)
P = paste("p = ",round(p,3),sep ="")
if(p<0.001){P = "p < 0.001"}
d=c(0,1)
pdf(paste(file_name,"_RF_ROC.pdf", sep=""))
plot(perf1, avg="vertical", spread.estimate="boxplot",
main="")
plot(perf3, avg="vertical", spread.estimate="boxplot",
main="",add=TRUE, col = 'blue')
abline(d, lty=2)
text(0.15, 0.9, cex=1, paste("All: ",
round(mean(auc1),3)*100,
"% (", format(ci1[1], nsmall=1),
'...',format(ci1[3], nsmall=1),
")", sep=""))
text(0.15, 1, cex=1, paste("EFS: ",round(mean(auc3),3)*100,
"% (",format(ci3[1], nsmall=1),
'...',format(ci3[3], nsmall=1),
")", sep=""), col='blue')
text(0.8, 0.1, cex=1, paste("ROC-test: ",P,sep=""))
dev.off()
return(rbind(round(mean(auc1),3)*100,round(mean(auc3),3)*100,p))
}
perm_logreg_test <- function(data, efs_table, file_name, classnumber,
NA_threshold){
klasse = data[[1]]
data = data.frame(data[,-1])
e.features = which(colSums(efs_table)>
mean(colSums(efs_table)))
k=length(data[,1])
prob1=c()
prob2=c()
prob3=c()
for(i in 1:k){
Train = seq(i,to=nrow(data),by=k)
training <- data[-Train, ]
training.cl <- klasse[-Train]
testing <- data[ Train, ]
testing.cl <- klasse[Train]
logreg1 = glm(as.factor(training.cl)~.,
data = training, family = binomial,control = list(maxit = 50))
logreg3 = glm(as.factor(training.cl)~.,
data = training[,e.features],
family = binomial,control = list(maxit = 50))
prob1= (c(prob1,predict(logreg1, newdata=testing)))
prob3= (c(prob3,predict(logreg3, newdata=testing)))
}
prob1=prob1[order(as.numeric(names(prob1)))]
roc1=roc(klasse, prob1, ci=T)
ci1=roc1$ci
ci1= gsub('95% CI:', '',ci1)
ci1=round(as.numeric(ci1),1)*100
pred1 <- prediction(prob1, klasse)
perf1 <- performance(pred1, measure = "tpr",
x.measure = "fpr")
auc1 <- performance(pred1, measure = "auc")
auc1 <- [email protected][[1]]
prob3=prob3[order(as.numeric(names(prob3)))]
roc3=roc(klasse, prob3, ci=T)
ci3=roc3$ci
ci3= gsub('95% CI:', '',ci3)
ci3=round(as.numeric(ci3),3)*100
pred3 <- prediction(prob3, klasse)
perf3 <- performance(pred3, measure = "tpr",
x.measure = "fpr")
auc3 <- performance(pred3, measure = "auc")
auc3 <- [email protected][[1]]
r=roc.test(roc1,roc3)
p = as.numeric(r$p.value)
P = paste("p = ",round(p,3),sep ="")
if(p<0.001){P = "p < 0.001"}
return(auc3)
}
permutation_test <- function(classnumber, NA_threshold, efs_table, p_num){
klasse = data[,clnr]
data = data[,-clnr]
dat.all = cbind(klasse,data)
AUC1 = perm_logreg_test (dat.all, efs_table, file_name, classnumber, NA_threshold)
AUC = c()
for(i in 1:p_num){
klasse = sample(klasse,replace=FALSE)
data.1 = cbind(klasse,data)
AUC[i] = perm_logreg_test(data.1, efs_table, file_name, classnumber, NA_threshold)
}
ttest = t.test(AUC,alternative = "less", mu=AUC1)
p.values = as.vector(ttest[["p.value"]])
return(p.values)
}
stability_test <- function(data, classnumber, bs_num, bs_percentage){
pos = which(data[,clnr]==1)
neg = which(data[,clnr]==0)
importances <- NULL
print("Conducting boostrapping:")
for(i in 1:bs_num){
print(i)
pos_sam = sample(pos, bs_percentage*length(pos), replace = FALSE)
neg_sam = sample(neg, bs_percentage*length(neg), replace = FALSE)
df=0
df = rbind(data[pos_sam,], data[neg_sam,])
efs_table = ensemble_fs(df, clnr, NA_threshold=0.2, cor_threshold=0.7, runs=100, selection = c(T,T,T,T,T,T,T,T))
importances = cbind(importances, colSums(efs_table))
}
return(importances)
}
feature_var <- function(importances){
m=NULL
for(i in 1: length(importances[,1])){
m = c(m,mean(importances[i,],na.rm=TRUE))
}
m = order(m,decreasing=TRUE)[1:5]
vars=NULL
for(i in 1:length(importances[m,1])){
vars = c(vars,var(importances[m,][i,],na.rm=TRUE))
}
pdf(paste(file_name,'_Variances.pdf', sep=""))
boxplot(t(importances[m,-1]),main = file_name, ylim = c(0,1))
dev.off()
return(vars)
}
jaccard_index <- function(importances){
importances = importances[ , colSums(is.na(importances)) == 0]
e.features = which(rowMeans(importances) > mean(rowMeans(importances)))
l = length(e.features)
x = apply(importances, 2 , order)
y = tail(x,l)
z = list(y)
schnitt = Reduce(intersect, z)
vereinigung = Reduce(union, z)
index = length(schnitt)/length(vereinigung)
return(index)
}
if(logreg == TRUE){
lg.efs = logreg_test(data, efs_table, file_name, classnumber)
}
else{lg.efs = rbind("Not conducted", "Not conducted","Not conducted")}
if(rf == TRUE){
rf.efs = rf_test(data, efs_table, file_name, classnumber)
}
else{rf.efs = rbind("Not conducted", "Not conducted","Not conducted")}
if(permutation == TRUE){
permutation.p.values = permutation_test(classnumber, NA_threshold, efs_table,p_num)
}
else{permutation.p.values = "Not conducted"}
if(variances == TRUE| jaccard ==TRUE){
importances = stability_test(data, classnumber, bs_num, bs_percentage)
}
if(variances == TRUE){
vars = feature_var(importances)
}
else{vars = "Not conducted"}
if(jaccard == TRUE){
jaccard_index = jaccard_index(importances)
}
else{jaccard_index = "Not conducted"}
results = NULL
results = list("AUC of LR of all parameters" = as.vector(lg.efs[1,1]), "AUC of LR of EFS parameters" = as.vector(lg.efs[2,1]), "P-value of LG-ROC test" = as.vector(lg.efs[3,1]),
"AUC of RF of all parameters" = as.vector(rf.efs[1,1]), "AUC of RF of EFS parameters" = as.vector(rf.efs[2,1]), "P-value of RF-ROC test" = as.vector(rf.efs[3,1]),
"P-value of permutation" = as.vector(permutation.p.values), "Variances of feature importances"= vars,
"Jaccard-index"=jaccard_index)
return(results)
}
|
pvm <- function(theta, m, k, rads = FALSE) {
if ( !rads ) {
u <- u * pi / 180
m <- m * pi / 180
}
theta <- theta %% (2 * pi)
if ( k > 0 ) {
theta <- theta %% (2 * pi)
f <- 2 * pi * besselI(k, 0)
funa <- function(u) exp(k * cos(u - m) )
prob <- as.numeric( integrate(funa, 0, theta)$value ) / f
} else prob <- theta / ( 2 * pi )
prob
}
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library(testthat)
library(synthACS)
context("LOCAL -- synthetic new attribute creation")
test_that("single synthetic dataset -- real df, fake ST (DF)", {
levels <- c("A", "B", "C")
ST <- data.frame(marital_status= rep(levels(test_micro$marital_status), each= 7),
race= rep(levels(test_micro$race), 5))
ST <- do.call("rbind", replicate(3, ST, simplify=FALSE))
ST <- ST[order(ST$marital_status, ST$race),]
ST$pct <- rep(c(0.33, 0.34, 0.33), 35)
ST$levels <- rep(levels, 35)
syn <- synthetic_new_attribute(df= test_micro, prob_name= "p", attr_name= "variable",
conditional_vars= c("marital_status", "race"), sym_tbl= ST)
expect_equal(sum(syn$p), 1)
expect_equal(tapply(syn$p, syn$gender, sum),
tapply(test_micro$p, test_micro$gender, sum))
expect_equal(tapply(syn$p, syn$marital_status, sum),
tapply(test_micro$p, test_micro$marital_status, sum))
expect_equal(tapply(syn$p, syn$race, sum),
tapply(test_micro$p, test_micro$race, sum))
expect_equal(nrow(test_micro) * length(levels), nrow(syn))
expect_equal(ncol(test_micro), ncol(syn) - 1)
expect_true(all.equal(as.vector(tapply(syn$p, syn$variable, sum)), c(0.33, 0.34, 0.33), check.attributes=FALSE))
})
test_that("single synthetic dataset -- real df, fake ST (DT)", {
levels <- c("A", "B", "C")
ST <- data.frame(marital_status= rep(levels(test_micro$marital_status), each= 7),
race= rep(levels(test_micro$race), 5))
ST <- do.call("rbind", replicate(3, ST, simplify=FALSE))
ST <- ST[order(ST$marital_status, ST$race),]
ST$pct <- rep(c(0.33, 0.34, 0.33), 35)
ST$levels <- rep(levels, 35)
data.table::setDT(test_micro); data.table::setDT(ST)
syn <- synthetic_new_attribute(df= test_micro, prob_name= "p", attr_name= "variable",
conditional_vars= c("marital_status", "race"), sym_tbl= ST)
expect_equal(sum(syn$p), 1)
expect_equal(tapply(syn$p, syn$gender, sum),
tapply(test_micro$p, test_micro$gender, sum))
expect_equal(tapply(syn$p, syn$marital_status, sum),
tapply(test_micro$p, test_micro$marital_status, sum))
expect_equal(tapply(syn$p, syn$race, sum),
tapply(test_micro$p, test_micro$race, sum))
expect_equal(nrow(test_micro) * length(levels), nrow(syn))
expect_equal(ncol(test_micro), ncol(syn) - 1)
expect_true(all.equal(as.vector(tapply(syn$p, syn$variable, sum)), c(0.33, 0.34, 0.33), check.attributes=FALSE))
})
test_that("single synthetic dataset -- real DF, ST (DF)", {
test_micro <- syn[[1]][[2]]
work_ST <- towork[[1]]
class(work_ST) <- "data.frame"
syn <- synthetic_new_attribute(df= test_micro, prob_name= "p", attr_name= "transit",
conditional_vars= c("emp_status", "age"), sym_tbl= work_ST)
expect_equal(sum(syn$p), 1)
expect_equal(tapply(syn$p, syn$emp_status, sum),
tapply(test_micro$p, test_micro$emp_status, sum))
expect_equal(tapply(syn$p, syn$age, sum),
tapply(test_micro$p, test_micro$age, sum))
expect_equal(tapply(syn$p, syn$race, sum),
tapply(test_micro$p, test_micro$race, sum))
expect_equal(ncol(test_micro), ncol(syn) - 1)
})
test_that("single synthetic dataset -- real DF, ST (DT)", {
test_micro <- syn[[4]][[2]]
work_ST <- towork[[4]]
syn <- synthetic_new_attribute(df= test_micro, prob_name= "p", attr_name= "transit",
conditional_vars= c("emp_status", "age"), sym_tbl= work_ST)
expect_equal(sum(syn$p), 1)
expect_equal(tapply(syn$p, syn$emp_status, sum),
tapply(test_micro$p, test_micro$emp_status, sum))
expect_equal(tapply(syn$p, syn$age, sum),
tapply(test_micro$p, test_micro$age, sum))
expect_equal(tapply(syn$p, syn$race, sum),
tapply(test_micro$p, test_micro$race, sum))
expect_equal(ncol(test_micro), ncol(syn) - 1)
})
test_that("parallel - real DF, fake ST", {
levels <- c("A", "B", "C")
ST <- data.frame(marital_status= rep(levels(syn[[1]][[2]]$marital_status), each= 7),
race= rep(levels(syn[[2]][[2]]$race), 5))
ST <- do.call("rbind", replicate(3, ST, simplify=FALSE))
ST <- ST[order(ST$marital_status, ST$race),]
ST$pct <- rep(c(0.33, 0.34, 0.33), 35)
ST$levels <- rep(levels, 35)
st_list <- replicate(4, ST, simplify= FALSE)
syn2 <- all_geog_synthetic_new_attribute(syn, prob_name= "p", attr_name= "variable",
conditional_vars= c("marital_status", "race"),
st_list= st_list)
expect_equal(class(syn2), c("synthACS", "list"))
expect_true(is.synthACS(syn2))
expect_true(all(unlist(lapply(syn2, function(l) is.micro_synthetic(l[[2]])))))
expect_true(all.equal(unlist(lapply(syn2, function(l) sum(l[[2]]$p))), rep(1, 4), check.attributes = FALSE))
expect_equal(lapply(syn2, function(l) {tapply(l[[2]]$p, l[[2]]$marital_status, sum)}),
lapply(syn, function(l) {tapply(l[[2]]$p, l[[2]]$marital_status, sum)}) )
expect_equal(lapply(syn2, function(l) {tapply(l[[2]]$p, l[[2]]$race, sum)}),
lapply(syn, function(l) {tapply(l[[2]]$p, l[[2]]$race, sum)}) )
expect_true(all.equal(lapply(syn2, function(l) {as.vector(tapply(l[[2]]$p, l[[2]]$variable, sum))}),
replicate(4, c(0.33, 0.34, 0.33), simplify = FALSE), check.attributes = FALSE))
})
|
weighted_quantile_type_selection <- function( type, pp, N, dfr, weights_NULL)
{
eps <- 1E-10
mm <- NULL
set1 <- FALSE
if ( ! weights_NULL ){
type <- -9
a1 <- dfr$w_cum <=pp
if ( sum(a1) > 0 ){
ind <- which( a1 )
} else {
ind <- 0
}
jj <- max(ind)
jj1 <- jj + 1
if (jj1 > N){ jj1 <- N}
if (jj %in% c(0,-Inf)){
jj <- 1
set1 <- TRUE
jj1 <- 1
}
if ( jj !=jj1){
GAMMA0 <- ( pp - dfr[jj,"w_cum"] )/ ( eps + dfr[jj1,"w_cum"] - dfr[jj,"w_cum"] )
} else {
GAMMA0 <- 0
}
GAMMA <- GAMMA0
}
if (type==6){
mm <- pp
jj <- floor(N*pp + mm)
gg <- N*pp + mm - jj
GAMMA <- gg
}
if (type==7){
mm <- 1 - pp
jj <- floor(N*pp + mm)
gg <- N*pp + mm - jj
GAMMA <- gg
}
if ( ! set1){
jj1 <- jj+1
}
if (jj1 > N){ jj1 <- N}
if (jj==0){ jj <- 1}
res <- list(mm=mm, jj=jj, GAMMA=GAMMA, jj1=jj1)
return(res)
}
|
ffs_adp_outcomes_week <- function(scoring_history,
pos_filter = c("QB", "RB", "WR", "TE")) {
checkmate::assert_character(pos_filter)
checkmate::assert_data_frame(scoring_history)
assert_columns(scoring_history, c("gsis_id", "week", "season", "points"))
gsis_id <- NULL
fantasypros_id <- NULL
pos <- NULL
rank <- NULL
points <- NULL
week <- NULL
week_outcomes <- NULL
player_name <- NULL
fantasypros_id <- NULL
len <- NULL
sh <- data.table::as.data.table(scoring_history)[!is.na(gsis_id) & week <= 16,c("gsis_id","week", "season", "points")]
fp_rh <- data.table::as.data.table(ffsimulator::fp_rankings_history_week)[,-"page_pos"]
dp_id <- data.table::as.data.table(ffscrapr::dp_playerids())[!is.na(gsis_id) & !is.na(fantasypros_id),c("fantasypros_id","gsis_id")]
ao <- fp_rh[dp_id,on = "fantasypros_id", nomatch = 0
][!is.na(gsis_id) & pos %in% pos_filter
][sh, on = c("season","week","gsis_id"),nomatch = 0
][,list(week_outcomes = list(points), games_played = .N),
by = c("season","pos","rank","fantasypros_id","player_name")
][,rank := lapply(rank, .ff_triplicate)]
ao <- tidytable::unnest.(ao,"rank", .drop = FALSE)
ao <- ao[
,list(week_outcomes = list(c(unlist(week_outcomes))),
player_name = list(player_name),
fantasypros_id = list(fantasypros_id)
),
by = c("pos","rank")
][,len := sapply(week_outcomes,length)
][,len := max(len)-len
][,`:=`(week_outcomes = mapply(.ff_rep_na,week_outcomes,len, SIMPLIFY = FALSE),len = NULL)
][order(pos,rank)]
return(ao)
}
.ff_rep_na <- function(week_outcomes,len){
c(unlist(week_outcomes),rep(NA,times = len))
}
|
list.load <- function(file, type = tools::file_ext(file), ..., guess = c("json",
"yaml", "rds", "rdata", "xml"), action = c("none", "merge", "ungroup"), progress = length(file) >=
5L) {
if (length(file) == 0L)
return(list())
nztype <- !is.na(type) & nzchar(type)
fun <- paste("list.loadfile", tolower(type), sep = ".")
fun[!nztype] <- NA_character_
guess <- tolower(guess)
pb <- if (progress)
txtProgressBar(min = 0L, max = length(file), style = 3L) else NULL
res <- if (length(file) == 1L)
list.loadfile(file, fun, guess, ..., pb = pb, index = 1L) else {
items <- map(list.loadfile, list(file, fun, index = seq_along(file)), list(guess = guess,
..., pb = pb))
switch(match.arg(action), merge = do.call("list.merge", items), ungroup = list.ungroup(items),
items)
}
if (!is.null(pb))
close(pb)
res
}
list.loadfile <- function(file, fun, guess, ..., pb = NULL, index = NULL) {
res <- NULL
if (is.na(fun)) {
if (!missing(guess) && length(guess) > 0L) {
exprs <- lapply(paste("list.loadfile", guess, sep = "."), function(f) call(f,
file))
res <- try_list(exprs, stop("Unrecognized type of file: ", file, call. = FALSE))
if (!is.null(pb))
pb$up(index)
} else stop("Unrecognized type of file: ", file, call. = FALSE)
} else if (exists(fun, mode = "function")) {
fun <- get(fun, mode = "function")
res <- fun(file, ...)
if (!is.null(pb))
pb$up(index)
} else {
stop("Unrecognized type of file: ", file, call. = FALSE)
}
res
}
list.loadfile.json <- function(file, ...) {
callwith(jsonlite::fromJSON, list(file, simplifyDataFrame = FALSE), list(...))
}
list.loadfile.yaml <- function(file, ...) {
yaml::yaml.load_file(file, ...)
}
list.loadfile.yml <- list.loadfile.yaml
list.loadfile.xml <- function(file, ...) {
xmlData <- XML::xmlParse(file, ...)
XML::xmlToList(xmlData)
}
list.loadfile.rdata <- function(file, name = "x") {
env <- new.env(parent = parent.frame(), size = 1L)
load(file, env)
env[[name]]
}
list.loadfile.rds <- function(file, ...) readRDS(file, ...)
|
idotplot <-
function(x, y, indID=NULL, group=NULL, chartOpts=NULL, digits=5)
{
stopifnot(length(x) == length(y))
if(is.null(group)) group <- rep(1, length(x))
stopifnot(length(group) == length(x))
group <- group2numeric(group)
if(is.null(indID))
indID <- get_indID(length(x), names(x), names(y), names(group))
stopifnot(length(indID) == length(x))
indID <- as.character(indID)
if(is.factor(x)) x_levels <- levels(x)
else x_levels <- sort(unique(x))
x <- group2numeric(x, preserveNA=TRUE)
names(x) <- NULL
names(y) <- NULL
names(indID) <- NULL
names(group) <- NULL
chartOpts <- add2chartOpts(chartOpts, ylab="y", title="", xlab="group",
xcategories=seq(along=x_levels), xcatlabels=x_levels)
x <- list(data=list(x=x, y=y, indID=indID, group=group), chartOpts=chartOpts)
if(!is.null(digits))
attr(x, "TOJSON_ARGS") <- list(digits=digits)
defaultAspect <- 1
browsersize <- getPlotSize(defaultAspect)
htmlwidgets::createWidget("idotplot", x,
width=chartOpts$width,
height=chartOpts$height,
sizingPolicy=htmlwidgets::sizingPolicy(
browser.defaultWidth=browsersize$width,
browser.defaultHeight=browsersize$height,
knitr.defaultWidth=1000,
knitr.defaultHeight=1000/defaultAspect),
package="qtlcharts")
}
idotplot_output <- function(outputId, width="100%", height="530") {
htmlwidgets::shinyWidgetOutput(outputId, "idotplot", width, height, package="qtlcharts")
}
idotplot_render <- function(expr, env=parent.frame(), quoted=FALSE) {
if(!quoted) { expr <- substitute(expr) }
htmlwidgets::shinyRenderWidget(expr, idotplot_output, env, quoted=TRUE)
}
|
print.nl <- function(x, ...)
{
util_print.nl(x)
util_print.experiment(x@experiment)
util_print.simdesign(x@simdesign)
util_print.summary(x)
}
util_print.nl <- function(x, ...) {
style_heading <- crayon::black$bold$bgWhite
style_def <- crayon::green
style_opt <- crayon::yellow
style_na <- crayon::red
cat(style_heading(paste0("\n", " NL OBJECT ", "\n")))
cat("NetLogo version = ")
output <- paste0(x@nlversion, "\n")
cat(ifelse(nchar(x@nlversion) > 0, style_def(output), style_na(output)))
cat("NetLogo path = ")
output <- paste0(x@nlpath, "\n")
cat(ifelse(!identical(x@nlpath, character(0)), style_def(output), style_na(output)))
cat("Model path = ")
output <- paste0(x@modelpath, "\n")
cat(ifelse(!identical(x@modelpath, character(0)), style_def(output), style_na(output)))
cat("JVM memory = ")
output <- paste0(x@jvmmem, "\n")
cat(ifelse(!is.na(x@jvmmem), style_def(output), style_na(output)))
}
util_print.summary <- function(x, ...)
{
style_heading <- crayon::black$bold$bgWhite
style_def <- crayon::green
style_opt <- crayon::yellow
style_na <- crayon::red
cat(style_heading(paste0("\n", " SUMMARY ", "\n")))
cat("supported nlversion: ")
output <- ifelse(x@nlversion %in% c("5.3.1", "6.0", "6.0.1", "6.0.2", "6.0.3", "6.0.4", "6.1.0", "6.1.1"), style_def("\u2713"), style_na("\u2717"))
cat(paste0(output, "\n"))
cat("nlpath exists on local system: ")
output <- ifelse(dir.exists(x@nlpath), style_def("\u2713"), style_na("\u2717"))
cat(paste0(output, "\n"))
cat("modelpath exists on local system: ")
output <- ifelse(file.exists(x@modelpath), style_def("\u2713"), style_na("\u2717"))
cat(paste0(output, "\n"))
cat("valid jvm memory: ")
output <- ifelse(is.numeric(x@jvmmem), style_def("\u2713"), style_na("\u2717"))
cat(paste0(output, "\n"))
cat("valid experiment name: ")
output <- ifelse(is.na(x@experiment@expname) | grepl("\\s", getexp(x, "expname")), style_na("\u2717"), style_def("\u2713"))
cat(paste0(output, "\n"))
cat("outpath exists on local system: ")
output <- ifelse(dir.exists(x@experiment@outpath), style_def("\u2713"), style_na("\u2717"))
cat(paste0(output, "\n"))
cat("setup and go defined: ")
output <- ifelse(!all(is.na(x@experiment@idsetup), is.na(x@experiment@idgo)), style_def("\u2713"), style_na("\u2717"))
cat(paste0(output, "\n"))
cat("variables defined: ")
output <- ifelse(length(x@experiment@variables) > 0, style_def("\u2713"), style_na("\u2717"))
cat(paste0(output, "\n"))
if(!identical(x@modelpath, character(0))){
if(file.exists(x@modelpath)){
cat("variables present in model: ")
output <- ifelse(length(x@experiment@variables) > 0 & all(names(x@experiment@variables) %in% names(report_model_parameters(x))),
style_def("\u2713"), style_na("\u2717"))
cat(paste0(output, "\n"))
}
}
cat("constants defined: ")
output <- ifelse(length(x@experiment@constants) > 0, style_def("\u2713"), style_na("\u2717"))
cat(paste0(output, "\n"))
if(!identical(x@modelpath, character(0))){
if(file.exists(x@modelpath)){
cat("constants present in model: ")
output <- ifelse(length(x@experiment@constants) > 0 & all(names(x@experiment@constants) %in% names(report_model_parameters(x))),
style_def("\u2713"), style_na("\u2717"))
cat(paste0(output, "\n"))
}
}
cat("metrics defined: ")
output <- ifelse(length(x@experiment@metrics) > 0, style_def("\u2713"), style_na("\u2717"))
cat(paste0(output, "\n"))
cat("spatial Metrics defined: ")
output <- ifelse(length(x@[email protected]) > 0 | length(x@[email protected]) > 0 | length(x@[email protected]) > 0,
style_def("\u2713"), style_na("\u2717"))
cat(paste0(output, "\n"))
cat("simdesign attached: ")
output <- ifelse(!is.na(x@simdesign@simmethod), style_def("\u2713"), style_na("\u2717"))
cat(paste0(output, "\n"))
cat("siminput parameter matrix: ")
output <- ifelse(nrow(x@simdesign@siminput) > 0, style_def("\u2713"), style_na("\u2717"))
cat(paste0(output, "\n"))
cat("number of siminputrows: ")
output <- ifelse(nrow(x@simdesign@siminput) > 0, style_def(nrow(x@simdesign@siminput)), style_na("\u2717"))
cat(paste0(output, "\n"))
cat("number of random seeds: ")
output <- ifelse(!all(is.na(x@simdesign@simseeds)), style_def(length(x@simdesign@simseeds)), style_na("\u2717"))
cat(paste0(output, "\n"))
cat("estimated number of runs: ")
output <- ifelse(!all(nrow(x@simdesign@siminput) == 0, is.na(x@simdesign@simseeds)),
style_def(nrow(x@simdesign@siminput) * length(x@simdesign@simseeds)),
style_na("\u2717"))
cat(paste0(output, "\n"))
cat("simoutput results attached: ")
output <- ifelse(nrow(x@simdesign@simoutput) > 0, style_def("\u2713"), style_na("\u2717"))
cat(paste0(output, "\n"))
cat("number of runs calculated: ")
output <- ifelse(nrow(x@simdesign@simoutput) > 0,
style_def(length(unique(paste(x@simdesign@simoutput$'random-seed', x@simdesign@simoutput$siminputrow)))),
style_na("\u2717"))
cat(paste0(output, "\n"))
}
util_print.experiment <- function(x, ...)
{
style_heading <- crayon::black$bold$bgWhite
style_def <- crayon::green
style_opt <- crayon::yellow
style_na <- crayon::red
cat(style_heading(paste0("\n", " EXPERIMENT ", "\n")))
cat("Experiment name = ")
output <- paste0(x@expname, "\n")
cat(ifelse(!is.na(x@expname), style_def(output), style_na(output)))
cat("Output path = ")
output <- paste0(x@outpath, "\n")
cat(ifelse(!is.na(x@outpath), style_def(output), style_na(output)))
cat("NetLogo repetitions = ")
output <- paste0(x@repetition, "\n")
cat(ifelse(!is.na(x@repetition), style_def(output), style_na(output)))
cat("Measure on each tick? = ")
output <- paste0(x@tickmetrics, "\n")
cat(ifelse(!is.na(x@tickmetrics), style_def(output), style_na(output)))
cat("Setup procedure(s) = ")
output <- paste0(paste(x@idsetup, collapse=", "), "\n")
cat(ifelse(!all(is.na(x@idsetup)), style_def(output), style_na(output)))
cat("Go procedure(s) = ")
output <- paste0(paste(x@idgo, collapse=", "), "\n")
cat(ifelse(!all(is.na(x@idgo)), style_def(output), style_na(output)))
cat("Final procedure(s) = ")
output <- paste0(paste(x@idfinal, collapse=", "), "\n")
cat(ifelse(!all(is.na(x@idfinal)), style_def(output), style_opt(output)))
cat("Run nr. widget name = ")
output <- paste0(x@idrunnum, "\n")
cat(ifelse(!is.na(x@idrunnum), style_def(output), style_opt(output)))
cat("Runtime (ticks) = ")
output <- paste0(x@runtime, "\n")
cat(ifelse(!is.na(x@runtime), style_def(output), style_na(output)))
cat("Report output on ticks = ")
output <- paste0(paste(x@evalticks, collapse=", "), "\n")
cat(ifelse(!all(is.na(x@evalticks)), style_def(output), style_opt(output)))
cat("Stop condition = ")
output <- paste0(x@stopcond, "\n")
cat(ifelse(!is.na(x@stopcond), style_def(output), style_opt(output)))
cat("Metrics (output) = ")
output <- paste0(paste(x@metrics, collapse=", "), "\n")
cat(ifelse(!all(is.na(x@metrics)), style_def(output), style_na(output)))
cat(paste0("\n", "Turtle metrics (output)", "\n"))
output <- paste0(paste(paste0(" ", names([email protected])), paste(unlist([email protected]), collapse = ", "), sep=" = "), "\n")
cat(ifelse(!all(is.na([email protected])), style_def(output), style_opt(output)))
cat(paste0("\n", "Patch metrics (output)", "\n"))
output <- paste0(" ", paste(unlist([email protected]), collapse = ", "), "\n")
cat(ifelse(!all(is.na([email protected])), style_def(output), style_opt(output)))
cat(paste0("\n", "Link metrics (output)", "\n"))
output <- paste0(paste(paste0(" ", names([email protected])), paste(unlist([email protected]), collapse = ", "), sep=" = "), "\n")
cat(ifelse(!all(is.na([email protected])), style_def(output), style_opt(output)))
cat(paste0("\n", "Variable parameters (input)", "\n"))
output <- paste0(paste(paste0(" ", names(x@variables)), x@variables, collapse="\n", sep=" = "), "\n")
cat(ifelse(!all(is.na(x@variables)), style_def(output), style_opt(output)))
cat(paste0("\n", "Constant parameters (input)", "\n"))
output <- paste0(paste(paste0(" ", names(x@constants)), x@constants, sep=" = ", collapse="\n"), "\n")
cat(ifelse(!all(is.na(x@constants)), style_def(output), style_opt(output)))
}
util_print.simdesign <- function(x, ...)
{
style_heading <- crayon::black$bold$bgWhite
style_def <- crayon::green
style_opt <- crayon::yellow
style_na <- crayon::red
cat(style_heading(paste0("\n", " SIMDESIGN ", "\n")))
cat("Simulation method = ")
output <- paste0(x@simmethod, "\n")
cat(ifelse(!identical(x@simmethod, character(0)), style_def(output), style_na(output)))
cat("Simulation object = ")
output <- paste0(x@simobject, "\n")
cat(ifelse(length(x@simmethod) > 0, style_def(output), style_opt(output)))
cat("Generated random seeds = ")
output <- paste0(paste(x@simseeds, collapse=", "), "\n")
cat(ifelse(!all(is.na(x@simseeds)), style_def(output), style_opt(output)))
cat(paste0("\n", "Parameter matrix (input)", "\n"))
print(x@siminput, width = Inf)
cat(paste0("\n", "Simulation results (output)", "\n"))
print(x@simoutput, width = Inf)
}
|
run_tuner <- function(app_title, soln_templates_dir, knit_wd,
tabs = c("lint","html","correctness"),
lint_list, corr_cols_to_drop = c(1,2,4,5),
max_time = 120, summary_header = "
permission_to_install = FALSE,
...) {
soln_fnames <- list.files(soln_templates_dir, full.names = TRUE)
soln_choices <- sapply(soln_fnames, function(x) rmarkdown::yaml_front_matter(x)$title,
USE.NAMES = FALSE)
if(anyDuplicated(soln_choices)){
stop("Duplicate titles found in solution templates. Please resolve.")
}
tabs <- match.arg(tabs,several.ok = TRUE)
soln_out_all <- lapply(soln_fnames, populate_soln_env, knit_root_dir = knit_wd)
names(soln_out_all) <- soln_choices
chunk_out_all <- lapply(soln_fnames, get_summary_output)
names(chunk_out_all) <- soln_choices
if(missing(lint_list)) {
lint_list <- c(lintr::T_and_F_symbol_linter,
lintr::assignment_linter,
lintr::closed_curly_linter,
lintr::commas_linter,
lintr::equals_na_linter,
lintr::function_left_parentheses_linter,
lintr::infix_spaces_linter,
lintr::line_length_linter,
lintr::no_tab_linter,
lintr::open_curly_linter,
lintr::paren_brace_linter,
lintr::absolute_path_linter,
lintr::pipe_continuation_linter,
lintr::spaces_inside_linter,
lintr::trailing_blank_lines_linter,
lintr::trailing_whitespace_linter,
lintr::unneeded_concatenation_linter)
}
full_tabs = c("lint","html","correctness")
lint_tabs <- tabPanel("Lint Check",uiOutput(outputId = "lint_check"))
html_tabs <- tabPanel("HTML Check",uiOutput(outputId = "html_check"))
correctness_tabs <- tabPanel("Correctness check",tableOutput(outputId = "corr_check"))
list1 <- list(lint_tabs,html_tabs,correctness_tabs)
index_match = match(tabs , full_tabs)
list_of_tabs <- list1[index_match]
internalwrapper <- function(...){
tabsetPanel(..., id = NULL, selected = NULL, type ="tabs")
}
panelOutput <- do.call("internalwrapper",list_of_tabs)
ui <- fluidPage(
titlePanel(title = app_title),
fluidRow(
column(4, wellPanel(
selectInput(
inputId = "selectfileweek",
label = h4("Select tutorial number:"),
choices = soln_choices
),
fileInput(
inputId = "fileupload",
label = h4("Upload your solution:"),
width = '100%',
multiple = FALSE
),
actionButton("goButton", "Check solution!"),
hr(),
shiny::tags$small('autoharp solution checker, 2020.', br(),
'Found a bug? Report it',
a(href='mailto:[email protected]','here.'))
)
),
column(8, panelOutput)
)
)
server <- function(input, output, session) {
lint_df <- reactive({
input$goButton
isolate({
if(is.null(input$fileupload)){
return(NULL)
} else {
file1 <- input$fileupload
all_lints <- lintr::lint(file1$datapath, linters= lint_list)
if(length(all_lints) > 0) {
ll <- sapply(all_lints, function(x) x$line_number)
mm <- sapply(all_lints, function(x) x$message)
l_df <- data.frame(ll, mm, stringsAsFactors = FALSE)
colnames(l_df) <- c("Line", "Message")
l_df2 <- renderTable(l_df)
} else {
l_df2 <- h5(br(), "No lints found. Nice job!")
}
}
})
l_df2
})
sess_tmp_dir <- tempfile("rmd_out")
dir.create(sess_tmp_dir)
session$onSessionEnded(function() {
unlink(sess_tmp_dir, TRUE)
})
output$lint_check <- renderUI({
fluidPage(lint_df())
})
htmlUI <- reactive({
input$goButton
isolate({
if(is.null(input$fileupload)) {
return(NULL)
} else {
file1 <- input$fileupload
progress <- shiny::Progress$new(session, min=1, max=10)
on.exit(progress$close())
progress$set(message="Checking libraries")
lib_used <- get_libraries(file1$datapath)
lib_used_msg <- paste('Libraries used: ',
paste0(lib_used, collapse=", "))
lib_to_install <- 'Libraries to install: None'
lib_error <- 'Installation logs: --'
if(!is.null(lib_used)) {
id <- !(sapply(lib_used, quietly=TRUE, requireNamespace))
lib_needed <- lib_used[id]
if(length(lib_needed) > 0) {
if(!permission_to_install) {
lib_error <- paste('Installation logs: Do Not Install')
lib_to_install <- paste('Need to install:',
paste0(lib_needed, collapse=", "))
return(list(used=lib_used_msg, install=lib_to_install,
error=lib_error, html=NULL, correctness=NULL))
}
lib_to_install <- paste('Need to install:',
paste0(lib_needed, collapse=", "))
showNotification("Installing packages, please wait a while..")
progress$set(message="Installing libraries", value=2)
try_install <- tryCatch(utils::install.packages(lib_needed,
dependencies = TRUE),
error = function(e) return(e))
if("error" %in% class(try_install)) {
lib_error <- paste('Installation logs:', conditionMessage(try_install))
return(list(used=lib_used_msg, install=lib_to_install, error=lib_error,
html=NULL, correctness=NULL))
} else {
lib_error <- paste('Installation logs: All OK!')
}
}
}
soln_to_use <- input$selectfileweek
progress$set(message="Rendering file", value=4)
try_html <- tryCatch(render_one(file1$datapath, out_dir = sess_tmp_dir,
knit_root_dir = knit_wd,
max_time_per_run = max_time,
soln_stuff = soln_out_all[[soln_to_use]]),
error = function(e) return(e))
progress$set(message="Almost done.. ", value=8)
return(list(used=lib_used_msg, install=lib_to_install, error=lib_error,
html=try_html))
}
})
})
output$html_check <- renderUI({
if(is.null(htmlUI()$html)){
return(p(h5(htmlUI()$used), h5(htmlUI()$install), h5(htmlUI()$error), hr()))
} else {
if(!("data.frame" %in% class(htmlUI()$html))) {
log_out <- log_summary(file.path(sess_tmp_dir, "render_one.log"))
render_error <- paste('Render logs:', utils::tail(log_out$error_message, 1))
return(p(h6(htmlUI()$used), h6(htmlUI()$install), h6(htmlUI()$error),
hr(), h6(render_error)))
}
if(htmlUI()$html$run_status[1] == "FAIL") {
log_out <- log_summary(file.path(sess_tmp_dir, "render_one.log"))
render_error <- paste('Render logs:', utils::tail(log_out$error_message, 1))
return(p(h6(htmlUI()$used), h6(htmlUI()$install), h6(htmlUI()$error),
hr(), h6(render_error)))
}
addResourcePath("test", sess_tmp_dir)
return(p(h6(htmlUI()$used), h6(htmlUI()$install), h6(htmlUI()$error),
hr(),
shiny::tags$iframe(src="test/0.html", height="1000", width="800", frameborder="0")))
}
})
summary_df <- reactive({
input$goButton
isolate({
if(is.null(input$fileupload)){
return(NULL)
} else {
soln_to_use <- input$selectfileweek
summary_df2 <- chunk_out_all[[soln_to_use]]
}
})
summary_df2
})
output$corr_check <- renderUI({
fluidPage(
fluidRow(
renderTable({
if(is.null(htmlUI())){
return(NULL)
}
if("data.frame" %in% class(htmlUI()$html)){
if(ncol(htmlUI()$html) == 3) {
return( htmlUI()$html[,3])
} else {
return(htmlUI()$html[,-corr_cols_to_drop])
}
}
})
),
fluidRow(
fluidPage(summary_df())
)
)
})
}
app <- shiny::shinyApp(ui, server)
app
}
get_summary_output <- function (rmd_file, summary_header = "
dir = tempdir()){
all_non_chunks <- extract_non_chunks(rmd_file)
ind <- which(all_non_chunks == summary_header)
if(length(ind) != 0) {
display_chunks <- all_non_chunks[ind:length(all_non_chunks)]
fp <- file.path(dir, 'summary_chunk_info.Rmd')
writeLines(display_chunks, con = fp)
summary_df <- shiny::withMathJax(shiny::includeMarkdown(fp))
unlink(fp)
} else {
summary_df <- h5(br(), "No summary found")
}
return(summary_df)
}
|
DLtest <-
function(y,p)
{
ym <- as.matrix(y-mean(y))
n <- nrow(ym); s2 <- sum(ym^2)/(n-p)
sum3 <- numeric(n-p)
sum2 <- 0
for(j in (p+1):n) {
sum1 <- 0
for(i in (p+1):n){
indicate <- 0
zi <- ym[(i-1):(i-p),1]
zj <- ym[(j-1):(j-p),1]
tem1 <- as.numeric(zi <= zj)
if( prod(tem1) == 1) indicate <- 1
sum1 <- sum1 + ym[i,1]*indicate
}
sum2 <- sum2 + sum1^2
sum3[j-p] <- abs(sum1/sqrt(n-p))
}
Cp <- sum2/(s2*(n-p)^2)
Kp <- max(sum3)/sqrt(s2)
return(list(Cpstat=Cp,Kpstat=Kp))
}
|
fdepthv2<-function(m,pts=NA,plotit=TRUE){
m<-elimna(m)
if(!is.na(pts[1]))remm<-m
if(!is.matrix(m))dep<-unidepth(m)
if(is.matrix(m)){
nm<-nrow(m)
nt<-nm
nm1<-nm+1
if(!is.na(pts[1])){
if(ncol(m)!=ncol(pts))stop("Number of columns of m is not equal to number of columns for pts")
nt<-nm+nrow(pts)
}}
if(ncol(m)==1)depth<-unidepth(m)
if(ncol(m)>1){
m<-elimna(m)
nc<-(nrow(m)^2-nrow(m))/2
if(is.na(pts[1]))mdep <- matrix(0,nrow=nc,ncol=nrow(m))
if(!is.na(pts[1])){
mdep <- matrix(0,nrow=nc,ncol=nrow(pts))
}
ic<-0
for (iall in 1:nm){
for (i in 1:nm){
if(iall < i){
ic<-ic+1
B<-m[i,]-m[iall,]
dis<-NA
BB<-B^2
bot<-sum(BB)
if(bot!=0){
if(is.na(pts[1])){
for (j in 1:nrow(m)){
A<-m[j,]-m[iall,]
temp<-sum(A*B)*B/bot
dis[j]<-sign(sum(A*B))*sqrt(sum(temp^2))
}}
if(!is.na(pts[1])){
m<-rbind(remm,pts)
for (j in 1:nrow(m)){
A<-m[j,]-m[iall,]
temp<-sum(A*B)*B/bot
dis[j]<-sign(sum(A*B))*sqrt(sum(temp^2))
}}
if(is.na(pts[1]))mdep[ic,]<-unidepth(dis)
if(!is.na(pts[1])){
mdep[ic,]<-unidepth(dis[1:nm],dis[nm1:nrow(m)])
}}
if(bot==0)mdep[ic,]<-rep(0,ncol(mdep))
}}}
dep<-apply(mdep,2,min)
}
if(ncol(m)==2 &&is.na(pts[1])){
flag<-chull(m)
dep[flag]<-min(dep)
}
if(ncol(m)==2){
if(is.na(pts[1]) && plotit){
plot(m)
x<-m
temp<-dep
flag<-(temp>=median(temp))
xx<-x[flag,]
xord<-order(xx[,1])
xx<-xx[xord,]
temp<-chull(xx)
xord<-order(xx[,1])
xx<-xx[xord,]
temp<-chull(xx)
lines(xx[temp,])
lines(xx[c(temp[1],temp[length(temp)]),])
}}
dep
}
|
Tensor$set("public", "__and__", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = '__and__',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "__iand__", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = '__iand__',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "__ilshift__", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = '__ilshift__',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "__ior__", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = '__ior__',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "__irshift__", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = '__irshift__',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "__ixor__", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = '__ixor__',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "__lshift__", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = '__lshift__',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "__or__", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = '__or__',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "__rshift__", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = '__rshift__',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "__xor__", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = '__xor__',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("private", "__backward", function(inputs, gradient = list(), retain_graph = NULL, create_graph = FALSE) { args <- mget(x = c("inputs", "gradient", "retain_graph", "create_graph"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", inputs = "TensorList", gradient = "Tensor",
retain_graph = "bool", create_graph = "bool")
nd_args <- c("self", "inputs")
return_types <- list(list("void"))
call_c_function(
fun_name = '_backward',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "_coalesced_", function(coalesced) { args <- mget(x = c("coalesced"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", coalesced = "bool")
nd_args <- c("self", "coalesced")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = '_coalesced_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "_dimI", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('int64_t'))
call_c_function(
fun_name = '_dimI',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "_dimV", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('int64_t'))
call_c_function(
fun_name = '_dimV',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "_fw_primal", function(level) { args <- mget(x = c("level"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", level = "int64_t")
nd_args <- c("self", "level")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = '_fw_primal',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "_indices", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = '_indices',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "_nnz", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('int64_t'))
call_c_function(
fun_name = '_nnz',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "_values", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = '_values',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "_version", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('int64_t'))
call_c_function(
fun_name = '_version',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "abs", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'abs',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "abs_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'abs_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "absolute", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'absolute',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "absolute_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'absolute_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "acos", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'acos',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "acos_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'acos_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "acosh", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'acosh',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "acosh_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'acosh_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "add", function(other, alpha = 1L) { args <- mget(x = c("other", "alpha"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"), alpha = "Scalar")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'add',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "add_", function(other, alpha = 1L) { args <- mget(x = c("other", "alpha"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"), alpha = "Scalar")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'add_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "addbmm", function(batch1, batch2, beta = 1L, alpha = 1L) { args <- mget(x = c("batch1", "batch2", "beta", "alpha"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", batch1 = "Tensor", batch2 = "Tensor", beta = "Scalar",
alpha = "Scalar")
nd_args <- c("self", "batch1", "batch2")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'addbmm',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "addbmm_", function(batch1, batch2, beta = 1L, alpha = 1L) { args <- mget(x = c("batch1", "batch2", "beta", "alpha"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", batch1 = "Tensor", batch2 = "Tensor", beta = "Scalar",
alpha = "Scalar")
nd_args <- c("self", "batch1", "batch2")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'addbmm_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "addcdiv", function(tensor1, tensor2, value = 1L) { args <- mget(x = c("tensor1", "tensor2", "value"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", tensor1 = "Tensor", tensor2 = "Tensor",
value = "Scalar")
nd_args <- c("self", "tensor1", "tensor2")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'addcdiv',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "addcdiv_", function(tensor1, tensor2, value = 1L) { args <- mget(x = c("tensor1", "tensor2", "value"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", tensor1 = "Tensor", tensor2 = "Tensor",
value = "Scalar")
nd_args <- c("self", "tensor1", "tensor2")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'addcdiv_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "addcmul", function(tensor1, tensor2, value = 1L) { args <- mget(x = c("tensor1", "tensor2", "value"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", tensor1 = "Tensor", tensor2 = "Tensor",
value = "Scalar")
nd_args <- c("self", "tensor1", "tensor2")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'addcmul',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "addcmul_", function(tensor1, tensor2, value = 1L) { args <- mget(x = c("tensor1", "tensor2", "value"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", tensor1 = "Tensor", tensor2 = "Tensor",
value = "Scalar")
nd_args <- c("self", "tensor1", "tensor2")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'addcmul_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "addmm", function(mat1, mat2, beta = 1L, alpha = 1L) { args <- mget(x = c("mat1", "mat2", "beta", "alpha"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", mat1 = "Tensor", mat2 = "Tensor", beta = "Scalar",
alpha = "Scalar")
nd_args <- c("self", "mat1", "mat2")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'addmm',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "addmm_", function(mat1, mat2, beta = 1L, alpha = 1L) { args <- mget(x = c("mat1", "mat2", "beta", "alpha"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", mat1 = "Tensor", mat2 = "Tensor", beta = "Scalar",
alpha = "Scalar")
nd_args <- c("self", "mat1", "mat2")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'addmm_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "addmv", function(mat, vec, beta = 1L, alpha = 1L) { args <- mget(x = c("mat", "vec", "beta", "alpha"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", mat = "Tensor", vec = "Tensor", beta = "Scalar",
alpha = "Scalar")
nd_args <- c("self", "mat", "vec")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'addmv',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "addmv_", function(mat, vec, beta = 1L, alpha = 1L) { args <- mget(x = c("mat", "vec", "beta", "alpha"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", mat = "Tensor", vec = "Tensor", beta = "Scalar",
alpha = "Scalar")
nd_args <- c("self", "mat", "vec")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'addmv_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "addr", function(vec1, vec2, beta = 1L, alpha = 1L) { args <- mget(x = c("vec1", "vec2", "beta", "alpha"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", vec1 = "Tensor", vec2 = "Tensor", beta = "Scalar",
alpha = "Scalar")
nd_args <- c("self", "vec1", "vec2")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'addr',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "addr_", function(vec1, vec2, beta = 1L, alpha = 1L) { args <- mget(x = c("vec1", "vec2", "beta", "alpha"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", vec1 = "Tensor", vec2 = "Tensor", beta = "Scalar",
alpha = "Scalar")
nd_args <- c("self", "vec1", "vec2")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'addr_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "alias", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'alias',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "align_as", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'align_as',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "align_to", function(names, order, ellipsis_idx) { args <- mget(x = c("names", "order", "ellipsis_idx"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", names = "DimnameList", order = "DimnameList",
ellipsis_idx = "int64_t")
nd_args <- c("self", "names", "order", "ellipsis_idx")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'align_to',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "all", function(dim, keepdim = FALSE) { args <- mget(x = c("dim", "keepdim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), keepdim = "bool")
nd_args <- c("self", "dim")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'all',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "allclose", function(other, rtol = 0.000010, atol = 0.000000, equal_nan = FALSE) { args <- mget(x = c("other", "rtol", "atol", "equal_nan"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor", rtol = "double", atol = "double",
equal_nan = "bool")
nd_args <- c("self", "other")
return_types <- list(list('bool'))
call_c_function(
fun_name = 'allclose',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "amax", function(dim = list(), keepdim = FALSE) { args <- mget(x = c("dim", "keepdim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = "IntArrayRef", keepdim = "bool")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'amax',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "amin", function(dim = list(), keepdim = FALSE) { args <- mget(x = c("dim", "keepdim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = "IntArrayRef", keepdim = "bool")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'amin',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "angle", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'angle',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "any", function(dim, keepdim = FALSE) { args <- mget(x = c("dim", "keepdim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), keepdim = "bool")
nd_args <- c("self", "dim")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'any',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "arccos", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'arccos',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "arccos_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'arccos_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "arccosh", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'arccosh',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "arccosh_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'arccosh_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "arcsin", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'arcsin',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "arcsin_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'arcsin_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "arcsinh", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'arcsinh',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "arcsinh_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'arcsinh_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "arctan", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'arctan',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "arctan_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'arctan_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "arctanh", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'arctanh',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "arctanh_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'arctanh_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("private", "_argmax", function(dim = NULL, keepdim = FALSE) { args <- mget(x = c("dim", "keepdim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = "int64_t", keepdim = "bool")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'argmax',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("private", "_argmin", function(dim = NULL, keepdim = FALSE) { args <- mget(x = c("dim", "keepdim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = "int64_t", keepdim = "bool")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'argmin',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("private", "_argsort", function(dim = -1L, descending = FALSE) { args <- mget(x = c("dim", "descending"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), descending = "bool")
nd_args <- c("self", "dim")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'argsort',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "as_strided", function(size, stride, storage_offset = NULL) { args <- mget(x = c("size", "stride", "storage_offset"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", size = "IntArrayRef", stride = "IntArrayRef",
storage_offset = "int64_t")
nd_args <- c("self", "size", "stride")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'as_strided',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "as_strided_", function(size, stride, storage_offset = NULL) { args <- mget(x = c("size", "stride", "storage_offset"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", size = "IntArrayRef", stride = "IntArrayRef",
storage_offset = "int64_t")
nd_args <- c("self", "size", "stride")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'as_strided_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "asin", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'asin',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "asin_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'asin_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "asinh", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'asinh',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "asinh_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'asinh_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "atan", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'atan',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "atan_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'atan_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "atan2", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'atan2',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "atan2_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'atan2_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "atanh", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'atanh',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "atanh_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'atanh_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "baddbmm", function(batch1, batch2, beta = 1L, alpha = 1L) { args <- mget(x = c("batch1", "batch2", "beta", "alpha"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", batch1 = "Tensor", batch2 = "Tensor", beta = "Scalar",
alpha = "Scalar")
nd_args <- c("self", "batch1", "batch2")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'baddbmm',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "baddbmm_", function(batch1, batch2, beta = 1L, alpha = 1L) { args <- mget(x = c("batch1", "batch2", "beta", "alpha"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", batch1 = "Tensor", batch2 = "Tensor", beta = "Scalar",
alpha = "Scalar")
nd_args <- c("self", "batch1", "batch2")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'baddbmm_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "bernoulli", function(p, generator = NULL) { args <- mget(x = c("p", "generator"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", p = "double", generator = "Generator")
nd_args <- c("self", "p")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'bernoulli',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "bernoulli_", function(p = 0.500000, generator = NULL) { args <- mget(x = c("p", "generator"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", p = c("Tensor", "double"), generator = "Generator")
nd_args <- c("self", "p")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'bernoulli_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "bincount", function(weights = list(), minlength = 0L) { args <- mget(x = c("weights", "minlength"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", weights = "Tensor", minlength = "int64_t")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'bincount',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "bitwise_and", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'bitwise_and',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "bitwise_and_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'bitwise_and_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "bitwise_not", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'bitwise_not',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "bitwise_not_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'bitwise_not_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "bitwise_or", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'bitwise_or',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "bitwise_or_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'bitwise_or_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "bitwise_xor", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'bitwise_xor',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "bitwise_xor_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'bitwise_xor_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "bmm", function(mat2) { args <- mget(x = c("mat2"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", mat2 = "Tensor")
nd_args <- c("self", "mat2")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'bmm',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "broadcast_to", function(size) { args <- mget(x = c("size"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", size = "IntArrayRef")
nd_args <- c("self", "size")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'broadcast_to',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "cauchy_", function(median = 0L, sigma = 1L, generator = NULL) { args <- mget(x = c("median", "sigma", "generator"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", median = "double", sigma = "double", generator = "Generator")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'cauchy_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "ceil", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'ceil',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "ceil_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'ceil_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "cholesky", function(upper = FALSE) { args <- mget(x = c("upper"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", upper = "bool")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'cholesky',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "cholesky_inverse", function(upper = FALSE) { args <- mget(x = c("upper"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", upper = "bool")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'cholesky_inverse',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "cholesky_solve", function(input2, upper = FALSE) { args <- mget(x = c("input2", "upper"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", input2 = "Tensor", upper = "bool")
nd_args <- c("self", "input2")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'cholesky_solve',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "chunk", function(chunks, dim = 1L) { args <- mget(x = c("chunks", "dim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", chunks = "int64_t", dim = "int64_t")
nd_args <- c("self", "chunks")
return_types <- list(list('TensorList'))
call_c_function(
fun_name = 'chunk',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "clamp", function(min = NULL, max = NULL) { args <- mget(x = c("min", "max"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", min = c("Scalar", "Tensor"), max = c("Scalar",
"Tensor"))
nd_args <- c("self", "min", "max")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'clamp',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "clamp_", function(min = NULL, max = NULL) { args <- mget(x = c("min", "max"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", min = c("Scalar", "Tensor"), max = c("Scalar",
"Tensor"))
nd_args <- c("self", "min", "max")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'clamp_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "clamp_max", function(max) { args <- mget(x = c("max"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", max = c("Scalar", "Tensor"))
nd_args <- c("self", "max")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'clamp_max',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "clamp_max_", function(max) { args <- mget(x = c("max"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", max = c("Scalar", "Tensor"))
nd_args <- c("self", "max")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'clamp_max_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "clamp_min", function(min) { args <- mget(x = c("min"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", min = c("Scalar", "Tensor"))
nd_args <- c("self", "min")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'clamp_min',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "clamp_min_", function(min) { args <- mget(x = c("min"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", min = c("Scalar", "Tensor"))
nd_args <- c("self", "min")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'clamp_min_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "clip", function(min = NULL, max = NULL) { args <- mget(x = c("min", "max"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", min = c("Scalar", "Tensor"), max = c("Scalar",
"Tensor"))
nd_args <- c("self", "min", "max")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'clip',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "clip_", function(min = NULL, max = NULL) { args <- mget(x = c("min", "max"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", min = c("Scalar", "Tensor"), max = c("Scalar",
"Tensor"))
nd_args <- c("self", "min", "max")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'clip_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "clone", function(memory_format = NULL) { args <- mget(x = c("memory_format"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", memory_format = "MemoryFormat")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'clone',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "coalesce", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'coalesce',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "col_indices", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'col_indices',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "conj", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'conj',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "contiguous", function(memory_format = torch_contiguous_format()) { args <- mget(x = c("memory_format"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", memory_format = "MemoryFormat")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'contiguous',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("private", "_copy_", function(src, non_blocking = FALSE) { args <- mget(x = c("src", "non_blocking"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", src = "Tensor", non_blocking = "bool")
nd_args <- c("self", "src")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'copy_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "copysign", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'copysign',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "copysign_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'copysign_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "cos", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'cos',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "cos_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'cos_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "cosh", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'cosh',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "cosh_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'cosh_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "count_nonzero", function(dim = NULL) { args <- mget(x = c("dim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "int64_t"))
nd_args <- c("self", "dim")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'count_nonzero',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "cross", function(other, dim = NULL) { args <- mget(x = c("other", "dim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor", dim = "int64_t")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'cross',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "crow_indices", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'crow_indices',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "cummax", function(dim) { args <- mget(x = c("dim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"))
nd_args <- c("self", "dim")
return_types <- list(list("Tensor", "Tensor"))
call_c_function(
fun_name = 'cummax',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "cummin", function(dim) { args <- mget(x = c("dim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"))
nd_args <- c("self", "dim")
return_types <- list(list("Tensor", "Tensor"))
call_c_function(
fun_name = 'cummin',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "cumprod", function(dim, dtype = NULL) { args <- mget(x = c("dim", "dtype"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), dtype = "ScalarType")
nd_args <- c("self", "dim")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'cumprod',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "cumprod_", function(dim, dtype = NULL) { args <- mget(x = c("dim", "dtype"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), dtype = "ScalarType")
nd_args <- c("self", "dim")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'cumprod_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "cumsum", function(dim, dtype = NULL) { args <- mget(x = c("dim", "dtype"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), dtype = "ScalarType")
nd_args <- c("self", "dim")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'cumsum',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "cumsum_", function(dim, dtype = NULL) { args <- mget(x = c("dim", "dtype"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), dtype = "ScalarType")
nd_args <- c("self", "dim")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'cumsum_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "data", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'data',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "deg2rad", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'deg2rad',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "deg2rad_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'deg2rad_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "dense_dim", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('int64_t'))
call_c_function(
fun_name = 'dense_dim',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "dequantize", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'dequantize',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "det", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'det',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "detach", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'detach',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "detach_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'detach_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "diag", function(diagonal = 0L) { args <- mget(x = c("diagonal"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", diagonal = "int64_t")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'diag',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "diag_embed", function(offset = 0L, dim1 = -2L, dim2 = -1L) { args <- mget(x = c("offset", "dim1", "dim2"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", offset = "int64_t", dim1 = "int64_t", dim2 = "int64_t")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'diag_embed',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "diagflat", function(offset = 0L) { args <- mget(x = c("offset"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", offset = "int64_t")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'diagflat',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "diagonal", function(outdim, dim1 = 1L, dim2 = 2L, offset = 0L) { args <- mget(x = c("outdim", "dim1", "dim2", "offset"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", outdim = "Dimname", dim1 = c("int64_t",
"Dimname"), dim2 = c("int64_t", "Dimname"), offset = "int64_t")
nd_args <- c("self", "outdim", "dim1", "dim2")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'diagonal',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "diff", function(n = 1L, dim = -1L, prepend = list(), append = list()) { args <- mget(x = c("n", "dim", "prepend", "append"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", n = "int64_t", dim = "int64_t", prepend = "Tensor",
append = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'diff',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "digamma", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'digamma',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "digamma_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'digamma_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "dist", function(other, p = 2L) { args <- mget(x = c("other", "p"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor", p = "Scalar")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'dist',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "div", function(other, rounding_mode) { args <- mget(x = c("other", "rounding_mode"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"), rounding_mode = "std::string")
nd_args <- c("self", "other", "rounding_mode")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'div',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "div_", function(other, rounding_mode) { args <- mget(x = c("other", "rounding_mode"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"), rounding_mode = "std::string")
nd_args <- c("self", "other", "rounding_mode")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'div_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "divide", function(other, rounding_mode) { args <- mget(x = c("other", "rounding_mode"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"), rounding_mode = "std::string")
nd_args <- c("self", "other", "rounding_mode")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'divide',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "divide_", function(other, rounding_mode) { args <- mget(x = c("other", "rounding_mode"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"), rounding_mode = "std::string")
nd_args <- c("self", "other", "rounding_mode")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'divide_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "dot", function(tensor) { args <- mget(x = c("tensor"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", tensor = "Tensor")
nd_args <- c("self", "tensor")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'dot',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "dsplit", function(indices, sections) { args <- mget(x = c("indices", "sections"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", indices = "IntArrayRef", sections = "int64_t")
nd_args <- c("self", "indices", "sections")
return_types <- list(list('TensorList'))
call_c_function(
fun_name = 'dsplit',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "eig", function(eigenvectors = FALSE) { args <- mget(x = c("eigenvectors"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", eigenvectors = "bool")
nd_args <- "self"
return_types <- list(list("Tensor", "Tensor"))
call_c_function(
fun_name = 'eig',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "eq", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'eq',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "eq_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'eq_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "equal", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('bool'))
call_c_function(
fun_name = 'equal',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "erf", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'erf',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "erf_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'erf_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "erfc", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'erfc',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "erfc_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'erfc_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "erfinv", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'erfinv',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "erfinv_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'erfinv_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "exp", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'exp',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "exp_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'exp_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "exp2", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'exp2',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "exp2_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'exp2_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "expand", function(size, implicit = FALSE) { args <- mget(x = c("size", "implicit"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", size = "IntArrayRef", implicit = "bool")
nd_args <- c("self", "size")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'expand',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "expand_as", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'expand_as',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "expm1", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'expm1',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "expm1_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'expm1_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "exponential_", function(lambd = 1L, generator = NULL) { args <- mget(x = c("lambd", "generator"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", lambd = "double", generator = "Generator")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'exponential_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "fill_", function(value) { args <- mget(x = c("value"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", value = c("Scalar", "Tensor"))
nd_args <- c("self", "value")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'fill_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "fill_diagonal_", function(fill_value, wrap = FALSE) { args <- mget(x = c("fill_value", "wrap"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", fill_value = "Scalar", wrap = "bool")
nd_args <- c("self", "fill_value")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'fill_diagonal_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "fix", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'fix',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "fix_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'fix_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "flatten", function(dims, start_dim = 1L, end_dim = -1L, out_dim) { args <- mget(x = c("dims", "start_dim", "end_dim", "out_dim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dims = "DimnameList", start_dim = c("int64_t",
"Dimname"), end_dim = c("int64_t", "Dimname"), out_dim = "Dimname")
nd_args <- c("self", "dims", "start_dim", "end_dim", "out_dim")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'flatten',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "flip", function(dims) { args <- mget(x = c("dims"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dims = "IntArrayRef")
nd_args <- c("self", "dims")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'flip',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "fliplr", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'fliplr',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "flipud", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'flipud',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "float_power", function(exponent) { args <- mget(x = c("exponent"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", exponent = c("Tensor", "Scalar"))
nd_args <- c("self", "exponent")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'float_power',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "float_power_", function(exponent) { args <- mget(x = c("exponent"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", exponent = c("Scalar", "Tensor"))
nd_args <- c("self", "exponent")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'float_power_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "floor", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'floor',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "floor_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'floor_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "floor_divide", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'floor_divide',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "floor_divide_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'floor_divide_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "fmax", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'fmax',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "fmin", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'fmin',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "fmod", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'fmod',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "fmod_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'fmod_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "frac", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'frac',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "frac_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'frac_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "frexp", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list("Tensor", "Tensor"))
call_c_function(
fun_name = 'frexp',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "gather", function(dim, index, sparse_grad = FALSE) { args <- mget(x = c("dim", "index", "sparse_grad"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), index = "Tensor",
sparse_grad = "bool")
nd_args <- c("self", "dim", "index")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'gather',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "gcd", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'gcd',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "gcd_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'gcd_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "ge", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'ge',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "ge_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'ge_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "geometric_", function(p, generator = NULL) { args <- mget(x = c("p", "generator"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", p = "double", generator = "Generator")
nd_args <- c("self", "p")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'geometric_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "geqrf", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list("Tensor", "Tensor"))
call_c_function(
fun_name = 'geqrf',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "ger", function(vec2) { args <- mget(x = c("vec2"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", vec2 = "Tensor")
nd_args <- c("self", "vec2")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'ger',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "greater", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'greater',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "greater_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'greater_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "greater_equal", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'greater_equal',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "greater_equal_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'greater_equal_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "gt", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'gt',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "gt_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'gt_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "hardshrink", function(lambd = 0.500000) { args <- mget(x = c("lambd"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", lambd = "Scalar")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'hardshrink',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "hardshrink_backward", function(grad_out, lambd) { args <- mget(x = c("grad_out", "lambd"))
args <- append(list(self = self), args)
expected_types <- list(grad_out = "Tensor", self = "Tensor", lambd = "Scalar")
nd_args <- c("grad_out", "self", "lambd")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'hardshrink_backward',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "heaviside", function(values) { args <- mget(x = c("values"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", values = "Tensor")
nd_args <- c("self", "values")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'heaviside',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "heaviside_", function(values) { args <- mget(x = c("values"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", values = "Tensor")
nd_args <- c("self", "values")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'heaviside_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "histc", function(bins = 100L, min = 0L, max = 0L) { args <- mget(x = c("bins", "min", "max"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", bins = "int64_t", min = "Scalar", max = "Scalar")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'histc',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "hsplit", function(indices, sections) { args <- mget(x = c("indices", "sections"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", indices = "IntArrayRef", sections = "int64_t")
nd_args <- c("self", "indices", "sections")
return_types <- list(list('TensorList'))
call_c_function(
fun_name = 'hsplit',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "hypot", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'hypot',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "hypot_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'hypot_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "i0", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'i0',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "i0_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'i0_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "igamma", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'igamma',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "igamma_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'igamma_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "igammac", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'igammac',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "igammac_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'igammac_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "index", function(indices) { args <- mget(x = c("indices"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", indices = "const c10::List<c10::optional<Tensor>> &")
nd_args <- c("self", "indices")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'index',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "index_add", function(dim, index, source, alpha = 1L) { args <- mget(x = c("dim", "index", "source", "alpha"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), index = "Tensor",
source = "Tensor", alpha = "Scalar")
nd_args <- c("self", "dim", "index", "source", "alpha")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'index_add',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "index_add_", function(dim, index, source, alpha) { args <- mget(x = c("dim", "index", "source", "alpha"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = "int64_t", index = "Tensor", source = "Tensor",
alpha = "Scalar")
nd_args <- c("self", "dim", "index", "source", "alpha")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'index_add_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "index_copy", function(dim, index, source) { args <- mget(x = c("dim", "index", "source"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), index = "Tensor",
source = "Tensor")
nd_args <- c("self", "dim", "index", "source")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'index_copy',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "index_copy_", function(dim, index, source) { args <- mget(x = c("dim", "index", "source"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), index = "Tensor",
source = "Tensor")
nd_args <- c("self", "dim", "index", "source")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'index_copy_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "index_fill", function(dim, index, value) { args <- mget(x = c("dim", "index", "value"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), index = "Tensor",
value = c("Scalar", "Tensor"))
nd_args <- c("self", "dim", "index", "value")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'index_fill',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "index_fill_", function(dim, index, value) { args <- mget(x = c("dim", "index", "value"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), index = "Tensor",
value = c("Scalar", "Tensor"))
nd_args <- c("self", "dim", "index", "value")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'index_fill_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "index_put", function(indices, values, accumulate = FALSE) { args <- mget(x = c("indices", "values", "accumulate"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", indices = "const c10::List<c10::optional<Tensor>> &",
values = "Tensor", accumulate = "bool")
nd_args <- c("self", "indices", "values")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'index_put',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "index_put_", function(indices, values, accumulate = FALSE) { args <- mget(x = c("indices", "values", "accumulate"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", indices = "const c10::List<c10::optional<Tensor>> &",
values = "Tensor", accumulate = "bool")
nd_args <- c("self", "indices", "values")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'index_put_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "index_select", function(dim, index) { args <- mget(x = c("dim", "index"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), index = "Tensor")
nd_args <- c("self", "dim", "index")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'index_select',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "indices", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'indices',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "inner", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'inner',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "int_repr", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'int_repr',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "inverse", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'inverse',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "is_coalesced", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('bool'))
call_c_function(
fun_name = 'is_coalesced',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "is_complex", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('bool'))
call_c_function(
fun_name = 'is_complex',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "is_distributed", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('bool'))
call_c_function(
fun_name = 'is_distributed',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "is_floating_point", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('bool'))
call_c_function(
fun_name = 'is_floating_point',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("private", "_is_leaf", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('bool'))
call_c_function(
fun_name = 'is_leaf',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "is_nonzero", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('bool'))
call_c_function(
fun_name = 'is_nonzero',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "is_pinned", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('bool'))
call_c_function(
fun_name = 'is_pinned',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "is_same_size", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('bool'))
call_c_function(
fun_name = 'is_same_size',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "is_set_to", function(tensor) { args <- mget(x = c("tensor"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", tensor = "Tensor")
nd_args <- c("self", "tensor")
return_types <- list(list('bool'))
call_c_function(
fun_name = 'is_set_to',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "is_signed", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('bool'))
call_c_function(
fun_name = 'is_signed',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "isclose", function(other, rtol = 0.000010, atol = 0.000000, equal_nan = FALSE) { args <- mget(x = c("other", "rtol", "atol", "equal_nan"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor", rtol = "double", atol = "double",
equal_nan = "bool")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'isclose',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "isfinite", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'isfinite',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "isinf", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'isinf',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "isnan", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'isnan',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "isneginf", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'isneginf',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "isposinf", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'isposinf',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "isreal", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'isreal',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "istft", function(n_fft, hop_length = NULL, win_length = NULL, window = list(), center = TRUE, normalized = FALSE, onesided = NULL, length = NULL, return_complex = FALSE) { args <- mget(x = c("n_fft", "hop_length", "win_length", "window", "center", "normalized", "onesided", "length", "return_complex"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", n_fft = "int64_t", hop_length = "int64_t",
win_length = "int64_t", window = "Tensor", center = "bool",
normalized = "bool", onesided = "bool", length = "int64_t",
return_complex = "bool")
nd_args <- c("self", "n_fft")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'istft',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "item", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Scalar'))
call_c_function(
fun_name = 'item',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "kron", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'kron',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "kthvalue", function(k, dim = -1L, keepdim = FALSE) { args <- mget(x = c("k", "dim", "keepdim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", k = "int64_t", dim = c("int64_t", "Dimname"
), keepdim = "bool")
nd_args <- c("self", "k", "dim")
return_types <- list(list("Tensor", "Tensor"))
call_c_function(
fun_name = 'kthvalue',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "lcm", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'lcm',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "lcm_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'lcm_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "ldexp", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'ldexp',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "ldexp_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'ldexp_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "le", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'le',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "le_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'le_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "lerp", function(end, weight) { args <- mget(x = c("end", "weight"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", end = "Tensor", weight = c("Scalar", "Tensor"
))
nd_args <- c("self", "end", "weight")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'lerp',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "lerp_", function(end, weight) { args <- mget(x = c("end", "weight"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", end = "Tensor", weight = c("Scalar", "Tensor"
))
nd_args <- c("self", "end", "weight")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'lerp_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "less", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'less',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "less_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'less_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "less_equal", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'less_equal',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "less_equal_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'less_equal_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "lgamma", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'lgamma',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "lgamma_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'lgamma_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "log", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'log',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "log_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'log_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "log_normal_", function(mean = 1L, std = 2L, generator = NULL) { args <- mget(x = c("mean", "std", "generator"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", mean = "double", std = "double", generator = "Generator")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'log_normal_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "log_softmax", function(dim, dtype = NULL) { args <- mget(x = c("dim", "dtype"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), dtype = "ScalarType")
nd_args <- c("self", "dim")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'log_softmax',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "log10", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'log10',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "log10_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'log10_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "log1p", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'log1p',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "log1p_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'log1p_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "log2", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'log2',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "log2_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'log2_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "logaddexp", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'logaddexp',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "logaddexp2", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'logaddexp2',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "logcumsumexp", function(dim) { args <- mget(x = c("dim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"))
nd_args <- c("self", "dim")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'logcumsumexp',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "logdet", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'logdet',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "logical_and", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'logical_and',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "logical_and_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'logical_and_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "logical_not", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'logical_not',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "logical_not_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'logical_not_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "logical_or", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'logical_or',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "logical_or_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'logical_or_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "logical_xor", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'logical_xor',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "logical_xor_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'logical_xor_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "logit", function(eps = NULL) { args <- mget(x = c("eps"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", eps = "double")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'logit',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "logit_", function(eps = NULL) { args <- mget(x = c("eps"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", eps = "double")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'logit_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "logsumexp", function(dim, keepdim = FALSE) { args <- mget(x = c("dim", "keepdim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "DimnameList"),
keepdim = "bool")
nd_args <- c("self", "dim")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'logsumexp',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "lstsq", function(A) { args <- mget(x = c("A"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", A = "Tensor")
nd_args <- c("self", "A")
return_types <- list(list("Tensor", "Tensor"))
call_c_function(
fun_name = 'lstsq',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "lt", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'lt',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "lt_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'lt_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "lu_solve", function(LU_data, LU_pivots) { args <- mget(x = c("LU_data", "LU_pivots"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", LU_data = "Tensor", LU_pivots = "Tensor")
nd_args <- c("self", "LU_data", "LU_pivots")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'lu_solve',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "masked_fill", function(mask, value) { args <- mget(x = c("mask", "value"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", mask = "Tensor", value = c("Scalar", "Tensor"
))
nd_args <- c("self", "mask", "value")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'masked_fill',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "masked_fill_", function(mask, value) { args <- mget(x = c("mask", "value"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", mask = "Tensor", value = c("Scalar", "Tensor"
))
nd_args <- c("self", "mask", "value")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'masked_fill_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "masked_scatter", function(mask, source) { args <- mget(x = c("mask", "source"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", mask = "Tensor", source = "Tensor")
nd_args <- c("self", "mask", "source")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'masked_scatter',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "masked_scatter_", function(mask, source) { args <- mget(x = c("mask", "source"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", mask = "Tensor", source = "Tensor")
nd_args <- c("self", "mask", "source")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'masked_scatter_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "masked_select", function(mask) { args <- mget(x = c("mask"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", mask = "Tensor")
nd_args <- c("self", "mask")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'masked_select',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "matmul", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'matmul',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "matrix_exp", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'matrix_exp',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "matrix_power", function(n) { args <- mget(x = c("n"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", n = "int64_t")
nd_args <- c("self", "n")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'matrix_power',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("private", "_max", function(dim, other, keepdim = FALSE) { args <- mget(x = c("dim", "other", "keepdim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), other = "Tensor",
keepdim = "bool")
nd_args <- c("self", "dim", "other")
return_types <- list(list("Tensor", "Tensor"), list('Tensor'))
call_c_function(
fun_name = 'max',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "maximum", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'maximum',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "mean", function(dim, keepdim = FALSE, dtype = NULL) { args <- mget(x = c("dim", "keepdim", "dtype"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "DimnameList"),
keepdim = "bool", dtype = "ScalarType")
nd_args <- c("self", "dim")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'mean',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "median", function(dim, keepdim = FALSE) { args <- mget(x = c("dim", "keepdim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), keepdim = "bool")
nd_args <- c("self", "dim")
return_types <- list(list('Tensor'), list("Tensor", "Tensor"))
call_c_function(
fun_name = 'median',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("private", "_min", function(dim, other, keepdim = FALSE) { args <- mget(x = c("dim", "other", "keepdim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), other = "Tensor",
keepdim = "bool")
nd_args <- c("self", "dim", "other")
return_types <- list(list("Tensor", "Tensor"), list('Tensor'))
call_c_function(
fun_name = 'min',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "minimum", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'minimum',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "mm", function(mat2) { args <- mget(x = c("mat2"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", mat2 = "Tensor")
nd_args <- c("self", "mat2")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'mm',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "mode", function(dim = -1L, keepdim = FALSE) { args <- mget(x = c("dim", "keepdim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), keepdim = "bool")
nd_args <- c("self", "dim")
return_types <- list(list("Tensor", "Tensor"))
call_c_function(
fun_name = 'mode',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "moveaxis", function(source, destination) { args <- mget(x = c("source", "destination"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", source = c("IntArrayRef", "int64_t"), destination = c("IntArrayRef",
"int64_t"))
nd_args <- c("self", "source", "destination")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'moveaxis',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "movedim", function(source, destination) { args <- mget(x = c("source", "destination"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", source = c("IntArrayRef", "int64_t"), destination = c("IntArrayRef",
"int64_t"))
nd_args <- c("self", "source", "destination")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'movedim',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "msort", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'msort',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "mul", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'mul',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "mul_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'mul_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "multinomial", function(num_samples, replacement = FALSE, generator = NULL) { args <- mget(x = c("num_samples", "replacement", "generator"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", num_samples = "int64_t", replacement = "bool",
generator = "Generator")
nd_args <- c("self", "num_samples")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'multinomial',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "multiply", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'multiply',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "multiply_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'multiply_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "mv", function(vec) { args <- mget(x = c("vec"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", vec = "Tensor")
nd_args <- c("self", "vec")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'mv',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "mvlgamma", function(p) { args <- mget(x = c("p"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", p = "int64_t")
nd_args <- c("self", "p")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'mvlgamma',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "mvlgamma_", function(p) { args <- mget(x = c("p"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", p = "int64_t")
nd_args <- c("self", "p")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'mvlgamma_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "nan_to_num", function(nan = NULL, posinf = NULL, neginf = NULL) { args <- mget(x = c("nan", "posinf", "neginf"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", nan = "double", posinf = "double", neginf = "double")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'nan_to_num',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "nan_to_num_", function(nan = NULL, posinf = NULL, neginf = NULL) { args <- mget(x = c("nan", "posinf", "neginf"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", nan = "double", posinf = "double", neginf = "double")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'nan_to_num_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "nanmedian", function(dim, keepdim = FALSE) { args <- mget(x = c("dim", "keepdim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), keepdim = "bool")
nd_args <- c("self", "dim")
return_types <- list(list('Tensor'), list("Tensor", "Tensor"))
call_c_function(
fun_name = 'nanmedian',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "nanquantile", function(q, dim = NULL, keepdim = FALSE, interpolation) { args <- mget(x = c("q", "dim", "keepdim", "interpolation"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", q = c("double", "Tensor"), dim = "int64_t",
keepdim = "bool", interpolation = "std::string")
nd_args <- c("self", "q", "dim", "keepdim", "interpolation")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'nanquantile',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "nansum", function(dim, keepdim = FALSE, dtype = NULL) { args <- mget(x = c("dim", "keepdim", "dtype"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = "IntArrayRef", keepdim = "bool",
dtype = "ScalarType")
nd_args <- c("self", "dim")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'nansum',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("private", "_narrow", function(dim, start, length) { args <- mget(x = c("dim", "start", "length"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = "int64_t", start = c("int64_t", "Tensor"
), length = "int64_t")
nd_args <- c("self", "dim", "start", "length")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'narrow',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("private", "_narrow_copy", function(dim, start, length) { args <- mget(x = c("dim", "start", "length"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = "int64_t", start = "int64_t", length = "int64_t")
nd_args <- c("self", "dim", "start", "length")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'narrow_copy',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "ne", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'ne',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "ne_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'ne_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "neg", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'neg',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "neg_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'neg_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "negative", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'negative',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "negative_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'negative_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "new_empty", function(size, options = list()) { args <- mget(x = c("size", "options"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", size = "IntArrayRef", options = "TensorOptions")
nd_args <- c("self", "size")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'new_empty',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "new_empty_strided", function(size, stride, options = list()) { args <- mget(x = c("size", "stride", "options"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", size = "IntArrayRef", stride = "IntArrayRef",
options = "TensorOptions")
nd_args <- c("self", "size", "stride")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'new_empty_strided',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "new_full", function(size, fill_value, options = list()) { args <- mget(x = c("size", "fill_value", "options"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", size = "IntArrayRef", fill_value = "Scalar",
options = "TensorOptions")
nd_args <- c("self", "size", "fill_value")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'new_full',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "new_zeros", function(size, options = list()) { args <- mget(x = c("size", "options"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", size = "IntArrayRef", options = "TensorOptions")
nd_args <- c("self", "size")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'new_zeros',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "nextafter", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'nextafter',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "nextafter_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'nextafter_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("private", "_nonzero", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'nonzero',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("private", "_nonzero_numpy", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('TensorList'))
call_c_function(
fun_name = 'nonzero_numpy',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("private", "_norm", function(p = 2L, dim, keepdim = FALSE, dtype) { args <- mget(x = c("p", "dim", "keepdim", "dtype"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", p = "Scalar", dim = c("IntArrayRef", "DimnameList"
), keepdim = "bool", dtype = "ScalarType")
nd_args <- c("self", "p", "dim", "keepdim", "dtype")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'norm',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "normal_", function(mean = 0L, std = 1L, generator = NULL) { args <- mget(x = c("mean", "std", "generator"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", mean = "double", std = "double", generator = "Generator")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'normal_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "not_equal", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'not_equal',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "not_equal_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'not_equal_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "numpy_T", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'numpy_T',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "orgqr", function(input2) { args <- mget(x = c("input2"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", input2 = "Tensor")
nd_args <- c("self", "input2")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'orgqr',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "ormqr", function(input2, input3, left = TRUE, transpose = FALSE) { args <- mget(x = c("input2", "input3", "left", "transpose"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", input2 = "Tensor", input3 = "Tensor", left = "bool",
transpose = "bool")
nd_args <- c("self", "input2", "input3")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'ormqr',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "outer", function(vec2) { args <- mget(x = c("vec2"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", vec2 = "Tensor")
nd_args <- c("self", "vec2")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'outer',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "output_nr", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('int64_t'))
call_c_function(
fun_name = 'output_nr',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "permute", function(dims) { args <- mget(x = c("dims"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dims = "IntArrayRef")
nd_args <- c("self", "dims")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'permute',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "pin_memory", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'pin_memory',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "pinverse", function(rcond = 0.000000) { args <- mget(x = c("rcond"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", rcond = "double")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'pinverse',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "polygamma_", function(n) { args <- mget(x = c("n"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", n = "int64_t")
nd_args <- c("self", "n")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'polygamma_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "positive", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'positive',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "pow", function(exponent) { args <- mget(x = c("exponent"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", exponent = c("Tensor", "Scalar"))
nd_args <- c("self", "exponent")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'pow',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "pow_", function(exponent) { args <- mget(x = c("exponent"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", exponent = c("Scalar", "Tensor"))
nd_args <- c("self", "exponent")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'pow_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "prelu", function(weight) { args <- mget(x = c("weight"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", weight = "Tensor")
nd_args <- c("self", "weight")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'prelu',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "prelu_backward", function(grad_output, weight) { args <- mget(x = c("grad_output", "weight"))
args <- append(list(self = self), args)
expected_types <- list(grad_output = "Tensor", self = "Tensor", weight = "Tensor")
nd_args <- c("grad_output", "self", "weight")
return_types <- list(list("Tensor", "Tensor"))
call_c_function(
fun_name = 'prelu_backward',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "prod", function(dim, keepdim = FALSE, dtype = NULL) { args <- mget(x = c("dim", "keepdim", "dtype"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), keepdim = "bool",
dtype = "ScalarType")
nd_args <- c("self", "dim")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'prod',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "put", function(index, source, accumulate = FALSE) { args <- mget(x = c("index", "source", "accumulate"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", index = "Tensor", source = "Tensor", accumulate = "bool")
nd_args <- c("self", "index", "source")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'put',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "put_", function(index, source, accumulate = FALSE) { args <- mget(x = c("index", "source", "accumulate"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", index = "Tensor", source = "Tensor", accumulate = "bool")
nd_args <- c("self", "index", "source")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'put_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "q_per_channel_axis", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('int64_t'))
call_c_function(
fun_name = 'q_per_channel_axis',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "q_per_channel_scales", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'q_per_channel_scales',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "q_per_channel_zero_points", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'q_per_channel_zero_points',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "q_scale", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('double'))
call_c_function(
fun_name = 'q_scale',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "q_zero_point", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('int64_t'))
call_c_function(
fun_name = 'q_zero_point',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "qr", function(some = TRUE) { args <- mget(x = c("some"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", some = "bool")
nd_args <- "self"
return_types <- list(list("Tensor", "Tensor"))
call_c_function(
fun_name = 'qr',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "qscheme", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('QScheme'))
call_c_function(
fun_name = 'qscheme',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "quantile", function(q, dim = NULL, keepdim = FALSE, interpolation) { args <- mget(x = c("q", "dim", "keepdim", "interpolation"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", q = c("double", "Tensor"), dim = "int64_t",
keepdim = "bool", interpolation = "std::string")
nd_args <- c("self", "q", "dim", "keepdim", "interpolation")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'quantile',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "rad2deg", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'rad2deg',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "rad2deg_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'rad2deg_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "random_", function(from, to, generator = NULL) { args <- mget(x = c("from", "to", "generator"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", from = "int64_t", to = "int64_t", generator = "Generator")
nd_args <- c("self", "from", "to")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'random_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "ravel", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'ravel',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "reciprocal", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'reciprocal',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "reciprocal_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'reciprocal_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "record_stream", function(s) { args <- mget(x = c("s"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", s = "Stream")
nd_args <- c("self", "s")
return_types <- list(list("void"))
call_c_function(
fun_name = 'record_stream',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "refine_names", function(names) { args <- mget(x = c("names"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", names = "DimnameList")
nd_args <- c("self", "names")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'refine_names',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "relu", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'relu',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "relu_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'relu_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "remainder", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'remainder',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "remainder_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'remainder_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("private", "_rename", function(names) { args <- mget(x = c("names"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", names = "DimnameList")
nd_args <- c("self", "names")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'rename',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("private", "_rename_", function(names) { args <- mget(x = c("names"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", names = "DimnameList")
nd_args <- c("self", "names")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'rename_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "renorm", function(p, dim, maxnorm) { args <- mget(x = c("p", "dim", "maxnorm"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", p = "Scalar", dim = "int64_t", maxnorm = "Scalar")
nd_args <- c("self", "p", "dim", "maxnorm")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'renorm',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "renorm_", function(p, dim, maxnorm) { args <- mget(x = c("p", "dim", "maxnorm"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", p = "Scalar", dim = "int64_t", maxnorm = "Scalar")
nd_args <- c("self", "p", "dim", "maxnorm")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'renorm_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "repeat", function(repeats) { args <- mget(x = c("repeats"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", repeats = "IntArrayRef")
nd_args <- c("self", "repeats")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'repeat',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "repeat_interleave", function(repeats, dim = NULL) { args <- mget(x = c("repeats", "dim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", repeats = c("Tensor", "int64_t"), dim = "int64_t")
nd_args <- c("self", "repeats")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'repeat_interleave',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "requires_grad_", function(requires_grad = TRUE) { args <- mget(x = c("requires_grad"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", requires_grad = "bool")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'requires_grad_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "reshape", function(shape) { args <- mget(x = c("shape"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", shape = "IntArrayRef")
nd_args <- c("self", "shape")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'reshape',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "reshape_as", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'reshape_as',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "resize_", function(size, memory_format = NULL) { args <- mget(x = c("size", "memory_format"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", size = "IntArrayRef", memory_format = "MemoryFormat")
nd_args <- c("self", "size")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'resize_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "resize_as_", function(the_template, memory_format = NULL) { args <- mget(x = c("the_template", "memory_format"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", the_template = "Tensor", memory_format = "MemoryFormat")
nd_args <- c("self", "the_template")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'resize_as_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("private", "_retain_grad", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list("void"))
call_c_function(
fun_name = 'retain_grad',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "roll", function(shifts, dims = list()) { args <- mget(x = c("shifts", "dims"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", shifts = "IntArrayRef", dims = "IntArrayRef")
nd_args <- c("self", "shifts")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'roll',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "rot90", function(k = 1L, dims = c(0,1)) { args <- mget(x = c("k", "dims"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", k = "int64_t", dims = "IntArrayRef")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'rot90',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "round", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'round',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "round_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'round_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "rsqrt", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'rsqrt',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "rsqrt_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'rsqrt_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("private", "_scatter", function(dim, index, src, value) { args <- mget(x = c("dim", "index", "src", "value"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), index = "Tensor",
src = "Tensor", value = "Scalar")
nd_args <- c("self", "dim", "index", "src", "value")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'scatter',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("private", "_scatter_", function(dim, index, src, value, reduce) { args <- mget(x = c("dim", "index", "src", "value", "reduce"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = "int64_t", index = "Tensor", src = "Tensor",
value = "Scalar", reduce = "std::string")
nd_args <- c("self", "dim", "index", "src", "value", "reduce")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'scatter_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "scatter_add", function(dim, index, src) { args <- mget(x = c("dim", "index", "src"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), index = "Tensor",
src = "Tensor")
nd_args <- c("self", "dim", "index", "src")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'scatter_add',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "scatter_add_", function(dim, index, src) { args <- mget(x = c("dim", "index", "src"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = "int64_t", index = "Tensor", src = "Tensor")
nd_args <- c("self", "dim", "index", "src")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'scatter_add_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "select", function(dim, index) { args <- mget(x = c("dim", "index"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("Dimname", "int64_t"), index = "int64_t")
nd_args <- c("self", "dim", "index")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'select',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "set_", function(source, storage_offset, size, stride = list()) { args <- mget(x = c("source", "storage_offset", "size", "stride"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", source = c("Storage", "Tensor"), storage_offset = "int64_t",
size = "IntArrayRef", stride = "IntArrayRef")
nd_args <- c("self", "source", "storage_offset", "size")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'set_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "set_data", function(new_data) { args <- mget(x = c("new_data"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", new_data = "Tensor")
nd_args <- c("self", "new_data")
return_types <- list(list("void"))
call_c_function(
fun_name = 'set_data',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "sgn", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'sgn',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "sgn_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'sgn_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "sigmoid", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'sigmoid',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "sigmoid_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'sigmoid_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "sign", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'sign',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "sign_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'sign_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "signbit", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'signbit',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "sin", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'sin',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "sin_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'sin_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "sinc", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'sinc',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "sinc_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'sinc_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "sinh", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'sinh',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "sinh_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'sinh_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("private", "_size", function(dim) { args <- mget(x = c("dim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = "Dimname")
nd_args <- c("self", "dim")
return_types <- list(list('int64_t'))
call_c_function(
fun_name = 'size',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "slice", function(dim = 1L, start = NULL, end = NULL, step = 1L) { args <- mget(x = c("dim", "start", "end", "step"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = "int64_t", start = "int64_t", end = "int64_t",
step = "int64_t")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'slice',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "slogdet", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list("Tensor", "Tensor"))
call_c_function(
fun_name = 'slogdet',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "smm", function(mat2) { args <- mget(x = c("mat2"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", mat2 = "Tensor")
nd_args <- c("self", "mat2")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'smm',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "softmax", function(dim, dtype = NULL) { args <- mget(x = c("dim", "dtype"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), dtype = "ScalarType")
nd_args <- c("self", "dim")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'softmax',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "solve", function(A) { args <- mget(x = c("A"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", A = "Tensor")
nd_args <- c("self", "A")
return_types <- list(list("Tensor", "Tensor"))
call_c_function(
fun_name = 'solve',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "sort", function(dim = -1L, descending = FALSE, stable) { args <- mget(x = c("dim", "descending", "stable"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), descending = "bool",
stable = "bool")
nd_args <- c("self", "dim", "stable")
return_types <- list(list("Tensor", "Tensor"))
call_c_function(
fun_name = 'sort',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "sparse_dim", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('int64_t'))
call_c_function(
fun_name = 'sparse_dim',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "sparse_mask", function(mask) { args <- mget(x = c("mask"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", mask = "Tensor")
nd_args <- c("self", "mask")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'sparse_mask',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "sparse_resize_", function(size, sparse_dim, dense_dim) { args <- mget(x = c("size", "sparse_dim", "dense_dim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", size = "IntArrayRef", sparse_dim = "int64_t",
dense_dim = "int64_t")
nd_args <- c("self", "size", "sparse_dim", "dense_dim")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'sparse_resize_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "sparse_resize_and_clear_", function(size, sparse_dim, dense_dim) { args <- mget(x = c("size", "sparse_dim", "dense_dim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", size = "IntArrayRef", sparse_dim = "int64_t",
dense_dim = "int64_t")
nd_args <- c("self", "size", "sparse_dim", "dense_dim")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'sparse_resize_and_clear_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("private", "_split", function(split_size, dim = 1L) { args <- mget(x = c("split_size", "dim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", split_size = "int64_t", dim = "int64_t")
nd_args <- c("self", "split_size")
return_types <- list(list('TensorList'))
call_c_function(
fun_name = 'split',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "split_with_sizes", function(split_sizes, dim = 1L) { args <- mget(x = c("split_sizes", "dim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", split_sizes = "IntArrayRef", dim = "int64_t")
nd_args <- c("self", "split_sizes")
return_types <- list(list('TensorList'))
call_c_function(
fun_name = 'split_with_sizes',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "sqrt", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'sqrt',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "sqrt_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'sqrt_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "square", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'square',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "square_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'square_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "squeeze", function(dim) { args <- mget(x = c("dim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"))
nd_args <- c("self", "dim")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'squeeze',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "squeeze_", function(dim) { args <- mget(x = c("dim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"))
nd_args <- c("self", "dim")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'squeeze_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "sspaddmm", function(mat1, mat2, beta = 1L, alpha = 1L) { args <- mget(x = c("mat1", "mat2", "beta", "alpha"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", mat1 = "Tensor", mat2 = "Tensor", beta = "Scalar",
alpha = "Scalar")
nd_args <- c("self", "mat1", "mat2")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'sspaddmm',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "std", function(dim, correction, unbiased = TRUE, keepdim = FALSE) { args <- mget(x = c("dim", "correction", "unbiased", "keepdim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "DimnameList"),
correction = "int64_t", unbiased = "bool", keepdim = "bool")
nd_args <- c("self", "dim", "correction")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'std',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "stft", function(n_fft, hop_length = NULL, win_length = NULL, window = list(), normalized = FALSE, onesided = NULL, return_complex = NULL) { args <- mget(x = c("n_fft", "hop_length", "win_length", "window", "normalized", "onesided", "return_complex"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", n_fft = "int64_t", hop_length = "int64_t",
win_length = "int64_t", window = "Tensor", normalized = "bool",
onesided = "bool", return_complex = "bool")
nd_args <- c("self", "n_fft")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'stft',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("private", "_stride", function(dim) { args <- mget(x = c("dim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"))
nd_args <- c("self", "dim")
return_types <- list(list('int64_t'))
call_c_function(
fun_name = 'stride',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "sub", function(other, alpha = 1L) { args <- mget(x = c("other", "alpha"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"), alpha = "Scalar")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'sub',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "sub_", function(other, alpha = 1L) { args <- mget(x = c("other", "alpha"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"), alpha = "Scalar")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'sub_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "subtract", function(other, alpha = 1L) { args <- mget(x = c("other", "alpha"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"), alpha = "Scalar")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'subtract',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "subtract_", function(other, alpha = 1L) { args <- mget(x = c("other", "alpha"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"), alpha = "Scalar")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'subtract_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "sum", function(dim, keepdim = FALSE, dtype = NULL) { args <- mget(x = c("dim", "keepdim", "dtype"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "DimnameList"),
keepdim = "bool", dtype = "ScalarType")
nd_args <- c("self", "dim")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'sum',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "sum_to_size", function(size) { args <- mget(x = c("size"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", size = "IntArrayRef")
nd_args <- c("self", "size")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'sum_to_size',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "svd", function(some = TRUE, compute_uv = TRUE) { args <- mget(x = c("some", "compute_uv"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", some = "bool", compute_uv = "bool")
nd_args <- "self"
return_types <- list(list("Tensor", "Tensor", "Tensor"))
call_c_function(
fun_name = 'svd',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "swapaxes", function(axis0, axis1) { args <- mget(x = c("axis0", "axis1"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", axis0 = "int64_t", axis1 = "int64_t")
nd_args <- c("self", "axis0", "axis1")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'swapaxes',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "swapaxes_", function(axis0, axis1) { args <- mget(x = c("axis0", "axis1"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", axis0 = "int64_t", axis1 = "int64_t")
nd_args <- c("self", "axis0", "axis1")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'swapaxes_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "swapdims", function(dim0, dim1) { args <- mget(x = c("dim0", "dim1"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim0 = "int64_t", dim1 = "int64_t")
nd_args <- c("self", "dim0", "dim1")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'swapdims',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "swapdims_", function(dim0, dim1) { args <- mget(x = c("dim0", "dim1"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim0 = "int64_t", dim1 = "int64_t")
nd_args <- c("self", "dim0", "dim1")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'swapdims_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "symeig", function(eigenvectors = FALSE, upper = TRUE) { args <- mget(x = c("eigenvectors", "upper"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", eigenvectors = "bool", upper = "bool")
nd_args <- "self"
return_types <- list(list("Tensor", "Tensor"))
call_c_function(
fun_name = 'symeig',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "t", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 't',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "t_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 't_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "take", function(index) { args <- mget(x = c("index"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", index = "Tensor")
nd_args <- c("self", "index")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'take',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "take_along_dim", function(indices, dim = NULL) { args <- mget(x = c("indices", "dim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", indices = "Tensor", dim = "int64_t")
nd_args <- c("self", "indices")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'take_along_dim',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "tan", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'tan',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "tan_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'tan_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "tanh", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'tanh',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "tanh_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'tanh_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "tensor_split", function(indices, sections, tensor_indices_or_sections, dim = 1L) { args <- mget(x = c("indices", "sections", "tensor_indices_or_sections", "dim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", indices = "IntArrayRef", sections = "int64_t",
tensor_indices_or_sections = "Tensor", dim = "int64_t")
nd_args <- c("self", "indices", "sections", "tensor_indices_or_sections"
)
return_types <- list(list('TensorList'))
call_c_function(
fun_name = 'tensor_split',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "tile", function(dims) { args <- mget(x = c("dims"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dims = "IntArrayRef")
nd_args <- c("self", "dims")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'tile',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("private", "_to", function(device, options = list(), other, dtype, non_blocking = FALSE, copy = FALSE, memory_format = NULL) { args <- mget(x = c("device", "options", "other", "dtype", "non_blocking", "copy", "memory_format"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", device = "Device", options = "TensorOptions",
other = "Tensor", dtype = "ScalarType", non_blocking = "bool",
copy = "bool", memory_format = "MemoryFormat")
nd_args <- c("self", "device", "other", "dtype")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'to',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "to_dense", function(dtype = NULL) { args <- mget(x = c("dtype"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dtype = "ScalarType")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'to_dense',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "to_mkldnn", function(dtype = NULL) { args <- mget(x = c("dtype"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dtype = "ScalarType")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'to_mkldnn',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "to_sparse", function(sparse_dim) { args <- mget(x = c("sparse_dim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", sparse_dim = "int64_t")
nd_args <- c("self", "sparse_dim")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'to_sparse',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("private", "_topk", function(k, dim = -1L, largest = TRUE, sorted = TRUE) { args <- mget(x = c("k", "dim", "largest", "sorted"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", k = "int64_t", dim = "int64_t", largest = "bool",
sorted = "bool")
nd_args <- c("self", "k")
return_types <- list(list("Tensor", "Tensor"))
call_c_function(
fun_name = 'topk',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "trace", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'trace',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "transpose", function(dim0, dim1) { args <- mget(x = c("dim0", "dim1"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim0 = c("int64_t", "Dimname"), dim1 = c("int64_t",
"Dimname"))
nd_args <- c("self", "dim0", "dim1")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'transpose',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "transpose_", function(dim0, dim1) { args <- mget(x = c("dim0", "dim1"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim0 = "int64_t", dim1 = "int64_t")
nd_args <- c("self", "dim0", "dim1")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'transpose_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "triangular_solve", function(A, upper = TRUE, transpose = FALSE, unitriangular = FALSE) { args <- mget(x = c("A", "upper", "transpose", "unitriangular"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", A = "Tensor", upper = "bool", transpose = "bool",
unitriangular = "bool")
nd_args <- c("self", "A")
return_types <- list(list("Tensor", "Tensor"))
call_c_function(
fun_name = 'triangular_solve',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "tril", function(diagonal = 0L) { args <- mget(x = c("diagonal"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", diagonal = "int64_t")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'tril',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "tril_", function(diagonal = 0L) { args <- mget(x = c("diagonal"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", diagonal = "int64_t")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'tril_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "triu", function(diagonal = 0L) { args <- mget(x = c("diagonal"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", diagonal = "int64_t")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'triu',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "triu_", function(diagonal = 0L) { args <- mget(x = c("diagonal"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", diagonal = "int64_t")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'triu_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "true_divide", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'true_divide',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "true_divide_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'true_divide_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "trunc", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'trunc',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "trunc_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'trunc_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "type_as", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'type_as',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "unbind", function(dim = 1L) { args <- mget(x = c("dim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"))
nd_args <- c("self", "dim")
return_types <- list(list('TensorList'))
call_c_function(
fun_name = 'unbind',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "unflatten", function(dim, sizes, names = NULL) { args <- mget(x = c("dim", "sizes", "names"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), sizes = "IntArrayRef",
names = "DimnameList")
nd_args <- c("self", "dim", "sizes", "names")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'unflatten',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "unfold", function(dimension, size, step) { args <- mget(x = c("dimension", "size", "step"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dimension = "int64_t", size = "int64_t",
step = "int64_t")
nd_args <- c("self", "dimension", "size", "step")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'unfold',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "uniform_", function(from = 0L, to = 1L, generator = NULL) { args <- mget(x = c("from", "to", "generator"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", from = "double", to = "double", generator = "Generator")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'uniform_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "unsafe_chunk", function(chunks, dim = 1L) { args <- mget(x = c("chunks", "dim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", chunks = "int64_t", dim = "int64_t")
nd_args <- c("self", "chunks")
return_types <- list(list('TensorList'))
call_c_function(
fun_name = 'unsafe_chunk',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "unsafe_split", function(split_size, dim = 1L) { args <- mget(x = c("split_size", "dim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", split_size = "int64_t", dim = "int64_t")
nd_args <- c("self", "split_size")
return_types <- list(list('TensorList'))
call_c_function(
fun_name = 'unsafe_split',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "unsafe_split_with_sizes", function(split_sizes, dim = 1L) { args <- mget(x = c("split_sizes", "dim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", split_sizes = "IntArrayRef", dim = "int64_t")
nd_args <- c("self", "split_sizes")
return_types <- list(list('TensorList'))
call_c_function(
fun_name = 'unsafe_split_with_sizes',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "unsqueeze", function(dim) { args <- mget(x = c("dim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = "int64_t")
nd_args <- c("self", "dim")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'unsqueeze',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "unsqueeze_", function(dim) { args <- mget(x = c("dim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = "int64_t")
nd_args <- c("self", "dim")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'unsqueeze_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "values", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'values',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "var", function(dim, correction, unbiased = TRUE, keepdim = FALSE) { args <- mget(x = c("dim", "correction", "unbiased", "keepdim"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "DimnameList"),
correction = "int64_t", unbiased = "bool", keepdim = "bool")
nd_args <- c("self", "dim", "correction")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'var',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "vdot", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'vdot',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("private", "_view", function(dtype, size) { args <- mget(x = c("dtype", "size"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", dtype = "ScalarType", size = "IntArrayRef")
nd_args <- c("self", "dtype", "size")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'view',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "view_as", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = "Tensor")
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'view_as',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "vsplit", function(indices, sections) { args <- mget(x = c("indices", "sections"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", indices = "IntArrayRef", sections = "int64_t")
nd_args <- c("self", "indices", "sections")
return_types <- list(list('TensorList'))
call_c_function(
fun_name = 'vsplit',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "where", function(condition, other) { args <- mget(x = c("condition", "other"))
args <- append(list(self = self), args)
expected_types <- list(condition = "Tensor", self = "Tensor", other = "Tensor")
nd_args <- c("condition", "self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'where',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "xlogy", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'xlogy',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "xlogy_", function(other) { args <- mget(x = c("other"))
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"))
nd_args <- c("self", "other")
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'xlogy_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
Tensor$set("public", "zero_", function() { args <- list()
args <- append(list(self = self), args)
expected_types <- list(self = "Tensor")
nd_args <- "self"
return_types <- list(list('Tensor'))
call_c_function(
fun_name = 'zero_',
args = args,
expected_types = expected_types,
nd_args = nd_args,
return_types = return_types,
fun_type = 'method'
)})
|
fabric_text <- function(cid,
cwidth = 800,
cheight = 600,
cfill = "
textId,
text,
left = 100,
top = 100,
fill = "
angle = 0,
opacity = 1,
fontFamily = 'Comic Sans',
fontSize = 40,
fontStyle = 'normal',
strokecolor = "
strokewidth = 1,
fontWeight = "normal",
underline = FALSE,
linethrough = FALSE,
overline = FALSE,
selectable = TRUE,
shadow = FALSE,
shadowCol = "
textAlign = "center",
lineHeight = 1,
textBackgroundColor = NULL,
isDrawingMode = FALSE){
if (!fontStyle %in% c("normal",
"italic")) {
stop(paste0("fontStyle accepts two values: 'normal' or 'italic'"))
}
selectable <- ifelse(selectable == TRUE, "true", "false")
isDrawingMode <- ifelse(isDrawingMode == TRUE, "true", "false")
underline <- ifelse(underline == TRUE, "true", "false")
linethrough <- ifelse(linethrough == TRUE, "true", "false")
overline <- ifelse(overline == TRUE, "true", "false")
shadow <- ifelse(shadow == TRUE, glue::glue("shadow:'{shadowCol} 5px 5px 5px'"), "")
tBG <- ifelse(is.null(textBackgroundColor), character(0), glue::glue("textBackgroundColor: '{textBackgroundColor}',"))
htmltools::tagList(
htmltools::tags$canvas(id = cid, width = cwidth, height = cheight),
fabric_dependency(),
htmltools::tags$script(htmltools::HTML(glue::glue(
"
var {cid} = new fabric.Canvas('{cid}', {{
isDrawingMode: {isDrawingMode}
}});
{cid}.backgroundColor = '{cfill}';
var {textId} = new fabric.Text(\"{text}\", {{
left: {left},
top: {top},
fontFamily: '{fontFamily}',
fontSize: {fontSize},
fontStyle: '{fontStyle}',
fontWeight: '{fontWeight}',
underline: {underline},
linethrough: {linethrough},
overline: {overline},
fill: '{fill}',
angle: {angle},
opacity: {opacity},
stroke: '{strokecolor}',
strokeWidth: {strokewidth},
textAlign: '{textAlign}',
lineHeight: {lineHeight},
{tBG}
selectable: {selectable},
{shadow}
}});
{cid}.add({textId});
"
, .na = "")))
)
}
|
ABC_P2_norm <-
function(n,ObsMean,M_Lo,M_Hi,SD_Lo,SD_Hi,delta,iter){
posterior<-c()
discard<-c()
Norm<-c()
Avg<-c()
Std<-c()
i<-1
j<-1
k<-1
l<-1
m<-1
while(i <= iter){
avg<-runif(1,M_Lo,M_Hi)
std<-runif(1,SD_Lo,SD_Hi)
while(j<=n){
norm<-round(rnorm(1, mean=avg, sd=std))
if(norm>0){
Norm[j]<-norm
j<-j+1}
}
P2<-runif(1,0,1)
sire2<-rbinom(n,Norm,P2)
meanP2<-mean(sire2)
if(abs(meanP2 - ObsMean)>delta){
discard[k]<-P2
k<-k+1
}else
if(abs(meanP2 - ObsMean)<=delta){
posterior[i]<-P2
Avg[l]<-avg
Std[m]<-std
i<-i+1
l<-l+1
m<-m+1
}
}
list(posterior = posterior, Avg = Avg, Std = Std)
}
|
createSpecificRef <- function(currRefference, modelSize, neighborhoodSize, genePercents, chosenCells, chosenNeigCells){
specificRef = do.call(cbind,lapply(chosenCells,function(chosenCell){
currRefference[,chosenNeigCells[[chosenCell]]]
}))
colnames(specificRef) = unlist(lapply(chosenCells,function(cell){
rep(paste(colnames(currRefference)[cell],cell,sep="_"),neighborhoodSize)
}))
row.names(specificRef) = row.names(currRefference)
specificRef = specificRef[sample(1:dim(currRefference)[1],round(genePercents*dim(currRefference)[1])),]
list(ref = specificRef, chosenCells = chosenCells)
}
createNoCellDupeReference <- function(refference){
currCellNames = colnames(refference)
refferenceNoDups = as.data.frame(matrix(0,nrow = dim(refference)[1], ncol=length(unique(currCellNames))))
for (i in 1:length(unique(currCellNames))){
if (length(which(currCellNames == unique(currCellNames)[i]))!=1){
refferenceNoDups[,i] = rowMeans(refference[,which(currCellNames == unique(currCellNames)[i])])
}else{
refferenceNoDups[,i] = refference[,which(currCellNames == unique(currCellNames)[i])]
}
}
row.names(refferenceNoDups) = row.names(refference)
colnames(refferenceNoDups) = unique(currCellNames)
return(refferenceNoDups)
}
GeneBasedAnova <- function(specificRefference, nonZeroRatio = NULL){
cellNamesForAnova = colnames(specificRefference)
genes_to_take = row.names(specificRefference)
genes_to_take = names(which(rowMeans(specificRefference[genes_to_take,])!=0))
if(!is.null(nonZeroRatio)){
genes_to_take = unlist(apply(as.data.frame(genes_to_take),1,function(gene){
if((length(which(as.numeric(specificRefference[gene,which(cellNamesForAnova==1)])!=0))/length(as.numeric(specificRefference[gene,which(cellNamesForAnova==1)])))>nonZeroRatio){
gene
}else{
NULL
}
}))
}
dat = cbind(rep(0,length(genes_to_take)),specificRefference[genes_to_take,])
group = c("",cellNamesForAnova)
dmat <- stats::model.matrix(~ group)
fit <- limma::lmFit(dat, dmat)
fit = fit[,-1]
fit <- limma::eBayes(fit)
fitF = fit$F
res = as.data.frame(cbind(gsub("group","",colnames(fit$coefficients)[apply(fit$coefficients,1,function(x){order(x,decreasing = T)[1]})]),fitF))
colnames(res) = c("group", "score")
listOfGenes = apply(as.data.frame(unique(cellNamesForAnova)),1,function(cellGroup){
selectedIndexes = which(as.character(res$group)==as.character(cellGroup))
(genes_to_take[selectedIndexes])[order(res$score[selectedIndexes],decreasing = T)]
})
return(listOfGenes)
}
selectGenesUsingKappa <- function(refferenceNoDups, allGenes){
bestKappa = Inf
bestG = 0
mul = 1
maxNumberOfGenesPerCell = 50
bestGenes = c()
indexRange = 2:maxNumberOfGenesPerCell
for (i in indexRange){
selectedGenes = unique(as.character(unlist(lapply(1:length(allGenes), function(listIndex){
unlist(allGenes[listIndex])[which(!is.na(unlist(allGenes[listIndex])[1:as.numeric(i*mul)]))]
}))))
currRefferenceNoDups = refferenceNoDups[match(selectedGenes, row.names(refferenceNoDups)),]
newKappa = kappa(currRefferenceNoDups)
if (newKappa<bestKappa){
bestKappa = newKappa
bestG = i
bestGenes = unique(selectedGenes)
}
}
finalRefference = refferenceNoDups[which(row.names(refferenceNoDups) %in% bestGenes),]
return(list(reference = finalRefference, G = bestG, kappa = bestKappa))
}
checkVariableGenes = function(a, ratio) {
count_nonZeros = length(which(a > min(a)))
if (count_nonZeros/length(a) > ratio) {
var(a)/ mean(a)
} else {
0
}
}
runLibLinear = function(ref_matrix, sample_matrix){
X <- data.matrix(ref_matrix)
X <- X[apply(X,1,sd)!=0,]
Y <- data.matrix(sample_matrix)
Y = Y[match(row.names(X),row.names(Y)),]
Y <- Y[row.names(Y) %in% row.names(X),]
X <- X[row.names(X) %in% row.names(Y),]
X <- t(apply(X,1,function(rowX){
(rowX - mean(rowX)) / sd(as.vector(rowX))
}))
C = LiblineaR::heuristicC(X)
predictionMatrix = do.call(rbind,lapply(1:dim(Y)[2],function(index){
y <- Y[,index]
y <- (y - mean(y)) / sd(y)
(LiblineaR::LiblineaR(data = X, target = y, type = 11, cost = C)$W)[1:dim(X)[2]]
}))
colnames(predictionMatrix) = colnames(X)
row.names(predictionMatrix) = colnames(Y)
predictionMatrix
}
choseCellsForRuns = function(XY, refNames, modelSize, minSelection, neighborhoodSize){
k = floor(modelSize/length(unique(refNames)))
if(k==0){
k=1
}
minValueToReduceTo = 10^-10
initialGrids = lapply(unique(refNames), function(currCluster){
clusterIndexes = which(refNames==currCluster)
nbins = max(k,length(clusterIndexes)/neighborhoodSize)
if(is.null(dim(XY))){
currXY = XY[clusterIndexes]
breaks = seq(min(currXY)-10^-7,max(currXY)+10^-7, (max(currXY)-min(currXY)+2*10^-7)/ceiling(nbins))
grid <- rep(NA,ceiling(nbins))
cellLocationOnGrid = rep(NA,length(currXY))
for(currBreakIndex in 2:length(breaks)){
cellLocationOnGrid[which(currXY>breaks[currBreakIndex-1] & currXY<breaks[currBreakIndex])] = currBreakIndex-1
}
tab <- table(cellLocationOnGrid)
grid[as.numeric(names(tab))] <- tab
}else{
currXY = XY[clusterIndexes,]
ch <- grDevices::chull(currXY)
coords <- currXY[c(ch, ch[1]), ]
poly = sp::SpatialPolygons(list(sp::Polygons(list(sp::Polygon(coords)), "x")))
grid <- raster::raster(raster::extent(poly), nrows = ceiling(sqrt(nbins)), ncols= ceiling(sqrt(nbins)))
sp::proj4string(grid)<-sp::proj4string(poly)
cellLocationOnGrid = raster::cellFromXY(grid, currXY)
tab <- table(cellLocationOnGrid)
grid[as.numeric(names(tab))] <- tab
}
list(grid = grid, clusterIndexes = clusterIndexes, cellLocationOnGrid = cellLocationOnGrid, nFullbins = length(tab), maxBinSize = max(tab))
})
numOfRuns = ceiling(minSelection*max(unlist(lapply(initialGrids,function(clusterData){ clusterData$nFullbins * clusterData$maxBinSize }))) / k)
meanDistMatrix = rep(1,length(refNames))
chosenCellList = lapply(1:numOfRuns, function(runNum){
chosenCells = as.numeric(unlist(lapply(unique(refNames),function(currCluster){
initialGrid = initialGrids[[which(unique(refNames)==currCluster)]]
clusterIndexes = initialGrid$clusterIndexes
grid = initialGrid$grid
cellLocationOnGrid = initialGrid$cellLocationOnGrid
kToUse = k
if(k>length(which(!is.na(grid[])))){
kToUse = length(which(!is.na(grid[])))
}
gridCellsToUse = sample(which(!is.na(grid[])),kToUse,replace = F)
chosenCellsForCluster = clusterIndexes[unlist(lapply(gridCellsToUse, function(currCell){
chosenCell = which(cellLocationOnGrid==currCell)
if(length(chosenCell)>1){
chosenCell = sample(chosenCell,1,prob = meanDistMatrix[clusterIndexes[chosenCell]])
}
chosenCell
}))]
chosenCellsForCluster
})))
cellsToReduce = chosenCells[which(meanDistMatrix[chosenCells]>minValueToReduceTo)]
meanDistMatrix[cellsToReduce] <<- meanDistMatrix[cellsToReduce]/10
chosenCells
})
chosenNeigList = lapply(1:length(refNames),function(cellIndex){
selectedCellType = refNames[cellIndex]
selectedCellIndexes = which(refNames == selectedCellType)
cellXY = XY[cellIndex,]
cellDist = fields::rdist(t(as.matrix(cellXY)),XY[selectedCellIndexes,])
chosenRepeats = order(as.numeric(cellDist),decreasing = F)[1:neighborhoodSize]
chosenRepeats = chosenRepeats[!is.na(chosenRepeats)]
selectedCellIndexes[chosenRepeats]
})
list(chosenCellList = chosenCellList, chosenNeigList = chosenNeigList, numOfRuns = numOfRuns)
}
CPMMain = function(refference,refferenceNames, Y, chosenCellList, chosenCellNeigList ,numOfRuns, modelSize, neighborhoodSize,
no_cores, genePercents, quantifyTypes, typeTransformation, calculateCI){
YReduced = Y[row.names(Y) %in% row.names(refference), , drop = FALSE]
geneVarianceRef = apply(refference,1,function(gene){checkVariableGenes(as.numeric(as.matrix(gene)),0.1)})
geneVarianceFinalRef = sort(geneVarianceRef[geneVarianceRef>0],decreasing = T)
mutualGenes = names(geneVarianceFinalRef)[names(geneVarianceFinalRef) %in% row.names(YReduced)]
YReduced = YReduced[mutualGenes, , drop = FALSE]
refferenceSmaller = refference[mutualGenes,]
if(is.null(no_cores)){
no_cores = max(1, parallel::detectCores() - 1)
}
cl<-parallel::makeCluster(no_cores)
parallel::clusterExport(cl=cl, varlist=c("refferenceNames", "refferenceSmaller", "YReduced","neighborhoodSize","modelSize",
"createSpecificRef","GeneBasedAnova", "chosenCellList", "chosenCellNeigList" ,"createNoCellDupeReference", "selectGenesUsingKappa", "runLibLinear", "genePercents"),
envir=environment())
doSNOW::registerDoSNOW(cl)
pb <- utils::txtProgressBar(min = 1, max = numOfRuns, style = 3)
progress <- function(n) setTxtProgressBar(pb, n)
opts <- list(progress = progress)
`%dopar2%` <- foreach::`%dopar%`
runNumber = NULL
resultSmallMatrixes <- foreach::foreach(runNumber = 1:numOfRuns, .options.snow = opts) %dopar2% {
print(runNumber)
completeSpecificRefBefore = createSpecificRef(refferenceSmaller, modelSize, neighborhoodSize, genePercents, chosenCellList[[runNumber]], chosenCellNeigList)
completeSpecificRef = completeSpecificRefBefore$ref
clusterNamesVector = rep("", dim(completeSpecificRef)[2])
for(cluster in unique(refferenceNames)){
selectedCellsForCluster = completeSpecificRefBefore$chosenCells[which(refferenceNames[completeSpecificRefBefore$chosenCells] == cluster)]
selectedNamesForCluster = paste(colnames(refferenceSmaller)[selectedCellsForCluster],selectedCellsForCluster,sep="_")
clusterNamesVector[!is.na(match(colnames(completeSpecificRef),selectedNamesForCluster))] = cluster
}
allGenes = c()
if(quantifyTypes){
allGenesList = GeneBasedAnova(completeSpecificRef)
}else{
allGenesList = lapply(unique(refferenceNames), function(cluster){
specificClusterRef = completeSpecificRef[,which(clusterNamesVector == cluster)]
colnames(specificClusterRef) = colnames(completeSpecificRef)[clusterNamesVector == cluster]
GeneBasedAnova(specificClusterRef)
})
}
for (list in allGenesList){
allGenes = c(allGenes, list)
}
specificRefNoDupes = createNoCellDupeReference(completeSpecificRef)
results = selectGenesUsingKappa(specificRefNoDupes, allGenes)
X = results$reference
X = refferenceSmaller[row.names(X),completeSpecificRefBefore$chosenCells]
X = X[rowSums(X)!=0,]
YRefinedReduced = YReduced[row.names(YReduced) %in% row.names(X),]
PBSReductionData = YRefinedReduced
setTxtProgressBar(pb, runNumber)
resMatrix = t(runLibLinear(X, PBSReductionData))
row.names(resMatrix) = chosenCellList[[runNumber]]
resMatrix
}
parallel::stopCluster(cl)
close(pb)
print("Combining CPM iterations")
predictedCells = matrix(0, nrow = dim(YReduced)[2], ncol = dim(refferenceSmaller)[2])
predictedCellsCounts = matrix(0, nrow = dim(YReduced)[2], ncol = dim(refferenceSmaller)[2])
for(resultMatrix in resultSmallMatrixes){
completeResultMatrix = matrix(0, nrow = dim(resultMatrix)[2], ncol = dim(refferenceSmaller)[2])
completeResultMatrix[,as.numeric(as.matrix(row.names(resultMatrix)))] = t(resultMatrix)
predictedCells = predictedCells + completeResultMatrix
predictedCellsCounts = predictedCellsCounts + abs(sign(completeResultMatrix))
}
predictedCellsFinal = predictedCells/predictedCellsCounts
print("Smoothing")
allClusterMeansMatrix = t(do.call(rbind,lapply(1:length(refferenceNames),function(cell){
rowMeans(predictedCellsFinal[,chosenCellNeigList[[cell]]])
})))
colnames(allClusterMeansMatrix) = colnames(refference)
row.names(allClusterMeansMatrix) = colnames(Y)
cellTypeRes = NULL
seRes = NULL
confMatrix = NULL
if(quantifyTypes){
print("Calculating cell type quantities")
allClusterMeansMatrixForCellTypes = allClusterMeansMatrix
if(typeTransformation){
allClusterMeansMatrixForCellTypes = t(apply(t(allClusterMeansMatrixForCellTypes),2,function(x){
x-min(x)
}))
}
cellTypeRes = do.call(cbind,lapply(unique(refferenceNames),function(currCluster){
rowMeans(allClusterMeansMatrixForCellTypes[,currCluster==refferenceNames])
}))
colnames(cellTypeRes) = unique(refferenceNames)
if(typeTransformation){
cellTypeRes = t(apply(t(cellTypeRes),2,function(x){
x/sum(x)
})
)
}
}
if(calculateCI){
print("Calculating the confidence interval matrix")
resultOriginalSizeMatrixes = lapply(resultSmallMatrixes, function(resultSmallMatrix){
completeResultMatrix = matrix(NA, nrow = dim(resultSmallMatrix)[2], ncol = dim(refferenceSmaller)[2])
completeResultMatrix[,match(colnames(allClusterMeansMatrix)[as.numeric(as.matrix(row.names(resultSmallMatrix)))],colnames(refferenceSmaller))] = t(resultSmallMatrix)
completeResultMatrix
})
seRes <- do.call(rbind,lapply(colnames(YReduced), function(sample){
sampleMatrix = do.call(rbind, lapply(resultOriginalSizeMatrixes,function(currRes){
currRes[which(colnames(YReduced)==sample),]
}))
apply(sampleMatrix,2,function(x){
sd(x[!is.na(x)])/sqrt(length(which(!is.na(x))))
})
}))
seResNorm = t(do.call(rbind,lapply(1:length(refferenceNames),function(cell){
rowMeans(seRes[,chosenCellNeigList[[cell]]])
})))
confMatrix = matrix(paste(allClusterMeansMatrix-1.96*seResNorm,allClusterMeansMatrix+1.96*seResNorm,sep = " <-> "),ncol = dim(allClusterMeansMatrix)[2])
colnames(seRes) = colnames(confMatrix) = colnames(refference)
row.names(seRes) = row.names(confMatrix) = colnames(Y)
}
print("Done")
list(predictions = allClusterMeansMatrix, cellTypePredictions = cellTypeRes, sePredictions = seRes, confMatrix = confMatrix)
}
CPM = function(SCData, SCLabels, BulkData, cellSpace, no_cores = NULL, neighborhoodSize = 10, modelSize = 50, minSelection = 5, quantifyTypes = F, typeTransformation = F, calculateCI = F){
genePercents = 0.4
if(min(table(SCLabels))<neighborhoodSize){
neighborhoodSize = min(table(SCLabels))
print(paste("Neighborhood size was switched to:",neighborhoodSize,sep=" "))
}
if(quantifyTypes){
modelSize = length(unique(SCLabels))
print(paste("Model size was switched to:",modelSize,sep=" "))
}else if(length(SCLabels)<modelSize){
modelSize = length(SCLabels)
print(paste("Model size was switched to:",modelSize,sep=" "))
}
if(!is.null(SCData) & !is.null(SCLabels) & !is.null(BulkData) & !is.null(cellSpace)){
print("Selecting cells for each iteration")
}
cellSelection = choseCellsForRuns(cellSpace, SCLabels, modelSize, minSelection,neighborhoodSize)
numOfRunsToUse = cellSelection$numOfRuns
print(paste("Number of iteration:",numOfRunsToUse,sep=" "))
cellSelectionList = cellSelection$chosenCellList
cellNeigSelectionList = cellSelection$chosenNeigList
print("Running CPM, this may take a few minutes")
deconvolutionRes = CPMMain(SCData, SCLabels,BulkData, cellSelectionList, cellNeigSelectionList, numOfRunsToUse,modelSize, neighborhoodSize, no_cores, genePercents, quantifyTypes, typeTransformation, calculateCI)
list(predicted = deconvolutionRes$predictions, cellTypePredictions = deconvolutionRes$cellTypePredictions, confIntervals = deconvolutionRes$confMatrix, numOfRuns = numOfRunsToUse)
}
"BulkFlu"
"SCFlu"
"SCLabels"
"SCCellSpace"
|
scanoneboot <-
function(cross, chr, pheno.col=1, model=c("normal","binary","2part","np"),
method=c("em","imp","hk","ehk","mr","mr-imp","mr-argmax"),
addcovar=NULL, intcovar=NULL, weights=NULL,
use=c("all.obs", "complete.obs"), upper=FALSE,
ties.random=FALSE, start=NULL, maxit=4000, tol=1e-4,
n.boot=1000, verbose=FALSE)
{
if(!missing(chr)) cross <- subset(cross, chr)
if(nchr(cross) != 1) {
warning("Considering just the first chromosome (", names(cross$geno)[1], ").")
cross <- subset(cross, names(cross$geno)[1])
}
if(LikePheVector(pheno.col, nind(cross), nphe(cross))) {
cross$pheno <- cbind(pheno.col, cross$pheno)
pheno.col <- 1
}
if(length(pheno.col) > 1)
stop("pheno.col should indicate a single phenotype")
if(is.character(pheno.col)) {
num <- find.pheno(cross, pheno.col)
if(is.na(num))
stop("Couldn't identify phenotype \"", pheno.col, "\"")
pheno.col <- num
}
if(pheno.col < 1 || pheno.col > nphe(cross))
stop("pheno.col should be between 1 and ", nphe(cross))
out <- scanone(cross, pheno.col=pheno.col, model=model, method=method,
addcovar=addcovar, intcovar=intcovar, weights=weights,
use=use, upper=upper, ties.random=ties.random, start=start,
maxit=maxit, tol=tol)
maxlod <- max(out[,3],na.rm=TRUE)
w <- which(!is.na(out[,3]) & out[,3] == maxlod)
results <- rep(NA, n.boot)
n.ind <- nind(cross)
n.prnt <- floor(n.boot/20)
for(i in 1:n.boot) {
temp <- subset(cross, ind=sample(n.ind, replace=TRUE))
out <- scanone(temp, pheno.col=pheno.col, model=model, method=method,
addcovar=addcovar, intcovar=intcovar, weights=weights,
use=use, upper=upper, ties.random=ties.random, start=start,
maxit=maxit, tol=tol)
mx <- max(out[,3],na.rm=TRUE)
w <- out[!is.na(out[,3]) & out[,3]==mx,2]
if(length(w) > 1) w <- sample(w,1)
results[i] <- w
if(verbose && ((i-1) %% n.prnt) == 0)
cat("replicate", i, "\n")
}
attr(results, "results") <- out
class(results) <- "scanoneboot"
results
}
summary.scanoneboot <-
function(object, prob=0.95, expandtomarkers=FALSE, ...)
{
lo <- (1-prob)/2
results <- attr(object, "results")
o <- max(results)
qu <- quantile(object, c(lo, 1-lo))
wh1 <- which(results[,2] <= qu[1])
wh1 <- wh1[length(wh1)]
wh2 <- which(results[,2] >= qu[2])
wh2 <- wh2[1]
if(expandtomarkers) {
markerpos <- (1:nrow(results))[-grep("^c.+\\.loc-*[0-9]+(\\.[0-9]+)*$", rownames(results))]
if(any(markerpos <= wh1))
wh1 <- max(markerpos[markerpos <= wh1])
if(any(markerpos >= wh2))
wh2 <- min(markerpos[markerpos >= wh2])
}
rbind(results[wh1,], o, results[wh2,])
}
print.scanoneboot <-
function(x, ...)
{
print(as.numeric(x))
}
|
context("AUC")
set.seed(SEED)
N <- 100
x0 <- rnorm(N, mean = runif(1))
x1 <- rnorm(N, mean = 2 * runif(1))
x <- c(x0, x1)
y <- c(rep(0, N), rep(1, N))
auc.conf <- AUCBoot(x, y, seed = 1)
auc.conf2 <- AUCBoot(x, y, seed = 1)
test_that("Same results of AUC with seed", {
expect_equal(auc.conf, auc.conf2)
})
test_that("Same results of AUC in particular cases", {
expect_equal(AUC(c(0, 0), 0:1), 0.5)
expect_equal(AUC(c(0.2, 0.1, 1), c(0, 0, 1)), 1)
expect_warning(auc1 <- AUCBoot(c(0, 0), 0:1))
expect_equivalent(auc1, c(rep(0.5, 3), 0))
expect_warning(auc2 <- AUCBoot(c(0.2, 0.1, 1), c(0, 0, 1)))
expect_equivalent(auc2, c(rep(1, 3), 0))
})
test_that("Same as wilcox test", {
expect_equivalent(AUC(x, y), wilcox.test(x1, x0)$statistic / N^2)
})
test_that("Same as package ModelMetrics (AUC < 0.5)", {
skip_if_not_installed("ModelMetrics")
for (i in 1:5) {
N <- 10^i
x4 <- c(sample(10, size = N, replace = TRUE),
sample(5, size = N, replace = TRUE))
y4 <- rep(0:1, each = N)
expect_equivalent(AUC(x4, y4), ModelMetrics::auc(y4, x4))
}
})
test_that("AUC() does not accept missing values", {
expect_error(AUC(c(0, 1, NA), c(0, 1, 1)), "missing values")
expect_error(AUC(c(0, 1, 0), c(0, 0, NA)), "composed of 0s and 1s")
expect_error(AUC(c(0, 1, 0), c(0, 1, NA)), "composed of 0s and 1s")
expect_error(AUCBoot(c(0, 1, NA), c(0, 1, 1)), "missing values")
expect_error(AUCBoot(c(0, 1, 0), c(0, 0, NA)), "composed of 0s and 1s")
expect_error(AUCBoot(c(0, 1, 0), c(0, 1, NA)), "composed of 0s and 1s")
})
test_that("AUCBoot() seems okay", {
aucs <- AUCBoot(sample(5, 1e4, replace = TRUE),
sample(0:1, 1e4, replace = TRUE))
expect_gt(aucs[2], 0.47)
expect_lt(aucs[3], 0.53)
aucs <- AUCBoot(rnorm(1e4), sample(0:1, 1e4, replace = TRUE))
expect_gt(aucs[2], 0.47)
expect_lt(aucs[3], 0.53)
})
|
enspls.od <- function(
x, y,
maxcomp = 5L,
cvfolds = 5L,
alpha = seq(0.2, 0.8, 0.2),
reptimes = 500L,
method = c("mc", "boot"),
ratio = 0.8,
parallel = 1L) {
if (missing(x) | missing(y)) stop("Please specify both x and y")
method <- match.arg(method)
x.row <- nrow(x)
samp.idx <- vector("list", reptimes)
samp.idx.remain <- vector("list", reptimes)
if (method == "mc") {
for (i in 1L:reptimes) {
samp.idx[[i]] <- sample(1L:x.row, round(x.row * ratio))
samp.idx.remain[[i]] <- setdiff(1L:x.row, samp.idx[[i]])
}
}
if (method == "boot") {
for (i in 1L:reptimes) {
samp.idx[[i]] <- sample(1L:x.row, x.row, replace = TRUE)
samp.idx.remain[[i]] <- setdiff(1L:x.row, unique(samp.idx[[i]]))
}
}
if (parallel < 1.5) {
errorlist <- vector("list", reptimes)
for (i in 1L:reptimes) {
x.sample <- x[samp.idx[[i]], ]
x.remain <- x[samp.idx.remain[[i]], ]
y.sample <- y[samp.idx[[i]]]
y.remain <- y[samp.idx.remain[[i]]]
errorlist[[i]] <- enspls.od.core(
x.sample, y.sample, x.remain, y.remain,
maxcomp, cvfolds, alpha
)
}
} else {
registerDoParallel(parallel)
errorlist <- foreach(i = 1L:reptimes) %dopar% {
x.sample <- x[samp.idx[[i]], ]
x.remain <- x[samp.idx.remain[[i]], ]
y.sample <- y[samp.idx[[i]]]
y.remain <- y[samp.idx.remain[[i]]]
enspls.od.core(
x.sample, y.sample, x.remain, y.remain,
maxcomp, cvfolds, alpha
)
}
}
prederrmat <- matrix(NA, ncol = x.row, nrow = reptimes)
for (i in 1L:reptimes) {
for (j in 1L:length(samp.idx.remain[[i]])) {
prederrmat[i, samp.idx.remain[[i]][j]] <- errorlist[[i]][j]
}
}
errmean <- abs(colMeans(prederrmat, na.rm = TRUE))
errmedian <- apply(prederrmat, 2L, median, na.rm = TRUE)
errsd <- apply(prederrmat, 2L, sd, na.rm = TRUE)
res <- list(
"error.mean" = errmean,
"error.median" = errmedian,
"error.sd" = errsd,
"predict.error.matrix" = prederrmat
)
class(res) <- "enspls.od"
res
}
enspls.od.core <- function(
x.sample, y.sample, x.remain, y.remain,
maxcomp, cvfolds, alpha) {
invisible(capture.output(
spls.cvfit <- cv.spls(
x.sample,
y.sample,
fold = cvfolds,
K = maxcomp,
eta = alpha,
scale.x = TRUE,
scale.y = FALSE,
plot.it = FALSE
)
))
cv.bestcomp <- spls.cvfit$"K.opt"
cv.bestalpha <- spls.cvfit$"eta.opt"
spls.fit <- spls(
x.sample,
y.sample,
K = cv.bestcomp,
eta = cv.bestalpha,
scale.x = TRUE,
scale.y = FALSE
)
pred <- predict(spls.fit, newx = x.remain)
error <- y.remain - pred
names(error) <- NULL
error
}
|
scRNAtools_DEGsA <-
function(example,types_all,type1,type2,num)
{
edgeR::cpm
n<-ncol(example)
data1<-example[,-1]
zcpm<-cpm(data1)
keep<-rowSums(zcpm>1)>=((n-1)*num)
zset<-example[keep,]
subtype1<-types_all[which(types_all[,1]%in%type1),2]
subtype2<-types_all[which(types_all[,1]%in%type2),2]
type1_exp<-zset[,which(as.numeric(zset[1,])%in%subtype1)]
type2_exp<-zset[,which(as.numeric(zset[1,])%in%subtype2)]
group1<-apply(type1_exp[,-1],1, mean)
group2<-apply(type2_exp[,-1],1, mean)
group1<-as.matrix(group1)
group2<-as.matrix(group2)
FC1<-group2/group1
FC2<-cbind(as.matrix(zset[,1]),FC1)
FC2<-FC2[-1,]
FC2[which(FC2[,2]%in%"Inf"),2]<-5
colnames(FC2)<-c("Gene_symbol","Fold_change")
up<-FC2[which(FC2[,2]>2),]
down<-FC2[which(FC2[,2]<0.5),]
no_diff1<-FC2[-which(FC2[,1]%in%up[,1]),]
no_diff2<-no_diff1[-which(no_diff1[,1]%in%down[,1]),]
main=paste("Differentially expressed genes between",type1,"and",type2)
max_v<-as.numeric(max(FC2[,2]))
max_n<-max(nrow(up),nrow(down),nrow(no_diff2))
p<-plot(1:nrow(up),up[,2],main=main,ylim=c(0,max_v),xlim=c(0,max_n),xlab = "Gene number",ylab=paste("log2 (",type1,"/",type2,")"),col="red",pch=19,lwd=2)
lines(1:nrow(down),down[,2],type="p",ylim=c(0,max_v),xlab = "Gene number",ylab=paste("log2(",type1,"/",type2,")"),col="green",pch=19,lwd=2)
lines(1:nrow(no_diff2),no_diff2[,2],type="p",ylim=c(0,max_v),xlab = "Gene number",ylab=paste("log2(",type1,"/",type2,")"),col="gray",pch=19,lwd=2)
abline(h = c(0.5, 2),lwd=0.5,lty=3)
text((max_n-5),0.2,"log2(FC)=0.5")
text((max_n-5),2.2,"log2(FC)=2")
legend((max_n-20),(max_v-0.2),c("Up-regulated genes","Not DEGs","Down-regulated genes"),col=c("red","gray","green"),text.col=c("red","gray","green"),pch=19,cex=0.7)
return(FC2)
}
|
createJSON <- function(repo, pkg_name, descr_df, scm_df, docdir, rev_deps,
suffix = paste0("_", descr_df$Version, ".json")) {
reponame <- paste0("GRAN", repo_name(repo))
descr_df$id <- encode_string(paste0(reponame,
descr_df$Package,
descr_df$Version))
descr_df$gran_repo <- reponame
scm_info <- scm_df[scm_df$name == pkg_name, ]
descr_df$scm_url <- scm_info$url
descr_df$scm_type <- scm_info$type
descr_df$scm_branch <- scm_info$branch
descr_df$scm_subdir <- scm_info$subdir
descr_df$r_version <- R.version$version.string
bldresults <- repo_results(repo)
bldresults <- bldresults[bldresults$name == pkg_name, ]
descr_df$last_attempt_version <- bldresults$lastAttemptVersion
descr_df$last_attempt_status <- bldresults$lastAttemptStatus
descr_df$last_attempt_date <- bldresults$lastAttempt
descr_df$last_built_version <- bldresults$lastbuiltversion
descr_df$last_built_status <- bldresults$lastbuiltstatus
descr_df$last_built_date <- bldresults$lastbuilt
descr_df$is_suspended <- bldresults$suspended
doc_url <- paste0(repo_url(repo), basename(pkg_doc_dir(repo)), pkg_name)
descr_df$pkgdocs_url <- doc_url
descr_df$pkg_sticker <- paste0(doc_url, "/", pkg_name, ".png")
descr_list <- lapply(as.list(descr_df), as.vector)
reverse_deps <- lapply(as.list(rev_deps), as.vector)
combo_list <- append(descr_list, reverse_deps)
if ("Imports" %in% names(combo_list))
combo_list$Imports <- .stringToVec(combo_list$Imports)
if ("Depends" %in% names(combo_list))
combo_list$Depends <- .stringToVec(combo_list$Depends)
if ("Suggests" %in% names(combo_list))
combo_list$Suggests <- .stringToVec(combo_list$Suggests)
if ("Enhances" %in% names(combo_list))
combo_list$Enhances <- .stringToVec(combo_list$Enhances)
if ("LinkingTo" %in% names(combo_list))
combo_list$LinkingTo <- .stringToVec(combo_list$LinkingTo)
if ("ReverseImports" %in% names(combo_list))
combo_list$ReverseImports <- .stringToVec(combo_list$ReverseImports)
if ("ReverseDependencies" %in% names(combo_list))
combo_list$ReverseDependencies <- .stringToVec(combo_list$ReverseDependencies)
if ("ReverseLinkingTo" %in% names(combo_list))
combo_list$ReverseLinkingTo <- .stringToVec(combo_list$ReverseLinkingTo)
if ("ReverseSuggests" %in% names(combo_list))
combo_list$ReverseSuggests <- .stringToVec(combo_list$ReverseSuggests)
if ("ReverseEnhances" %in% names(combo_list))
combo_list$ReverseEnhances <- .stringToVec(combo_list$ReverseEnhances)
desc_json <- toJSON(combo_list, pretty = TRUE)
json_outfile <- file.path(docdir, paste0(pkg_name, suffix))
logfun(repo)(pkg_name, "Writing package metadata JSON file")
write(desc_json, json_outfile)
}
.stringToVec <- function(x) {
unlist(strsplit(gsub("[[:blank:]]", "",x), ","))
}
|
fec.power.limits <- function(meanepg=200, g.faeces=3, sensitivity=1/25, replicates=1, animals=10, coeffvarrep=0.4, coeffvarind=0.3, coeffvargroup=0.7, true.sample=FALSE, lower.limit=NA, upper.limit=NA, iterations=100000, power = 0.95, confidence = 0.99, feedback=FALSE, forcesim=FALSE){
if(power >= 1) stop("Required power must be < 1")
if(confidence >= 1) stop("Confidence must be < 1")
conf <- (1-confidence)/2
lci <- 0+conf
uci <- 1-conf
if(replicates < 1 | animals < 1) stop("Specified values for animals and replicates must be greater than 0")
if(!is.na(lower.limit) & !is.na(upper.limit)) stop("One or both of lower.limit or upper.limit must be non-fixed (NA)")
fix.lower <- !is.na(lower.limit)
fix.upper <- !is.na(upper.limit)
fixed.lower <- lower.limit
fixed.upper <- upper.limit
target <- power
if(feedback){
if(any(.Platform$GUI == c("AQUA", "Rgui"))){
warning("Printing the progress of the function using the GUI versions of R may massively increase the time taken, I suggest setting feedback=FALSE or using a command line version of R instead")
}
}
if(animals==1 & true.sample==TRUE) coeffvargroup <- 10^-10
if(coeffvargroup > (coeffvarrep+coeffvarind)/10) approximate <- FALSE else approximate <- TRUE
if(forcesim) approximate <- FALSE
if(animals==1 & true.sample==FALSE) warning("NOTE: The power calculated is for the population from which the animal is derived, to calculate the power for the true mean of this individual use true.sample=TRUE")
lowerl <- lower.limit
upperl <- upper.limit
start <- Sys.time()
if(!approximate){
if(true.sample){
out <- .C("poweranalysissamplefixed", as.numeric(meanepg), as.numeric(g.faeces), as.numeric(sensitivity), as.integer(replicates), as.integer(animals), as.numeric(coeffvarrep), as.numeric(coeffvarind), as.numeric(coeffvargroup), as.integer(iterations), as.integer(feedback), numeric(iterations), PACKAGE="bayescount")
lo <- length(out)
meancounts <- out[[lo]]
}else{
out <- .C("poweranalysispopulationfixed", as.numeric(meanepg), as.numeric(g.faeces), as.numeric(sensitivity), as.integer(replicates), as.integer(animals), as.numeric(coeffvarrep), as.numeric(coeffvarind), as.numeric(coeffvargroup), as.integer(iterations), as.integer(feedback), numeric(iterations), PACKAGE="bayescount")
lo <- length(out)
meancounts <- out[[lo]]
}
mcs <- meancounts
f <- function(limit){
prob <- limit
limits <- quantile(mcs, probs=c(0+((1-prob)/2), 1-((1-prob)/2)))
nin <- sum(mcs <= limits[2] & mcs >= limits[1])
nout <- length(mcs)-nin
med <- qbeta(0.5, nin+1, nout+1)
return(med)
}
if(fix.lower){
f <- function(limit){
nin <- sum(mcs <= limit & mcs >= fixed.lower)
nout <- length(mcs)-nin
med <- qbeta(0.5, nin+1, nout+1)
return(med)
}
}
if(fix.upper){
f <- function(limit){
nin <- sum(mcs <= fixed.upper & mcs >= -limit)
nout <- length(mcs)-nin
med <- qbeta(0.5, nin+1, nout+1)
return(med)
}
}
limits <- c(0,1)
if(fix.upper) limits <- c(-Inf,0)
if(fix.lower) limits <- c(0,Inf)
bsres <- binary.search(f, target, limits)
if(bsres$status!="OK"){
if(bsres$status!="Absolute value not possible but this is closest") stop(bsres$status)
}
if(fix.upper) limits <- c(-bsres$value, fixed.upper)
if(fix.lower) limits <- c(fixed.lower, bsres$value)
if(!fix.upper & !fix.lower) limits <- quantile(mcs, prob=c(0+((1-bsres$value)/2), 1-((1-bsres$value)/2)))
nin <- sum(mcs <= limits[2] & mcs >= limits[1])
nout <- length(mcs)-nin
power <- qbeta(c(lci,0.5,uci), nin+1, nout+1)
names(limits) <- c("lower.limit", "upper.limit")
names(power) <- c(paste("lower.", confidence*100, "%.estimate", sep=""), "median.estimate", paste("upper.", confidence*100, "%.estimate", sep=""))
return(list(limits=limits, power=power))
}else{
if(true.sample==FALSE | animals > 1){
coeffvarind <- sqrt(coeffvarind^2 + coeffvargroup^2 + coeffvarind^2*coeffvargroup^2)
replicates <- replicates*animals
}
coeff.var <- sqrt(coeffvarind^2 + (coeffvarrep^2)/g.faeces + (coeffvarind^2 * (coeffvarrep^2)/g.faeces))
eggs.counted <- replicates*meanepg*sensitivity
shape <- replicates / coeff.var^2
f <- function(limit){
accuracy <- limit
upper.tol <- replicates*sensitivity*(meanepg+meanepg*accuracy)
lower.tol <- replicates*sensitivity*(meanepg-meanepg*accuracy)
return(pnbinom(upper.tol, size=shape, mu=eggs.counted, lower.tail=TRUE) - pnbinom(lower.tol, size=shape, mu=eggs.counted, lower.tail=TRUE) + suppressWarnings(dnbinom(lower.tol, size=shape, mu=eggs.counted)))
}
if(fix.lower){
f <- function(limit){
upper.tol <- replicates*sensitivity*(limit)
lower.tol <- replicates*sensitivity*(fixed.lower)
return(pnbinom(upper.tol, size=shape, mu=eggs.counted, lower.tail=TRUE) - pnbinom(lower.tol, size=shape, mu=eggs.counted, lower.tail=TRUE) + suppressWarnings(dnbinom(lower.tol, size=shape, mu=eggs.counted)))
}
}
if(fix.upper){
f <- function(limit){
upper.tol <- replicates*sensitivity*(fixed.upper)
lower.tol <- replicates*sensitivity*(-limit)
return(pnbinom(upper.tol, size=shape, mu=eggs.counted, lower.tail=TRUE) - pnbinom(lower.tol, size=shape, mu=eggs.counted, lower.tail=TRUE) + suppressWarnings(dnbinom(lower.tol, size=shape, mu=eggs.counted)))
}
}
limits <- c(0,Inf)
if(fix.upper) limits <- c(-Inf,0)
if(fix.lower) limits <- c(0,Inf)
bsres <- binary.search(f, target, limits)
if(bsres$status!="OK"){
if(bsres$status!="Absolute value not possible but this is closest") stop(bsres$status)
}
if(fix.upper) limits <- c(-bsres$value, fixed.upper)
if(fix.lower) limits <- c(fixed.lower, bsres$value)
if(!fix.upper & !fix.lower) limits <- c((meanepg-meanepg*bsres$value), (meanepg+meanepg*bsres$value))
power <- replicate(3, bsres$objective)
names(limits) <- c("lower.limit", "upper.limit")
names(power) <- c("absolute.value", "absolute.value", "absolute.value")
return(list(limits=limits, power=power))
}
}
fec.power <- function(meanepg=200, g.faeces=3, sensitivity=1/25, replicates=1, animals=10, coeffvarrep=0.4, coeffvarind=0.3, coeffvargroup=0.7, true.sample=FALSE, accuracy=0.1, lower.limit=meanepg*(1-accuracy), upper.limit=meanepg*(1+accuracy), maxiterations=1000000, precision=2, confidence = 0.99, feedback=FALSE, forcesim=FALSE){
if(confidence >= 1) stop("Confidence must be < 1")
conf <- (1-confidence)/2
lci <- 0+conf
uci <- 1-conf
if(feedback){
if(any(.Platform$GUI == c("AQUA", "Rgui"))){
warning("Printing the progress of the function using the GUI versions of R may massively increase the time taken, I suggest setting feedback=FALSE or using a command line version of R instead")
}
}
if(replicates < 1 | animals < 1) stop("Specified values for animals and replicates must be greater than 0")
if(animals==1 & true.sample==TRUE) coeffvargroup <- 10^-10
if(coeffvargroup > (coeffvarrep+coeffvarind)/10) approximate <- FALSE else approximate <- TRUE
if(forcesim) approximate <- FALSE
if(animals==1 & true.sample==FALSE) warning("NOTE: The power calculated is for the population from which the animal is derived, to calculate the power for the true mean of this individual use true.sample=TRUE")
lowerl <- lower.limit
upperl <- upper.limit
start <- Sys.time()
if(approximate){
if(true.sample==FALSE){
coeffvarind <- sqrt(coeffvarind^2 + coeffvargroup^2 + coeffvarind^2*coeffvargroup^2)
replicates <- replicates*animals
}
coeff.var <- sqrt(coeffvarind^2 + (coeffvarrep^2)/g.faeces + (coeffvarind^2 * (coeffvarrep^2)/g.faeces))
eggs.counted <- replicates*meanepg*sensitivity
upper.tol <- replicates*sensitivity*(upperl)
lower.tol <- replicates*sensitivity*(lowerl)
shape <- replicates / coeff.var^2
nbpower <- pnbinom(upper.tol, size=shape, mu=eggs.counted, lower.tail=TRUE) - pnbinom(lower.tol, size=shape, mu=eggs.counted, lower.tail=TRUE) + suppressWarnings(dnbinom(lower.tol, size=shape, mu=eggs.counted))
time <- timestring(start, Sys.time(), units="s", show.units=FALSE)
output = list(roundedci=replicate(3, round(nbpower, precision)), ci=replicate(3, nbpower))
}else{
if(true.sample){
if(is.na(precision)){
out <- .C("poweranalysissamplefixed", as.numeric(meanepg), as.numeric(g.faeces), as.numeric(sensitivity), as.integer(replicates), as.integer(animals), as.numeric(coeffvarrep), as.numeric(coeffvarind), as.numeric(coeffvargroup), as.integer(maxiterations), as.integer(feedback), numeric(maxiterations), PACKAGE="bayescount")
lo <- length(out)
meancounts <- out[[lo]]
nin <- sum(meancounts >= lowerl & meancounts <= upperl)
nout <- length(meancounts)-nin
time <- timestring(start, Sys.time(), units="s", show.units=FALSE)
output=list(roundedci=round(qbeta(c(lci, 0.5, uci), nin+1, nout+1), precision), ci=qbeta(c(lci, 0.5, uci), nin+1, nout+1), within=nin, without=nout, total=nin+nout)
}else{
out <- .C("poweranalysissample", as.numeric(meanepg), as.numeric(g.faeces), as.numeric(sensitivity), as.integer(replicates), as.integer(animals), as.numeric(coeffvarrep), as.numeric(coeffvarind), as.numeric(coeffvargroup), as.numeric(lowerl), as.numeric(upperl), as.integer(maxiterations), as.integer(precision), as.numeric(lci), as.numeric(uci), as.integer(feedback), as.integer(0), as.integer(0), PACKAGE="bayescount")
lo <- length(out)
time <- timestring(start, Sys.time(), units="s", show.units=FALSE)
output=list(roundedci = round(qbeta(c(lci, 0.5, uci), out[[lo-1]]+1, (out[[lo]]-out[[lo-1]])+1), precision), ci=qbeta(c(lci, 0.5, uci), out[[lo-1]]+1, (out[[lo]]-out[[lo-1]])+1), within=out[[lo-1]], without=out[[lo]]-out[[lo-1]], total=out[[lo]])
}
}else{
if(is.na(precision)){
out <- .C("poweranalysispopulationfixed", as.numeric(meanepg), as.numeric(g.faeces), as.numeric(sensitivity), as.integer(replicates), as.integer(animals), as.numeric(coeffvarrep), as.numeric(coeffvarind), as.numeric(coeffvargroup), as.integer(maxiterations), as.integer(feedback), numeric(maxiterations), PACKAGE="bayescount")
lo <- length(out)
meancounts <- out[[lo]]
nin <- sum(meancounts >= lowerl & meancounts <= upperl)
nout <- length(meancounts)-nin
time <- timestring(start, Sys.time(), units="s", show.units=FALSE)
output=list(roundedci=round(qbeta(c(lci, 0.5, uci), nin+1, nout+1), precision), ci=qbeta(c(lci, 0.5, uci), nin+1, nout+1), within=nin, without=nout, total=nin+nout)
}else{
out <- .C("poweranalysispopulation", as.numeric(meanepg), as.numeric(g.faeces), as.numeric(sensitivity), as.integer(replicates), as.integer(animals), as.numeric(coeffvarrep), as.numeric(coeffvarind), as.numeric(coeffvargroup), as.numeric(lowerl), as.numeric(upperl), as.integer(maxiterations), as.integer(precision), as.numeric(lci), as.numeric(uci), as.integer(feedback), as.integer(0), as.integer(0), PACKAGE="bayescount")
lo <- length(out)
time <- timestring(start, Sys.time(), units="s", show.units=FALSE)
output=list(roundedci = round(qbeta(c(lci, 0.5, uci), out[[lo-1]]+1, (out[[lo]]-out[[lo-1]])+1), precision), ci=qbeta(c(lci, 0.5, uci), out[[lo-1]]+1, (out[[lo]]-out[[lo-1]])+1), within=out[[lo-1]], without=out[[lo]]-out[[lo-1]], total=out[[lo]])
}
}
}
if(approximate){
names(output$roundedci) <- c("absolute.value", "absolute.value", "absolute.value")
names(output$ci) <- c("absolute.value", "absolute.value", "absolute.value")
}else{
names(output$roundedci) <- c(paste("lower.", confidence*100, "%", sep=""), "median", paste("upper.", confidence*100, "%", sep=""))
names(output$ci) <- c(paste("lower.", confidence*100, "%", sep=""), "median", paste("upper.", confidence*100, "%", sep=""))
}
return(output)
}
FEC.power.limits <- fec.power.limits
FEC.precision <- fec.power.limits
fec.precision <- fec.power.limits
count.precision <- fec.precision
FEC.power <- fec.power
count.power <- fec.power
|
sdp_multi <- function (Q, capacity, target, surface_area, max_depth, evap,
R_max = 2 * target, spill_targ = 0.95, vol_targ = 0.75, Markov = FALSE,
weights = c(0.7, 0.2, 0.1), S_disc = 1000, R_disc = 10,
Q_disc = c(0.0, 0.2375, 0.4750, 0.7125, 0.95, 1.0),
loss_exp = c(2,2,2), S_initial = 1, plot = TRUE, tol = 0.99, rep_rrv = FALSE){
frq <- frequency(Q)
if (is.ts(Q)==FALSE) stop("Q must be seasonal time series object with frequency of 12 or 4")
if (frq != 12 && frq != 4) stop("Q must have frequency of 4 or 12")
if (missing(evap)) {
evap <- ts(rep(0, length(Q)), start = start(Q), frequency = frq)
}
if(length(evap) == 1) {
evap <- ts(rep(evap, length(Q)), start = start(Q), frequency = frq)
}
if (length(evap) != length(Q) && length(evap) != frq){
stop("Evaporation must be either a time series of length Q, a vector of length frequency(Q), or a single numeric constant")
}
if (start(Q)[2] != 1){
message("NOTE: First incomplete year of time series removed")
Q <- window(Q, start = c(start(Q)[1] + 1, 1), frequency = frq)
}
if(end(Q)[2] != frq){
message("NOTE: Final incomplete year of time series removed")
Q <- window(Q, end = c(end(Q)[1] - 1, frq), frequency = frq)
}
if (length(evap) == frq){
evap <- ts(rep(evap, length(Q) / frq), start = start(Q), frequency = frq)
} else {
if(is.ts(evap)==FALSE) stop("Evaporation must be either a time series of length Q or a vector of length frequency(Q) for a seasonal evaporation profile")
evap <- window(evap, start = start(Q), end = end(Q), frequency = frq)
}
if (missing(surface_area)) {
surface_area <- 0
}
evap_seas <- as.vector(tapply(evap, cycle(evap), FUN = mean))
if (Markov == FALSE){
Q_month_mat <- matrix(Q, byrow = TRUE, ncol = frq)
Q.probs <- diff(Q_disc)
Q_class_med <- apply(Q_month_mat, 2, quantile, type = 8,
probs = Q_disc[-1] - (Q.probs / 2))
S_states <- seq(from = 0, to = capacity, by = capacity / S_disc)
R_disc_x <- seq(from = 0, to = R_max, by = R_max / R_disc)
Shell.array <- array(0,dim=c(length(S_states),length(R_disc_x),length(Q.probs)))
Cost_to_go <- vector("numeric",length=length(S_states))
Results_mat <- matrix(0,nrow=length(S_states),ncol=frq)
R_policy <- matrix(0,nrow=length(S_states),ncol=frq)
Bellman <- R_policy
R_policy_test <- R_policy
} else if (Markov == TRUE){
Q_month_mat <- matrix(Q, byrow = TRUE, ncol = frq)
n_Qcl <- length(Q_disc) - 1
Q.probs <- diff(Q_disc)
Q_class_med <- apply(Q_month_mat, 2, quantile, type = 8,
probs = Q_disc[-1] - (Q.probs / 2))
S_states <- seq(from = 0, to = capacity, by = capacity / S_disc)
R_disc_x <- seq(from = 0, to = R_max, by = R_max / R_disc)
Shell.array <- array(0, dim = c(length(S_states), length(R_disc_x),
length(Q.probs)))
Q_class.mat <- matrix(nrow=length(Q_month_mat[,1]),ncol=frq)
for (m in 1:frq){
Q_disc_x <- gtools::quantcut(Q_month_mat[,m], Q_disc)
Q_class.mat[,m] <- as.numeric(as.vector(factor(Q_disc_x,
labels = c(1:n_Qcl))))
}
Q_trans_probs <- array(0, c(length(Q_disc) - 1, length(Q_disc) - 1, frq))
for (m in 1 : frq){
for (cl in 1 : n_Qcl){
if (m == frq){
Tr.count <- table(factor(Q_class.mat[which(Q_class.mat[1:(length(Q_month_mat[,1]) - 1),
frq] == cl) + 1, 1], 1:n_Qcl))
}else{
Tr.count <- table(factor(Q_class.mat[which(Q_class.mat[,m] == cl),
m + 1], 1:n_Qcl))
}
Tr.freq <- Tr.count / sum(Tr.count)
Q_trans_probs[cl,,m] <- Tr.freq
}}
Cost_to_go <- matrix(0, nrow = (length(S_states)), ncol = n_Qcl)
R_policy <- array(0,dim = c(length(S_states), n_Qcl, frq))
Bellman <- R_policy
R_policy_test <- R_policy
}
if (missing(max_depth)){
c <- sqrt(2) / 3 * (surface_area * 10 ^ 6) ^ (3/2) / (capacity * 10 ^ 6)
GetLevel <- function(c, V){
y <- (6 * V / (c ^ 2)) ^ (1 / 3)
return(y)
}
GetArea <- function(c, V){
Ay <- (((3 * c * V) / (sqrt(2))) ^ (2 / 3))
return(Ay)
}
} else {
c <- 2 * capacity / (max_depth * surface_area)
GetLevel <- function(c, V){
y <- max_depth * (V / (capacity * 10 ^ 6)) ^ (c / 2)
return(y)
}
GetArea <- function(c, V){
Ay <- ((2 * (capacity * 10 ^ 6)) / (c * max_depth * (V / (capacity * 10 ^ 6)) ^ (c / 2))) * ((V / (capacity * 10 ^ 6)) ^ (c / 2)) ^ (2 / c)
Ay[which(is.nan(Ay) == TRUE)] <- 0
return(Ay)
}
}
GetEvap <- function(s, q, r, ev){
e <- GetArea(c, V = s * 10 ^ 6) * ev / 10 ^ 6
n <- 0
repeat{
n <- n + 1
s_plus_1 <- max(min(s + q - r - e, capacity), 0)
e_x <- GetArea(c, V = ((s + s_plus_1) / 2) * 10 ^ 6) * ev / 10 ^ 6
if (abs(e_x - e) < 0.001 || n > 20){
break
} else {
e <- e_x
}
}
return(e)
}
S_area_rel <- GetArea(c, V = S_states * 10 ^ 6)
message(paste0("policy converging... (>", tol,")"))
if (Markov == FALSE){
repeat{
for (t in frq:1){
R.cstr <- sweep(Shell.array, 3, Q_class_med[,t], "+") +
sweep(Shell.array, 1, S_states, "+") -
sweep(Shell.array, 1, evap_seas[t] * S_area_rel / 10 ^ 6, "+")
R.star <- aperm(apply(Shell.array, c(1, 3), "+", R_disc_x), c(2, 1, 3))
R.star[,2:(R_disc + 1),][which(R.star[,2:(R_disc + 1),] > R.cstr[,2 : (R_disc + 1),])] <- NaN
Deficit.arr <- (target - R.star) / target
Deficit.arr[which(Deficit.arr < 0)] <- 0
Cost_arr <- ( (abs(Deficit.arr)) ^ loss_exp[1])
S.t_plus_1 <- R.cstr - R.star
S.t_plus_1[which(S.t_plus_1 < 0)] <- 0
Spill_costs <- S.t_plus_1 - capacity
Spill_costs[which(Spill_costs < 0)] <- 0
Spill_costs <- (Spill_costs / quantile(Q, spill_targ)) ^ loss_exp[2]
S.t_plus_1[which(S.t_plus_1 > capacity)] <- capacity
Vol_costs <- abs(((S.t_plus_1 - vol_targ * capacity) / (vol_targ * capacity))) ^ loss_exp[3]
Implied_S_state <- round(1 + (S.t_plus_1 / capacity)
* (length(S_states) - 1))
Cost_arr <- weights[1] * Cost_arr + weights[2] * Spill_costs + weights[3] * Vol_costs
Cost_to_go.arr <- array(Cost_to_go[Implied_S_state],
dim = c(length(S_states), length(R_disc_x) , length(Q.probs)))
Min_cost_arr <- Cost_arr + Cost_to_go.arr
Min_cost_arr_weighted <- sweep(Min_cost_arr, 3, Q.probs, "*")
Min_cost_expected <- apply(Min_cost_arr_weighted, c(1, 2), sum)
Bellman[,t] <- Cost_to_go
Cost_to_go <- apply(Min_cost_expected, 1, min, na.rm = TRUE)
Results_mat[,t] <- Cost_to_go
R_policy[,t] <- apply(Min_cost_expected, 1, which.min)
}
message(sum(R_policy == R_policy_test) /
(frq * length(S_states)))
if (sum(R_policy == R_policy_test) /
(frq * length(S_states)) > tol){
break
}
R_policy_test <- R_policy
}
} else if (Markov == TRUE){
repeat{
for (t in frq:1){
R.cstr <- sweep(Shell.array, 3, Q_class_med[,t], "+") +
sweep(Shell.array, 1, S_states, "+") -
sweep(Shell.array, 1, evap_seas[t] * S_area_rel / 10 ^ 6, "+")
R.star <- aperm(apply(Shell.array, c(1, 3), "+", R_disc_x), c(2, 1, 3))
R.star[,2:(R_disc + 1),][which(R.star[,2:(R_disc + 1),] > R.cstr[,2 : (R_disc + 1),])] <- NaN
Deficit.arr <- (target - R.star) / target
Deficit.arr[which(Deficit.arr < 0)] <- 0
Cost_arr <- ( (abs(Deficit.arr)) ^ loss_exp[1])
S.t_plus_1 <- R.cstr - R.star
S.t_plus_1[which(S.t_plus_1 < 0)] <- 0
Spill_costs <- S.t_plus_1 - capacity
Spill_costs[which(Spill_costs < 0)] <- 0
Spill_costs <- (Spill_costs / quantile(Q, spill_targ)) ^ loss_exp[2]
S.t_plus_1[which(S.t_plus_1 > capacity)] <- capacity
Vol_costs <- abs(((S.t_plus_1 - vol_targ * capacity) / (vol_targ * capacity))) ^ loss_exp[3]
Implied_S_state <- round(1 + (S.t_plus_1 / capacity)
* (length(S_states) - 1))
Cost_arr <- weights[1] * Cost_arr + weights[2] * Spill_costs + weights[3] * Vol_costs
Cost_to_go.arr <- array(Cost_to_go,
dim = c(length(S_states), n_Qcl, n_Qcl))
Expectation <- apply(sweep(Cost_to_go.arr, c(2,3),
t(Q_trans_probs[,,t]), "*"), c(1,3), sum)
Exp.arr <- Shell.array
for (Qt in 1:n_Qcl){
Exp.arr[,,Qt] <- matrix(Expectation[,Qt][Implied_S_state[,,Qt]],
ncol = length(R_disc_x))
}
R_policy[,,t] <- apply( (Cost_arr + Exp.arr), c(1,3), which.min)
Cost_to_go <- apply( (Cost_arr + Exp.arr), c(1,3), min, na.rm = TRUE)
Bellman[,,t] <- Cost_to_go
}
message(sum(R_policy == R_policy_test) / (frq * length(S_states) * n_Qcl))
if (sum(R_policy == R_policy_test) / (frq * length(S_states) * n_Qcl) > tol){
break
}
R_policy_test <- R_policy
}
}
S <- vector("numeric",length(Q) + 1); S[1] <- S_initial * capacity
R_rec <- vector("numeric",length(Q))
E <- vector("numeric", length(Q))
y <- vector("numeric", length(Q))
Spill <- vector("numeric",length(Q))
for (yr in 1:nrow(Q_month_mat)) {
for (month in 1:frq) {
t_index <- (frq * (yr - 1)) + month
S_state <- which.min(abs(S_states - S[t_index]))
Qx <- Q_month_mat[yr,month]
if (Markov == FALSE){
R <- R_disc_x[R_policy[S_state,month]]
} else if (Markov == TRUE){
Q_class <- which.min(abs(as.vector(Q_class_med[,month] - Qx)))
R <- R_disc_x[R_policy[S_state,Q_class,month]]
}
R_rec[t_index] <- R
E[t_index] <- GetEvap(s = S[t_index], q = Qx, r = R, ev = evap[t_index])
y[t_index] <- GetLevel(c, S[t_index] * 10 ^ 6)
if ( (S[t_index] - R + Qx - E[t_index]) > capacity) {
S[t_index + 1] <- capacity
Spill[t_index] <- S[t_index] - R + Qx - capacity - E[t_index]
}else{
if ( (S[t_index] - R + Qx) < 0) {
S[t_index + 1] <- 0
R_rec[t_index] <- max(0, S[t_index] + Qx - E[t_index])
}else{
S[t_index + 1] <- S[t_index] - R + Qx - E[t_index]
}
}
}
}
R_policy <- (R_policy - 1) / R_disc
S <- ts(S[1:(length(S) - 1)],start = start(Q),frequency = frq)
R_rec <- ts(R_rec, start = start(Q), frequency = frq)
E <- ts(E, start = start(Q), frequency = frequency(Q))
y <- ts(y, start = start(Q), frequency = frequency(Q))
Spill <- ts(Spill, start = start(Q), frequency = frq)
if(plot) {
plot(R_rec, ylab = "Controlled release", ylim = c(0, R_max)); abline(h = target, lty = 2)
plot(S, ylab = "Storage", ylim = c(0, capacity)); abline(h = vol_targ * capacity, lty = 2)
plot(Spill, ylab = "Uncontrolled spill")
}
total_release_cost <- sum((1 - R_rec/target)[which((R_rec/target) < 1)] ^ loss_exp[1])
total_spill_cost <- sum((Spill / quantile(Q, spill_targ)) ^ loss_exp[2])
total_volume_cost <- sum(((S - vol_targ * capacity) / (vol_targ * capacity)) ^ loss_exp[3])
total_weighted_cost <- weights[1] * total_release_cost + weights[2] * total_spill_cost + weights[3] * total_volume_cost
costs <- list(total_release_cost, total_spill_cost, total_volume_cost, total_weighted_cost)
names(costs) <- c("total_release_cost", "total_spill_cost", "total_volume_cost", "total_weighted_cost")
if (rep_rrv == TRUE){
deficit <- ts(round(1 - (R_rec / target),5), start = start(Q), frequency = frequency(Q))
rel_ann <- sum(aggregate(deficit, FUN = mean) == 0) /
length(aggregate(deficit, FUN = mean))
rel_time <- sum(deficit == 0) / length(deficit)
rel_vol <- sum(R_rec) / (target * length(deficit))
fail.periods <- which(deficit > 0)
if (length(fail.periods) == 0) {
resilience <- NA
vulnerability <- NA
} else {
if (length(fail.periods) == 1) {
resilience <- 1
vulnerability <- max(deficit)
} else {
resilience <- (sum(diff(which(deficit > 0)) > 1) + 1) / (length(which(deficit > 0)))
fail.refs <- vector("numeric", length = length(fail.periods))
fail.refs[1] <- 1
for (j in 2:length(fail.periods)) {
if (fail.periods[j] > (fail.periods[j - 1] + 1)) {
fail.refs[j] <- fail.refs[j - 1] + 1
} else {
fail.refs[j] <- fail.refs[j - 1]
}
}
n.events <- max(fail.refs)
event.starts <- by(fail.periods, fail.refs, FUN = min)
event.ends <- by(fail.periods, fail.refs, FUN = max)
max.deficits <- vector("numeric", length = n.events)
for (k in 1:n.events) {
max.deficits[k] <- max(deficit[event.starts[k]:event.ends[k]])
}
vulnerability <- mean(max.deficits)
}
}
results <- list(R_policy, Bellman, S, R_rec, E, y, Spill, rel_ann, rel_time, rel_vol, resilience, vulnerability, Q_disc, costs)
names(results) <- c("release_policy", "Bellman", "storage", "releases", "evap_loss", "water_level", "spill", "annual_reliability",
"time_based_reliability", "volumetric_reliability",
"resilience", "vulnerability", "flow_disc", "costs")
} else {
results <- list(R_policy, Bellman, S, R_rec, E, y, Spill, Q_disc, costs)
names(results) <- c("release_policy", "Bellman", "storage", "releases", "evap_loss", "water_level", "spill", "flow_disc", "total_costs")
}
return(results)
}
|
dynsi <- function(formula, model, factors, cumul=FALSE, simulonly=FALSE, nb.outp=NULL, Name.File=NULL, ...)
{
if(is.null(dim(factors))){
multisensi.design=planfact.as
d.args=factors
}else{
multisensi.design=factors
d.args=list()
}
result <- multisensi(design=multisensi.design, model=model, reduction=NULL, dimension=nb.outp, center=FALSE, scale=FALSE, analysis=analysis.anoasg, cumul=cumul, simulonly=simulonly, Name.File=Name.File, design.args=d.args, analysis.args=list(formula=formula,keep.ouputs=FALSE), ...)
cat("Warning : dynsi function can now be replaced by a call to the multisensi function. It is kept for compatibility with Version 1 of the multisensi package.\n")
cat("You may use multisensi function instead, like this :\n")
print(result$call.info$call)
return(result)
}
|
rinvchisq <- function (n, df, scale = 1/df)
{
if ((length(scale) != 1) & (length(scale) != n))
stop("scale should have the same length as n")
if (df <= 0)
stop("df must be greater than zero")
if (any(scale <= 0))
stop("scale must be greater than zero")
(df * scale)/rchisq(n, df = df)
}
|
loadThresholds <- function(cpmType, ARL0, desiredLength=5000,lambda=NA, startup=20) {
if (!is.na(lambda)) {
lambda <- gsub("\\.","",lambda)
cpmType <- sprintf("%s%s",cpmType,lambda)
}
str <- sprintf("%sARL%s",cpmType,ARL0)
thresholds <- c(rep(99999,19),cpmthresholds[[str]][,1])
thresholds <- thresholds[!is.na(thresholds)]
len <- length(thresholds)
if (len< desiredLength) {
thresholds <- c(thresholds,rep(mean(thresholds[(len-100):len]), desiredLength-len))
}
return(thresholds)
}
|
data_dir <- file.path("..", "testdata")
tempfile_nc <- function() {
tempfile_helper("monvar_")
}
file_out <- tempfile_nc()
monvar("SIS",
file.path(data_dir, "ex_mon.nc"),
file_out)
file <- nc_open(file_out)
test_that("data is correct", {
actual <- ncvar_get(file, "SIS")
expected_data <- c(
18600.0,18600.0,18600.0,
18600.0,18600.0,18600.0,
18600.0,18600.0,18600.0,
18600.0,18600.0,18600.0,
18600.0,18600.0,18600.0,
16312.5,16312.5,16312.5,
16312.5,16312.5,16312.5,
16312.5,16312.5,16312.5,
16312.5,16312.5,16312.5,
16312.5,16312.5,16312.5,
115245.164,115245.164,115245.164,
115245.164,104890.32,115245.164,
115245.164,115245.164,104890.32,
104890.32,115245.164,115245.164,
115245.164,104890.32,104890.32
)
expected <- array(expected_data, dim = c(5,3,3))
expect_equivalent(actual, expected, tolerance = 1e-8)
})
test_that("attributes are correct", {
actual <- ncatt_get(file, "lon", "units")$value
expect_equal(actual, "degrees_east")
actual <- ncatt_get(file, "lon", "long_name")$value
expect_equal(actual, "longitude")
actual <- ncatt_get(file, "lon", "standard_name")$value
expect_equal(actual, "longitude")
actual <- ncatt_get(file, "lon", "axis")$value
expect_equal(actual, "X")
actual <- ncatt_get(file, "lat", "units")$value
expect_equal(actual, "degrees_north")
actual <- ncatt_get(file, "lat", "long_name")$value
expect_equal(actual, "latitude")
actual <- ncatt_get(file, "lat", "standard_name")$value
expect_equal(actual, "latitude")
actual <- ncatt_get(file, "lat", "axis")$value
expect_equal(actual, "Y")
actual <- ncatt_get(file, "time", "units")$value
expect_equal(actual, "hours since 1983-01-01 00:00:00")
actual <- ncatt_get(file, "time", "long_name")$value
expect_equal(actual, "time")
actual <- ncatt_get(file, "time", "standard_name")$value
expect_equal(actual, "time")
actual <- ncatt_get(file, "time", "calendar")$value
expect_equal(actual, "standard")
actual <- ncatt_get(file, "SIS", "standard_name")$value
expect_equal(actual, "SIS_standard")
actual <- ncatt_get(file, "SIS", "long_name")$value
expect_equal(actual, "Surface Incoming Shortwave Radiation")
actual <- ncatt_get(file, "SIS", "units")$value
expect_equal(actual, "W m-2")
actual <- ncatt_get(file, "SIS", "_FillValue")$value
expect_equal(actual, -999)
actual <- ncatt_get(file, "SIS", "cmsaf_info")$value
expect_equal(actual, "cmsafops::monvar for variable SIS")
global_attr <- ncatt_get(file, 0)
expect_equal(length(global_attr), 1)
actual <- names(global_attr[1])
expect_equal(actual, "Info")
actual <- global_attr[[1]]
expect_equal(actual, "Created with the CM SAF R Toolbox.")
})
test_that("coordinates are correct", {
actual <- ncvar_get(file, "lon")
expect_identical(actual, array(seq(5, 6, by = 0.5)))
actual <- ncvar_get(file, "lat")
expect_identical(actual, array(seq(45, 47, by = 0.5)))
actual <- ncvar_get(file, "time")
expect_equal(actual, array(c(149016, 149760, 150456)))
})
nc_close(file)
test_that("no error is thrown if var does not exist", {
file_out <- tempfile_nc()
expect_warning(monsd("someVariable",
file.path(data_dir, "ex_mon.nc"),
file_out),
"Variable 'someVariable' not found. Variable 'SIS' will be used instead.")
})
test_that("no error is thrown if var is empty", {
file_out <- tempfile_nc()
expect_warning(monsd("",
file.path(data_dir, "ex_mon.nc"),
file_out),
"Variable '' not found. Variable 'SIS' will be used instead.")
})
test_that("error is thrown if var is NULL", {
file_out <- tempfile_nc()
expect_error(
monsd(NULL,
file.path(data_dir, "ex_mon.nc"),
file_out),
"variable must not be NULL"
)
})
test_that("error is thrown if input file does not exist", {
file_out <- tempfile_nc()
expect_error(
monsd("SIS",
file.path(data_dir, "ex_doesNotExist.nc"),
file_out),
"Input file does not exist")
})
test_that("error is thrown if input file is empty", {
file_out <- tempfile_nc()
expect_error(
monsd("SIS",
"",
file_out),
"Input file does not exist")
})
test_that("error is thrown if input file is NULL", {
file_out <- tempfile_nc()
expect_error(
monsd("SIS",
NULL,
file_out),
"Input filepath must be of length one and not NULL")
})
test_that("no error is thrown if output file already exists", {
file_out <- tempfile_nc()
cat("test\n", file = file_out)
expect_error(
monsd("SIS",
file.path(data_dir, "ex_mon.nc"),
file_out),
paste0("File '",
file_out,
"' already exists. Specify 'overwrite = TRUE' if you want to overwrite it."),
fixed = TRUE
)
expect_equal(readLines(con = file_out), "test")
})
test_that("no error is thrown if overwrite = TRUE", {
file_out <- tempfile_nc()
cat("test\n", file = file_out)
expect_error(
monsd("SIS",
file.path(data_dir, "ex_mon.nc"),
file_out,
overwrite = TRUE),
NA
)
})
test_that("no error is thrown if output file already exists", {
expect_error(
monsd("SIS",
file.path(data_dir, "ex_mon.nc"),
NULL),
"Output filepath must be of length one and not NULL"
)
})
|
library(testthat)
library(d3r)
test_check("d3r")
|
fit.gpd <- function(x,method="LM",na.rm=TRUE, record.cpu.time = TRUE,return.data=FALSE,LambdaZeroEpsilon=1e-15){
if(record.cpu.time) {
time.1 <- as.numeric(proc.time()[3]) } else { time.1 <- "not timing"
}
if (method == "LM") {method.name="Method of L-Moments"}
if (method == "SM") {method.name="Starship"}
if (method == "starship") {
method.name="Starship"
method="SM" }
if (method == "LM") {
results <- fit.gpd.lmom(data=x,na.rm=na.rm,LambdaZeroEpsilon=LambdaZeroEpsilon)
}
if (method == "SM") {
if (na.rm) {
original.n = length(x)
x = x[!is.na(x)]
}
starship.results <- starship(data=x,param="gpd",return.data=FALSE)
region = gldGPDRegionID(pars=starship.results$lambda)
if (region == "A"){
results = list(estA=starship.results$lambda,estB=NULL,param="gpd",starship=starship.results) }
if (region == "B"){
results = list(estA=NULL,estB=starship.results$lambda,param="gpd",starship=starship.results) }
}
if (record.cpu.time) {
time.2 <- as.numeric(proc.time()[3]); runtime <- round(time.2-time.1,2)
results$cpu <- runtime
}
if (return.data) {results$data = x}
class(results) <- "GldGPDFit"
results
}
fit.gpd.lmom <- function(data,na.rm=TRUE,LambdaZeroEpsilon=1e-15){
if (na.rm){ dataNArm <- data[!is.na(data)]
} else { if (any(is.na(data))) {
stop(paste("NA values in ",deparse(substitute(data)),". use na.rm=TRUE to fit these data.",sep=""))} else {dataNArm <- data}
}
fit.gpd.lmom.given(lmoms=lmom::samlmu(dataNArm,nmom=4),n=length(dataNArm),LambdaZeroEpsilon=LambdaZeroEpsilon)
}
fit.gpd.lmom.given <- function(lmoms,n=NULL,LambdaZeroEpsilon=1e-15){
if (length(lmoms) < 4) {stop("4 L-Moments are required to fit the GLD gpd.\nArgument lmoms of fit.gpd.lmom.given is less than 4 long.")}
t4 <- lmoms[4]
t3 <- lmoms[3]
l2 <- lmoms[2]
l1 <- lmoms[1]
el.1 <- (3+7*t4)
if (abs(t3)>=1){problem=paste("No estimates possible, impossible sample Tau 3 value: Tau3=",t3,"outside (-1,1) range\n")
warning(problem)
res <- list(estA=NA,estB=NA,warn=problem,param="gpd")
class(res) <- "GldGPDFit"
return(res)}
if ( (5*t3^2-1)/4 > t4 ){problem = paste("No estimates possible, impossible sample Tau3/Tau4 combination. (5*Tau3^2-1)/4 =",(5*t3^2-1)/4,"must be <= Tau4 =",t4,"\n")
warning(problem)
res <- list(estA=NA,estB=NA,warn=problem,param="gpd")
class(res) <- "GldGPDFit"
return(res)}
if (t4 < -0.25){problem = paste("No estimates possible, impossible sample Tau 4 value: Tau4=",t4,"< -0.25\n")
warning(problem)
res <- list(estA=NA,estB=NA,warn=problem,param="gpd")
class(res) <- "GldGPDFit"
return(res)}
if (t4>=1){problem = paste("No estimates possible, impossible sample Tau 4 value: Tau4=",t4,">= 1\n")
warning(problem)
res <- list(estA=NA,estB=NA,warn=problem,param="gpd")
class(res) <- "GldGPDFit"
return(res)}
if ((t4^2+98*t4+1)<0) {problem = paste("No estimates possible, Tau4 too low (lowest possible value is approx -0.0102051). Tau4 here is ",t4,"\n")
warning(problem)
res <- list(estA=NA,estB=NA,warn=problem,param="gpd")
class(res) <- "GldGPDFit"
return(res)}
el.2 <- sqrt(t4^2+98*t4+1)
denom <- (2*(1-t4))
lambdahatA <- (el.1 - el.2 )/ denom
lambdahatB <- (el.1 + el.2 )/ denom
deltahatA <- 0.5*(1-(t3*(lambdahatA+3))/(lambdahatA-1))
deltahatB <- 0.5*(1-(t3*(lambdahatB+3))/(lambdahatB-1))
betahatA <- l2*(lambdahatA+1)*(lambdahatA+2)
betahatB <- l2*(lambdahatB+1)*(lambdahatB+2)
alphahatA <- l1+(betahatA*(1-2*deltahatA))/(lambdahatA+1)
alphahatB <- l1+(betahatB*(1-2*deltahatB))/(lambdahatB+1)
if (abs(lambdahatA)<LambdaZeroEpsilon) {
AisSLD = TRUE
lambdahatA = 0
} else {AisSLD = FALSE}
lmomestA <- c(alphahatA,betahatA,deltahatA,lambdahatA)
if (gl.check.lambda(lmomestA,param="gpd")) {
names(lmomestA) <- c("alpha","beta","delta","lambda")
RegionAest = TRUE} else {lmomestA <- NA
RegionAest = FALSE}
lmomestB <- c(alphahatB,betahatB,deltahatB,lambdahatB)
if (gl.check.lambda(lmomestB,param="gpd")) {
names(lmomestB) <- c("alpha","beta","delta","lambda")
RegionBest = TRUE
} else {
lmomestB = NA
RegionBest = FALSE}
if (!is.null(n)){
if (RegionAest){
if (AisSLD){
warning("Since lambda estimate is zero, the estimated distribution\nis a special case, the Quantile Based Skew Logistic Distribution.\nNo standard errors are available for lambda, but SEs for the other\nparameters are given from the Quantile Based SLD.\n")
omega = lmomestA[3]*(1-lmomestA[3])
se.alpha = lmomestA[2] * sqrt((57 + (125*pi^2-1308)*omega)/(15*n))
se.beta = lmomestA[2] * sqrt(4/(3*n) * (1 - (pi^2-8)*omega))
se.delta = sqrt((8-(397+160*omega-20*pi^2*(omega+2))*omega)/(15*n))
lmomestA <- cbind(lmomestA,c(se.alpha,se.beta,se.delta,NA))
dimnames(lmomestA) <- list(c("alpha","beta","delta","lambda"),c("Estimate","Std. Error"))
} else {
if (lambdahatA > -0.5) {
alphahatA.se = se.alphahat(alphahatA,betahatA,deltahatA,lambdahatA,n)
betahatA.se = se.betahat(alphahatA,betahatA,deltahatA,lambdahatA,n)
deltahatA.se = se.deltahat(alphahatA,betahatA,deltahatA,lambdahatA,n)
lambdahatA.se = se.lambdahat(alphahatA,betahatA,deltahatA,lambdahatA,n)
SEs.A = c(alphahatA.se,betahatA.se,deltahatA.se,lambdahatA.se)
lmomestA = cbind(lmomestA,SEs.A)
dimnames(lmomestA)[[2]] = c("Estimate","Std. Error")
} else { warning("Region A Standard Errors are undefined since lambda is estimated as <= -0.5\n")}
}
}
if (RegionBest) {
if (lmomestB[4] == 0){
warning("Since lambda estimate is zero, the estimate is a special case,\nthe Quantile Based Skew Logistic Distribution. No standard errors are available for lambda,\nbut SEs for the other parameters are given from the Quantile Based SLD.\n")
omega = lmomestB$delta*(1-lmomestB$delta)
se.alpha = lmomestB$beta * sqrt((57 + (125*pi^2-1308)*omega)/(15*n))
se.beta = lmomestB$beta * sqrt(4/(3*n) * (1 - (pi^2-8)*omega))
se.delta = sqrt((8-(397+160*omega-20*pi^2*(omega+2))*omega)/(15*n))
lmomestB <- cbind(lmomestB,c(se.alpha,se.beta,se.delta,NA))
dimnames(lmomestB) <- list(c("alpha","beta","delta","lambda"),c("Estimate","Std. Error"))
} else {
if (lambdahatB > -0.5) {
alphahatB.se = se.alphahat(alphahatB,betahatB,deltahatB,lambdahatB,n)
betahatB.se = se.betahat(alphahatB,betahatB,deltahatB,lambdahatB,n)
deltahatB.se = se.deltahat(alphahatB,betahatB,deltahatB,lambdahatB,n)
lambdahatB.se = se.lambdahat(alphahatB,betahatB,deltahatB,lambdahatB,n)
SEs.B = c(alphahatB.se,betahatB.se,deltahatB.se,lambdahatB.se)
lmomestB = cbind(lmomestB,SEs.B)
dimnames(lmomestB)[[2]] = c("Estimate","Std. Error")
} else {
warning("Region B Standard Errors are undefined since lambda is estimated as <= -0.5\n")
}
}
}
ret <- list(estA=lmomestA,estB=lmomestB,param="gpd")
}
if (RegionAest){
ret <- list(estA=lmomestA,estB=lmomestB,param="gpd")
} else {
if (RegionBest) {
ret <- list(estB=lmomestB,param="gpd")
} else {
ret <- list(param="gpd")
}
}
class(ret) <- "GldGPDFit"
ret
}
|
skip_on_cran()
test_that("no errors/warnings with standard use in tbl_summary() and add_p()", {
tbl_summary_comp <- tbl_summary(mtcars, by = am) %>%
add_p()
expect_error(bold_p(tbl_summary_comp), NA)
expect_warning(bold_p(tbl_summary_comp), NA)
})
test_that("expect error with use in tbl_summary() but NO add_p()", {
table1_without_comp <-
tbl_summary(mtcars, by = am)
expect_error(bold_p(table1_without_comp), NULL)
})
test_that("no errors/warnings with q=TRUE and add_q() used in tbl_summary", {
table1_comp_with_q <-
tbl_summary(mtcars, by = am) %>%
add_p() %>%
add_q()
expect_error(bold_p(table1_comp_with_q, q = TRUE), NA)
expect_warning(bold_p(table1_comp_with_q, q = TRUE), NA)
})
test_that("expect error with q=TRUE and add_q() NOT USED in tbl_summary", {
table1_comp_without_q <-
tbl_summary(mtcars, by = am) %>%
add_p()
expect_error(bold_p(table1_comp_without_q, q = TRUE), NULL)
})
test_that("no errors/warnings with standard use in tbl_regression()", {
fmt_reg <- lm(mpg ~ hp + am, mtcars) %>%
tbl_regression()
expect_error(bold_p(fmt_reg), NA)
expect_warning(bold_p(fmt_reg), NA)
})
test_that("no errors/warnings with standard use in tbl_uvregression()", {
fmt_uni_reg <- trial %>%
tbl_uvregression(
method = lm,
y = age
)
expect_error(bold_p(fmt_uni_reg, t = 0.3), NA)
expect_warning(bold_p(fmt_uni_reg, t = 0.3), NA)
})
test_that("no errors/warnings with use in tbl_uvregression() with add_global_p()", {
skip_if_not(requireNamespace("car"))
fmt_uni_reg_global_p <- trial %>%
tbl_uvregression(
method = lm,
y = age
) %>%
add_global_p()
expect_error(bold_p(fmt_uni_reg_global_p, t = 0.3), NA)
expect_warning(bold_p(fmt_uni_reg_global_p, t = 0.3), NA)
})
|
getSummary.polr <- function(obj,
alpha=.05,
...){
smry <- summary(obj)
N <- if(length(weights(obj))) sum(weights(obj))
else nobs(obj)
coef <- smry$coef
if (smry$df.residual) {
pvals <- 2*pt(-abs(coef[,3]), smry$df.residual)
} else {
pvals <- 2*pnorm(-abs(coef[,3]))
}
lower <- qnorm(p=alpha/2,mean=coef[,1],sd=coef[,2])
upper <- qnorm(p=1-alpha/2,mean=coef[,1],sd=coef[,2])
coef <- cbind(coef,pvals,lower,upper)
colnames(coef) <- c("est","se","stat","p","lwr","upr")
null.model <- update(obj, .~1)
LR <- deviance(null.model) - deviance(obj)
df <- null.model$df.residual - smry$df.resid
ll <- logLik(obj)
dev <- deviance(obj)
if(df > 0){
p <- pchisq(LR,df,lower.tail=FALSE)
L0.pwr <- exp(-deviance(null.model)/N)
Aldrich.Nelson <- LR/(LR+N)
McFadden <- 1 - dev/deviance(null.model)
Cox.Snell <- 1 - exp(-LR/N)
Nagelkerke <- Cox.Snell/(1-L0.pwr)
}
else {
LR <- NA
df <- NA
p <- NA
Aldrich.Nelson <- NA
McFadden <- NA
Cox.Snell <- NA
Nagelkerke <- NA
}
AIC <- AIC(obj)
BIC <- AIC(obj,k=log(N))
sumstat <- c(
LR = LR,
df = df,
p = p,
logLik = ll,
deviance = dev,
Aldrich.Nelson = Aldrich.Nelson,
McFadden = McFadden,
Cox.Snell = Cox.Snell,
Nagelkerke = Nagelkerke,
AIC = AIC,
BIC = BIC,
N = N
)
list(coef=coef,sumstat=sumstat,contrasts=obj$contrasts,xlevels=smry$xlevels,call=obj$call)
}
getSummary.simex <- function(obj,
alpha=.05,
...){
smry <- summary(obj)
modsmry <- summary(obj$model)
N <- if(length(weights(obj$model))) sum(weights(obj$model))
else {
if(length(modsmry$df) > 1) sum(modsmry$df[1:2])
else obj$model$nobs
}
coef <- smry$coef$jackknife
lower <- qnorm(p=alpha/2,mean=coef[,1],sd=coef[,2])
upper <- qnorm(p=1-alpha/2,mean=coef[,1],sd=coef[,2])
coef <- cbind(coef,lower,upper)
colnames(coef) <- c("est","se","stat","p","lwr","upr")
sumstat <- c(N=N)
list(coef=coef,sumstat=sumstat,contrasts=obj$contrasts,xlevels=smry$xlevels,call=obj$call)
}
setSummaryTemplate("simex" = c(
"N" = "($N:d)"
))
|
apply_mistnet <- function(file, pvolfile_out, verbose = FALSE,
mount = dirname(file), load = TRUE,
mistnet_elevations = c(0.5, 1.5, 2.5, 3.5, 4.5)) {
assert_that(file.exists(file))
if (!.pkgenv$mistnet) {
stop("MistNet has not been installed, see update_docker() for install instructions")
}
assert_that(is.numeric(mistnet_elevations))
assert_that(length(mistnet_elevations) == 5)
if (!missing(pvolfile_out)) {
if (!file.exists(dirname(pvolfile_out))) {
stop(paste("output directory", dirname(pvolfile_out), "not found"))
}
if (file.access(dirname(pvolfile_out), 2) == -1) {
stop(paste("No write permission in directory", dirname(pvolfile_out)))
}
}
opt.names <- c("USE_MISTNET", "MISTNET_ELEVS")
opt.values <- c(
"TRUE",
paste("{", paste(as.character(mistnet_elevations), collapse = ", "), paste = "}", sep = "")
)
opt <- data.frame(
"option" = opt.names, "is" = rep("=", length(opt.values)),
"value" = opt.values
)
optfile <- paste(normalizePath(mount, winslash = "/"),
"/options.conf",
sep = ""
)
if (file.exists(optfile)) {
optfile_save <- paste(optfile, ".", format(Sys.time(), "%Y%m%d%H%M%S"), sep = "")
warning(paste("options.conf file found in directory ", mount,
". Renamed to ", basename(optfile_save), " to prevent overwrite...",
sep = ""
))
file.rename(optfile, optfile_save)
}
write.table(opt,
file = optfile, col.names = FALSE,
row.names = FALSE, quote = FALSE
)
pvol_tmp <- nexrad_to_odim_tempfile(file, verbose, mount)
if (load) output <- read_pvolfile(pvol_tmp) else output <- TRUE
if (missing(pvolfile_out)) {
file.remove(pvol_tmp)
} else {
file.rename(pvol_tmp, pvolfile_out)
}
if (file.exists(optfile)) {
file.remove(optfile)
}
return(output)
}
|
context("Test pmx options")
test_that("can get pmx options", {
pmxOptions(template_dir = "/home/agstudy")
default_options <- pmxOptions()
expect_identical(default_options$template_dir, "/home/agstudy")
})
test_that("can set option", {
pmxOptions(myOption = 10L)
expect_identical(getPmxOption("myOption"), 10L)
})
|
cnd_signal <- function(cnd, ...) {
check_dots_empty0(...)
.__signal_frame__. <- TRUE
if (is_null(cnd)) {
return(invisible(NULL))
}
switch(
cnd_type(cnd),
error = {
if (is_environment(cnd$call)) {
frame <- cnd$call
cnd$call <- error_call(cnd$call)
} else {
frame <- caller_env()
}
if (is_null(cnd$trace)) {
info <- abort_context(frame, cnd$parent)
with_options(
"rlang:::visible_bottom" = info$bottom_frame,
{ cnd$trace <- trace_back() }
)
}
signal_abort(cnd)
},
warning = warning(cnd),
message = message(cnd),
interrupt = interrupt(),
condition = invisible(withRestarts(
rlang_muffle = function() NULL,
signalCondition(cnd)
))
)
}
warn <- function(message = NULL,
class = NULL,
...,
body = NULL,
footer = NULL,
use_cli_format = NULL,
.frequency = c("always", "regularly", "once"),
.frequency_id = NULL,
.subclass = deprecated()) {
message <- validate_signal_args(message, class, NULL, .subclass, "warn")
message_info <- cnd_message_info(
message,
body,
footer,
caller_env(),
use_cli_format = use_cli_format
)
message <- message_info$message
extra_fields <- message_info$extra_fields
use_cli_format <- message_info$use_cli_format
.frequency <- arg_match0(.frequency, c("always", "regularly", "once"))
if (!needs_signal(.frequency, .frequency_id, warning_freq_env, "rlib_warning_verbosity")) {
return(invisible(NULL))
}
cnd <- warning_cnd(
class,
message = message,
!!!extra_fields,
use_cli_format = use_cli_format,
...
)
cnd$footer <- c(cnd$footer, message_freq(message, .frequency, "warning"))
local_long_messages()
warning(cnd)
}
inform <- function(message = NULL,
class = NULL,
...,
body = NULL,
footer = NULL,
use_cli_format = NULL,
.file = NULL,
.frequency = c("always", "regularly", "once"),
.frequency_id = NULL,
.subclass = deprecated()) {
message <- message %||% ""
validate_signal_args(message, class, NULL, .subclass, "inform")
message_info <- cnd_message_info(
message,
body,
footer,
caller_env(),
use_cli_format = use_cli_format
)
message <- message_info$message
extra_fields <- message_info$extra_fields
use_cli_format <- message_info$use_cli_format
.frequency <- arg_match0(.frequency, c("always", "regularly", "once"))
if (!needs_signal(.frequency, .frequency_id, message_freq_env, "rlib_message_verbosity")) {
return(invisible(NULL))
}
cnd <- message_cnd(
class,
message = message,
!!!extra_fields,
use_cli_format = use_cli_format,
...
)
cnd$footer <- c(cnd$footer, message_freq(message, .frequency, "message"))
withRestarts(muffleMessage = function() NULL, {
signalCondition(cnd)
msg <- paste0(conditionMessage(cnd), "\n")
cat(msg, file = .file %||% default_message_file())
})
invisible()
}
signal <- function(message,
class,
...,
.subclass = deprecated()) {
validate_signal_args(message, class, NULL, .subclass, "signal")
message <- .rlang_cli_format_fallback(message)
cnd <- cnd(class, ..., message = message)
cnd_signal(cnd)
}
local_long_messages <- function(..., frame = caller_env()) {
if (peek_option("warning.length") == 1000) {
local_options(warning.length = 8170, .frame = frame)
}
}
default_message_file <- function() {
opt <- peek_option("rlang:::message_file")
if (!is_null(opt)) {
return(opt)
}
if (is_interactive() &&
sink.number("output") == 0 &&
sink.number("message") == 2) {
stdout()
} else {
stderr()
}
}
deprecate_subclass <- function(subclass, fn, env = caller_env()) {
msg <- sprintf(
"The %s argument of %s has been renamed to %s.",
format_arg(".subclass"),
format_fn(fn),
format_arg("class")
)
if (is_true(peek_option("force_subclass_deprecation"))) {
signal_soft_deprecated(msg)
}
env_bind(env, class = subclass)
}
interrupt <- function() {
.Call(ffi_interrupt)
}
validate_signal_args <- function(message,
class,
call,
subclass,
fn,
env = caller_env()) {
local_error_call("caller")
if (!is_missing(subclass)) {
deprecate_subclass(subclass, fn, env)
}
check_required(class, call = env)
if (!is_missing(call)) {
if (!is_null(call) && !is_environment(call) && !is_call(call)) {
stop_input_type(call, "a call or environment", arg = "call", call = env)
}
}
if (is_null(message)) {
if (is_null(class)) {
abort("Either `message` or `class` must be supplied.", call = env)
}
message <- ""
}
check_character(message, call = env)
if (!is_null(class)) {
check_character(class, call = env)
}
message
}
warning_freq_env <- new.env(parent = emptyenv())
message_freq_env <- new.env(parent = emptyenv())
needs_signal <- function(frequency,
id,
env,
opt) {
local_error_call("caller")
switch(
peek_verbosity(opt),
verbose = return(TRUE),
quiet = return(FALSE),
default = NULL
)
if (is_string(frequency, "always")) {
return(TRUE)
}
if (is_true(peek_option("rlang:::message_always"))) {
return(TRUE)
}
if (is_null(id)) {
abort(sprintf(
"%s must be supplied with %s.",
format_arg(".frequency_id"),
format_arg(".frequency")
))
}
if (!is_string(id)) {
abort(sprintf(
"%s must be a string.",
format_arg(".frequency")
))
}
sentinel <- env[[id]]
if (is_null(sentinel)) {
env_poke(env, id, Sys.time())
return(TRUE)
}
if (is_string(frequency, "once")) {
return(FALSE)
}
if (!inherits(sentinel, "POSIXct")) {
abort("Expected `POSIXct` value.", .internal = TRUE)
}
(Sys.time() - sentinel) > (8 * 60 * 60)
}
peek_verbosity <- function(opt, call = caller_env()) {
arg_match0(
peek_option(opt) %||% "default",
c("default", "verbose", "quiet"),
opt,
error_call = call
)
}
message_freq <- function(message, frequency, type) {
if (is_string(frequency, "always")) {
return(chr())
}
if (is_string(frequency, "regularly")) {
info <- silver("This %s is displayed once every 8 hours.")
} else {
info <- silver("This %s is displayed once per session.")
}
sprintf(info, type)
}
|
LLR.fun <- function(outcomes, mu, mu0, mu1, dfun, ...) {
llr.res <- t(apply(outcomes,1, function(y) {
llr <- dfun(y, mu=mu1, log=TRUE,...) - dfun(y, mu=mu0, log=TRUE, ...)
p <- dfun(y, mu=mu, ...)
return(c(llr=llr,p=p))
}))
res <- cbind(outcomes,llr.res)
colnames(res) <- c(paste("y",1:ncol(outcomes),sep=""),"llr","p")
return(res)
}
outcomeFunStandard <- function(k,n) {
args <- list() ; for (j in seq_len(k)) args[[j]] <- 0:n
outcomes <- as.matrix(do.call("expand.grid", args))
if (!is.null(n)) {
outcomes <- outcomes[apply(outcomes,1,sum) <= n,,drop=FALSE]
}
return(outcomes)
}
LRCUSUM.runlength <- function(mu,mu0,mu1,h,dfun, n, g=5,outcomeFun=NULL,...) {
if ( ((ncol(mu) != ncol(mu0)) | (ncol(mu0) != ncol(mu1))) |
((nrow(mu) != nrow(mu0)) | (nrow(mu0) != nrow(mu1)))) {
stop("Error: dimensions of mu, mu0 and mu1 have to match")
}
if (missing(h)) {
stop("No threshold specified!")
}
if (is.null(outcomeFun)) {
outcomeFun <- outcomeFunStandard
}
S <- c(-Inf,seq(0,h,length=g))
names <- c(levels(cut(1,S,right=TRUE)),">=h")
t <- 1:ncol(mu)
km1 <- nrow(mu)
P <- array(0, dim=c(length(t),g+1,g+1),dimnames=list(t,names,names))
P[,g+1,g+1] <- 1
for (i in seq_len(length(t))) {
cat("Looking at t=",i," out of ",length(t),"\n")
outcomes <- outcomeFun(km1,n[i])
llr <- LLR.fun(outcomes,mu=mu[,i],mu0=mu0[,i],mu1=mu1[,i],dfun=dfun,size=n[i],...)
F <- stepfun(sort(llr[,"llr"]),c(0,cumsum(llr[order(llr[,"llr"]),"p"])))
for (j in 1:g) {
for (k in 1:g) {
a <- S[k] ; b <- S[k+1] ; c <- S[j] ; d <- S[j+1] ; m <- (c+d)/2
if (j == 1) {
P[i,j,k] <- F(b) - F(a)
} else {
P[i,j,k] <- (F(b-c) + 4*F(b-m) + F(b-d) - F(a-c) - 4*F(a-m) - F(a-d))/6
}
}
}
P[i,-(g+1),(g+1)] <- pmax(0,1-apply(P[i,-(g+1),-(g+1)],1,sum))
}
Ppower <- P[1,,]
alarmUntilTime <- numeric(ncol(mu0))
alarmUntilTime[1] <- Ppower[1,ncol(P)]
for (time in t[-1]) {
Ppower <- Ppower %*% P[time,,]
alarmUntilTime[time] <- Ppower[1,ncol(P)]
}
pRL <- c(alarmUntilTime[1],diff(alarmUntilTime))
mom <- NA
if (length(t) == 1) {
R <- P[,1:g,1:g]
I <- diag(nrow=g)
mom <- rowSums(solve(I-R))
}
return(list(P=P,pmf=pRL,cdf=alarmUntilTime,arl=mom[1]))
}
|
tidy_regression <- function(data, model, type = "ols", robust = FALSE, ...) {
if (is_ols(type)) {
call <- "ols_regression"
} else if (is_log(type)) {
call <- "logistic_regression"
} else if (is_qlog(type)) {
call <- "quasilogistic_regression"
} else if (is_pois(type)) {
call <- "poisson_regression"
} else if (is_qpois(type)) {
call <- "quasipoisson_regression"
}else if (is_negbin(type)) {
call <- "negbinom_regression"
} else {
stop("cannot recognized type", call. = FALSE)
}
args <- list(model, data = data, robust = robust, ...)
m <- do.call(call, args)
m
}
NULL
ols_regression <- function(data, model, robust = FALSE, ...) {
if (robust) {
e <- rlang::expr(MASS::rlm(!!model, data, ...))
} else {
e <- rlang::expr(lm(!!model, data = data, ...))
}
m <- eval(e)
attr(m, "tidycall") <- store_tidycall(m, e)
m
}
logistic_regression <- function(data, model, robust = FALSE, ...) {
if (robust) {
e <- rlang::expr(robust::glmRob(!!model, data = data, family = binomial))
} else {
e <- rlang::expr(glm(!!model, data = data, family = binomial))
}
m <- eval(e)
attr(m, "tidycall") <- store_tidycall(m, e)
m
}
quasilogistic_regression <- function(data, model, robust = FALSE, ...) {
if (robust) {
e <- rlang::expr(robust::glmRob(!!model, data = data, family = quasibinomial))
} else {
e <- rlang::expr(glm(!!model, data = data, family = quasibinomial))
}
m <- eval(e)
attr(m, "tidycall") <- store_tidycall(m, e)
m
}
poisson_regression <- function(data, model, robust = FALSE, ...) {
if (robust) {
e <- rlang::expr(robust::glmRob(!!model, data = data, family = poisson))
} else {
e <- rlang::expr(glm(!!model, data = data, family = poisson))
}
m <- eval(e)
attr(m, "tidycall") <- store_tidycall(m, e)
m
}
quasipoisson_regression <- function(data, model, robust = FALSE, ...) {
if (robust) {
e <- rlang::expr(robust::glmRob(!!model, data = data, family = quasipoisson))
} else {
e <- rlang::expr(glm(!!model, data = data, family = quasipoisson))
}
m <- eval(e)
attr(m, "tidycall") <- store_tidycall(m, e)
m
}
negbinom_regression <- function(data, model, robust = FALSE, ...) {
if (robust) {
stop(paste0("Robust is not currently available for negative binomial models.\n",
" If you know of a package that offers such a model, please file an\n",
" issue at https://github.com/mkearney/tidyversity/issues. Otherwise,\n",
" you can try the function found at the following link---though the\n",
" output is rather limited: https://github.com/williamaeberhard/glmrob.nb"),
call. = FALSE)
}
e <- rlang::expr(MASS::glm.nb(!!model, data = data, ...))
m <- eval(e)
attr(m, "tidycall") <- store_tidycall(m, e)
m
}
|
mapdeck_style <- function(
style = c("dark","light","outdoors","streets","satellite","satellite-streets")
) {
style <- match.arg(style)
return(
switch(
style
, "dark" = "mapbox://styles/mapbox/dark-v10"
, "light" = "mapbox://styles/mapbox/light-v10"
, "outdoors" = "mapbox://styles/mapbox/outdoors-v11"
, "streets" = "mapbox://styles/mapbox/streets-v11"
, "satellite" = "mapbox://styles/mapbox/satellite-v9"
, "satellite-streets" = "mapbox://styles/mapbox/satellite-streets-v11"
)
)
}
|
`weibplot` <- function(x,
plot.pos = "exp",
shape = NULL,
scale = NULL,
labels = NULL,
mono = TRUE,
...){
if (mono) {
col1 <- "darkgray"
} else {
col <- col2rgb("SteelBlue3")/256
col1 <- rgb(col[1], col[2], col[3], 0.3)
}
xs <- sort(x)
n <- length(x)
if (!(plot.pos %in% c("exp", "med")))
stop("plot.pas must be either \"exp\" or \"med\"")
if (plot.pos == "medrank") F <- ((1:n) - 0.3)/(n + 0.4)
else F <- (1:n) / (n + 1)
transF <- log(-log(1-F))
par(xlog = TRUE)
plot(x = xs,
y = transF,
pch = 16,
col = col1,
yaxt = "n",
ylab = "prob",
log = "x",
...)
probs <- c(0.01, 0.05, 0.10, 0.25, 0.50, 0.60, 0.70, 0.80, 0.90)
abline(h = log(-log(probs)),
lty = "dotted",
col = "gray")
abline(v = axTicks(side = 1),
lty = "dotted",
col = "gray")
axis(side = 2,
at = log(-log(probs)),
labels = formatC(1 - probs, format = "f", digits = 2))
if (!is.null(shape)) {
if (is.null(scale)) {
warning("'scale' is NULL, hence no use of 'shape'")
} else {
x.g <- seq(from = min(x), to = max(x) , length = 50)
nW <- max(c(length(shape), length(scale)))
shape <- rep(shape, length.out = nW)
scale <- rep(scale, length.out = nW)
if (mono) {
cols <- c("black", "darkgray")
ltys <- c("solid", "dashed", "dotted")
} else {
ltys <- "solid"
cols <- c("orangered", "DarkOliveGreen3", "purple", "pink")
}
ltys <- rep(ltys, length.out = nW)
cols <- rep(cols, length.out = nW)
for (i in 1:nW) {
col1 <- col2rgb(cols[i]) / 256
cols[i] <- rgb(col1[1], col1[2], col1[3], 0.9)
transF.g <- log(-log(1 - pweibull(x.g, shape[i], scale[i])))
lines(x = x.g,
y = transF.g,
col = cols[i],
lty = ltys[i])
}
coords <- par()$usr
if (is.null(labels)) labels <- paste("shape = ", formatC(shape, format = "f", digits = 2),
"scale = ", formatC(scale, format = "f", digits = 2))
else labels <- rep(labels, length.out = nW)
legend(x = range(x)[2] * 0.9,
y = 0.60 * coords[3] + 0.40 * coords[4],
xjust = 1,
yjust = 1,
lty = ltys,
lwd = rep(2, nW),
col = cols,
horiz = FALSE,
legend = labels)
}
}
}
|
validate.Gamma.arguments <- function(data,surv.times,m,gamma,strata,gamma.factor,DCO.time,Call){
is.valid.call(Call)
if(nrow(data)==0){
stop("Empty data frame!")
}
if(any(c("impute.time","impute.event","internal_gamma_val","internalDCO.time") %in% colnames(data))){
stop("Cannot use a data frame with columns impute.time, impute.event",
" internalDCO.time or internal_gamma_val")
}
if(!.internal.is.finite.number(m) ||!.internal.is.wholenumber(m) || m < 2){
stop("m must be an integer and at least 2")
}
if(!is.numeric(gamma.factor) || is.na(gamma.factor) || length(gamma.factor)>1){
stop("gamma.factor must be a single number that is multiplied by the values",
" of the gamma argument in order to create subject specific jumps in the hazard rate.",
" see help(gammaImpute) for examples")
}
if(length(strata) != nrow(data)){
stop("Invalid strata argument it must be a vector the same length as the number of rows in the dataset")
}
validateDCO.time(DCO.time=DCO.time,data=data,times=surv.times[,1])
if(!is.null(gamma)){
validateGammaVal(gamma,data)
}
if(any(surv.times[,1]<= 0)){
stop("Time on study must be positive")
}
}
validateDCO.time <- function(DCO.time,data,times){
if(is.character(DCO.time)){
if(length(DCO.time) != 1 || !DCO.time %in% colnames(data)){
stop("Invalid DCO.time argument")
}
DCO.time <- data[,DCO.time]
}
else if(length(DCO.time)==1){
DCO.time <- rep(DCO.time,nrow(data))
}
else if(length(DCO.time)!=nrow(data)){
stop("Invalid DCO.time length")
}
if(any(!is.numeric(DCO.time) | is.infinite(DCO.time))){
stop("DCO times must be numeric and finite")
}
if(any(DCO.time < times)){
stop("DCO.time must be >= time for all subjects ")
}
}
validateGammaVal <- function(gamma,data){
if(is.character(gamma)){
if(length(gamma)!= 1 || !gamma %in% colnames(data)){
stop("Invalid gamma column name")
}
gamma <- data[,gamma]
}
if(length(gamma)!= nrow(data)){
stop("Invalid length of gamma its length must be the number of subjects in the",
" the dataset. use the gamma.factor argument if you want to use a single number",
" for each subject's jump in hazard. See help(gammaImpute) for futher details")
}
if(any(!is.na(gamma) & !is.numeric(gamma))){
stop("gamma must be numeric")
}
}
|
df_format_stdstyle <- c(
"padding-top"="3px",
"padding-bottom"="3px",
"padding-left"="0.5ex",
"padding-right"="0.5ex",
"margin-top"="0px",
"margin-bottom"="0px",
"border-style"="none",
"border-width"="0px"
)
format_html.data.frame <- function(x,
toprule=2,midrule=1,bottomrule=2,
split.dec=TRUE,
row.names=TRUE,
digits=getOption("digits"),
format="f",
style=df_format_stdstyle,
margin="2ex auto",
...){
firstcol <- c("padding-left"="0.3em")
lastcol <- c("padding-right"="0.3em")
toprule <- c("border-top"=paste0(midrule,"px solid"))
bottomrule <- c("border-bottom"=paste0(midrule,"px solid"))
midrule_above <- c("border-top"=paste0(midrule,"px solid"))
midrule <- c("border-bottom"=paste0(midrule,"px solid"))
align.right <- c("text-align"="right")
align.left <- c("text-align"="left")
align.center <- c("text-align"="center")
row_style <- c("border-style"="none")
table_style <- c("border-collapse"="collapse" ,"border-style"="none")
colsep <- ""
rowsep <- "\n"
n <- nrow(x)
m <- ncol(x)
d <- digits
is.int <- sapply(x,is.integer)
is.num <- sapply(x,is.numeric) & !is.int
m.num <- sum(is.num)
digits <- integer(m.num)
digits[] <- d
fdigits <- integer(m)
fdigits[is.num] <- digits
fo <- format
format <- character(m)
format[is.num] <- fo
colspan <- integer(0)
body <- matrix(nrow=nrow(x),ncol=0)
for(i in 1:m) {
tmp <- x[[i]]
dim.x.i <- dim(tmp)
ncol.tmp <- if(length(dim.x.i)) ncol(tmp) else 1
if(is.int[i]){
tmp <- formatC(tmp,format="d")
col <- html_td(tmp,vectorize=TRUE,style=css(style))
colspan <- c(colspan,ncol.tmp)
}
else if(is.num[i]){
tmp <- formatC(tmp,digits=fdigits[i],format=format[i])
if(split.dec){
tmp <- spltDec(tmp)
col <- html_td_spltDec(tmp,style=css(style))
colspan <- c(colspan,3L*ncol.tmp)
}
else{
col <- html_td(tmp,vectorize=TRUE,style=css(style))
colspan <- c(colspan,ncol.tmp)
}
}
else {
tmp <- as.character(tmp)
col <- html_td(tmp,vectorize=TRUE,style=css(style))
col <- setStyle(col,align.left)
colspan <- c(colspan,ncol.tmp)
}
dim(col) <- dim.x.i
body <- cbind(body,col)
}
if(row.names){
tmp <- rownames(x)
ldr <- html_td(tmp,vectorize=TRUE,style=css(c(style,firstcol,align.right)))
body <- cbind(ldr,body)
}
body[1,] <- lapply(body[1,],setStyle,toprule)
body[n,] <- lapply(body[n,],setStyle,bottomrule)
body <- apply(body,1,html_tr)
hdr <- colnames(x)
if(row.names) {
hdr <- c("",hdr)
colspan <- c(1L,colspan)
}
hdr <- html_td(hdr,vectorize=TRUE,style=css(style))
hdr[] <- mapply(setAttribs,hdr,colspan=colspan,SIMPLIFY=FALSE)
hdr <- lapply(hdr,setStyle,df_format_stdstyle)
hdr <- lapply(hdr,setStyle,align.center)
hdr <- lapply(hdr,setStyle,toprule)
hdr[[1]] <- setStyle(hdr[[1]],lastcol)
hdr[[length(hdr)]] <- setStyle(hdr[[length(hdr)]],lastcol)
hdr <- html_tr(hdr)
if(length(margin))
table_style <- c(table_style,margin=margin)
ans <- html_table(c(list(hdr),body),style=as.css(table_style))
ans <- as.character(ans)
return(ans)
}
|
download.database.all <- function(db, path = NULL) {
db_chunks <- listNCBIDatabases(db = db)
db_chunks <- db_chunks[-which(stringr::str_detect(db_chunks, "[.]json"))]
message("Starting download of the files: ",
paste0(db_chunks, collapse = ", "),
" ...")
message("This download process may take a while due to the large size of the individual data chunks ...")
if (is.null(path)) {
path <- db
}
dld_paths <- list(length(db_chunks))
for (i in seq_len(length(db_chunks))) {
dld_paths[i] <- list(download.database(db = db_chunks[i], path = path))
}
corrupt_md5 <- any(unlist(lapply(dld_paths, is.logical)))
which_corrupter_md5 <- which(unlist(lapply(dld_paths, is.logical)))
if (corrupt_md5)
warning("The file(s) ", paste0(db_chunks[which_corrupter_md5], collapse = ", "), " had corrupted md5 check sum(s). You can simply re-run this function to re-download corrupted files.")
message("Download process is finished and files are stored in '",
path, "'.")
if (corrupt_md5)
return(unlist(dld_paths[-which_corrupter_md5]))
if (!corrupt_md5)
return(unlist(dld_paths))
}
|
generateSpFromBCA <- function(raster.stack.current, raster.stack.future, rescale = TRUE, niche.breadth = "any",
means = NULL, sds = NULL, bca = NULL,
sample.points = FALSE, nb.points = 10000,
plot = TRUE)
{
if(!(is(raster.stack.current, "RasterStack"))){
stop("raster.stack.current must be a raster stack object")
}
if(!(is(raster.stack.future, "RasterStack"))){
stop("raster.stack.future must be a raster stack object")
}
if(!all((names(raster.stack.future) %in% names(raster.stack.current)))){
stop("The variables names in raster.stack.future must be the same as variables names in raster.stack.current")
}
if((calc(raster.stack.current, sum) == calc(raster.stack.future, sum))@data@min==1){
stop("Please provide two different rasters")
}
if(sample.points){
if(!is.numeric(nb.points))
{stop("nb.points must be a numeric value corresponding to the number of pixels to sample from raster.stack.current
and from raster.stack.future")}
env.df.current <- sampleRandom(raster.stack.current, size = nb.points, na.rm = TRUE)
env.df.future <- sampleRandom(raster.stack.future , size = nb.points, na.rm = TRUE)
} else
{
if(canProcessInMemory(raster.stack.current, n = 4)){
env.df.current <- getValues(raster.stack.current)
env.df.future <- getValues(raster.stack.future)
if(any(is.na(env.df.current)))
{
env.df.current <- env.df.current[-unique(which(is.na(env.df.current), arr.ind = T)[, 1]), ]
}
if(any(is.na(env.df.future)))
{
env.df.future <- env.df.future[-unique(which(is.na(env.df.future), arr.ind = T)[, 1]), ]
}
} else
{
stop("Your computer does not have enough memory to extract all the values from raster.stack.current.
Use the argument sample.points = TRUE, and adjust the number of points to use with nb.points.
More details in ?generateSpFromBCA")
}
}
if(!is.null(bca)){
if(!all(class(bca) %in% c("between", "dudi"))) {
stop("Please provide an appropriate bca object (output of bca()) to make the bca plot.")
}
if(any(!(names(bca$tab) %in% names(raster.stack.current)))){
stop("The variables used to make the bca must be the same as variables names in raster.stack.current")
}
if (is.null(bca$cent) | is.null(bca$norm) ){
stop("Please provide an appropriate bca object (output of generateSpFromBCA) to make the bca plot.")
}
between.object <- bca
rm(bca)
sel.vars <- names(raster.stack.current)
} else
{
sel.vars <- names(raster.stack.current)
xpoint <- sample(nrow(env.df.future), 1)
env.df <- rbind(env.df.current,
env.df.future,
env.df.future[xpoint, ],
deparse.level = 0)
condition <- as.factor(c(rep('Current', nrow(env.df.current)),
rep('Future' , nrow(env.df.future )), "X"))
message(" - Perfoming the between component analysis\n")
pca.object <- ade4::dudi.pca(env.df, scannf = F, nf = 2)
between.object <- ade4::bca(pca.object, condition, scannf = F, nf = 2)
between.object$xpoint <- xpoint
between.object$cent <- pca.object$cent
between.object$norm <- pca.object$norm
if(!ncol(between.object$ls)==2){
stop("A two dimension BCA can not be performed with provided rasters stack")
}
}
message(" - Defining the response of the species along the axis\n")
if(!is.null(means)){
if(!is.numeric(means))
{stop("Please provide numeric means for the gaussian function to compute probabilities of presence")}
if(!is.vector(means) | length(means) != 2)
{stop("Please provide a vector with 2 means for the gaussian function (one for each of the two between axes)")}
} else
{
means <- between.object$ls[sample(1:nrow(between.object$ls), 1), ]
means <- c(mean1 = means[1, 1],
mean2 = means[1, 2])
}
if(!is.null(sds)){
if(!is.numeric(sds))
{stop("Please provide numeric standard deviations for the gaussian function to compute probabilities of presence")}
if(!is.vector(sds) | length(sds) != 2)
{stop("Please provide a vector with 2 standard deviations for the gaussian function (one for each of the two pca axes)")}
if(any(sds < 0))
{stop("The standard deviations must have a positive value!")}
message(" - You have provided standard deviations, so argument niche.breadth will be ignored.\n")
} else
{
axis1 <- c(min = max(min(between.object$ls[, 1]),
quantile(between.object$ls[, 1], probs = .25) -
5 * (quantile(between.object$ls[, 1], probs = .75) -
quantile(between.object$ls[, 1], probs = .25))),
max = min(max(between.object$ls[, 1]),
quantile(between.object$ls[, 1], probs = .75) +
5 * (quantile(between.object$ls[, 1], probs = .75) -
quantile(between.object$ls[, 1], probs = .25))))
axis2 <- c(min = max(min(between.object$ls[, 2]),
quantile(between.object$ls[, 2], probs = .25) -
5 * (quantile(between.object$ls[, 2], probs = .75) -
quantile(between.object$ls[, 2], probs = .25))),
max = min(max(between.object$ls[, 2]),
quantile(between.object$ls[, 2], probs = .75) +
5 * (quantile(between.object$ls[, 2], probs = .75) -
quantile(between.object$ls[, 2], probs = .25))))
if(niche.breadth == "any")
{
sds <- c(sd1 = sample(seq((axis1[2] - axis1[1])/100, (axis1[2] - axis1[1])/2, length = 1000), 1),
sd2 = sample(seq((axis2[2] - axis2[1])/100, (axis2[2] - axis2[1])/2, length = 1000), 1))
} else if (niche.breadth == "narrow")
{
sds <- c(sd1 = sample(seq((axis1[2] - axis1[1])/100, (axis1[2] - axis1[1])/10, length = 1000), 1),
sd2 = sample(seq((axis2[2] - axis2[1])/100, (axis2[2] - axis2[1])/10, length = 1000), 1))
} else if (niche.breadth == "wide")
{
sds <- c(sd1 = sample(seq((axis1[2] - axis1[1])/10, (axis1[2] - axis1[1])/2, length = 1000), 1),
sd2 = sample(seq((axis2[2] - axis2[1])/10, (axis2[2] - axis2[1])/2, length = 1000), 1))
} else
{
stop("niche.breadth must be one of these: 'any', 'narrow', 'wide")
}
}
message(" - Calculating current suitability values\n")
rasters.env.current <- calc(raster.stack.current[[sel.vars]], fun = function(x, ...)
{.pca.coordinates(x, pca = between.object, na.rm = TRUE, axes = c(1, 2))})
suitab.raster.current <- calc(rasters.env.current, fun = function(x, ...){.prob.gaussian(x, means = means, sds = sds)})
message(" - Calculating future suitability values\n")
rasters.env.future <- calc(raster.stack.future [[sel.vars]], fun = function(x, ...)
{.pca.coordinates(x, pca = between.object, na.rm = TRUE, axes = c(1, 2))})
suitab.raster.future <- calc(rasters.env.future , fun = function(x, ...){.prob.gaussian(x, means = means, sds = sds)})
if(!is.null(bca)){
between.env.current <- .pca.coordinates(env.df.current, pca = between.object, na.rm = TRUE)
between.env.future <- .pca.coordinates(env.df.future , pca = between.object, na.rm = TRUE)
between.object$ls <- as.data.frame( rbind(between.env.current, between.env.future) )
}
if(rescale){
suitab.raster.current <- (suitab.raster.current - suitab.raster.current@data@min) / (suitab.raster.current@data@max - suitab.raster.current@data@min)
suitab.raster.future <- (suitab.raster.future - suitab.raster.future@data@min) / (suitab.raster.future@data@max - suitab.raster.future@data@min)
}
stack.lengths <- c(nrow(env.df.current), nrow(env.df.future))
if(plot){
message(" - Ploting response and suitability\n")
op <- par(no.readonly = TRUE)
par(mar = c(5.1, 4.1, 4.1, 2.1))
layout(matrix(nrow = 2, ncol = 2, c(1, 1, 2, 3 )))
plotResponse(x = raster.stack.current, approach = "bca",
parameters = list(bca = between.object,
means = means,
sds = sds,
stack.lengths = stack.lengths), no.plot.reset = T)
image(suitab.raster.current, axes = T, ann = F, asp = 1,
las = 1, col = rev(terrain.colors(12)))
legend(title = "Pixel\nsuitability", "right", inset = c(-0.14, 0),
legend = c(1, 0.8, 0.6, 0.4, 0.2, 0),
fill = terrain.colors(6), bty = "n")
title("Current environmental suitability of the virtual species")
image(suitab.raster.future, axes = T, ann = F, asp = 1,
las = 1, col = rev(terrain.colors(12)))
legend(title = "Pixel\nsuitability", "right", inset = c(-0.14, 0),
legend = c(1, 0.8, 0.6, 0.4, 0.2, 0),
fill = terrain.colors(6), bty = "n")
title("Future environmental suitability of the virtual species")
par(op)
}
results <- list(approach = "bca",
details = list(variables = sel.vars,
bca = between.object,
rescale = rescale,
axes = c(1, 2),
means = means,
sds = sds,
stack.lengths = stack.lengths),
suitab.raster.current = suitab.raster.current,
suitab.raster.future = suitab.raster.future)
class(results) <- append("virtualspecies", class(results))
return(results)
}
|
factor_detector <- function(y_column,x_column_nn,tabledata)
{
n_x<-length( x_column_nn)
error1<-try({tabledata[y_column]},silent=TRUE)
if('try-error' %in% class(error1))
{
stop("undefined columns selected in data as parameter.")
}
for (num in 1: n_x)
{
x_column <- x_column_nn[num]
error1<-try({tabledata[x_column]},silent=TRUE)
if('try-error' %in% class(error1))
{
stop("undefined columns selected in data as parameter.")
}
}
if(is.character(y_column))
{
y_colname<-y_column
y_column<-which(names(tabledata) == y_colname)
}
y_colname<-names(tabledata)[y_column]
x_column_n <- vector()
for(num in 1:n_x)
{
x_column <- x_column_nn[num]
if(is.character(x_column))
{
x_colname<-x_column
x_column<-which(names(tabledata) == x_colname)
}
x_column_n <- rbind(x_column_n,c(x_column))
if(x_column==y_column)
{
stop("Y variable and X variables should be the different data.")
}
}
lgnull<-is.null(tabledata)
num_null=sum(lgnull)
if(num_null > 0)
{
stop("data hava some objects with value NULL")
}
long=length(tabledata[[y_column]])
Na_check <- vector()
for(i in 1:long)
{
if(is.na(tabledata[[y_column]][i]))
{
Na_check <-rbind(Na_check,c(y_column,as.character(i)))
}
}
for (num in 1: n_x)
{
x_column <- x_column_n[num]
for(i in 1:long)
{
if(is.na(tabledata[[x_column]][i]))
{
Na_check <-rbind(Na_check,c(x_column,as.character(i)))
}
}
}
for (num in 1: n_x)
{
x_column <- x_column_n[num]
if((class(tabledata[[x_column]])=="factor")|(class(tabledata[[x_column]])=="character") )
{
for(i in 1:long){
if(tabledata[[x_column]][i]=="")
{
Na_check <-rbind(Na_check,c(x_column,as.character(i)))
}
}
}
}
if(length(Na_check)!=0)
{
mes=""
for(i in 1:length(Na_check[,1])){
mes=paste(mes,"data hava NA in column: ",Na_check[i,1]," ,at row: ",Na_check[i,2],"\n")
}
stop(mes)
}
for(i in 1:long)
{
if(class(tabledata[[y_column]][i])=="character")
{
stop("the data type of Y variable can not be character ,in column :",y_column)
}
}
lginfi<-is.infinite(tabledata[[y_column]])
num_infi=sum(lginfi)
if(num_infi > 0)
{
stop("Y variable data hava some objects with value Not finite")
}
for (num in 1: n_x)
{
x_column <- x_column_n[num]
uni_x=unique(tabledata[x_column])
long2=long/2
if(length(uni_x[[1]])> long2)
{
stop("For column ",x_column,":data should be dispersed.")
}
if(length(uni_x[[1]]) < 2)
{
stop("For column ",x_column,":the number of types(or groups) in a x variable should be more than 1.")
}
}
Result_factorDetector_n<-list()
for (num in 1: n_x)
{
x_column <- x_column_n[num]
x_colname<-names(tabledata)[x_column]
vec <- tabledata[,x_column]
vec.sort <- sort(vec)
vec.unique <- unique(vec.sort)
N_popu <- nrow(tabledata)
N_stra <- length(vec.unique)
N_var <- var(tabledata[,y_column])
strataVarSum <- 0
lamda_1st_sum <- 0
lamda_2nd_sum <- 0
for(i in vec.unique)
{
LenInter <- length(which(vec == i))
strataVar <- 0
lamda_1st <- 0
lamda_2nd <- 0
if(LenInter <= 1)
{
strataVar <- 0
lamda_1st <- (tabledata[which(vec == i),y_column])^2
lamda_2nd <- tabledata[which(vec == i),y_column]
}else
{
strataVar <- (LenInter-1) * var(tabledata[which(vec == i),y_column])
lamda_1st <- (mean(tabledata[which(vec == i),y_column]))^2
lamda_2nd <- sqrt(LenInter) * mean(tabledata[which(vec == i),y_column])
}
strataVarSum <- strataVarSum + strataVar;
lamda_1st_sum <- lamda_1st_sum + lamda_1st
lamda_2nd_sum <- lamda_2nd_sum + lamda_2nd
}
TotalVar <- (nrow(tabledata)-1)*N_var
pd <- 1 - strataVarSum/TotalVar;
lamda <- (lamda_1st_sum - lamda_2nd_sum^2 / N_popu) / N_var
F_value <- (N_popu - N_stra)* pd / ((N_stra - 1)* (1 - pd))
p_value <- pf(F_value,df1= N_stra - 1, df2= N_popu - N_stra, ncp=lamda, lower.tail = F)
Result_factorDetector <- data.frame(pd,p_value)
colnames(Result_factorDetector) <- c("q-statistic","p-value")
rownames(Result_factorDetector) <- c(x_colname)
Result_factorDetector_n[num]<-list(Result_factorDetector)
}
return(Result_factorDetector_n)
}
|
`SHradfoc` <-
function(A , MEC, GU, pscale, col)
{
C = RPMG::circle()
imageSH(MEC$az1, MEC$dip1, MEC$rake1, SCALE=FALSE, UP=MEC$UP, col=col )
lines(C$x, C$y, type='l', col=grey(.6))
}
|
fpca2s <- function(Y = NULL, ydata = NULL, argvals = NULL, npc = NA, center = TRUE,
smooth = TRUE) {
stopifnot(!is.null(Y))
if (any(is.na(Y)))
stop("No missing values in <Y> allowed.")
if (!is.null(ydata)) {
stop(paste("<ydata> argument for irregular data is not supported,", "please use fpca.sc instead."))
}
X <- Y
data_dim <- dim(X)
I <- data_dim[1]
J <- data_dim[2]
if (is.na(npc)) {
npc <- getNPC.DonohoGavish(X)
}
irregular <- FALSE
if (!is.null(argvals)) {
stopifnot(is.numeric(argvals), length(argvals) == J, all(!is.na(argvals)))
if (any(diff(argvals)/mean(diff(argvals)) > 1.05 | diff(argvals)/mean(diff(argvals)) <
0.95)) {
warning(paste("non-equidistant <argvals>-grid detected:", "fpca2s will return orthonormal eigenvectors of the function evaluations",
"not evaluations of the orthonormal eigenvectors.", "Use fpca.sc() for the latter instead."))
irregular <- TRUE
}
} else {
argvals <- seq(0, 1, length = J)
}
meanX <- rep(0, J)
if (center) {
meanX <- apply(X, 2, function(x) mean(x, na.rm = TRUE))
meanX <- smooth.spline(argvals, meanX, all.knots = TRUE)$y
X <- t(t(X) - meanX)
}
if (J > I) {
VV <- X %*% t(X)
Eigen <- eigen(VV)
D <- Eigen$values
sel <- (D > 0)
V <- Eigen$vectors[, sel == 1]
D <- D[sel == 1]
D <- sqrt(D)
U <- t(X) %*% V %*% diag(1/D)
}
if (J <= I) {
UU <- t(X) %*% X
Eigen <- eigen(UU)
D <- Eigen$values
U <- Eigen$vectors[, D > 0]
D <- D[D > 0]
D <- sqrt(D)
}
lambda <- D^2/(I - 1)/J
if (!is.numeric(npc))
stop("Invalid <npc>.")
if (npc < 1 | npc > min(I, J))
stop("Invalid <npc>.")
message("Extracted ", npc, " smooth components.")
if (smooth == TRUE) {
for (j in 1:npc) {
temp = smooth.spline(argvals, U[, j], all.knots = TRUE)$y
U[, j] = temp
}
}
if (!irregular) {
scale <- sqrt(mean(diff(argvals)))
} else {
scale <- 1
}
eigenvectors = U[, 1:npc, drop = FALSE]/scale
scores = unname(t(lm.fit(x = eigenvectors, y = t(X))$coefficients))
eigenvalues = diag(var(scores))
Yhat = t(eigenvectors %*% t(scores) + meanX)
ret = list(Yhat = Yhat, Y = Y, scores = scores, mu = meanX, efunctions = eigenvectors,
evalues = eigenvalues, npc = npc, argvals = argvals)
class(ret) = "fpca"
return(ret)
}
|
test_that("mpm_to_ functions work correctly", {
xmax <- 20
lx <- mpm_to_lx(mat_u, start = 1, xmax = xmax, lx_crit = 0)
expect_length(lx, xmax + 1)
expect_true(all(lx >= 0))
expect_true(all(lx <= 1))
expect_true(all(lx == cummin(lx)))
lx_zero <- suppressWarnings(mpm_to_lx(mat_u_zero, start = 1, xmax = xmax))
expect_equal(lx_zero[1], 1)
expect_true(all(lx_zero[-1] == 0))
lx_a <- mpm_to_lx(mat_u, start = 2)
lx_b <- mpm_to_lx(mat_u, start = c(0, 1, 0, 0))
expect_equal(lx_a, lx_b)
px <- mpm_to_px(mat_u, start = 1, xmax = xmax, lx_crit = 0)
expect_length(px, length(lx))
hx <- mpm_to_hx(mat_u, start = 1, xmax = xmax, lx_crit = 0)
expect_length(hx, length(lx))
mx <- mpm_to_mx(mat_u, mat_f, start = 1, xmax = xmax,lx_crit = 0)
expect_length(mx, length(lx))
expect_true(all(mx >= 0))
mx_zero <- suppressWarnings(mpm_to_mx(mat_u_zero, mat_f,
start = 1, xmax = xmax))
expect_true(all(mx_zero == 0))
lx_named <- mpm_to_lx(mat_u_named, start = "sm", xmax = xmax, lx_crit = 0)
px_named <- mpm_to_px(mat_u_named, start = 1, xmax = xmax, lx_crit = 0)
hx_named <- mpm_to_hx(mat_u_named, start = 1, xmax = xmax,lx_crit = 0)
mx_named <- mpm_to_mx(mat_u_named, mat_f_named, start = 1, xmax = xmax, lx_crit = 0)
expect_equal(lx, lx_named)
expect_equal(px, px_named)
expect_equal(hx, hx_named)
expect_equal(mx, mx_named)
})
test_that("mpm_to_ functions warn and fail gracefully", {
expect_error(mpm_to_lx(mat_u, start = 10))
expect_error(mpm_to_lx(mat_u_na))
expect_error(mpm_to_lx(mat_u, start = "stage name"))
expect_error(mpm_to_px(mat_u, start = 10))
expect_error(mpm_to_px(mat_u_na))
expect_error(mpm_to_px(mat_u, start = "stage name"))
expect_error(mpm_to_hx(mat_u, start = 10))
expect_error(mpm_to_hx(mat_u_na))
expect_error(mpm_to_hx(mat_u, start = "stage name"))
expect_error(mpm_to_mx(mat_u, mat_f, start = 10))
expect_error(mpm_to_mx(mat_u_na, mat_f))
expect_error(mpm_to_mx(mat_u, mat_f_na))
expect_error(mpm_to_mx(mat_u, mat_f, start = "stage name"))
})
|
betamfxest <-
function(formula, data, atmean = TRUE, robust = FALSE, clustervar1 = NULL,
clustervar2 = NULL, control = betareg.control(),
link.phi = NULL, type = "ML"){
if(is.null(formula)){
stop("formula is missing")
}
if(!is.data.frame(data)){
stop("data arguement must contain data.frame object")
}
if(is.null(clustervar1) & !is.null(clustervar2)){
stop("use clustervar1 arguement before clustervar2 arguement")
}
if(!is.null(clustervar1)){
if(is.null(clustervar2)){
if(!(clustervar1 %in% names(data))){
stop("clustervar1 not in data.frame object")
}
data = data.frame(model.frame(formula, data, na.action=NULL),data[,clustervar1])
names(data)[dim(data)[2]] = clustervar1
data=na.omit(data)
}
if(!is.null(clustervar2)){
if(!(clustervar1 %in% names(data))){
stop("clustervar1 not in data.frame object")
}
if(!(clustervar2 %in% names(data))){
stop("clustervar2 not in data.frame object")
}
data = data.frame(model.frame(formula, data, na.action=NULL),
data[,c(clustervar1,clustervar2)])
names(data)[c(dim(data)[2]-1):dim(data)[2]] = c(clustervar1,clustervar2)
data=na.omit(data)
}
}
fit = betareg(formula, data=data, x=T, control = control,
link = "logit", link.phi = link.phi, type = type)
x1 = model.matrix(fit)
if (any(alias <- is.na(coef(fit)))) {
x1 <- x1[, !alias, drop = FALSE]
}
xm = as.matrix(colMeans(x1))
be = as.matrix(na.omit(coef(fit)))[1:NROW(xm)]
k1 = NROW(xm)
xb = t(xm) %*% be
fxb = ifelse(atmean==TRUE, plogis(xb)*(1-plogis(xb)), mean(plogis(x1 %*% be)*(1-plogis(x1 %*% be))))
vcv = vcov(fit)[1:NROW(xm),1:NROW(xm)]
if(robust){
if(is.null(clustervar1)){
vcv = sandwich(fit, bread. = bread(fit), meat. = meatHCbeta(fit,"HC0"))[1:NROW(xm),1:NROW(xm)]
} else {
if(is.null(clustervar2)){
vcv = clusterVCV(data=data, fm=fit, cluster1=clustervar1,cluster2=NULL)[1:NROW(xm),1:NROW(xm)]
} else {
vcv = clusterVCV(data=data, fm=fit, cluster1=clustervar1,cluster2=clustervar2)[1:NROW(xm),1:NROW(xm)]
}
}
}
if(robust==FALSE & is.null(clustervar1)==FALSE){
if(is.null(clustervar2)){
vcv = clusterVCV(data=data, fm=fit, cluster1=clustervar1,cluster2=NULL)[1:NROW(xm),1:NROW(xm)]
} else {
vcv = clusterVCV(data=data, fm=fit, cluster1=clustervar1,cluster2=clustervar2)[1:NROW(xm),1:NROW(xm)]
}
}
mfx = data.frame(mfx=fxb*be, se=NA)
row.names(mfx) = colnames(x1)
if(atmean){
gr = (as.numeric(fxb))*(diag(k1) + as.numeric(1 - 2*plogis(xb))*(be %*% t(xm)))
mfx$se = sqrt(diag(gr %*% vcv %*% t(gr)))
} else {
gr = apply(x1, 1, function(x){
as.numeric(as.numeric(plogis(x %*% be)*(1-plogis(x %*% be)))*
(diag(k1) - (1 - 2*as.numeric(plogis(x %*% be)))*(be %*% t(x))))
})
gr = matrix(apply(gr,1,mean),nrow=k1)
mfx$se = sqrt(diag(gr %*% vcv %*% t(gr)))
}
temp1 = apply(x1,2,function(x)length(table(x))==1)
const = names(temp1[temp1==TRUE])
mfx = mfx[row.names(mfx)!=const,]
temp1 = apply(x1,2,function(x)length(table(x))==2)
disch = names(temp1[temp1==TRUE])
if(length(disch)!=0){
for(i in 1:length(disch)){
if(atmean){
disx0 = disx1 = xm
disx1[disch[i],] = max(x1[,disch[i]])
disx0[disch[i],] = min(x1[,disch[i]])
mfx[disch[i],1] = plogis(t(be) %*% disx1) - plogis(t(be) %*% disx0)
gr = dlogis(t(be) %*% disx1) %*% t(disx1) - dlogis(t(be) %*% disx0) %*% t(disx0)
mfx[disch[i],2] = sqrt(gr %*% vcv %*% t(gr))
} else {
disx0 = disx1 = x1
disx1[,disch[i]] = max(x1[,disch[i]])
disx0[,disch[i]] = min(x1[,disch[i]])
mfx[disch[i],1] = mean(plogis(disx1 %*% be) - plogis(disx0 %*% be))
gr = as.numeric(dlogis(disx1 %*% be)) * disx1 - as.numeric(dlogis(disx0 %*% be)) * disx0
avegr = as.matrix(colMeans(gr))
mfx[disch[i],2] = sqrt(t(avegr) %*% vcv %*% avegr)
}
}
}
mfx$discretechgvar = ifelse(rownames(mfx) %in% disch, 1, 0)
output = list(fit=fit, mfx=mfx)
return(output)
}
|
spip_exists <- function() {
file.exists(spip_binary_path())
}
|
library(RXMCDA)
tree <- xmlTreeParse(system.file("extdata","testFile.xml",package="RXMCDA"), useInternalNodes=TRUE)
altIDs <- getAlternativesIDs(tree)
perfTable <- getPerformanceTables(tree)
comps <- getAlternativesComparisons(tree, perfTable[[1]])
stopifnot(all.equal(dim(comps[[1]]), c(13,11)))
|
library(OutliersO3)
library(ggplot2)
data(Election2005)
data <- Election2005[, c(6, 10, 17, 28)]
O3d <- O3prep(data, method=c("HDo", "PCS", "BAC", "adjOut", "DDC", "MCD"), tolHDo=0.05, tolPCS=0.5, tolBAC=0.95, toladj=0.25, tolDDC=0.01, tolMCD=0.5)
O3d1 <- O3plotM(O3d)
O3d1$nOut
O3p <- O3prep(data, method=c("HDo", "PCS", "BAC", "adjOut", "DDC", "MCD"))
O3p1 <- O3plotM(O3p)
O3p1$nOut
O3p1$gO3
data(etymology, package="languageR")
data <- etymology[, c(2, 4, 5, 10, 13, 14)]
O3q <- O3prep(data, method=c("HDo", "PCS", "BAC", "adjOut", "DDC", "MCD"), tolHDo=0.01, tolPCS=0.005, tolBAC=0.005, toladj=0.1, tolDDC=0.01, tolMCD=0.000001)
O3q1 <- O3plotM(O3q)
O3q1$nOut
library(dplyr)
outHD <- O3q1$outsTable %>% filter(Method=="HDo") %>% group_by(Combination) %>% summarise(num=n()) %>% filter(num>5)
knitr::kable(outHD, row.names=FALSE)
O3r <- O3prep(data, method=c("HDo", "PCS", "BAC", "adjOut", "DDC", "MCD"), k1=2, tolHDo=0.01, tolPCS=0.0025, tolBAC=0.005, toladj=0.1, tolDDC=0.005, tolMCD=0.000001)
O3r1 <- O3plotM(O3r, caseNames=etymology$Verb)
O3r1$nOut
library(gridExtra)
grid.arrange(O3r1$gO3 + theme(plot.margin = unit(c(0, 1, 0, 0), "cm")), O3r1$gpcp, ncol=1, heights=c(2,1))
|
gemMoney_3_2 <- function(dstl,
supply.labor = 100,
supply.money = 300,
names.commodity = c("product", "labor", "money"),
names.agent = c("firm", "household"),
...) {
ge <- sdm2(
A = dstl,
B = matrix(c(
1, 0,
0, 0,
0, 0
), 3, 2, TRUE,
dimnames = list(names.commodity, names.agent)
),
S0Exg = {
tmp <- matrix(NA, 3, 2, dimnames = list(names.commodity, names.agent))
tmp[2, 2] <- supply.labor
tmp[3, 2] <- supply.money
tmp
},
names.commodity = names.commodity,
names.agent = names.agent,
...
)
return(ge)
}
|
test_aregImpute <- function(X_hat, list) {
index <- lapply(list, is.na)
aregImpute_imp <- function(X) {
Xdf <- as.data.frame(X)
Xcolnames <- colnames(Xdf)
Xformula <- stats::as.formula(paste("~", paste(Xcolnames, collapse = "+")))
hmisc_algo <- Hmisc::aregImpute(formula = Xformula, data = Xdf, n.impute = 1,
burnin = 5, nk = 3, type = "pmm", pmmtype = 2)
completeData <- as.data.frame(Hmisc::impute.transcan(hmisc_algo, imputation = 1,
data = Xdf, list.out = TRUE, pr = FALSE, check = FALSE))
imp_matrix <- as.matrix(completeData)
list(Imputed = imp_matrix)
}
print("Hmisc aregImpute imputation - in progress")
start_time <- Sys.time()
log_output <- utils::capture.output(results <- lapply(list, aregImpute_imp))
end_time <- Sys.time()
time <- as.numeric(end_time - start_time, units = "mins")
orig_MCAR <- X_hat[index[[1]]]
orig_MAR <- X_hat[index[[2]]]
orig_MNAR <- X_hat[index[[3]]]
if (length(index) == 4)
orig_MAP <- X_hat[index[[4]]]
imp_MCAR <- results$MCAR_matrix$Imputed[index[[1]]]
imp_MAR <- results$MAR_matrix$Imputed[index[[2]]]
imp_MNAR <- results$MNAR_matrix$Imputed[index[[3]]]
if (length(index) == 4)
imp_MAP <- results$MAP_matrix$Imputed[index[[4]]]
rmse_MCAR <- sqrt(mean((orig_MCAR - imp_MCAR)^2))
rmse_MAR <- sqrt(mean((orig_MAR - imp_MAR)^2))
rmse_MNAR <- sqrt(mean((orig_MNAR - imp_MNAR)^2))
if (length(index) == 4)
rmse_MAP <- sqrt(mean((orig_MAP - imp_MAP)^2))
mae_MCAR <- mean(abs(orig_MCAR - imp_MCAR))
mae_MAR <- mean(abs(orig_MAR - imp_MAR))
mae_MNAR <- mean(abs(orig_MNAR - imp_MNAR))
if (length(index) == 4)
mae_MAP <- mean(abs(orig_MAP - imp_MAP))
ks_MCAR <- stats::ks.test(orig_MCAR, imp_MCAR, exact=TRUE)$statistic
ks_MAR <- stats::ks.test(orig_MAR, imp_MAR, exact=TRUE)$statistic
ks_MNAR <- stats::ks.test(orig_MNAR, imp_MNAR, exact=TRUE)$statistic
if (length(index) == 4)
ks_MAP <- stats::ks.test(orig_MAP, imp_MAP, exact=TRUE)$statistic
if (length(index) == 4)
list(Comp_time = time, MCAR_RMSE = rmse_MCAR, MAR_RMSE = rmse_MAR, MNAR_RMSE = rmse_MNAR, MAP_RMSE = rmse_MAP, MCAR_MAE = mae_MCAR, MAR_MAE = mae_MAR, MNAR_MAE = mae_MNAR, MAP_MAE = mae_MAP, MCAR_KS = ks_MCAR, MAR_KS = ks_MAR, MNAR_KS = ks_MNAR, MAP_KS = ks_MAP) else list(Comp_time = time, MCAR_RMSE = rmse_MCAR, MAR_RMSE = rmse_MAR, MNAR_RMSE = rmse_MNAR, MCAR_MAE = mae_MCAR, MAR_MAE = mae_MAR, MNAR_MAE = mae_MNAR, MCAR_KS = ks_MCAR, MAR_KS = ks_MAR, MNAR_KS = ks_MNAR)
}
|
if(FALSE) {
tools:::.install_packages(c("--preclean", "--no-multiarch",
"tree"))
status <- tryCatch(
tools:::.install_packages(c("--no-clean-on-error", "--no-multiarch",
"tree"), no.q = TRUE)
, error = function(e) as.numeric(sub(".* exit status *", "",
conditionMessage(e))))
debugonce(tools:::.install_packages)
tools:::.install_packages(c("-c", "--debug", "--no-clean-on-error", "--no-multiarch",
"tree"))
debug(do_install)
}
.install_packages <- function(args = NULL, no.q = interactive(), warnOption = 1)
{
curPkg <- character()
lockdir <- ""
is_first_package <- TRUE
stars <- "*"
user.tmpdir <- Sys.getenv("PKG_BUILD_DIR")
keep.tmpdir <- nzchar(user.tmpdir)
tmpdir <- ""
clean_on_error <- TRUE
R_runR_deps_only <- function(cmd, deps_only_env, multiarch = FALSE, ...) {
deps_only <-
config_val_to_logical(Sys.getenv("_R_CHECK_INSTALL_DEPENDS_",
"FALSE"))
env <- if (deps_only) deps_only_env
else ""
env <- paste(env, "R_TESTS=")
opts <- "--no-save --no-restore --no-echo"
if (deps_only) {
opts <- paste(opts, "--no-init-file --no-site-file")
if (!multiarch)
opts <- paste(opts, "--no-environ")
}
R_runR(cmd = cmd, Ropts = opts, env = env, ...)
}
do_exit <-
if(no.q)
function(status) stop(".install_packages() exit status ", status)
else
function(status) q("no", status = status, runLast = FALSE)
do_exit_on_error <- function(status = 1L)
{
if(clean_on_error && length(curPkg)) {
pkgdir <- file.path(lib, curPkg)
if (nzchar(pkgdir) && dir.exists(pkgdir) &&
is_subdir(pkgdir, lib)) {
starsmsg(stars, "removing ", sQuote(pkgdir))
unlink(pkgdir, recursive = TRUE)
}
if (nzchar(lockdir) &&
dir.exists(lp <- file.path(lockdir, curPkg)) &&
is_subdir(lp, lockdir)) {
starsmsg(stars, "restoring previous ", sQuote(pkgdir))
if (WINDOWS) {
file.copy(lp, dirname(pkgdir), recursive = TRUE,
copy.date = TRUE)
unlink(lp, recursive = TRUE)
} else {
setwd(startdir)
if(system(paste("mv -f", shQuote(lp), shQuote(pkgdir))))
message(" restoration failed\n")
}
}
}
do_cleanup()
do_exit(status=status)
}
do_cleanup <- function()
{
if(!keep.tmpdir && nzchar(tmpdir)) do_cleanup_tmpdir()
if (!is_first_package) {
if (lib == .Library && "html" %in% build_help_types)
utils::make.packages.html(.Library, docdir = R.home("doc"))
}
if (nzchar(lockdir)) unlink(lockdir, recursive = TRUE)
}
do_cleanup_tmpdir <- function()
{
setwd(startdir)
if (!keep.tmpdir && dir.exists(tmpdir)) unlink(tmpdir, recursive=TRUE)
}
quote_path <- function(path, quote = "'") {
path <- gsub("\\", "\\\\", path, fixed = TRUE)
path <- gsub(quote, paste0("\\", quote), path, fixed = TRUE)
paste0(quote, path, quote)
}
quote_replacement <- function(r)
paste0(gsub("\\", "\\\\", r, fixed=TRUE))
on.exit(do_exit_on_error())
WINDOWS <- .Platform$OS.type == "windows"
cross <- Sys.getenv("R_CROSS_BUILD")
have_cross <- nzchar(cross)
if(have_cross && !cross %in% c("i386", "x64"))
stop("invalid value ", sQuote(cross), " for R_CROSS_BUILD")
if (have_cross) {
WINDOWS <- TRUE
Sys.setenv(R_OSTYPE = "windows")
}
if (WINDOWS) MAKE <- "make"
else MAKE <- Sys.getenv("MAKE")
rarch <- Sys.getenv("R_ARCH")
if (WINDOWS && nzchar(.Platform$r_arch))
rarch <- paste0("/", .Platform$r_arch)
cross <- Sys.getenv("R_CROSS_BUILD")
if(have_cross && !cross %in% c("i386", "x64"))
stop("invalid value ", sQuote(cross), " for R_CROSS_BUILD")
test_archs <- rarch
if (have_cross) {
WINDOWS <- TRUE
r_arch <- paste0("/", cross)
test_archs <- c()
}
SHLIB_EXT <- if (WINDOWS) ".dll" else {
mconf <- file.path(R.home(), paste0("etc", rarch), "Makeconf")
sub(".*= ", "", grep("^SHLIB_EXT", readLines(mconf), value = TRUE,
perl = TRUE))
}
if(getOption("warn") < warnOption) {
op <- options(warn = warnOption)
on.exit(options(op), add = TRUE)
}
invisible(Sys.setlocale("LC_COLLATE", "C"))
if (WINDOWS) {
rhome <- chartr("\\", "/", R.home())
Sys.setenv(R_HOME = rhome)
if (nzchar(rarch)) Sys.setenv(R_ARCH = rarch, R_ARCH_BIN = rarch)
}
Usage <- function() {
cat("Usage: R CMD INSTALL [options] pkgs",
"",
"Install the add-on packages specified by pkgs. The elements of pkgs can",
"be relative or absolute paths to directories with the package",
"sources, or to gzipped package 'tar' archives. The library tree",
"to install to can be specified via '--library'. By default, packages are",
"installed in the library tree rooted at the first directory in",
".libPaths() for an R session run in the current environment.",
"",
"Options:",
" -h, --help print short help message and exit",
" -v, --version print INSTALL version info and exit",
" -c, --clean remove files created during installation",
" --preclean remove files created during a previous run",
" -d, --debug turn on debugging messages",
if(WINDOWS) " and build a debug DLL",
" -l, --library=LIB install packages to library tree LIB",
" --no-configure do not use the package's configure script",
" --no-docs do not install HTML, LaTeX or examples help",
" --html build HTML help",
" --no-html do not build HTML help",
" --latex install LaTeX help",
" --example install R code for help examples",
" --fake do minimal install for testing purposes",
" --no-lock install on top of any existing installation",
" without using a lock directory",
" --lock use a per-library lock directory (default)",
" --pkglock use a per-package lock directory",
" (default for a single package)",
" --build build binaries of the installed package(s)",
" --install-tests install package-specific tests (if any)",
" --no-R, --no-libs, --no-data, --no-help, --no-demo, --no-exec,",
" --no-inst",
" suppress installation of the specified part of the",
" package for testing or other special purposes",
" --no-multiarch build only the main architecture",
" --libs-only only install the libs directory",
" --data-compress= none, gzip (default), bzip2 or xz compression",
" to be used for lazy-loading of data",
" --resave-data re-save data files as compactly as possible",
" --compact-docs re-compress PDF files under inst/doc",
" --with-keep.source",
" --without-keep.source",
" use (or not) 'keep.source' for R code",
" --with-keep.parse.data",
" --without-keep.parse.data",
" use (or not) 'keep.parse.data' for R code",
" --byte-compile byte-compile R code",
" --no-byte-compile do not byte-compile R code",
" --staged-install install to a temporary directory and then move",
" to the target directory (default)",
" --no-staged-install install directly to the target directory",
" --no-test-load skip test of loading installed package",
" --no-clean-on-error do not remove installed package on error",
" --merge-multiarch multi-arch by merging (from a single tarball only)",
" --use-vanilla do not read any Renviron or Rprofile files",
" --use-LTO use Link-Time Optimization",
" --no-use-LTO do not use Link-Time Optimization",
"\nfor Unix",
" --configure-args=ARGS",
" set arguments for the configure scripts (if any)",
" --configure-vars=VARS",
" set variables for the configure scripts (if any)",
" --strip strip shared object(s)",
" --strip-lib strip static/dynamic libraries under lib/",
" --dsym (macOS only) generate dSYM directory",
" --built-timestamp=STAMP",
" set timestamp for Built: entry in DESCRIPTION",
"\nand on Windows only",
" --force-biarch attempt to build both architectures",
" even if there is a non-empty configure.win",
" --compile-both compile both architectures on 32-bit Windows",
"",
"Which of --html or --no-html is the default depends on the build of R:",
paste0("for this one it is ",
if(static_html) "--html" else "--no-html", "."),
"",
"Report bugs at <https://bugs.R-project.org>.", sep = "\n")
}
is_subdir <- function(dir, parent) {
rl <- Sys.readlink(dir)
(!is.na(rl) && nzchar(rl)) ||
normalizePath(parent) == normalizePath(file.path(dir, ".."))
}
fullpath <- function(dir)
{
owd <- setwd(dir)
full <- getwd()
setwd(owd)
full
}
parse_description_field <- function(desc, field, default)
str_parse_logic(desc[field], default = default,
otherwise = quote(
errmsg("invalid value of ", field, " field in DESCRIPTION")))
starsmsg <- function(stars, ...)
message(stars, " ", ..., domain = NA)
errmsg <- function(...)
{
message("ERROR: ", ..., domain = NA)
do_exit_on_error()
}
pkgerrmsg <- function(msg, pkg)
errmsg(msg, " for package ", sQuote(pkg))
do_install <- function(pkg)
{
if (WINDOWS && endsWith(pkg, ".zip")) {
pkg_name <- basename(pkg)
pkg_name <- sub("\\.zip$", "", pkg_name)
pkg_name <- sub("_[0-9.-]+$", "", pkg_name)
reuse_lockdir <- lock && !pkglock
if (pkglock)
lock <- "pkglock"
utils:::unpackPkgZip(pkg, pkg_name, lib, libs_only, lock,
reuse_lockdir = reuse_lockdir)
return()
}
setwd(pkg)
desc <- tryCatch(read.dcf(fd <- file.path(pkg, "DESCRIPTION")),
error = identity)
if(inherits(desc, "error") || !length(desc))
stop(gettextf("error reading file '%s'", fd),
domain = NA, call. = FALSE)
desc <- desc[1L,]
if (!is.na(desc["Bundle"])) {
stop("this seems to be a bundle -- and they are defunct")
} else {
pkg_name <- desc["Package"]
if (is.na(pkg_name)) errmsg("no 'Package' field in 'DESCRIPTION'")
curPkg <<- pkg_name
}
instdir <- file.path(lib, pkg_name)
Sys.setenv(R_PACKAGE_NAME = pkg_name, R_PACKAGE_DIR = instdir)
status <- .Rtest_package_depends_R_version()
if (status) do_exit_on_error()
dir.create(instdir, recursive = TRUE, showWarnings = FALSE)
if (!dir.exists(instdir)) {
unlink(instdir, recursive = FALSE)
dir.create(instdir, recursive = TRUE, showWarnings = FALSE)
}
if (!dir.exists(instdir)) {
message("ERROR: unable to create ", sQuote(instdir), domain = NA)
do_exit_on_error()
}
if (!is_subdir(instdir, lib)) {
message("ERROR: ", sQuote(pkg_name), " is not a legal package name",
domain = NA)
do_exit_on_error()
}
owd <- setwd(instdir)
if (owd == getwd()) pkgerrmsg("cannot install to srcdir", pkg_name)
setwd(owd)
is_source_package <- is.na(desc["Built"])
if (is_source_package) {
sys_requires <- desc["SystemRequirements"]
if (!is.na(sys_requires)) {
sys_requires <- unlist(strsplit(sys_requires, ","))
for (i in cxx_standards) {
pattern <- paste0("^[[:space:]]*C[+][+]",i,"[[:space:]]*$")
if(any(grepl(pattern, sys_requires, ignore.case=TRUE))) {
Sys.setenv("R_PKG_CXX_STD"=i)
on.exit(Sys.unsetenv("R_PKG_CXX_STD"))
break
}
}
}
}
if (!is_first_package) cat("\n")
if (is_source_package)
do_install_source(pkg_name, instdir, pkg, desc)
else
do_install_binary(pkg_name, instdir, desc)
.Call(C_dirchmod, instdir, group.writable)
is_first_package <<- FALSE
if (tar_up) {
starsmsg(stars, "creating tarball")
version <- desc["Version"]
filename <- if (!grepl("darwin", R.version$os)) {
paste0(pkg_name, "_", version, "_R_",
Sys.getenv("R_PLATFORM"), ".tar.gz")
} else {
paste0(pkg_name, "_", version,".tgz")
}
filepath <- file.path(startdir, filename)
owd <- setwd(lib)
res <- utils::tar(filepath, curPkg, compression = "gzip",
compression_level = 9L,
tar = Sys.getenv("R_INSTALL_TAR"))
if (res)
errmsg(sprintf("packaging into %s failed", sQuote(filename)))
message("packaged installation of ",
sQuote(pkg_name), " as ", sQuote(filename),
domain = NA)
setwd(owd)
}
if (zip_up) {
starsmsg(stars, "MD5 sums")
.installMD5sums(instdir)
ZIP <- "zip"
version <- desc["Version"]
filename <- paste0(pkg_name, "_", version, ".zip")
filepath <- shQuote(file.path(startdir, filename))
unlink(filepath)
owd <- setwd(lib)
res <- system(paste(shQuote(ZIP), "-r9Xq", filepath,
paste(curPkg, collapse = " ")))
setwd(owd)
if (res)
message("running 'zip' failed", domain = NA)
else
message("packaged installation of ",
sQuote(pkg_name), " as ", filename, domain = NA)
}
if (Sys.getenv("_R_INSTALL_NO_DONE_") != "yes") {
starsmsg(stars, "DONE (", pkg_name, ")")
}
curPkg <<- character()
}
do_install_binary <- function(pkg, instdir, desc)
{
starsmsg(stars, "installing *binary* package ", sQuote(pkg), " ...")
if (file.exists(file.path(instdir, "DESCRIPTION"))) {
if (nzchar(lockdir))
system(paste("mv -f", shQuote(instdir),
shQuote(file.path(lockdir, pkg))))
dir.create(instdir, recursive = TRUE, showWarnings = FALSE)
}
TAR <- Sys.getenv("TAR", 'tar')
res <- system(paste("cp -R .", shQuote(instdir),
"|| (", TAR, "cd - .| (cd", shQuote(instdir), "&&", TAR, "-xf -))"
))
if (res) errmsg("installing binary package failed")
if (tar_up) {
starsmsg(stars, sQuote(pkg),
" was already a binary package and will not be rebuilt")
tar_up <- FALSE
}
}
run_clean <- function()
{
if (dir.exists("src") && length(dir("src", all.files = TRUE) > 2L)) {
if (WINDOWS) archs <- c("i386", "x64")
else {
wd2 <- setwd(file.path(R.home("bin"), "exec"))
archs <- Sys.glob("*")
setwd(wd2)
}
if(length(archs))
for(arch in archs) {
ss <- paste0("src-", arch)
.Call(C_dirchmod, ss, group.writable)
unlink(ss, recursive = TRUE)
}
owd <- setwd("src")
if (WINDOWS) {
if (file.exists("Makefile.ucrt"))
system(paste(MAKE, "-f Makefile.ucrt clean"))
else if (file.exists("Makefile.win"))
system(paste(MAKE, "-f Makefile.win clean"))
else
unlink(c("Makedeps",
Sys.glob("*_res.rc"),
Sys.glob("*.[do]")))
} else {
if (file.exists("Makefile")) system(paste(MAKE, "clean"))
else
unlink(Sys.glob(paste0("*", SHLIB_EXT)))
}
setwd(owd)
}
if (WINDOWS) {
if (file.exists("cleanup.ucrt"))
system("sh ./cleanup.ucrt")
else if (file.exists("cleanup.win"))
system("sh ./cleanup.win")
} else if (file_test("-x", "cleanup")) system("./cleanup")
else if (file.exists("cleanup"))
warning("'cleanup' exists but is not executable -- see the 'R Installation and Administration Manual'", call. = FALSE)
revert_install_time_patches()
}
do_install_source <- function(pkg_name, instdir, pkg_dir, desc)
{
Sys.setenv("R_INSTALL_PKG" = pkg_name)
on.exit(Sys.unsetenv("R_INSTALL_PKG"))
shlib_install <- function(instdir, arch)
{
if (file.exists("install.libs.R")) {
message("installing via 'install.libs.R' to ", instdir,
domain = NA)
local.env <- local({ SHLIB_EXT <- SHLIB_EXT
R_PACKAGE_DIR <- instdir
R_PACKAGE_NAME <- pkg_name
R_PACKAGE_SOURCE <- pkg_dir
R_ARCH <- arch
WINDOWS <- WINDOWS
environment()})
parent.env(local.env) <- .GlobalEnv
source("install.libs.R", local = local.env)
return(TRUE)
}
files <- Sys.glob(paste0("*", SHLIB_EXT))
if (length(files)) {
libarch <- if (nzchar(arch)) paste0("libs", arch) else "libs"
dest <- file.path(instdir, libarch)
message('installing to ', dest, domain = NA)
dir.create(dest, recursive = TRUE, showWarnings = FALSE)
file.copy(files, dest, overwrite = TRUE)
if((do_strip || config_val_to_logical(Sys.getenv("_R_SHLIB_STRIP_",
"false"))) &&
nzchar(strip_cmd <- Sys.getenv("R_STRIP_SHARED_LIB"))) {
system(paste(c(strip_cmd,
shQuote(file.path(dest, files))),
collapse = " "))
}
if (!WINDOWS)
Sys.chmod(file.path(dest, files), dmode)
if (dsym && startsWith(R.version$os, "darwin")) {
starsmsg(stars, gettextf("generating debug symbols (%s)", "dSYM"))
dylib <- Sys.glob(paste0(dest, "/*", SHLIB_EXT))
for (d in dylib) system(paste0("dsymutil ", d))
}
if(config_val_to_logical(Sys.getenv("_R_SHLIB_BUILD_OBJECTS_SYMBOL_TABLES_",
"TRUE"))
&& file_test("-f", "symbols.rds")) {
file.copy("symbols.rds", dest)
}
}
}
run_shlib <- function(pkg_name, srcs, instdir, arch, use_LTO = NA)
{
args <- c(shargs,
if(isTRUE(use_LTO)) "--use-LTO",
if(isFALSE(use_LTO)) "--no-use-LTO",
"-o", paste0(pkg_name, SHLIB_EXT),
srcs)
if (WINDOWS && debug) args <- c(args, "--debug")
if (debug) message("about to run ",
"R CMD SHLIB ", paste(args, collapse = " "),
domain = NA)
if (.shlib_internal(args) == 0L) {
if(WINDOWS && !file.exists("install.libs.R")
&& !length(Sys.glob("*.dll"))) {
message("no DLL was created")
return(TRUE)
}
shlib_install(instdir, arch)
return(FALSE)
} else return(TRUE)
}
patch_rpaths <- function()
{
slibs <- list.files(instdir, recursive = TRUE, all.files = TRUE,
full.names = TRUE)
slibs <- grep("(\\.sl$)|(\\.so$)|(\\.dylib$)|(\\.dll$)", slibs,
value = TRUE)
if (!length(slibs)) return()
have_file <- nzchar(Sys.which("file"))
if (have_file) {
are_shared <- vapply(slibs,
function(l) any(grepl("(shared|dynamically linked)",
system(paste("file", shQuote(l)), intern = TRUE))),
NA)
slibs <- slibs[are_shared]
if (!length(slibs)) return()
}
starsmsg(stars, "checking absolute paths in shared objects and dynamic libraries")
uname <- system("uname -a", intern = TRUE)
os <- sub(" .*", "", uname)
have_chrpath <- nzchar(Sys.which("chrpath"))
have_patchelf <- nzchar(Sys.which("patchelf"))
have_readelf <- nzchar(Sys.which("readelf"))
have_macos_clt <- identical(os, "Darwin") &&
nzchar(Sys.which("otool")) &&
nzchar(Sys.which("install_name_tool"))
have_solaris_elfedit <- identical(os, "SunOS") &&
nzchar(Sys.which("elfedit"))
hardcoded_paths <- FALSE
failed_fix <- FALSE
if (have_solaris_elfedit) {
for (l in slibs) {
out <- suppressWarnings(
system(paste("elfedit -re dyn:value", shQuote(l)), intern = TRUE))
out <- grep("^[ \t]*\\[[0-9]+\\]", out, value = TRUE)
re <- "^[ \t]*\\[([0-9]+)\\][ \t]+([^ \t]+)[ \t]+([^ \t]+)[ \t]*(.*)"
paths <- gsub(re, "\\4", out)
idxs <- gsub(re, "\\1", out)
old_paths <- paths
paths <- gsub(instdir, quote_replacement(final_instdir),
paths, fixed = TRUE)
changed <- paths != old_paths
paths <- paths[changed]
old_paths <- old_paths[changed]
idxs <- idxs[changed]
for (i in seq_along(paths)) {
hardcoded_paths <- TRUE
qp <- gsub('([" \\])', "\\\\\\1", paths[i])
qp <- gsub("'", "\\\\'", qp)
cmd <- paste0("elfedit -e \"dyn:value -dynndx -s ",
idxs[i], " ", qp, "\" ", shQuote(l))
message(cmd)
ret <- suppressWarnings(system(cmd, intern = FALSE))
if (ret == 0)
message("NOTE: fixed path ", sQuote(old_paths[i]))
}
out <- suppressWarnings(
system(paste("elfedit -re dyn:value", shQuote(l)), intern = TRUE))
out <- grep("^[ \t]*\\[", out, value = TRUE)
paths <- gsub(re, "\\4", out)
if (any(grepl(instdir, paths, fixed = TRUE)))
failed_fix <- TRUE
}
} else if (have_macos_clt) {
for (l in slibs) {
out <- suppressWarnings(
system(paste("otool -D", shQuote(l)), intern = TRUE))
out <- out[-1L]
oldid <- out
if (length(oldid) == 1 &&
grepl(instdir, oldid, fixed = TRUE)) {
hardcoded_paths <- TRUE
newid <- gsub(instdir, quote_replacement(final_instdir),
oldid, fixed = TRUE)
cmd <- paste("install_name_tool -id", shQuote(newid),
shQuote(l))
message(cmd)
ret <- suppressWarnings(system(cmd, intern = FALSE))
if (ret == 0)
message("NOTE: fixed library identification name ",
sQuote(oldid))
}
out <- suppressWarnings(
system(paste("otool -L", shQuote(l)), intern = TRUE))
paths <- grep("\\(compatibility", out, value = TRUE)
paths <- gsub("^[ \t]*(.*) \\(compatibility.*", "\\1",
paths)
old_paths <- paths
paths <- gsub(instdir, quote_replacement(final_instdir),
paths, fixed = TRUE)
changed <- paths != old_paths
paths <- paths[changed]
old_paths <- old_paths[changed]
for(i in seq_along(paths)) {
hardcoded_paths <- TRUE
cmd <- paste("install_name_tool -change",
shQuote(old_paths[i]), shQuote(paths[i]),
shQuote(l))
message(cmd)
ret <- suppressWarnings(system(cmd, intern = FALSE))
if (ret == 0)
message("NOTE: fixed library path ",
sQuote(old_paths[i]))
}
out <- suppressWarnings(
system(paste("otool -L", shQuote(l)), intern = TRUE))
out <- grep("\\(compatibility", out, value = TRUE)
if (any(grepl(instdir, out, fixed = TRUE)))
failed_fix <- TRUE
out <- suppressWarnings(
system(paste("otool -l", shQuote(l)), intern = TRUE))
out <- grep("(^[ \t]*cmd )|(^[ \t]*path )", out,
value = TRUE)
rpidx <- grep("cmd LC_RPATH$", out)
if (length(rpidx)) {
paths <- gsub("^[ \t]*path ", "", out[rpidx+1])
paths <- gsub("(.*) \\(offset .*", "\\1", paths)
old_paths <- paths
paths <- gsub(instdir, quote_replacement(final_instdir),
paths, fixed = TRUE)
changed <- paths != old_paths
paths <- paths[changed]
old_paths <- old_paths[changed]
for(i in seq_along(paths)) {
hardcoded_paths <- TRUE
cmd <- paste("install_name_tool -rpath",
shQuote(old_paths[i]),
shQuote(paths[i]),
shQuote(l))
message(cmd)
ret <- suppressWarnings(system(cmd))
if (ret == 0)
message("NOTE: fixed rpath ",
sQuote(old_paths[i]))
}
}
out <- suppressWarnings(
system(paste("otool -l", shQuote(l)), intern = TRUE))
out <- out[-1L]
if (any(grepl(instdir, out, fixed = TRUE)))
failed_fix <- TRUE
}
} else if (have_patchelf) {
for(l in slibs) {
rpath <- suppressWarnings(
system(paste("patchelf --print-rpath", shQuote(l)),
intern = TRUE))
old_rpath <- rpath
rpath <- gsub(instdir, quote_replacement(final_instdir),
rpath, fixed = TRUE)
if (length(rpath) && nzchar(rpath) && old_rpath != rpath) {
hardcoded_paths <- TRUE
cmd <- paste("patchelf", "--set-rpath",
shQuote(rpath), shQuote(l))
message(cmd)
ret <- suppressWarnings(system(cmd))
if (ret == 0)
message("NOTE: fixed rpath ", sQuote(old_rpath))
rpath <- suppressWarnings(
system(paste("patchelf --print-rpath",
shQuote(l)), intern = TRUE))
if (any(grepl(instdir, rpath, fixed = TRUE)))
failed_fix <- TRUE
}
if (have_readelf) {
out <- suppressWarnings(
system(paste("readelf -d", shQuote(l)), intern = TRUE))
re0 <- "0x.*\\(NEEDED\\).*Shared library:"
out <- grep(re0, out, value = TRUE)
re <- "^[ \t]*0x[0-9]+[ \t]+\\(NEEDED\\)[ \t]+Shared library:[ \t]*\\[(.*)\\]"
paths <- gsub(re, "\\1", out)
old_paths <- paths
paths <- gsub(instdir, quote_replacement(final_instdir),
paths, fixed = TRUE)
changed <- paths != old_paths
paths <- paths[changed]
old_paths <- old_paths[changed]
for(i in seq_along(paths)) {
cmd <- paste("patchelf --replace-needed",
shQuote(old_paths[i]),
shQuote(paths[i]),
shQuote(l))
message(cmd)
ret <- suppressWarnings(system(cmd))
if (ret == 0)
message("NOTE: fixed library path ",
sQuote(old_paths[i]))
}
out <- suppressWarnings(
system(paste("readelf -d", shQuote(l)), intern = TRUE))
out <- grep(re0, out, value = TRUE)
if (any(grepl(instdir, out, fixed = TRUE)))
failed_fix <- TRUE
}
}
} else if (have_chrpath) {
for(l in slibs) {
out <- suppressWarnings(
system(paste("chrpath", shQuote(l)), intern = TRUE))
rpath <- grep(".*PATH=", out, value=TRUE)
rpath <- gsub(".*PATH=", "", rpath)
old_rpath <- rpath
rpath <- gsub(instdir, quote_replacement(final_instdir),
rpath, fixed = TRUE)
if (length(rpath) && nzchar(rpath) && old_rpath != rpath) {
hardcoded_paths <- TRUE
cmd <- paste("chrpath", "-r", shQuote(rpath),
shQuote(l))
message(cmd)
ret <- suppressWarnings(system(cmd))
if (ret == 0)
message("NOTE: fixed rpath ", sQuote(old_rpath))
out <- suppressWarnings(
system(paste("chrpath", shQuote(l)), intern = TRUE))
rpath <- grep(".*PATH=", out, value = TRUE)
rpath <- gsub(".*PATH=", "", rpath)
if (any(grepl(instdir, rpath, fixed = TRUE)))
failed_fix <- TRUE
}
}
}
if (hardcoded_paths)
message("WARNING: shared objects/dynamic libraries with hard-coded temporary installation paths")
if (failed_fix)
errmsg("some hard-coded temporary paths could not be fixed")
if (have_readelf) {
for(l in slibs) {
out <- suppressWarnings(
system(paste("readelf -d", shQuote(l)), intern = TRUE))
out <- grep("^[ \t]*0x", out, value = TRUE)
if (any(grepl(instdir, out, fixed = TRUE))) {
ll <- sub(file.path(instdir, ""), "", l, fixed = TRUE)
errmsg("absolute paths in ",
sQuote(ll),
" include the temporary installation directory:",
" please report to the package maintainer",
" and use ", sQuote("--no-staged-install"))
}
}
}
}
Sys.setenv(R_LIBRARY_DIR = lib)
if (nzchar(lib0)) {
rlibs <- Sys.getenv("R_LIBS")
rlibs <- if (nzchar(rlibs)) paste(lib, rlibs, sep = .Platform$path.sep) else lib
Sys.setenv(R_LIBS = rlibs)
.libPaths(c(lib, .libPaths()))
}
Type <- desc["Type"]
if (!is.na(Type) && Type == "Frontend") {
if (WINDOWS) errmsg("'Frontend' packages are Unix-only")
starsmsg(stars, "installing *Frontend* package ", sQuote(pkg_name), " ...")
if (preclean) system(paste(MAKE, "clean"))
if (use_configure) {
if (file_test("-x", "configure")) {
res <- system(paste(paste(configure_vars, collapse = " "),
"./configure",
paste(configure_args, collapse = " ")))
if (res) pkgerrmsg("configuration failed", pkg_name)
} else if (file.exists("configure"))
errmsg("'configure' exists but is not executable -- see the 'R Installation and Administration Manual'")
}
if (file.exists("Makefile"))
if (system(MAKE)) pkgerrmsg("make failed", pkg_name)
if (clean) system(paste(MAKE, "clean"))
return()
}
if (!is.na(Type) && Type == "Translation")
errmsg("'Translation' packages are defunct")
OS_type <- desc["OS_type"]
if (WINDOWS) {
if ((!is.na(OS_type) && OS_type == "unix") && !fake)
errmsg(" Unix-only package")
} else {
if ((!is.na(OS_type) && OS_type == "windows") && !fake)
errmsg(" Windows-only package")
}
if(group.writable) {
fmode <- "664"
dmode <- "775"
} else {
fmode <- "644"
dmode <- "755"
}
pkgInfo <- .split_description(.read_description("DESCRIPTION"))
R_install_force_depends_imports <- config_val_to_logical(Sys.getenv(
"_R_INSTALL_LIBS_ONLY_FORCE_DEPENDS_IMPORTS_", "TRUE"))
if (libs_only && isFALSE(R_install_force_depends_imports))
pkgs <- unique(c(names(names(pkgInfo$LinkingTo))))
else
pkgs <- unique(c(names(pkgInfo$Depends), names(pkgInfo$Imports),
names(pkgInfo$LinkingTo)))
if (length(pkgs)) {
miss <- character()
for (pkg in pkgs) {
if(!length(find.package(pkg, quiet = TRUE)))
miss <- c(miss, pkg)
}
if (length(miss) > 1)
pkgerrmsg(sprintf("dependencies %s are not available",
paste(sQuote(miss), collapse = ", ")),
pkg_name)
else if (length(miss))
pkgerrmsg(sprintf("dependency %s is not available",
sQuote(miss)), pkg_name)
}
starsmsg(stars, "installing *source* package ",
sQuote(pkg_name), " ...")
stars <- "**"
res <- checkMD5sums(pkg_name, getwd())
if(!is.na(res) && res) {
starsmsg(stars,
gettextf("package %s successfully unpacked and MD5 sums checked",
sQuote(pkg_name)))
}
if (file.exists(file.path(instdir, "DESCRIPTION"))) {
if (nzchar(lockdir)) {
if (debug) starsmsg(stars, "backing up earlier installation")
if(WINDOWS) {
file.copy(instdir, lockdir, recursive = TRUE,
copy.date = TRUE)
if (more_than_libs) unlink(instdir, recursive = TRUE)
} else if (more_than_libs)
system(paste("mv -f ", shQuote(instdir),
shQuote(file.path(lockdir, pkg_name))))
else
file.copy(instdir, lockdir, recursive = TRUE,
copy.date = TRUE)
} else if (more_than_libs) unlink(instdir, recursive = TRUE)
if (more_than_libs && dir.exists(instdir))
errmsg("cannot remove earlier installation, is it in use?")
dir.create(instdir, recursive = TRUE, showWarnings = FALSE)
}
pkg_staged_install <- SI <-
parse_description_field(desc, "StagedInstall", default = NA)
if (is.na(pkg_staged_install)) pkg_staged_install <- staged_install
if (have_cross) pkg_staged_install <- FALSE
if (pkg_staged_install && libs_only) {
pkg_staged_install <- FALSE
message("not using staged install with --libs-only")
}
if (pkg_staged_install && !lock) {
pkg_staged_install <- FALSE
message("staged installation is only possible with locking")
}
if (pkg_staged_install) {
starsmsg(stars, "using staged installation")
final_instdir <- instdir
final_lib <- lib
final_rpackagedir <- Sys.getenv("R_PACKAGE_DIR")
final_rlibs <- Sys.getenv("R_LIBS")
final_libpaths <- .libPaths()
instdir <- file.path(lockdir, "00new", pkg_name)
Sys.setenv(R_PACKAGE_DIR = instdir)
dir.create(instdir, recursive = TRUE, showWarnings = FALSE)
lib <- file.path(lockdir, "00new")
rlibs <- if (nzchar(final_rlibs))
paste(lib, final_rlibs, sep = .Platform$path.sep)
else
lib
Sys.setenv(R_LIBS = rlibs)
.libPaths(c(lib, final_libpaths))
} else {
if(isFALSE(SI))
starsmsg(stars,
"using non-staged installation via StagedInstall field")
else if (Sys.getenv("_R_INSTALL_SUPPRESS_NO_STAGED_MESSAGE_") != "yes")
starsmsg(stars, "using non-staged installation")
}
if (preclean) run_clean()
if (WINDOWS) {
it_patches_base <- Sys.getenv("_R_INSTALL_TIME_PATCHES_",
"https://www.r-project.org/nosvn/winutf8/ucrt3/")
if (!it_patches_base %in% c("no", "disabled", "false", "FALSE")) {
patches_idx <- tryCatch({
idxfile <- file(paste0(it_patches_base, "/",
"patches_idx.rds"))
patches_idx <- readRDS(idxfile)
close(idxfile)
patches_idx
},
error = function(e) NULL)
if (is.null(patches_idx))
message("WARNING: installation-time patches will not be applied, could not get the patches index")
else {
patches_msg <- FALSE
for(p in patches_idx[[pkg_name]]) {
if (!patches_msg) {
patches_msg <- TRUE
starsmsg(stars, "applying installation-time patches")
}
purl <- paste0(it_patches_base, "/", p)
have_patch <- nzchar(Sys.which("patch"))
if (!have_patch)
stop("patch utility is needed for installation-time patching")
dir.create("install_time_patches", recursive=TRUE)
fname <- paste0("install_time_patches/", basename(p))
if (grepl("^http", purl))
utils::download.file(purl, destfile = fname, mode = "wb")
else
file.copy(purl, fname)
if (system2("patch", args = c("--dry-run", "-p2", "--binary", "--force"),
stdin = fname, stdout = NULL, stderr = NULL) != 0) {
if (system2("patch", args = c("--dry-run", "-R", "-p2", "--binary",
"--force"), stdin = fname) == 0)
message("NOTE: Skipping installation-time patch ", purl,
" which seems to be already applied.\n")
else
message("WARNING: failed to apply patch ", purl, "\n")
} else {
if (system2("patch", args = c("-p2", "--binary", "--force"),
stdin = fname) != 0)
message("WARNING: failed to apply patch ", p, "\n")
else
message("Applied installation-time patch ", purl,
" and saved it as ", fname,
" in package installation\n")
}
}
}
}
}
if (use_configure) {
if (WINDOWS) {
if (file.exists("configure.ucrt")) {
res <- system("sh ./configure.ucrt")
if (res) pkgerrmsg("configuration failed", pkg_name)
} else if (file.exists("configure.win")) {
res <- system("sh ./configure.win")
if (res) pkgerrmsg("configuration failed", pkg_name)
} else if (file.exists("configure"))
message("\n",
" **********************************************\n",
" WARNING: this package has a configure script\n",
" It probably needs manual configuration\n",
" **********************************************\n\n", domain = NA)
} else {
if (file_test("-x", "configure")) {
cmd <- paste(paste(configure_vars, collapse = " "),
"./configure",
paste(configure_args, collapse = " "))
if (debug) message("configure command: ", sQuote(cmd),
domain = NA)
cmd <- paste("_R_SHLIB_BUILD_OBJECTS_SYMBOL_TABLES_=false",
cmd)
res <- system(cmd)
if (res) pkgerrmsg("configuration failed", pkg_name)
} else if (file.exists("configure"))
errmsg("'configure' exists but is not executable -- see the 'R Installation and Administration Manual'")
}
}
if (more_than_libs) {
for (f in c("NAMESPACE", "LICENSE", "LICENCE", "NEWS", "NEWS.md"))
if (file.exists(f)) {
file.copy(f, instdir, TRUE)
Sys.chmod(file.path(instdir, f), fmode)
}
res <- try(.install_package_description('.', instdir, built_stamp))
if (inherits(res, "try-error"))
pkgerrmsg("installing package DESCRIPTION failed", pkg_name)
if (!file.exists(namespace <- file.path(instdir, "NAMESPACE")) ) {
if(dir.exists("R"))
errmsg("a 'NAMESPACE' file is required")
else writeLines("
}
}
if (install_libs && dir.exists("src") &&
length(dir("src", all.files = TRUE)) > 2L) {
starsmsg(stars, "libs")
if (!file.exists(file.path(R.home("include"), "R.h")))
warning("R include directory is empty -- perhaps need to install R-devel.rpm or similar", call. = FALSE)
has_error <- FALSE
linkTo <- pkgInfo$LinkingTo
if (!is.null(linkTo)) {
lpkgs <- sapply(linkTo, function(x) x[[1L]])
paths <- find.package(lpkgs, quiet = TRUE)
bpaths <- basename(paths)
if (length(paths)) {
have_vers <-
(lengths(linkTo) > 1L) & lpkgs %in% bpaths
for (z in linkTo[have_vers]) {
p <- z[[1L]]
path <- paths[bpaths %in% p]
current <- readRDS(file.path(path, "Meta", "package.rds"))$DESCRIPTION["Version"]
target <- as.numeric_version(z$version)
if (!do.call(z$op, list(as.numeric_version(current), target)))
stop(gettextf("package %s %s was found, but %s %s is required by %s",
sQuote(p), current, z$op,
target, sQuote(pkgname)),
call. = FALSE, domain = NA)
}
clink_cppflags <- paste(paste0("-I'", paths, "/include'"),
collapse = " ")
Sys.setenv(CLINK_CPPFLAGS = clink_cppflags)
}
} else clink_cppflags <- ""
libdir <- file.path(instdir, paste0("libs", rarch))
dir.create(libdir, showWarnings = FALSE)
if (WINDOWS) {
owd <- setwd("src")
makefiles <- character()
if (!is.na(f <- Sys.getenv("R_MAKEVARS_USER",
NA_character_))) {
if (file.exists(f)) makefiles <- f
} else if (file.exists(f <- path.expand("~/.R/Makevars.ucrt")))
makefiles <- f
else if (file.exists(f <- path.expand("~/.R/Makevars.win")))
makefiles <- f
else if (file.exists(f <- path.expand("~/.R/Makevars")))
makefiles <- f
if (file.exists(f <- "Makefile.ucrt") || file.exists(f <- "Makefile.win")) {
makefiles <- c(f, makefiles)
message(paste0(" running 'src/", f, "' ..."), domain = NA)
res <- system(paste("make --no-print-directory",
paste("-f", shQuote(makefiles), collapse = " ")))
if (res == 0L) shlib_install(instdir, rarch)
else has_error <- TRUE
} else {
srcs <- dir(pattern = "\\.([cfmM]|cc|cpp|f90|f95|mm)$",
all.files = TRUE)
archs <- if(have_cross) cross
else if (!force_both && !grepl(" x64 ", utils::win.version()))
"i386"
else {
f <- dir(file.path(R.home(), "bin"))
f[f %in% c("i386", "x64")]
}
one_only <- !multiarch
has_configure_ucrt <- file.exists("../configure.ucrt")
if(!one_only && (has_configure_ucrt || file.exists("../configure.win"))) {
if(pkg_name %notin%
c("AnalyzeFMRI", "CORElearn", "PearsonDS",
"PKI", "RGtk2", "RNetCDF", "RODBC",
"RSclient", "Rcpp", "Runuran", "SQLiteMap",
"XML", "arulesSequences", "cairoDevice",
"diversitree", "foreign", "fastICA",
"glmnet", "gstat", "igraph", "jpeg", "png",
"proj4", "randtoolbox", "rgdal", "rngWELL",
"rphast", "rtfbs", "sparsenet", "tcltk2",
"tiff", "udunits2"))
one_only <- sum(nchar(readLines(
if(has_configure_ucrt) "../configure.ucrt" else "../configure.win",
warn = FALSE), "bytes")) > 0
if(one_only && !force_biarch) {
if(parse_description_field(desc, "Biarch", FALSE))
force_biarch <- TRUE
else if (has_configure_ucrt)
warning("this package has a non-empty 'configure.ucrt' file,\nso building only the main architecture\n", call. = FALSE, domain = NA)
else
warning("this package has a non-empty 'configure.win' file,\nso building only the main architecture\n", call. = FALSE, domain = NA)
}
}
if(force_biarch) one_only <- FALSE
if(one_only || length(archs) < 2L)
has_error <-
run_shlib(pkg_name, srcs, instdir, rarch, use_LTO)
else {
setwd(owd)
test_archs <- archs
for(arch in archs) {
message("", domain = NA)
starsmsg("***", "arch - ", arch)
ss <- paste0("src-", arch)
dir.create(ss, showWarnings = FALSE)
file.copy(Sys.glob("src/*"), ss, recursive = TRUE)
.Call(C_dirchmod, ss, group.writable)
setwd(ss)
ra <- paste0("/", arch)
Sys.setenv(R_ARCH = ra, R_ARCH_BIN = ra)
has_error <-
run_shlib(pkg_name, srcs, instdir, ra, use_LTO)
setwd(owd)
if (has_error) break
}
}
}
setwd(owd)
} else {
if (file.exists("src/Makefile")) {
arch <- substr(rarch, 2, 1000)
starsmsg(stars, "arch - ", arch)
owd <- setwd("src")
system_makefile <-
file.path(R.home(), paste0("etc", rarch), "Makeconf")
makefiles <- c(system_makefile,
makevars_site(),
"Makefile",
makevars_user())
res <- system(paste(MAKE,
paste("-f", shQuote(makefiles), collapse = " ")))
if (res == 0L) shlib_install(instdir, rarch)
else has_error <- TRUE
setwd(owd)
} else {
owd <- setwd("src")
srcs <- dir(pattern = "\\.([cfmM]|cc|cpp|f90|f95|mm)$",
all.files = TRUE)
allfiles <- if (file.exists("Makevars")) c("Makevars", srcs) else srcs
wd2 <- setwd(file.path(R.home("bin"), "exec"))
archs <- Sys.glob("*")
setwd(wd2)
if (length(allfiles)) {
use_LTO <-
if (!is.na(use_LTO)) use_LTO
else
parse_description_field(desc, "UseLTO", default = NA)
if (!multiarch || length(archs) <= 1 ||
file_test("-x", "../configure")) {
if (nzchar(rarch))
starsmsg("***", "arch - ",
substr(rarch, 2, 1000))
has_error <- run_shlib(pkg_name, srcs, instdir, rarch, use_LTO)
} else {
setwd(owd)
test_archs <- archs
for(arch in archs) {
if (arch == "R") {
has_error <- run_shlib(pkg_name, srcs, instdir, "", use_LTO)
} else {
starsmsg("***", "arch - ", arch)
ss <- paste0("src-", arch)
dir.create(ss, showWarnings = FALSE)
file.copy(Sys.glob("src/*"), ss, recursive = TRUE)
setwd(ss)
ra <- paste0("/", arch)
Sys.setenv(R_ARCH = ra)
has_error <- run_shlib(pkg_name, srcs, instdir, ra, use_LTO)
Sys.setenv(R_ARCH = rarch)
setwd(owd)
if (has_error) break
}
}
}
} else warning("no source files found", call. = FALSE)
}
setwd(owd)
}
if (has_error)
pkgerrmsg("compilation failed", pkg_name)
fi <- file.info(Sys.glob(file.path(instdir, "libs", "*")))
dirs <- basename(row.names(fi[fi$isdir %in% TRUE, ]))
if(WINDOWS) dirs <- dirs[dirs %in% c("i386", "x64")]
if (length(dirs)) {
descfile <- file.path(instdir, "DESCRIPTION")
olddesc <- readLines(descfile, warn = FALSE)
olddesc <- filtergrep("^Archs:", olddesc, useBytes = TRUE)
newdesc <- c(olddesc,
paste("Archs:", paste(dirs, collapse = ", "))
)
writeLines(newdesc, descfile, useBytes = TRUE)
saveRDS(.split_description(.read_description(descfile)),
file.path(instdir, "Meta", "package.rds"))
}
} else if (multiarch) {
if (WINDOWS) {
wd2 <- setwd(file.path(R.home(), "bin"))
archs <- Sys.glob("*")
setwd(wd2)
test_archs <- archs[archs %in% c("i386", "x64")]
} else {
wd2 <- setwd(file.path(R.home("bin"), "exec"))
test_archs <- Sys.glob("*")
setwd(wd2)
}
}
if (WINDOWS && "x64" %in% test_archs) {
if (!grepl(" x64 ", utils::win.version())) test_archs <- "i386"
}
if (have_cross) Sys.unsetenv("R_ARCH")
if (WINDOWS && dir.exists("install_time_patches"))
file.copy("install_time_patches", instdir, recursive = TRUE)
if (install_R && dir.exists("R") && length(dir("R"))) {
starsmsg(stars, "R")
dir.create(file.path(instdir, "R"), recursive = TRUE,
showWarnings = FALSE)
res <- try(.install_package_code_files(".", instdir))
if (inherits(res, "try-error"))
pkgerrmsg("unable to collate and parse R files", pkg_name)
if (file.exists(f <- file.path("R", "sysdata.rda"))) {
comp <- TRUE
if(!is.na(lazycompress <- desc["SysDataCompression"])) {
comp <- switch(lazycompress,
"none" = FALSE,
"gzip" = TRUE,
"bzip2" = 2L,
"xz" = 3L,
TRUE)
} else if(file.size(f) > 1e6) comp <- 3L
res <- try(sysdata2LazyLoadDB(f, file.path(instdir, "R"),
compress = comp))
if (inherits(res, "try-error"))
pkgerrmsg("unable to build sysdata DB", pkg_name)
}
if (fake) {
if (file.exists("NAMESPACE")) {
cat("",
'.onLoad <- .onAttach <- function(lib, pkg) NULL',
'.onUnload <- function(libpaths) NULL',
sep = "\n",
file = file.path(instdir, "R", pkg_name), append = TRUE)
writeLines(sub("useDynLib.*", 'useDynLib("")',
readLines("NAMESPACE", warn = FALSE),
perl = TRUE, useBytes = TRUE),
file.path(instdir, "NAMESPACE"))
} else {
cat("",
'.onLoad <- function (libname, pkgname) NULL',
'.onAttach <- function (libname, pkgname) NULL',
'.onDetach <- function(libpath) NULL',
'.onUnload <- function(libpath) NULL',
'.Last.lib <- function(libpath) NULL',
sep = "\n",
file = file.path(instdir, "R", pkg_name), append = TRUE)
}
}
}
if (install_data && dir.exists("data") && length(dir("data"))) {
starsmsg(stars, "data")
files <- Sys.glob(file.path("data", "*"))
if (length(files)) {
is <- file.path(instdir, "data")
dir.create(is, recursive = TRUE, showWarnings = FALSE)
file.remove(Sys.glob(file.path(instdir, "data", "*")))
file.copy(files, is, TRUE)
thislazy <- parse_description_field(desc, "LazyData",
default = lazy_data)
if (!thislazy && resave_data) {
paths <- Sys.glob(c(file.path(is, "*.rda"),
file.path(is, "*.RData")))
if (length(paths)) {
starsmsg(paste0(stars, "*"), "resaving rda files")
resaveRdaFiles(paths, compress = "auto")
}
}
Sys.chmod(Sys.glob(file.path(instdir, "data", "*")), fmode)
if (thislazy) {
starsmsg(paste0(stars, "*"),
"moving datasets to lazyload DB")
lazycompress <- desc["LazyDataCompression"]
if(!is.na(lazycompress))
data_compress <- switch(lazycompress,
"none" = FALSE,
"gzip" = TRUE,
"bzip2" = 2L,
"xz" = 3L,
TRUE)
res <- try(data2LazyLoadDB(pkg_name, lib,
compress = data_compress))
if (inherits(res, "try-error"))
pkgerrmsg("lazydata failed", pkg_name)
}
} else warning("empty 'data' directory", call. = FALSE)
}
if (install_demo && dir.exists("demo") && length(dir("demo"))) {
starsmsg(stars, "demo")
dir.create(file.path(instdir, "demo"), recursive = TRUE,
showWarnings = FALSE)
file.remove(Sys.glob(file.path(instdir, "demo", "*")))
res <- try(.install_package_demos(".", instdir))
if (inherits(res, "try-error"))
pkgerrmsg("ERROR: installing demos failed")
Sys.chmod(Sys.glob(file.path(instdir, "demo", "*")), fmode)
}
if (install_exec && dir.exists("exec") && length(dir("exec"))) {
starsmsg(stars, "exec")
dir.create(file.path(instdir, "exec"), recursive = TRUE,
showWarnings = FALSE)
file.remove(Sys.glob(file.path(instdir, "exec", "*")))
files <- Sys.glob(file.path("exec", "*"))
if (length(files)) {
file.copy(files, file.path(instdir, "exec"), TRUE)
if (!WINDOWS)
Sys.chmod(Sys.glob(file.path(instdir, "exec", "*")), dmode)
}
}
if (install_inst && dir.exists("inst") &&
length(dir("inst", all.files = TRUE)) > 2L) {
starsmsg(stars, "inst")
i_dirs <- list.dirs("inst")[-1L]
i_dirs <- filtergrep(.vc_dir_names_re, i_dirs)
ignore_file <- ".Rinstignore"
ignore <- if (file.exists(ignore_file)) {
ignore <- readLines(ignore_file, warn = FALSE)
ignore[nzchar(ignore)]
} else character()
for(e in ignore)
i_dirs <- filtergrep(e, i_dirs, perl = TRUE, ignore.case = TRUE)
lapply(gsub("^inst", quote_replacement(instdir), i_dirs),
function(p) dir.create(p, FALSE, TRUE))
i_files <- list.files("inst", all.files = TRUE,
full.names = TRUE, recursive = TRUE)
i_files <- filtergrep(.vc_dir_names_re, i_files)
for(e in ignore)
i_files <- filtergrep(e, i_files, perl = TRUE, ignore.case = TRUE)
i_files <- i_files %w/o% c("inst/doc/Rplots.pdf",
"inst/doc/Rplots.ps")
i_files <- filtergrep("inst/doc/.*[.](log|aux|bbl|blg|dvi)$",
i_files, perl = TRUE, ignore.case = TRUE)
if (!dir.exists("vignettes") && pkgname %notin% c("RCurl"))
i_files <- filtergrep("inst/doc/.*[.](png|jpg|jpeg|gif|ps|eps)$",
i_files, perl = TRUE, ignore.case = TRUE)
i_files <- i_files %w/o% "Makefile"
i2_files <- gsub("^inst", quote_replacement(instdir), i_files)
file.copy(i_files, i2_files)
if (!WINDOWS) {
modes <- file.mode(i_files)
execs <- as.logical(modes & as.octmode("100"))
Sys.chmod(i2_files[execs], dmode)
}
if (compact_docs) {
pdfs <- dir(file.path(instdir, "doc"), pattern="\\.pdf",
recursive = TRUE, full.names = TRUE,
all.files = TRUE)
res <- compactPDF(pdfs, gs_quality = "none")
print(res[res$old > 1e5, ])
}
}
rait <- Sys.getenv("R_ALWAYS_INSTALL_TESTS", "FALSE")
install_tests <- install_tests || config_val_to_logical(rait)
if (install_tests && dir.exists("tests") &&
length(dir("tests", all.files = TRUE)) > 2L) {
starsmsg(stars, "tests")
file.copy("tests", instdir, recursive = TRUE)
}
if (install_R && dir.exists("R") && length(dir("R"))) {
BC <- if (!is.na(byte_compile)) byte_compile
else
parse_description_field(desc, "ByteCompile", default = TRUE)
rcps <- Sys.getenv("R_COMPILE_PKGS")
rcp <- switch(rcps,
"TRUE"=, "true"=, "True"=, "yes"=, "Yes"= 1,
"FALSE"=,"false"=,"False"=, "no"=, "No" = 0,
as.numeric(rcps))
if (!is.na(rcp))
BC <- (rcp > 0)
if (BC) {
starsmsg(stars,
"byte-compile and prepare package for lazy loading")
cmd <- c("Sys.setenv(R_ENABLE_JIT = 0L)",
"invisible(compiler::enableJIT(0))",
"invisible(compiler::compilePKGS(1L))",
"compiler::setCompilerOptions(suppressAll = FALSE)",
"compiler::setCompilerOptions(suppressUndefined = TRUE)",
"compiler::setCompilerOptions(suppressNoSuperAssignVar = TRUE);")
} else {
starsmsg(stars, "preparing package for lazy loading")
cmd <- ""
}
keep.source <-
parse_description_field(desc, "KeepSource",
default = keep.source)
cmd <- append(cmd, paste0("setwd(", quote_path(getwd()), ")"))
cmd <- append(cmd,
paste0("if (isNamespaceLoaded(\"",pkg_name, "\"))",
" unloadNamespace(\"", pkg_name, "\")"))
cmd <- append(cmd,
"suppressPackageStartupMessages(.getRequiredPackages(quietly = TRUE))")
if (pkg_staged_install)
set.install.dir <- paste0(", set.install.dir = ",
quote_path(final_instdir))
else
set.install.dir <- ""
cmd <- append(cmd,
paste0("tools:::makeLazyLoading(\"", pkg_name, "\", ",
quote_path(lib), ", ",
"keep.source = ", keep.source, ", ",
"keep.parse.data = ", keep.parse.data,
set.install.dir, ")"))
cmd <- paste(cmd, collapse="\n")
out <- R_runR_deps_only(cmd,
setRlibs(LinkingTo = TRUE, quote = TRUE))
if(length(out))
cat(paste(c(out, ""), collapse = "\n"))
if(length(attr(out, "status")))
pkgerrmsg("lazy loading failed", pkg_name)
}
if (install_help) {
starsmsg(stars, "help")
if (!dir.exists("man") ||
!length(list_files_with_type("man", "docs")))
cat("No man pages found in package ", sQuote(pkg_name), "\n")
encoding <- desc["Encoding"]
if (is.na(encoding)) encoding <- "unknown"
res <- try(.install_package_Rd_objects(".", instdir, encoding))
if (inherits(res, "try-error"))
pkgerrmsg("installing Rd objects failed", pkg_name)
starsmsg(paste0(stars, "*"), "installing help indices")
.writePkgIndices(pkg_dir, instdir)
if (build_help) {
outenc <- desc["Encoding"]
if (is.na(outenc)) outenc <- "latin1"
.convertRdfiles(pkg_dir, instdir,
types = build_help_types,
outenc = outenc)
}
if (dir.exists(figdir <- file.path(pkg_dir, "man", "figures"))) {
starsmsg(paste0(stars, "*"), "copying figures")
dir.create(destdir <- file.path(instdir, "help", "figures"))
file.copy(Sys.glob(c(file.path(figdir, "*.png"),
file.path(figdir, "*.jpg"),
file.path(figdir, "*.jpeg"),
file.path(figdir, "*.svg"),
file.path(figdir, "*.pdf"))), destdir)
}
}
if (install_inst || install_demo || install_help) {
starsmsg(stars, "building package indices")
cmd <- c("tools:::.install_package_indices(\".\",",
quote_path(instdir), ")")
cmd <- paste(cmd, collapse="\n")
out <- R_runR_deps_only(cmd,
setRlibs(LinkingTo = TRUE, quote = TRUE))
if(length(out))
cat(paste(c(out, ""), collapse = "\n"))
if (length(attr(out, "status")))
errmsg("installing package indices failed")
if(dir.exists("vignettes")) {
starsmsg(stars, "installing vignettes")
enc <- desc["Encoding"]
if (is.na(enc)) enc <- ""
if (!fake &&
file_test("-f", file.path("build", "vignette.rds")))
installer <- .install_package_vignettes3
else
installer <- .install_package_vignettes2
res <- try(installer(".", instdir, enc))
if (inherits(res, "try-error"))
errmsg("installing vignettes failed")
}
}
if (install_R && file.exists("NAMESPACE")) {
res <- try(.install_package_namespace_info(if(fake) instdir else ".", instdir))
if (inherits(res, "try-error"))
errmsg("installing namespace metadata failed")
}
if (clean) run_clean()
do_test_load <- function(extra_cmd = NULL) {
cmd <- paste0("tools:::.test_load_package('", pkg_name, "', ", quote_path(lib), ")")
if (!is.null(extra_cmd))
cmd <- paste0(cmd, "\n", extra_cmd)
tlim <- get_timeout(Sys.getenv("_R_INSTALL_TEST_LOAD_ELAPSED_TIMEOUT_"))
if (length(test_archs) > 1L) {
msgs <- character()
for (arch in test_archs) {
starsmsg("***", "arch - ", arch)
out <- R_runR_deps_only(cmd,
deps_only_env = setRlibs(lib0, self = TRUE, quote = TRUE),
arch = arch, timeout = tlim, multiarch = TRUE)
if(length(attr(out, "status")))
msgs <- c(msgs, arch)
if(length(out))
cat(paste(c(out, ""), collapse = "\n"))
}
if (length(msgs)) {
msg <- paste("loading failed for",
paste(sQuote(msgs), collapse = ", "))
errmsg(msg)
}
} else {
out <- R_runR_deps_only(cmd,
deps_only_env = setRlibs(lib0, self = TRUE, quote = TRUE),
timeout = tlim)
if(length(out)) {
cat(paste(c(out, ""), collapse = "\n"))
}
if(length(attr(out, "status")))
errmsg("loading failed")
}
}
if (test_load && !have_cross) {
if (pkg_staged_install)
starsmsg(stars,
"testing if installed package can be loaded from temporary location")
else
starsmsg(stars, "testing if installed package can be loaded")
do_test_load()
}
if (pkg_staged_install) {
if (WINDOWS) {
unlink(final_instdir, recursive = TRUE)
if (!file.rename(instdir, final_instdir)) {
if (dir.exists(instdir) && !dir.exists(final_instdir)) {
message("WARNING: moving package to final location failed, copying instead")
ret <- file.copy(instdir, dirname(final_instdir),
recursive = TRUE, copy.date = TRUE)
if (any(!ret))
errmsg(" copying to final location failed")
unlink(instdir, recursive = TRUE)
} else
errmsg(" moving to final location failed")
}
} else {
patch_rpaths()
unlink(final_instdir, recursive = TRUE)
owd <- setwd(startdir)
status <- system(paste("mv -f",
shQuote(instdir),
shQuote(dirname(final_instdir))))
if (status) errmsg(" moving to final location failed")
setwd(owd)
}
instdir <- final_instdir
lib <- final_lib
Sys.setenv(R_PACKAGE_DIR = final_rpackagedir)
Sys.setenv(R_LIBS = final_rlibs)
.libPaths(final_libpaths)
if (test_load) {
starsmsg(stars,
"testing if installed package can be loaded from final location")
serf <- tempfile()
cmd <- paste0("f <- base::file(", quote_path(serf),
", \"wb\")")
cmd <- append(cmd,
paste0("base::invisible(base::suppressWarnings(base::serialize(",
"base::as.list(base::getNamespace(\"", pkg_name, "\"), all.names=TRUE), f)))"))
cmd <- append(cmd, "base::close(f)")
do_test_load(extra_cmd = paste(cmd, collapse = "\n"))
starsmsg(stars,
"testing if installed package keeps a record of temporary installation path")
r <- readBin(serf, "raw", n=file.size(serf))
unlink(serf)
if (length(grepRaw("00new", r, fixed = TRUE, all = FALSE,
value = FALSE)))
errmsg("hard-coded installation path: ",
"please report to the package maintainer and use ",
sQuote("--no-staged-install"))
}
}
if (do_strip_lib &&
nzchar(strip_cmd <- Sys.getenv("R_STRIP_STATIC_LIB")) &&
length(a_s <- Sys.glob(file.path(file.path(lib, curPkg),
"lib", "*.a")))) {
if(length(a_s) > 1L)
starsmsg(stars, "stripping static libraries under lib")
else
starsmsg(stars, "stripping static library under lib")
system(paste(c(strip_cmd, shQuote(a_s)), collapse = " "))
}
if (do_strip_lib &&
nzchar(strip_cmd <- Sys.getenv("R_STRIP_SHARED_LIB")) &&
length(so_s <- Sys.glob(file.path(file.path(lib, curPkg), "lib",
paste0("*", SHLIB_EXT))))) {
if(length(so_s) > 1L)
starsmsg(stars, "stripping dynamic libraries under lib")
else
starsmsg(stars, "stripping dynamic library under lib")
system(paste(c(strip_cmd, shQuote(so_s)), collapse = " "))
}
}
options(showErrorCalls = FALSE)
pkgs <- character()
if (is.null(args)) {
args <- commandArgs(TRUE)
args <- paste(args, collapse = " ")
args <- strsplit(args,'nextArg', fixed = TRUE)[[1L]][-1L]
}
args0 <- args
startdir <- getwd()
if (is.null(startdir))
stop("current working directory cannot be ascertained")
lib <- lib0 <- ""
clean <- FALSE
preclean <- FALSE
debug <- FALSE
static_html <- nzchar(system.file("html", "mean.html", package="base"))
build_html <- static_html
build_latex <- FALSE
build_example <- FALSE
use_configure <- TRUE
configure_args <- character()
configure_vars <- character()
fake <- FALSE
lazy_data <- FALSE
byte_compile <- NA
staged_install <- NA
lock <- getOption("install.lock", NA)
pkglock <- FALSE
libs_only <- FALSE
tar_up <- zip_up <- FALSE
shargs <- character()
multiarch <- TRUE
force_biarch <- FALSE
force_both <- FALSE
test_load <- TRUE
merge <- FALSE
dsym <- nzchar(Sys.getenv("PKG_MAKE_DSYM"))
get_user_libPaths <- FALSE
data_compress <- TRUE
resave_data <- FALSE
compact_docs <- FALSE
keep.source <- getOption("keep.source.pkgs")
keep.parse.data <- getOption("keep.parse.data.pkgs")
use_LTO <- NA
built_stamp <- character()
install_libs <- TRUE
install_R <- TRUE
install_data <- TRUE
install_demo <- TRUE
install_exec <- TRUE
install_inst <- TRUE
install_help <- TRUE
install_tests <- FALSE
do_strip <- do_strip_lib <- FALSE
while(length(args)) {
a <- args[1L]
if (a %in% c("-h", "--help")) {
Usage()
do_exit(0)
}
else if (a %in% c("-v", "--version")) {
cat("R add-on package installer: ",
R.version[["major"]], ".", R.version[["minor"]],
" (r", R.version[["svn rev"]], ")\n", sep = "")
cat("",
.R_copyright_msg(2000),
"This is free software; see the GNU General Public License version 2",
"or later for copying conditions. There is NO warranty.",
sep = "\n")
do_exit(0)
} else if (a %in% c("-c", "--clean")) {
clean <- TRUE
shargs <- c(shargs, "--clean")
} else if (a == "--preclean") {
preclean <- TRUE
shargs <- c(shargs, "--preclean")
} else if (a %in% c("-d", "--debug")) {
debug <- TRUE
} else if (a == "--no-configure") {
use_configure <- FALSE
} else if (a == "--no-docs") {
build_html <- build_latex <- build_example <- FALSE
} else if (a == "--no-html") {
build_html <- FALSE
} else if (a == "--html") {
build_html <- TRUE
} else if (a == "--latex") {
build_latex <- TRUE
} else if (a == "--example") {
build_example <- TRUE
} else if (a == "--use-zip-data") {
warning("use of '--use-zip-data' is defunct",
call. = FALSE, domain = NA)
warning("use of '--use-zip-data' is deprecated",
call. = FALSE, domain = NA)
} else if (a == "--auto-zip") {
warning("'--auto-zip' is defunct",
call. = FALSE, domain = NA)
} else if (a == "-l") {
if (length(args) >= 2L) {lib <- args[2L]; args <- args[-1L]}
else stop("-l option without value", call. = FALSE)
} else if (substr(a, 1, 10) == "--library=") {
lib <- substr(a, 11, 1000)
} else if (substr(a, 1, 17) == "--configure-args=") {
configure_args <- c(configure_args, substr(a, 18, 1000))
} else if (substr(a, 1, 17) == "--configure-vars=") {
configure_vars <- c(configure_vars, substr(a, 18, 1000))
} else if (a == "--fake") {
fake <- TRUE
} else if (a == "--no-lock") {
lock <- pkglock <- FALSE
} else if (a == "--lock") {
lock <- TRUE; pkglock <- FALSE
} else if (a == "--pkglock") {
lock <- pkglock <- TRUE
} else if (a == "--libs-only") {
libs_only <- TRUE
} else if (a == "--no-multiarch") {
multiarch <- FALSE
} else if (a == "--force-biarch") {
force_biarch <- TRUE
} else if (a == "--compile-both") {
force_both <- TRUE
} else if (a == "--maybe-get-user-libPaths") {
get_user_libPaths <- TRUE
} else if (a == "--build") {
if (WINDOWS) zip_up <- TRUE else tar_up <- TRUE
} else if (substr(a, 1, 16) == "--data-compress=") {
dc <- substr(a, 17, 1000)
dc <- match.arg(dc, c("none", "gzip", "bzip2", "xz"))
data_compress <- switch(dc,
"none" = FALSE,
"gzip" = TRUE,
"bzip2" = 2,
"xz" = 3)
} else if (a == "--resave-data") {
resave_data <- TRUE
} else if (a == "--install-tests") {
install_tests <- TRUE
} else if (a == "--no-inst") {
install_inst <- FALSE
} else if (a == "--no-R") {
install_R <- FALSE
} else if (a == "--no-libs") {
install_libs <- FALSE
} else if (a == "--no-data") {
install_data <- FALSE
} else if (a == "--no-demo") {
install_demo <- FALSE
} else if (a == "--no-exec") {
install_exec <- FALSE
} else if (a == "--no-help") {
install_help <- FALSE
} else if (a == "--no-test-load") {
test_load <- FALSE
} else if (a == "--no-clean-on-error") {
clean_on_error <- FALSE
} else if (a == "--merge-multiarch") {
merge <- TRUE
} else if (a == "--compact-docs") {
compact_docs <- TRUE
} else if (a == "--with-keep.source") {
keep.source <- TRUE
} else if (a == "--without-keep.source") {
keep.source <- FALSE
} else if (a == "--with-keep.parse.data") {
keep.parse.data <- TRUE
} else if (a == "--without-keep.parse.data") {
keep.parse.data <- FALSE
} else if (a == "--byte-compile") {
byte_compile <- TRUE
} else if (a == "--no-byte-compile") {
byte_compile <- FALSE
} else if (a == "--use-LTO") {
use_LTO <- TRUE
} else if (a == "--no-use-LTO") {
use_LTO <- FALSE
} else if (a == "--staged-install") {
staged_install <- TRUE
} else if (a == "--no-staged-install") {
staged_install <- FALSE
} else if (a == "--dsym") {
dsym <- TRUE
} else if (a == "--strip") {
do_strip <- TRUE
} else if (a == "--strip-lib") {
do_strip_lib <- TRUE
} else if (substr(a, 1, 18) == "--built-timestamp=") {
built_stamp <- substr(a, 19, 1000)
} else if (startsWith(a, "-")) {
message("Warning: unknown option ", sQuote(a), domain = NA)
} else pkgs <- c(pkgs, a)
args <- args[-1L]
}
if (keep.tmpdir) {
make_tmpdir <- function(prefix, nchars = 8, ntries = 100) {
for(i in 1:ntries) {
name <- paste(sample(c(0:9, letters, LETTERS), nchars, replace=TRUE), collapse="")
path <- paste(prefix, name, sep = "/")
if (dir.create(path, showWarnings = FALSE, recursive = T)) {
return(path)
}
}
stop("cannot create unique directory for build")
}
tmpdir <- make_tmpdir(user.tmpdir)
} else {
tmpdir <- tempfile("R.INSTALL")
if (!dir.create(tmpdir))
stop("cannot create temporary directory")
}
if (merge) {
if (length(pkgs) != 1L || !file_test("-f", pkgs))
stop("ERROR: '--merge-multiarch' applies only to a single tarball",
call. = FALSE)
if (WINDOWS) {
f <- dir(file.path(R.home(), "bin"))
archs <- f[f %in% c("i386", "x64")]
if (length(archs) > 1L) {
args <- args0 %w/o% c("--merge-multiarch", "--build")
Sys.setenv("_R_INSTALL_NO_DONE_" = "yes")
for (arch in archs) {
cmd <- c(shQuote(file.path(R.home(), "bin", arch,
"Rcmd.exe")),
"INSTALL", shQuote(args), "--no-multiarch")
if (arch == "x64") {
Sys.setenv("_R_INSTALL_SUPPRESS_NO_STAGED_MESSAGE_" = "yes")
cmd <- c(cmd, "--libs-only --no-staged-install",
if(zip_up) "--build")
Sys.unsetenv("_R_INSTALL_NO_DONE_")
}
cmd <- paste(cmd, collapse = " ")
if (debug) message("about to run ", cmd, domain = NA)
message("\n", "install for ", arch, "\n", domain = NA)
res <- system(cmd)
if (arch == "x64")
Sys.unsetenv("_R_INSTALL_SUPPRESS_NO_STAGED_MESSAGE_")
if(res) break
}
}
} else {
archs <- dir(file.path(R.home("bin"), "exec"))
if (length(archs) > 1L) {
args <- args0 %w/o% c("--merge-multiarch", "--build")
Sys.setenv("_R_INSTALL_NO_DONE_" = "yes")
last <- archs[length(archs)]
for (arch in archs) {
cmd <- c(shQuote(file.path(R.home("bin"), "R")),
"--arch", arch, "CMD",
"INSTALL", shQuote(args), "--no-multiarch")
if (arch != archs[1L]) {
Sys.setenv("_R_INSTALL_SUPPRESS_NO_STAGED_MESSAGE_" = "yes")
cmd <- c(cmd, "--libs-only --no-staged-install")
}
if (arch == last) {
Sys.unsetenv("_R_INSTALL_NO_DONE_")
if(tar_up) cmd <- c(cmd, "--build")
}
cmd <- paste(cmd, collapse = " ")
if (debug) message("about to run ", cmd, domain = NA)
message("\n", "install for ", arch, "\n", domain = NA)
res <- system(cmd)
if (arch != archs[1L])
Sys.unsetenv("_R_INSTALL_SUPPRESS_NO_STAGED_MESSAGE_")
if(res) break
}
}
}
if (length(archs) > 1L) {
if (res) do_exit_on_error()
do_cleanup()
on.exit()
return(invisible())
}
message("only one architecture so ignoring '--merge-multiarch'",
domain = NA)
}
allpkgs <- character()
for(pkg in pkgs) {
if (debug) message("processing ", sQuote(pkg), domain = NA)
if (file_test("-f", pkg)) {
if (WINDOWS && endsWith(pkg, ".zip")) {
if (debug) message("a zip file", domain = NA)
pkgname <- basename(pkg)
pkgname <- sub("\\.zip$", "", pkgname)
pkgname <- sub("_[0-9.-]+$", "", pkgname)
allpkgs <- c(allpkgs, pkg)
next
}
if (debug) message("a file", domain = NA)
of <- dir(tmpdir, full.names = TRUE)
if (utils::untar(pkg, exdir = tmpdir,
tar = Sys.getenv("R_INSTALL_TAR", "internal")))
errmsg("error unpacking tarball")
nf <- dir(tmpdir, full.names = TRUE)
new <- nf %w/o% of
if (!length(new))
errmsg("cannot extract package from ", sQuote(pkg))
if (length(new) > 1L)
errmsg("extracted multiple files from ", sQuote(pkg))
if (dir.exists(new)) pkgname <- basename(new)
else errmsg("cannot extract package from ", sQuote(pkg))
if (file.exists(file.path(tmpdir, pkgname, "DESCRIPTION"))) {
allpkgs <- c(allpkgs, file.path(tmpdir, pkgname))
} else errmsg("cannot extract package from ", sQuote(pkg))
} else if (file.exists(file.path(pkg, "DESCRIPTION"))) {
if (debug) message("a directory", domain = NA)
pkgname <- basename(pkg)
allpkgs <- c(allpkgs, fullpath(pkg))
} else {
warning("invalid package ", sQuote(pkg), call. = FALSE)
next
}
}
if (!length(allpkgs))
stop("ERROR: no packages specified", call.=FALSE)
if (!nzchar(lib)) {
lib <- if (get_user_libPaths) {
system(paste(shQuote(file.path(R.home("bin"), "Rscript")),
"-e 'cat(.libPaths()[1L])'"),
intern = TRUE)
}
else .libPaths()[1L]
starsmsg(stars, "installing to library ", sQuote(lib))
} else {
lib0 <- lib <- path.expand(lib)
cwd <- tryCatch(setwd(lib), error = function(e)
stop(gettextf("ERROR: cannot cd to directory %s", sQuote(lib)),
call. = FALSE, domain = NA))
lib <- getwd()
setwd(cwd)
}
ok <- dir.exists(lib)
if (ok) {
if (WINDOWS) {
fn <- file.path(lib, paste0("_test_dir_", Sys.getpid()))
unlink(fn, recursive = TRUE)
res <- try(dir.create(fn, showWarnings = FALSE))
if (inherits(res, "try-error") || !res) ok <- FALSE
else unlink(fn, recursive = TRUE)
} else ok <- file.access(lib, 2L) == 0L
}
if (!ok)
stop("ERROR: no permission to install to directory ",
sQuote(lib), call. = FALSE)
group.writable <- if(WINDOWS) FALSE else {
d <- as.octmode("020")
(file.mode(lib) & d) == d
}
if (libs_only) {
install_R <- FALSE
install_data <- FALSE
install_demo <- FALSE
install_exec <- FALSE
install_inst <- FALSE
install_help <- FALSE
}
more_than_libs <- !libs_only
mk_lockdir <- function(lockdir)
{
if (file.exists(lockdir)) {
message("ERROR: failed to lock directory ", sQuote(lib),
" for modifying\nTry removing ", sQuote(lockdir),
domain = NA)
do_cleanup_tmpdir()
do_exit(status = 3)
}
dir.create(lockdir, recursive = TRUE)
if (!dir.exists(lockdir)) {
message("ERROR: failed to create lock directory ", sQuote(lockdir),
domain = NA)
do_cleanup_tmpdir()
do_exit(status = 3)
}
if (debug) starsmsg(stars, "created lock directory ", sQuote(lockdir))
}
if (is.na(lock)) {
lock <- TRUE
pkglock <- length(allpkgs) == 1L
}
if (lock && !pkglock) {
lockdir <- file.path(lib, "00LOCK")
mk_lockdir(lockdir)
}
if (is.na(staged_install)) {
rsi <- Sys.getenv("R_INSTALL_STAGED")
rsi <- switch(rsi,
"TRUE"=, "true"=, "True"=, "yes"=, "Yes"= 1,
"FALSE"=,"false"=,"False"=, "no"=, "No" = 0,
as.numeric(rsi))
if (!is.na(rsi))
staged_install <- (rsi > 0)
else
staged_install <- TRUE
}
if ((tar_up || zip_up) && fake)
stop("building a fake installation is disallowed")
if (fake) {
use_configure <- FALSE
if("--html" %notin% args0)
build_html <- FALSE
build_latex <- FALSE
build_example <- FALSE
install_libs <- FALSE
install_demo <- FALSE
install_exec <- FALSE
}
build_help_types <- character()
if (build_html) build_help_types <- c(build_help_types, "html")
if (build_latex) build_help_types <- c(build_help_types, "latex")
if (build_example) build_help_types <- c(build_help_types, "example")
build_help <- length(build_help_types) > 0L
if (debug)
starsmsg(stars, "build_help_types=",
paste(build_help_types, collapse = " "))
if (debug)
starsmsg(stars, "DBG: 'R CMD INSTALL' now doing do_install()")
for(pkg in allpkgs) {
if (pkglock) {
lockdir <- file.path(lib, paste0("00LOCK-", basename(pkg)))
mk_lockdir(lockdir)
}
do_install(pkg)
}
do_cleanup()
on.exit()
invisible()
}
.SHLIB <- function()
{
status <- .shlib_internal(commandArgs(TRUE))
q("no", status = (status != 0), runLast=FALSE)
}
.shlib_internal <- function(args)
{
Usage <- function()
cat("Usage: R CMD SHLIB [options] files | linker options",
"",
"Build a shared object for dynamic loading from the specified source or",
"object files (which are automagically made from their sources) or",
"linker options. If not given via '--output', the name for the shared",
"object is determined from the first source or object file.",
"",
"Options:",
" -h, --help print short help message and exit",
" -v, --version print version info and exit",
" -o, --output=LIB use LIB as (full) name for the built library",
" -c, --clean remove files created during compilation",
" --preclean remove files created during a previous run",
" -n, --dry-run dry run, showing commands that would be used",
" --use-LTO use Link-Time Optimization",
" --no-use-LTO do not use Link-Time Optimization",
"",
"Windows only:",
" -d, --debug build a debug DLL",
"",
"Report bugs at <https://bugs.R-project.org>.",
sep = "\n")
p1 <- function(...) paste(..., collapse = " ")
WINDOWS <- .Platform$OS.type == "windows"
cross <- Sys.getenv("R_CROSS_BUILD")
if(nzchar(cross)) {
if(!cross %in% c("i386", "x64"))
stop("invalid value ", sQuote(cross), " for R_CROSS_BUILD")
WINDOWS <- TRUE
Sys.setenv(R_ARCH = paste0("/", cross))
}
if (!WINDOWS) {
mconf <- readLines(file.path(R.home(),
paste0("etc", Sys.getenv("R_ARCH")),
"Makeconf"))
SHLIB_EXT <- sub(".*= ", "", grep("^SHLIB_EXT", mconf, value = TRUE,
perl = TRUE))
SHLIB_LIBADD <- sub(".*= ", "", grep("^SHLIB_LIBADD", mconf,
value = TRUE, perl = TRUE))
MAKE <- Sys.getenv("MAKE")
rarch <- Sys.getenv("R_ARCH")
} else {
rhome <- chartr("\\", "/", R.home())
Sys.setenv(R_HOME = rhome)
SHLIB_EXT <- ".dll"
SHLIB_LIBADD <- ""
MAKE <- "make"
rarch <- Sys.getenv("R_ARCH", NA_character_)
if(is.na(rarch)) {
if (nzchar(.Platform$r_arch)) {
rarch <- paste0("/", .Platform$r_arch)
Sys.setenv(R_ARCH = rarch)
} else rarch <- ""
}
}
OBJ_EXT <- ".o"
objs <- character()
shlib <- ""
site <- Sys.getenv("R_MAKEVARS_SITE", NA_character_)
if (is.na(site))
site <- file.path(paste0(R.home("etc"), rarch), "Makevars.site")
makefiles <-
c(file.path(paste0(R.home("etc"), rarch), "Makeconf"),
if(file.exists(site)) site,
file.path(R.home("share"), "make",
if (WINDOWS) "winshlib.mk" else "shlib.mk"))
shlib_libadd <- if (nzchar(SHLIB_LIBADD)) SHLIB_LIBADD else character()
with_cxx <- FALSE
with_f77 <- FALSE
with_f9x <- FALSE
with_objc <- FALSE
use_cxxstd <- NULL
use_fc_link <- FALSE
use_lto <- NA
pkg_libs <- character()
clean <- FALSE
preclean <- FALSE
dry_run <- FALSE
debug <- FALSE
while(length(args)) {
a <- args[1L]
if (a %in% c("-h", "--help")) {
Usage()
return(0L)
}
else if (a %in% c("-v", "--version")) {
cat("R shared object builder: ",
R.version[["major"]], ".", R.version[["minor"]],
" (r", R.version[["svn rev"]], ")\n", sep = "")
cat("",
.R_copyright_msg(2000),
"This is free software; see the GNU General Public License version 2",
"or later for copying conditions. There is NO warranty.",
sep = "\n")
return(0L)
} else if (a %in% c("-n", "--dry-run")) {
dry_run <- TRUE
} else if (a %in% c("-d", "--debug")) {
debug <- TRUE
} else if (a %in% c("-c", "--clean")) {
clean <- TRUE
} else if (a == "--preclean") {
preclean <- TRUE
} else if (a == "--use-LTO") {
use_lto <- TRUE
} else if (a == "--no-use-LTO") {
use_lto <- FALSE
} else if (a == "-o") {
if (length(args) >= 2L) {shlib <- args[2L]; args <- args[-1L]}
else stop("-o option without value", call. = FALSE)
} else if (substr(a, 1, 9) == "--output=") {
shlib <- substr(a, 10, 1000)
} else {
base <- sub("\\.[[:alnum:]]*$", "", a)
ext <- sub(paste0(base, "."), "", a, fixed = TRUE)
nobj <- ""
if (nzchar(ext)) {
if (ext %in% c("cc", "cpp")) {
with_cxx <- TRUE
nobj <- base
} else if (ext == "m") {
with_objc <- TRUE
nobj <- base
} else if (ext %in% c("mm", "M")) {
with_objc <- with_cxx <- TRUE
nobj <- base
} else if (ext == "f") {
with_f77 <- TRUE
nobj <- base
} else if (ext %in% c("f90", "f95")) {
with_f9x <- TRUE
nobj <- base
} else if (ext == "c") {
nobj <- base
} else if (ext == "o") {
nobj <- base
}
if (nzchar(nobj) && !nzchar(shlib))
shlib <- paste0(nobj, SHLIB_EXT)
}
if (nzchar(nobj)) objs <- c(objs, nobj)
else pkg_libs <- c(pkg_libs, a)
}
args <- args[-1L]
}
if (length(objs)) objs <- paste0(objs, OBJ_EXT, collapse = " ")
if (WINDOWS) {
if (!is.na(f <- Sys.getenv("R_MAKEVARS_USER", NA_character_))) {
if (file.exists(f)) makefiles <- c(makefiles, f)
} else if (rarch == "/x64" &&
file.exists(f <- path.expand("~/.R/Makevars.ucrt")))
makefiles <- c(makefiles, f)
else if (rarch == "/x64" &&
file.exists(f <- path.expand("~/.R/Makevars.win64")))
makefiles <- c(makefiles, f)
else if (file.exists(f <- path.expand("~/.R/Makevars.win")))
makefiles <- c(makefiles, f)
else if (file.exists(f <- path.expand("~/.R/Makevars")))
makefiles <- c(makefiles, f)
} else {
makefiles <- c(makefiles, makevars_user())
}
makeobjs <- paste0("OBJECTS=", shQuote(objs))
if (WINDOWS && (file.exists(fn <- "Makevars.ucrt") || file.exists(fn <- "Makevars.win"))) {
makefiles <- c(fn, makefiles)
lines <- readLines(fn, warn = FALSE)
if (length(grep("^OBJECTS *=", lines, perl=TRUE, useBytes = TRUE)))
makeobjs <- ""
if (length(ll <- grep("^CXX_STD *=", lines, perl = TRUE,
value = TRUE, useBytes = TRUE)) == 1) {
val <- gsub("^CXX_STD *= *CXX", "", ll)
val <- gsub(" +$", "", val)
if (val %in% cxx_standards) {
use_cxxstd <- val
with_cxx <- TRUE
}
}
if (any(grepl("^USE_FC_TO_LINK", lines, perl=TRUE, useBytes = TRUE)))
use_fc_link <- TRUE
} else if (file.exists("Makevars")) {
makefiles <- c("Makevars", makefiles)
lines <- readLines("Makevars", warn = FALSE)
if (length(grep("^OBJECTS *=", lines, perl = TRUE, useBytes = TRUE)))
makeobjs <- ""
if (length(ll <- grep("^CXX_STD *=", lines, perl = TRUE,
value = TRUE, useBytes = TRUE)) == 1) {
val <- gsub("^CXX_STD *= *CXX", "", ll)
val <- gsub(" +$", "", val)
if (val %in% cxx_standards) {
use_cxxstd <- val
with_cxx <- TRUE
}
}
if (any(grepl("^USE_FC_TO_LINK", lines, perl=TRUE, useBytes = TRUE)))
use_fc_link <- TRUE
}
if (is.null(use_cxxstd)) {
for (i in cxx_standards) {
if (nzchar(Sys.getenv(paste0("USE_CXX", i)))) {
use_cxxstd <- i
break
}
}
}
if (is.null(use_cxxstd)) {
val <- Sys.getenv("R_PKG_CXX_STD")
if (val %in% cxx_standards) {
use_cxxstd <- val
}
}
if (with_cxx) {
checkCXX <- function(cxxstd) {
for (i in rev(seq_along(makefiles))) {
lines <- readLines(makefiles[i], warn = FALSE)
pattern <- paste0("^CXX", cxxstd, " *= *")
ll <- grep(pattern, lines, perl = TRUE, value = TRUE,
useBytes = TRUE)
for (j in rev(seq_along(ll))) {
cxx <- gsub(pattern, "", ll[j])
return(nzchar(cxx))
}
}
return(FALSE)
}
if (!is.null(use_cxxstd)) {
if (use_cxxstd == "98") {
stop("C++98 standard requested but unsupported",
call. = FALSE, domain = NA)
}
if (!checkCXX(use_cxxstd)) {
stop(paste0("C++", use_cxxstd, " standard requested but CXX",
use_cxxstd, " is not defined"),
call. = FALSE, domain = NA)
}
}
}
makeargs <- paste0("SHLIB=", shQuote(shlib))
if (with_cxx) {
if (!is.null(use_cxxstd)) {
cxx_makeargs <- sprintf(c("CXX='$(CXX%s) $(CXX%sSTD)'",
"CXXFLAGS='$(CXX%sFLAGS)'",
"CXXPICFLAGS='$(CXX%sPICFLAGS)'",
"SHLIB_LDFLAGS='$(SHLIB_CXX%sLDFLAGS)'",
"SHLIB_LD='$(SHLIB_CXX%sLD)'"),
use_cxxstd, use_cxxstd)
makeargs <- c(cxx_makeargs, makeargs)
}
else {
makeargs <- c("SHLIB_LDFLAGS='$(SHLIB_CXXLDFLAGS)'",
"SHLIB_LD='$(SHLIB_CXXLD)'", makeargs)
}
} else if (use_fc_link && (with_f77 || with_f9x))
makeargs <- c("SHLIB_LDFLAGS='$(SHLIB_FCLDFLAGS)'",
"SHLIB_LD='$(SHLIB_FCLD)'",
"ALL_LIBS='$(PKG_LIBS) $(SHLIB_LIBADD)'",
makeargs)
if (with_objc) shlib_libadd <- c(shlib_libadd, "$(OBJC_LIBS)")
if (with_f77 || with_f9x) {
if (use_fc_link)
shlib_libadd <- c(shlib_libadd, "$(FCLIBS_XTRA)")
else
shlib_libadd <- c(shlib_libadd, "$(FLIBS) $(FCLIBS_XTRA)")
}
if (length(pkg_libs))
makeargs <- c(makeargs,
paste0("PKG_LIBS='", p1(pkg_libs), "'"))
if (length(shlib_libadd))
makeargs <- c(makeargs,
paste0("SHLIB_LIBADD='", p1(shlib_libadd), "'"))
if (with_f9x && file.exists("Makevars") &&
length(grep("^\\s*PKG_FCFLAGS", lines, perl = TRUE, useBytes = TRUE)))
makeargs <- c(makeargs, "P_FCFLAGS='$(PKG_FCFLAGS)'")
if (WINDOWS && debug) makeargs <- c(makeargs, "DEBUG=T")
if (WINDOWS && rarch == "/x64") makeargs <- c(makeargs, "WIN=64 TCLBIN=")
build_objects_symbol_tables <-
config_val_to_logical(Sys.getenv("_R_SHLIB_BUILD_OBJECTS_SYMBOL_TABLES_",
"FALSE"))
makeargs <- c(makeargs,
if(isTRUE(use_lto))
c(paste0("LTO=", shQuote("$(LTO_OPT)")),
paste0("LTO_FC=", shQuote("$(LTO_FC_OPT)")))
else if(isFALSE(use_lto)) c("LTO=", "LTO_FC=")
)
cmd <- paste(MAKE, p1(paste("-f", shQuote(makefiles))), p1(makeargs),
p1(makeobjs))
if (dry_run) {
cat("make cmd is\n ", cmd, "\n\nmake would use\n", sep = "")
system(paste(cmd, "-n"))
res <- 0
} else {
if (preclean) system(paste(cmd, "shlib-clean"))
res <- system(cmd)
if((res == 0L) && build_objects_symbol_tables) {
system(paste(cmd, "symbols.rds"))
}
if (clean) system(paste(cmd, "shlib-clean"))
}
res
}
.writePkgIndices <-
function(dir, outDir, OS = .Platform$OS.type, html = TRUE)
{
re <- function(x)
{
xx <- rep.int(TRUE, length(x))
xx[grep("-package", x, fixed = TRUE)] <- FALSE
order(xx, toupper(x), x)
}
html_header <- function(pkg, title, version, conn)
{
cat(paste(HTMLheader(title, Rhome="../../..",
up="../../../doc/html/packages.html",
css = "R.css"),
collapse = "\n"),
'<h2>Documentation for package ‘', pkg, '’ version ',
version, '</h2>\n\n', sep = "", file = conn)
cat('<ul><li><a href="../DESCRIPTION">DESCRIPTION file</a>.</li>\n', file=conn)
if (file.exists(file.path(outDir, "doc")))
cat('<li><a href="../doc/index.html">User guides, package vignettes and other documentation.</a></li>\n', file=conn)
if (file.exists(file.path(outDir, "demo")))
cat('<li><a href="../demo">Code demos</a>. Use <a href="../../utils/help/demo">demo()</a> to run them.</li>\n',
sep = "", file=conn)
if (any(file.exists(c(file.path(outDir, "NEWS"), file.path(outDir, "NEWS.Rd")))))
cat('<li><a href="../NEWS">Package NEWS</a>.</li>\n',
sep = "", file=conn)
cat('</ul>\n\n<h2>Help Pages</h2>\n\n\n',
sep ="", file = conn)
}
firstLetterCategory <- function(x)
{
x[endsWith(x, "-package")] <- " "
x <- toupper(substr(x, 1, 1))
x[x > "Z"] <- "misc"
x[x < "A" & x != " "] <- "misc"
x
}
Rd <- if (file.exists(f <- file.path(outDir, "Meta", "Rd.rds")))
readRDS(f)
else {
db <- tryCatch(Rd_db(basename(outDir), lib.loc = dirname(outDir)),
error = function(e) NULL)
if (is.null(db)) db <- Rd_db(dir = dir)
Rd <- Rd_contents(db)
saveRDS(Rd, file.path(outDir, "Meta", "Rd.rds"))
Rd
}
topics <- Rd$Aliases
M <- if (!length(topics)) {
list2DF(list(Topic = character(),
File = character(),
Title = character(),
Internal = character()))
} else {
lens <- lengths(topics)
files <- sub("\\.[Rr]d$", "", Rd$File)
internal <- (vapply(Rd$Keywords,
function(x) match("internal", x, 0L),
0L) > 0L)
list2DF(list(Topic = unlist(topics),
File = rep.int(files, lens),
Title = rep.int(Rd$Title, lens),
Internal = rep.int(internal, lens)))
}
outman <- file.path(outDir, "help")
dir.create(outman, showWarnings = FALSE)
MM <- M[re(M[, 1L]), 1:2]
utils::write.table(MM, file.path(outman, "AnIndex"),
quote = FALSE, row.names = FALSE, col.names = FALSE,
sep = "\t")
a <- structure(MM[, 2L], names=MM[, 1L])
saveRDS(a, file.path(outman, "aliases.rds"))
outman <- file.path(outDir, "html")
dir.create(outman, showWarnings = FALSE)
outcon <- file(file.path(outman, "00Index.html"), "wt")
on.exit(close(outcon))
desc <- read.dcf(file.path(outDir, "DESCRIPTION"))[1L, ]
if(!is.na(enc <- desc["Encoding"])) {
desc <- iconv(desc, enc, "UTF-8", sub = "byte")
}
M <- M[!M[, 4L], ]
if (desc["Package"] %in% c("base", "graphics", "stats", "utils")) {
for(pass in 1:2) {
gen <- gsub("\\.data\\.frame", ".data_frame", M$Topic)
gen <- sub("\\.model\\.matrix$", ".modelmatrix", gen)
gen <- sub("^(all|as|is|file|Sys|row|na|model)\\.", "\\1_", gen)
gen <- sub("^(.*)\\.test", "\\1_test", gen)
gen <- sub("([-[:alnum:]]+)\\.[^.]+$", "\\1", gen)
last <- nrow(M)
nongen <- gen %in% c("ar", "bw", "contr", "dyn", "lm", "qr", "ts", "which", ".Call", ".External", ".Library", ".First", ".Last")
nc <- nchar(gen)
asg <- (nc > 3) & endsWith(gen, "<-")
skip <- (gen == c("", gen[-last])) & (M$File == c("", M$File[-last])) & !nongen
skip <- skip | asg
M <- M[!skip, ]
}
}
M$Topic <- sub("^([^,]*),.*-method$", "\\1-method", M$Topic)
M <- M[!duplicated(M[, c("Topic", "File")]),]
M <- M[re(M[, 1L]), ]
htmlize <- function(x, backtick)
{
x <- gsub("&", "&", x, fixed = TRUE)
x <- gsub("<", "<", x, fixed = TRUE)
x <- gsub(">", ">", x, fixed = TRUE)
if (backtick) {
x <- gsub("---", "-", x, fixed = TRUE)
x <- gsub("--", "-", x, fixed = TRUE)
}
x
}
M$HTopic <- htmlize(M$Topic, FALSE)
M$ Title <- htmlize(M$Title, TRUE)
html_header(desc["Package"], htmlize(desc["Title"], TRUE),
desc["Version"], outcon)
use_alpha <- (nrow(M) > 100)
if (use_alpha) {
first <- firstLetterCategory(M$Topic)
nm <- sort(names(table(first)))
m <- match(" ", nm, 0L)
if (m) nm <- c(" ", nm[-m])
m <- match("misc", nm, 0L)
if (m) nm <- c(nm[-m], "misc")
writeLines(c('<p style="text-align: center;">',
paste0("<a href=\"
"</p>\n"), outcon)
for (f in nm) {
MM <- M[first == f, ]
if (f != " ")
cat("\n<h2><a name=\"", f, "\">-- ", f, " --</a></h2>\n\n",
sep = "", file = outcon)
writeLines(c('<table width="100%">',
paste0('<tr><td style="width: 25%;"><a href="', MM[, 2L], '.html">',
MM$HTopic, '</a></td>\n<td>', MM[, 3L],'</td></tr>'),
"</table>"), outcon)
}
} else if (nrow(M)) {
writeLines(c('<table width="100%">',
paste0('<tr><td style="width: 25%;"><a href="', M[, 2L], '.html">',
M$HTopic, '</a></td>\n<td>', M[, 3L],'</td></tr>'),
"</table>"), outcon)
} else {
writeLines("There are no help pages in this package", outcon)
}
writeLines('</div></body></html>', outcon)
file.copy(file.path(R.home("doc"), "html", "R.css"), outman)
invisible(NULL)
}
.convertRdfiles <-
function(dir, outDir, types = "html", silent = FALSE, outenc = "UTF-8")
{
showtype <- function(type) {
if (!shown) {
nc <- nchar(bf)
if (nc < 38L)
cat(" ", bf, rep.int(" ", 40L - nc), sep = "")
else
cat(" ", bf, "\n", rep.int(" ", 44L), sep = "")
shown <<- TRUE
}
cat(type, rep.int(" ", max(0L, 6L - nchar(type))), sep = "")
}
dirname <- c("html", "latex", "R-ex")
ext <- c(".html", ".tex", ".R")
names(dirname) <- names(ext) <- c("html", "latex", "example")
mandir <- file.path(dir, "man")
if (!dir.exists(mandir)) return()
desc <- readRDS(file.path(outDir, "Meta", "package.rds"))$DESCRIPTION
pkg <- desc["Package"]
ver <- desc["Version"]
for(type in types)
dir.create(file.path(outDir, dirname[type]), showWarnings = FALSE)
cat(" converting help for package ", sQuote(pkg), "\n", sep = "")
if ("html" %in% types) {
if (!silent) message(" finding HTML links ...", appendLF = FALSE, domain = NA)
Links <- findHTMLlinks(outDir, level = 0:1)
if (!silent) message(" done")
.Links2 <- function() {
message("\n finding level-2 HTML links ...", appendLF = FALSE, domain = NA)
Links2 <- findHTMLlinks(level = 2)
message(" done", domain = NA)
Links2
}
delayedAssign("Links2", .Links2())
}
db <- tryCatch(Rd_db(basename(outDir), lib.loc = dirname(outDir)),
error = function(e) NULL)
if (is.null(db)) db <- Rd_db(dir = dir)
if (!length(db)) return()
.whandler <- function(e) {
.messages <<- c(.messages,
paste("Rd warning:", conditionMessage(e)))
tryInvokeRestart("muffleWarning")
}
.ehandler <- function(e) {
message("", domain = NA)
unlink(ff)
stop(conditionMessage(e), domain = NA, call. = FALSE)
}
.convert <- function(expr)
withCallingHandlers(tryCatch(expr, error = .ehandler),
warning = .whandler)
files <- names(db)
for(nf in files) {
.messages <- character()
Rd <- db[[nf]]
attr(Rd, "source") <- NULL
bf <- sub("\\.[Rr]d$", "", basename(nf))
f <- attr(Rd, "Rdfile")
shown <- FALSE
if ("html" %in% types) {
type <- "html"
ff <- file.path(outDir, dirname[type],
paste0(bf, ext[type]))
if (!file_test("-f", ff) || file_test("-nt", f, ff)) {
showtype(type)
.convert(Rd2HTML(Rd, ff, package = c(pkg, ver),
defines = NULL,
Links = Links, Links2 = Links2))
}
}
if ("latex" %in% types) {
type <- "latex"
ff <- file.path(outDir, dirname[type],
paste0(bf, ext[type]))
if (!file_test("-f", ff) || file_test("-nt", f, ff)) {
showtype(type)
.convert(Rd2latex(Rd, ff, defines = NULL,
outputEncoding = outenc))
}
}
if ("example" %in% types) {
type <- "example"
ff <- file.path(outDir, dirname[type],
paste0(bf, ext[type]))
if (!file_test("-f", ff) || file_test("-nt", f, ff)) {
.convert(Rd2ex(Rd, ff, defines = NULL))
if (file_test("-f", ff)) showtype(type)
}
}
if (shown) {
cat("\n")
if (length(.messages)) writeLines(unique(.messages))
}
}
bfs <- sub("\\.[Rr]d$", "", basename(files))
if ("html" %in% types) {
type <- "html"
have <- list.files(file.path(outDir, dirname[type]))
have2 <- sub(".html", "", basename(have), fixed=TRUE)
drop <- have[have2 %notin% c(bfs, "00Index", "R.css")]
unlink(file.path(outDir, dirname[type], drop))
}
if ("latex" %in% types) {
type <- "latex"
have <- list.files(file.path(outDir, dirname[type]))
have2 <- sub(".tex", "", basename(have), fixed=TRUE)
drop <- have[have2 %notin% bfs]
unlink(file.path(outDir, dirname[type], drop))
}
if ("example" %in% types) {
type <- "example"
have <- list.files(file.path(outDir, dirname[type]))
have2 <- sub(".R", "", basename(have), fixed=TRUE)
drop <- have[have2 %notin% bfs]
unlink(file.path(outDir, dirname[type], drop))
}
}
.makeDllRes <-
function(name="", version = "0.0")
{
if (file.exists(f <- "../DESCRIPTION") ||
file.exists(f <- "../../DESCRIPTION")) {
desc <- read.dcf(f)[[1L]]
if (!is.na(f <- desc["Package"])) name <- f
if (!is.na(f <- desc["Version"])) version <- f
}
writeLines(c('
'
'',
'VS_VERSION_INFO VERSIONINFO',
'FILEVERSION R_FILEVERSION',
'PRODUCTVERSION 3,0,0,0',
'FILEFLAGSMASK 0x3L',
'FILEOS VOS__WINDOWS32',
'FILETYPE VFT_APP',
'BEGIN',
' BLOCK "StringFileInfo"',
' BEGIN',
' BLOCK "040904E4"',
' BEGIN'))
cat(" VALUE \"FileDescription\", \"DLL for R package `", name,"'\\0\"\n",
" VALUE \"FileVersion\", \"", version, "\\0\"\n", sep = "")
writeLines(c(
' VALUE "Compiled under R Version", R_MAJOR "." R_MINOR " (" R_YEAR "-" R_MONTH "-" R_DAY ")\\0"',
' VALUE "Project info", "https://www.r-project.org\\0"',
' END',
' END',
' BLOCK "VarFileInfo"',
' BEGIN',
' VALUE "Translation", 0x409, 1252',
' END',
'END'))
}
makevars_user <-
function()
{
m <- character()
if(.Platform$OS.type == "windows") {
if(!is.na(f <- Sys.getenv("R_MAKEVARS_USER", NA_character_))) {
if(file.exists(f)) m <- f
}
else if((Sys.getenv("R_ARCH") == "/x64") &&
file.exists(f <- path.expand("~/.R/Makevars.ucrt")))
m <- f
else if((Sys.getenv("R_ARCH") == "/x64") &&
file.exists(f <- path.expand("~/.R/Makevars.win64")))
m <- f
else if(file.exists(f <- path.expand("~/.R/Makevars.win")))
m <- f
else if(file.exists(f <- path.expand("~/.R/Makevars")))
m <- f
}
else {
if(!is.na(f <- Sys.getenv("R_MAKEVARS_USER", NA_character_))) {
if(file.exists(f)) m <- f
}
else if(file.exists(f <- path.expand(paste0("~/.R/Makevars-",
Sys.getenv("R_PLATFORM")))))
m <- f
else if(file.exists(f <- path.expand("~/.R/Makevars")))
m <- f
}
m
}
revert_install_time_patches <- function()
{
WINDOWS <- .Platform$OS.type == "windows"
if (WINDOWS && dir.exists("install_time_patches")) {
patches <- sort(list.files("install_time_patches"),
decreasing = TRUE)
for(p in patches) {
fname <- paste0("install_time_patches/", p)
if (system2("patch",
args = c("-p2", "--binary", "--force", "--reverse"),
stdin = fname) != 0)
message("WARNING: failed to revert patch ", p, "\n")
else
message("Reverted installation-time patch ", p,
" in package installation\n")
}
unlink("install_time_patches", recursive = TRUE)
}
}
makevars_site <-
function()
{
m <- character()
if(is.na(f <- Sys.getenv("R_MAKEVARS_SITE", NA_character_)))
f <- file.path(paste0(R.home("etc"), Sys.getenv("R_ARCH")),
"Makevars.site")
if(file.exists(f))
m <- f
m
}
cxx_standards <- c("20", "17", "14", "11", "98")
|
compute.threshold.AROC.kernel <-
function(object, newcovariate, FPF = 0.5) {
if(class(object)[2] != "AROC.kernel") {
stop(paste0("This function can not be used for this object class: ", class(object)[2]))
}
ncov <- length(newcovariate)
np <- length(FPF)
thresholds <- matrix(0, nrow = np, ncol = ncov)
rownames(thresholds) <- FPF
colnames(thresholds) <- newcovariate
fit.mean.new <- npreg(object$bw.mean, exdat = newcovariate, residuals = TRUE)
fit.var.new <- npreg(object$bw.var, exdat = newcovariate, residuals = TRUE)
h.residuals <- object$fit.mean$resid/sqrt(object$fit.var$mean)
csf0 <- apply(outer(h.residuals, h.residuals, ">="), 2, mean)
csf0_inv <- apply(outer(csf0, FPF, "<="), 2, function(x, z) {
res <- min(c(z[x], max(z)))
res
}, z = h.residuals)
csf0_inv <- replace(csf0_inv, is.infinite(csf0_inv), max(h.residuals))
for(i in 1:ncov) {
thresholds[,i] <- fit.mean.new$mean[i] + sqrt(fit.var.new$mean[i])*csf0_inv
}
res <- list()
res$thresholds <- thresholds
res
}
|
context("test-geom_parallel_slopes")
library(ggplot2)
set.seed(202)
test_df <- dplyr::bind_rows(
tibble::tibble(a = runif(10, 0, 0.1), gr = 1),
tibble::tibble(a = runif(15, 0.1, 0.3), gr = 2),
tibble::tibble(a = runif(20, 0.4, 1), gr = 3)
) %>%
dplyr::mutate(
pan = factor(sample(1:3, dplyr::n(), replace = TRUE)),
gr = factor(gr),
b = as.integer(pan) * a + as.integer(gr) + runif(dplyr::n(), max = 0.1),
c = as.integer(pan) * a^2 + as.integer(gr) + runif(dplyr::n(), max = 0.1),
)
viz <- ggplot(test_df, aes(a, b, colour = gr)) +
geom_point()
test_that("geom_parallel_slopes works", {
expect_doppelganger(
"geom_parallel_slopes-basic-1",
viz + geom_parallel_slopes() + labs(title = "geom_parallel_slopes()")
)
expect_doppelganger(
"geom_parallel_slopes-basic-2",
viz + geom_parallel_slopes(se = FALSE) +
labs(title = "geom_parallel_slopes() with `se = FALSE`")
)
expect_doppelganger(
"geom_parallel_slopes-basic-3",
viz +
geom_parallel_slopes(
mapping = aes(group = gr), color = "red", size = 3
) +
labs(title = "geom_parallel_slopes() with extra aesthetics")
)
expect_doppelganger(
"geom_parallel_slopes-basic-4",
ggplot(test_df, aes(a, c, colour = gr)) +
geom_point() +
geom_parallel_slopes(formula = y ~ poly(x, 2)) +
labs(title = "Quadratic geom_parallel_slopes()")
)
expect_doppelganger(
"geom_parallel_slopes-basic-5",
viz + geom_parallel_slopes() +
facet_wrap(~pan) +
labs(title = "Faceted geom_parallel_slopes()")
)
expect_doppelganger(
"geom_parallel_slopes-basic-6",
ggplot(test_df, aes(a, b)) +
geom_point() +
geom_parallel_slopes(aes(group = gr)) +
labs(title = "geom_parallel_slopes() with `group` aesthetics grouping")
)
expect_doppelganger(
"geom_parallel_slopes-basic-7",
ggplot(test_df, aes(a, b)) +
geom_point() +
geom_parallel_slopes(aes(fill = gr)) +
labs(title = "geom_parallel_slopes() with `fill` aesthetics grouping")
)
expect_doppelganger(
"geom_parallel_slopes-fullrange",
viz + geom_parallel_slopes(fullrange = TRUE) +
xlim(c(-1, 2)) +
labs(title = "geom_parallel_slopes() with fullrange=TRUE")
)
expect_doppelganger(
"geom_parallel_slopes-level",
viz + geom_parallel_slopes(level = 0.25) +
labs(title = "geom_parallel_slopes() with level=0.25")
)
})
test_that("geom_parallel_slopes works in edge cases", {
expect_doppelganger(
"geom_parallel_slopes-edge-non-factor-grouping",
test_df %>%
dplyr::mutate(gr = as.integer(gr)) %>%
ggplot(aes(a, b, colour = gr, group = gr)) +
geom_point() +
geom_parallel_slopes() +
labs(title = "Works with non-factor grouping")
)
expect_warning(
expect_doppelganger(
"geom_parallel_slopes-edge-no-grouping",
ggplot(test_df, aes(a, b)) +
geom_point() +
geom_parallel_slopes() +
labs(title = "Works without grouping")
),
regexp = "grouping"
)
expect_warning(
expect_doppelganger(
"geom_parallel_slopes-edge-unique-grouping",
test_df %>%
dplyr::mutate(gr = 1) %>%
ggplot(aes(a, b, group = gr)) +
geom_point() +
geom_parallel_slopes() +
labs(title = "Works with grouping variable with one value")
),
regexp = "grouping.*unique"
)
expect_warning(
expect_doppelganger(
"geom_parallel_slopes-edge-method-arg",
viz + geom_parallel_slopes(method = "lm") +
labs(title = "geom_parallel_slopes() with `method` supplied")
),
regexp = "doesn't need a `method`"
)
})
|
context("getFirstLast")
test_that("getFirstLast", {
expect_equal(getFirst(1:3), 1L)
expect_equal(getLast(1:3), 3L)
expect_equal(getFirst(list(iris, 1)), iris)
expect_equal(getLast(list(iris, 1)), 1)
expect_equal(getFirst(c(a=1, 2)), 1)
expect_equal(names(getFirst(c(a=1, 2))), NULL)
expect_equal(getLast(c(a=1, 2)), 2)
expect_equal(names(getLast(c(a=1, 2))), NULL)
})
|
setMethod(
'ConstantOutFluxRate_by_PoolIndex'
,signature=signature(
sourceIndex='numeric'
,rate_constant='numeric'
)
,def=function(sourceIndex,rate_constant){
if (rate_constant<0){
stop(
"Negative rate constant.
A rate_constant defines a flux F with F = rate_constant*pool_content.
Since fluxes have to be positive and pool contents are positive
rate constants have to be positive too."
)
}
rate=new(
'ConstantOutFluxRate_by_PoolIndex'
,sourceIndex=PoolIndex(id=sourceIndex)
,rate_constant=rate_constant
)
rate
}
)
setMethod(
f="by_PoolName",
signature=c(obj='ConstantOutFluxRate_by_PoolIndex'),
def=function(obj,poolNames){
new(
'ConstantOutFluxRate_by_PoolName'
,sourceName=PoolName(id=obj@sourceIndex,poolNames)
,rate_constant=obj@rate_constant
)
}
)
|
path.rpart <- function(tree, nodes, pretty = 0, print.it = TRUE)
{
if (!inherits(tree, "rpart"))
stop("Not a legitimate \"rpart\" object")
splits <- labels.rpart(tree, pretty = pretty)
frame <- tree$frame
n <- row.names(frame)
node <- as.numeric(n)
which <- descendants(node)
path <- list()
if (missing(nodes)) {
xy <- rpartco(tree)
while(length(i <- identify(xy, n = 1L, plot = FALSE)) > 0L) {
path[[n[i]]] <- path.i <- splits[which[, i]]
if (print.it) {
cat("\n", "node number:", n[i], "\n")
cat(paste(" ", path.i), sep = "\n")
}
}
} else {
if (length(nodes <- node.match(nodes, node)) == 0L)
return(invisible())
for (i in nodes) {
path[[n[i]]] <- path.i <- splits[which[, i]]
if (print.it) {
cat("\n", "node number:", n[i], "\n")
cat(paste(" ", path.i), sep = "\n")
}
}
}
invisible(path)
}
|
plot.predMMSE <- function(x,legend.loc="topright",legend,add=FALSE,...)
{
if (!inherits(x, "predMMSE")) stop("use only with \"predMMSE\" objects")
if(is.na(as.logical(add))) stop("add should be TRUE or FALSE")
ng <- 1
ndistr <- grep("MMSEdistr",colnames(x))
if(length(ndistr))
{
ng <- length(ndistr)/3
}
else
{
if(ncol(x)>2)
{
ng <- ncol(x)-1
}
}
if(missing(legend)) legend <- paste("class",1:ng,sep="")
dots <- list(...)
if(length(list(...)$main))
{
main1 <- as.character(eval(match.call()$main))
dots <- dots[setdiff(names(dots),"main")]
}
else main1 <- "Prediction of MMSE scores"
if(length(list(...)$type))
{
type1 <- eval(match.call()$type)
dots <- dots[-which(names(dots)=="type")]
}
else type1 <- "l"
if(length(list(...)$col))
{
col1 <- eval(match.call()$col)
col1 <- rep(col1,length.out=ng)
dots <- dots[-which(names(dots)=="col")]
}
else col1 <- rainbow(ng)
if(length(list(...)$ylim))
{
ylim1 <- eval(match.call()$ylim)
dots <- dots[setdiff(names(dots),"ylim")]
}
else ylim1 <- c(0,30)
if(length(list(...)$xlab))
{
xlab1 <- as.character(eval(match.call()$xlab))
dots <- dots[setdiff(names(dots),"xlab")]
}
else xlab1 <- "prediction time"
if(length(list(...)$ylab))
{
ylab1 <- as.character(eval(match.call()$ylab))
dots <- dots[setdiff(names(dots),"ylab")]
}
else ylab1 <- "MMSE"
names.plot <- c("adj","ann","asp","axes","bg","bty","cex","cex.axis","cex.lab","cex.main","cex.sub","col","col.axis",
"col.lab","col.main","col.sub","crt","err","family","fig","fin","font","font.axis","font.lab","font.main","font.sub",
"frame.plot","lab","las","lend","lheight","ljoin","lmitre","lty","lwd","mai","main","mar","mex","mgp","mkh","oma",
"omd","omi","pch","pin","plt","ps","pty","smo","srt","sub","tck","tcl","type","usr","xaxp","xaxs","xaxt","xlab",
"xlim","xpd","yaxp","yaxs","yaxt","ylab","ylbias","ylim")
dots.plot <- dots[intersect(names(dots),names.plot)]
if(!isTRUE(add))
{
do.call("matplot",c(dots.plot,list(x=x[,rep(1,ng),drop=FALSE],y=x[,1+1:ng,drop=FALSE],type=type1,ylab=ylab1,xlab=xlab1,main=main1,ylim=ylim1,col=col1)))
}
else
{
do.call("matlines",c(dots.plot,list(x=x[,rep(1,ng),drop=FALSE],y=x[,1+1:ng,drop=FALSE],type=type1,col=col1)))
}
if(length(ndistr))
{
if(length(list(...)$lty)) dots.plot <- dots.plot[setdiff(names(dots),"lty")]
do.call("matlines",c(dots.plot,list(x=x[,rep(1,ng),drop=FALSE],y=x[,1+ng+1:ng,drop=FALSE],col=col1,lty=2)))
do.call("matlines",c(dots.plot,list(x=x[,rep(1,ng),drop=FALSE],y=x[,1+2*ng+1:ng,drop=FALSE],col=col1,lty=2)))
}
if(!is.null(legend))
{
if(length(list(...)$box.lty))
{
box.lty1 <- as.integer(eval(match.call()$box.lty))
dots <- dots[setdiff(names(dots),"box.lty")]
}
else box.lty1 <- 0
if(length(list(...)$inset))
{
inset1 <- eval(match.call()$inset)
dots <- dots[setdiff(names(dots),"inset")]
}
else inset1 <- c(0.02,0.02)
if(length(list(...)$lty))
{
lty1 <- eval(match.call()$lty)
dots <- dots[setdiff(names(dots),"lty")]
}
else lty1 <- 1
names.legend <- c("fill","border","lty","lwd","pch","angle","density","bg","box.lwd",
"box.lty","box.col","pt.bg","cex","pt.cex","pt.lwd","xjust","yjust","x.intersp","y.intersp","adj","text.width",
"text.col","text.font","merge","trace","plot","ncol","horiz","title","xpd","title.col","title.adj","seg.len")
dots.leg <- dots[intersect(names(dots),names.legend)]
if(type1=="l" | type1=="b") dots.leg <- c(dots.leg,list(lty=lty1))
if(!(type1 %in% c("l","b"))) dots.leg <- dots.leg[setdiff(names(dots),"lwd")]
do.call("legend",c(dots.leg,list(x=legend.loc, legend=legend, box.lty=box.lty1, inset=inset1,col=col1)))
}
return(invisible(NULL))
}
|
context('chapter 4')
library(rethinking)
expect_equiv_eps <- function( x , y , eps=0.01 ) {
expect_equivalent( x , y , tolerance=eps )
}
library(rethinking)
data(Howell1)
d <- Howell1
d2 <- d[ d$age >= 18 , ]
flist <- alist(
height ~ dnorm( mu , sigma ) ,
mu ~ dnorm( 178 , 20 ) ,
sigma ~ dunif( 0 , 50 )
)
m4.1 <- quap( flist , data=d2 )
test_that("R code 4.29",
expect_equiv_eps( round(coef(m4.1),2) , c(154.61,7.73) )
)
m4.2 <- quap(
alist(
height ~ dnorm( mu , sigma ) ,
mu ~ dnorm( 178 , 0.1 ) ,
sigma ~ dunif( 0 , 50 )
) , data=d2 )
test_that("R code 4.31",
expect_equiv_eps( round(coef(m4.2),2) , c(177.86,24.52) )
)
library(rethinking)
data(Howell1)
d <- Howell1
d2 <- d[ d$age >= 18 , ]
d2$xbar <- mean(d2$weight)
m4.3 <- quap(
alist(
height ~ dnorm( mu , sigma ) ,
mu <- a + b*( weight - xbar ) ,
a ~ dnorm( 178 , 20 ) ,
b ~ dlnorm( 0 , 1 ) ,
sigma ~ dunif( 0 , 50 )
) ,
data=d2 )
test_that("R code 4.42",
expect_equiv_eps( round(coef(m4.3),2) , c(154.60 , 0.90 , 5.07) )
)
m4.3b <- quap(
alist(
height ~ dnorm( mu , sigma ) ,
mu <- a + exp(log_b)*( weight - xbar ),
a ~ dnorm( 178 , 20 ) ,
log_b ~ dnorm( 0 , 1 ) ,
sigma ~ dunif( 0 , 50 )
) ,
data=d2 )
test_that("R code 4.43",
expect_equiv_eps( round(coef(m4.3b),2) , c(154.60 , -0.10 , 5.07) )
)
library(rethinking)
data(Howell1)
d <- Howell1
d$weight_s <- ( d$weight - mean(d$weight) )/sd(d$weight)
d$weight_s2 <- d$weight_s^2
m4.5 <- quap(
alist(
height ~ dnorm( mu , sigma ) ,
mu <- a + b1*weight_s + b2*weight_s2 ,
a ~ dnorm( 178 , 20 ) ,
b1 ~ dlnorm( 0 , 1 ) ,
b2 ~ dnorm( 0 , 1 ) ,
sigma ~ dunif( 0 , 50 )
) ,
data=d )
test_that("R code 4.65",
expect_equiv_eps( round(coef(m4.5),2) ,
c(146.06 , 21.73 , -7.80 , 5.77) )
)
d$weight_s3 <- d$weight_s^3
m4.6 <- quap(
alist(
height ~ dnorm( mu , sigma ) ,
mu <- a + b1*weight_s + b2*weight_s2 + b3*weight_s3 ,
a ~ dnorm( 178 , 20 ) ,
b1 ~ dlnorm( 0 , 1 ) ,
b2 ~ dnorm( 0 , 10 ) ,
b3 ~ dnorm( 0 , 10 ) ,
sigma ~ dunif( 0 , 50 )
) ,
data=d )
test_that("R code 4.69",
expect_equiv_eps( round(coef(m4.6),2) ,
c(146.74 , 15.00 , -6.54 , 3.60 , 4.82) )
)
library(rethinking)
data(cherry_blossoms)
d <- cherry_blossoms
d2 <- d[ complete.cases(d$temp) , ]
num_knots <- 15
knot_list <- quantile( d2$year , probs=seq(0,1,length.out=num_knots) )
library(splines)
B <- bs(d2$year,
knots=knot_list[-c(1,num_knots)] ,
degree=3 , intercept=TRUE )
m4.7 <- quap(
alist(
T ~ dnorm( mu , sigma ) ,
mu <- a + B %*% w ,
a ~ dnorm(6,10),
w ~ dnorm(0,1),
sigma ~ dexp(1)
),
data=list( T=d2$temp , B=B ) ,
start=list( w=rep( 0 , ncol(B) ) ) )
test_that("R code 4.76",
expect_equiv_eps( round(coef(m4.7),2) ,
c(0.10 , 0.24 , 1.20, -0.82 , 0.10, -1.40 , 1.16, -1.92 , 2.35 ,-2.32 , 0.94, -1.61 , 0.18 , -1.24 , 0.03 , 1.00 , 2.04 , 6.32 , 0.34 )
)
)
m4.7alt <- quap(
alist(
T ~ dnorm( mu , sigma ) ,
mu <- a + sapply( 1:1124 , function(i) sum( B[i,]*w ) ) ,
a ~ dnorm(6,10),
w ~ dnorm(0,1),
sigma ~ dexp(1)
),
data=list( T=d2$temp , B=B ) ,
start=list( w=rep( 0 , ncol(B) ) ) )
test_that("R code 4.76",
expect_equiv_eps( round(coef(m4.7alt),2) ,
c(0.10 , 0.24 , 1.20, -0.82 , 0.10, -1.40 , 1.16, -1.92 , 2.35 ,-2.32 , 0.94, -1.61 , 0.18 , -1.24 , 0.03 , 1.00 , 2.04 , 6.32 , 0.34 )
)
)
|
test_that("is_running", {
expect_false(is_running())
})
|
integration <- function(values, intervals){
n <- length(values)
sum(0.5 * (values[1:n-1] + values[2:n]) * diff(intervals))
}
getNC <- function(beta, subbetas, nvertex, ncolor,
edges, neighbors=NULL, blocks=NULL,
algorithm=c("SwendsenWang", "Gibbs", "Wolff"), n, burn){
if(length(subbetas) < 2)
stop("There has to be at least 2 intermidiate betas.")
if(max(subbetas) > beta)
stop("The maxmimum of the intermidiate betas has to be less than or equal to beta")
if(max(subbetas) < beta)
subbetas <- c(subbetas, beta)
if(is.null(edges))
stop("'edges' are needed to get the normalizing constant.")
algorithm <- match.arg(algorithm)
algorithm <- switch(algorithm, Gibbs = 1, SwendsenWang = 2, Wolff = 3)
if(algorithm == 1 && (is.null(neighbors) || is.null(blocks)))
stop("'neighbors' and 'blcoks' are needed to run Gibbs sampling.")
if(algorithm == 3 && is.null(neighbors))
stop("'neighbors' are needed to run the Wolff algorithm.")
if(algorithm ==1)
p.body <- quote(BlocksGibbs(n, nvertex, ncolor, neighbors, blocks, beta=subbetas[i]))
else if(algorithm == 2)
p.body <- quote(SW(n, nvertex, ncolor, edges, beta=subbetas[i]))
else
p.body <- quote(Wolff(n, nvertex, ncolor, neighbors, beta=subbetas[i]))
EUs <- rep(0, length(subbetas))
for (i in 1 : length(subbetas)){
colors <- eval(p.body)
EUs[i] <- mean(apply(colors[,-(1:burn)], 2, function(x) sum(x[edges[,1]]==x[edges[,2]])))
}
exp(integration(EUs, subbetas) + nvertex * log(ncolor))
}
|
tab_stackfrq <- function(items,
weight.by = NULL,
title = NULL,
var.labels = NULL,
value.labels = NULL,
wrap.labels = 20,
sort.frq = NULL,
alternate.rows = FALSE,
digits = 2,
string.total = "N",
string.na = "NA",
show.n = FALSE,
show.total = FALSE,
show.na = FALSE,
show.skew = FALSE,
show.kurtosis = FALSE,
digits.stats = 2,
file = NULL,
encoding = NULL,
CSS = NULL,
use.viewer = TRUE,
remove.spaces = TRUE) {
if (!is.null(sort.frq)) {
if (sort.frq == "first.asc") {
sort.frq <- "first"
reverseOrder <- FALSE
} else if (sort.frq == "first.desc") {
sort.frq <- "first"
reverseOrder <- TRUE
} else if (sort.frq == "last.asc") {
sort.frq <- "last"
reverseOrder <- FALSE
} else if (sort.frq == "last.desc") {
sort.frq <- "last"
reverseOrder <- TRUE
} else {
sort.frq <- NULL
reverseOrder <- FALSE
}
} else {
reverseOrder <- FALSE
}
encoding <- get.encoding(encoding, items)
if (is.null(value.labels)) {
value.labels <- sjlabelled::get_labels(
items[[1]],
attr.only = F,
values = "n",
non.labelled = T
)
}
if (is.null(var.labels)) {
var.labels <- sjlabelled::get_label(items, def.value = colnames(items))
}
minval <- as.numeric(min(apply(items, 2, function(x) min(x, na.rm = TRUE))))
maxval <- as.numeric(max(apply(items, 2, function(x) max(x, na.rm = TRUE))))
if (is.null(value.labels)) value.labels <- as.character(minval:maxval)
if (show.na) value.labels <- c(value.labels, `NA` = string.na)
catcount <- length(value.labels)
value.labels <- sjmisc::word_wrap(value.labels, wrap.labels, "<br>")
if (is.null(var.labels)) var.labels <- colnames(items)
var.labels <- sjmisc::word_wrap(var.labels, wrap.labels, "<br>")
if (show.skew) pstat_skewness <- datawizard::skewness(items)
if (show.kurtosis) pstat_kurtosis <- datawizard::kurtosis(items)
if (is.null(weight.by)) {
dummy <- sjmisc::frq(items, show.strings = TRUE, show.na = show.na)
} else {
items$weights <- weight.by
dummy <- sjmisc::frq(items, weights = items$weights, show.strings = TRUE, show.na = show.na)
}
mat.n <- .transform_data(dummy, col = "frq")
mat <- .transform_data(dummy, col = ifelse(isTRUE(show.na), "raw.prc", "valid.prc"))
facord <- seq_len(nrow(mat))
if (!is.null(sort.frq)) {
if (sort.frq == "first")
facord <- order(mat.n$V1)
else
facord <- order(mat.n[, ncol(mat.n)])
}
if (reverseOrder) facord <- rev(facord)
toWrite <- table.header <- sprintf("<html>\n<head>\n<meta http-equiv=\"Content-type\" content=\"text/html;charset=%s\">\n", encoding)
tag.table <- "table"
tag.caption <- "caption"
tag.thead <- "thead"
tag.tdata <- "tdata"
tag.arc <- "arc"
tag.centeralign <- "centeralign"
tag.firsttablecol <- "firsttablecol"
tag.ncol <- "ncol"
tag.skewcol <- "skewcol"
tag.kurtcol <- "kurtcol"
tag.summary <- "summary"
css.table <- "border-collapse:collapse; border:none; border-bottom:double black;"
css.caption <- "font-weight: bold; text-align:left;"
css.thead <- "border-top:double black; border-bottom:1px solid black; padding:0.2cm;"
css.tdata <- "padding:0.2cm;"
css.arc <- "background-color:
css.centeralign <- "text-align:center;"
css.firsttablecol <- "font-style:italic;"
css.ncol <- ""
css.summary <- ""
css.skewcol <- ""
css.kurtcol <- ""
if (!is.null(CSS)) {
if (!is.null(CSS[['css.table']])) css.table <- ifelse(substring(CSS[['css.table']],1,1) == '+', paste0(css.table, substring(CSS[['css.table']],2)), CSS[['css.table']])
if (!is.null(CSS[['css.thead']])) css.thead <- ifelse(substring(CSS[['css.thead']],1,1) == '+', paste0(css.thead, substring(CSS[['css.thead']],2)), CSS[['css.thead']])
if (!is.null(CSS[['css.caption']])) css.caption <- ifelse(substring(CSS[['css.caption']],1,1) == '+', paste0(css.caption, substring(CSS[['css.caption']],2)), CSS[['css.caption']])
if (!is.null(CSS[['css.summary']])) css.summary <- ifelse(substring(CSS[['css.summary']],1,1) == '+', paste0(css.summary, substring(CSS[['css.summary']],2)), CSS[['css.summary']])
if (!is.null(CSS[['css.arc']])) css.arc <- ifelse(substring(CSS[['css.arc']],1,1) == '+', paste0(css.arc, substring(CSS[['css.arc']],2)), CSS[['css.arc']])
if (!is.null(CSS[['css.tdata']])) css.tdata <- ifelse(substring(CSS[['css.tdata']],1,1) == '+', paste0(css.tdata, substring(CSS[['css.tdata']],2)), CSS[['css.tdata']])
if (!is.null(CSS[['css.centeralign']])) css.centeralign <- ifelse(substring(CSS[['css.centeralign']],1,1) == '+', paste0(css.centeralign, substring(CSS[['css.centeralign']],2)), CSS[['css.centeralign']])
if (!is.null(CSS[['css.firsttablecol']])) css.firsttablecol <- ifelse(substring(CSS[['css.firsttablecol']],1,1) == '+', paste0(css.firsttablecol, substring(CSS[['css.firsttablecol']],2)), CSS[['css.firsttablecol']])
if (!is.null(CSS[['css.ncol']])) css.ncol <- ifelse(substring(CSS[['css.ncol']],1,1) == '+', paste0(css.ncol, substring(CSS[['css.ncol']],2)), CSS[['css.ncol']])
if (!is.null(CSS[['css.skewcol']])) css.skewcol <- ifelse(substring(CSS[['css.skewcol']],1,1) == '+', paste0(css.skewcol, substring(CSS[['css.skewcol']],2)), CSS[['css.skewcol']])
if (!is.null(CSS[['css.kurtcol']])) css.kurtcol <- ifelse(substring(CSS[['css.kurtcol']],1,1) == '+', paste0(css.kurtcol, substring(CSS[['css.kurtcol']],2)), CSS[['css.kurtcol']])
}
page.style <- sprintf("<style>\nhtml, body { background-color: white; }\n%s { %s }\n%s { %s }\n.%s { %s }\n.%s { %s }\n.%s { %s }\n.%s { %s }\n.%s { %s }\n.%s { %s }\n.%s { %s }\n.%s { %s }\n.%s { %s }\n</style>",
tag.table, css.table, tag.caption, css.caption,
tag.thead, css.thead, tag.tdata, css.tdata,
tag.firsttablecol, css.firsttablecol, tag.arc, css.arc,
tag.centeralign, css.centeralign, tag.ncol, css.ncol,
tag.summary, css.summary, tag.kurtcol, css.kurtcol,
tag.skewcol, css.skewcol)
toWrite <- paste0(toWrite, page.style)
toWrite = paste(toWrite, "\n</head>\n<body>", "\n")
page.content <- "<table>\n"
if (!is.null(title)) page.content <- paste(page.content, sprintf(" <caption>%s</caption>\n", title))
page.content <- paste0(page.content, " <tr>\n")
page.content <- paste0(page.content, " <th class=\"thead\"> </th>\n")
for (i in 1:catcount) {
page.content <- paste0(page.content, sprintf(" <th class=\"thead\">%s</th>\n", value.labels[i]))
}
if (show.total) page.content <- paste0(page.content, sprintf(" <th class=\"thead ncol summary\">%s</th>\n", string.total))
if (show.skew) page.content <- paste0(page.content, " <th class=\"thead skewcol summary\">Skew</th>\n")
if (show.kurtosis) page.content <- paste0(page.content, " <th class=\"thead kurtcol summary\">Kurtosis</th>\n")
page.content <- paste0(page.content, " </tr>\n")
for (i in seq_len(nrow(mat))) {
arcstring <- ""
if (alternate.rows) arcstring <- ifelse(sjmisc::is_even(i), " arc", "")
page.content <- paste0(page.content, " <tr>\n")
page.content <- paste0(page.content, sprintf(" <td class=\"firsttablecol%s\">%s</td>\n", arcstring, var.labels[facord[i]]))
for (j in seq_len(ncol(mat))) {
if (show.n) {
page.content <- paste0(page.content, sprintf(" <td class=\"tdata centeralign%s\">%i<br>(%.*f %%)</td>\n", arcstring, as.integer(mat.n[facord[i], j]), digits, mat[facord[i], j]))
} else {
page.content <- paste0(page.content, sprintf(" <td class=\"tdata centeralign%s\">%.*f %%</td>\n", arcstring, digits, mat[facord[i], j]))
}
}
if (show.total) page.content <- paste0(page.content, sprintf(" <td class=\"tdata centeralign ncol summary%s\">%i</td>\n", arcstring, as.integer(sum(mat.n[facord[i], ]))))
if (show.skew) page.content <- paste0(page.content, sprintf(" <td class=\"tdata centeralign skewcol summary%s\">%.*f</td>\n", arcstring, digits.stats, pstat_skewness[facord[i]]))
if (show.kurtosis) page.content <- paste0(page.content, sprintf(" <td class=\"tdata centeralign kurtcol summary%s\">%.*f</td>\n", arcstring, digits.stats, pstat_kurtosis[facord[i]]))
page.content <- paste0(page.content, " </tr>\n")
}
page.content <- paste(page.content, "\n</table>")
toWrite <- paste(toWrite, page.content, "\n")
toWrite <- paste0(toWrite, "</body></html>")
knitr <- page.content
knitr <- gsub("class=", "style=", knitr, fixed = TRUE, useBytes = TRUE)
knitr <- gsub("<table", sprintf("<table style=\"%s\"", css.table), knitr, fixed = TRUE, useBytes = TRUE)
knitr <- gsub("<caption", sprintf("<caption style=\"%s\"", css.caption), knitr, fixed = TRUE, useBytes = TRUE)
knitr <- gsub(tag.tdata, css.tdata, knitr, fixed = TRUE, useBytes = TRUE)
knitr <- gsub(tag.thead, css.thead, knitr, fixed = TRUE, useBytes = TRUE)
knitr <- gsub(tag.centeralign, css.centeralign, knitr, fixed = TRUE, useBytes = TRUE)
knitr <- gsub(tag.firsttablecol, css.firsttablecol, knitr, fixed = TRUE, useBytes = TRUE)
knitr <- gsub(tag.ncol, css.ncol, knitr, fixed = TRUE, useBytes = TRUE)
knitr <- gsub(tag.skewcol, css.skewcol, knitr, fixed = TRUE, useBytes = TRUE)
knitr <- gsub(tag.kurtcol, css.kurtcol, knitr, fixed = TRUE, useBytes = TRUE)
knitr <- gsub(tag.summary, css.summary, knitr, fixed = TRUE, useBytes = TRUE)
knitr <- gsub(tag.arc, css.arc, knitr, fixed = TRUE, useBytes = TRUE)
if (remove.spaces) {
knitr <- sju.rmspc(knitr)
toWrite <- sju.rmspc(toWrite)
page.content <- sju.rmspc(page.content)
}
structure(class = c("sjTable", "sjtstackfrq"),
list(page.style = page.style,
page.content = page.content,
page.complete = toWrite,
header = table.header,
knitr = knitr,
file = file,
viewer = use.viewer))
}
.transform_data <- function(x, col) {
dat <- suppressWarnings(Reduce(function(x, y) merge(x, y, all = TRUE, sort = FALSE, by = "val"), x))
if (is.factor(dat$val)) {
reihe <- levels(dat$val)
if (anyNA(dat$val))
reihe <- c(reihe, NA)
dat <- dat[order(dat$val, reihe), ]
} else {
dat <- dat[order(dat$val), ]
}
colnames(dat) <- make.names(colnames(dat), unique = TRUE)
keep <- (colnames(dat) == "val") | grepl(paste0("^", col), colnames(dat))
dat <- as.data.frame(t(dat[, keep, drop = FALSE]))
dat <- as.data.frame(sapply(dat[-1, ], function(i) as.numeric(as.character(i))))
dat[is.na(dat)] <- 0
colnames(dat) <- sprintf("V%i", 1:ncol(dat))
dat
}
|
if(require("suppdata") & require("testthat")){
context("bioRxiv")
test_that("bioRxiv works", {
skip_on_cran()
expect_true(file.exists(suppdata("10.1101/016386", 1)))
})
test_that("bioRxiv fails with character SI info", {
skip_on_cran()
expect_error(suppdata("10.1101/016386", si = "999"), "numeric SI info")
})
}
|
plot.Meth <-
function( x,
which = NULL,
col.LoA = "blue",
col.pt = "black",
cex.name = 2,
var.range,
diff.range,
var.names = FALSE,
pch = 16,
cex = 0.7,
Transform,
... )
{
if( !missing(Transform) )
{
if( is.character(Transform) ) Transform <- choose.trans(Transform)$trans
if( !is.function(Transform) ) stop( "Transform= must be of mode character or function\n" )
x$y <- Transform( x$y )
}
x <- x[,c("meth","item","repl","y")]
data <- to.wide( x )
if( is.null(which) )
which <- match( levels(x$meth), names(data) )
if( is.logical( var.names ) )
{
plot.names <- var.names
if( is.character( which ) ) var.names <- which
else var.names <- names( data )[which]
}
else plot.names <- TRUE
pnl <-
function( x, y, ... )
{
abline( 0, 1, col="black" )
points( x, y, pch=pch, cex=cex, col=col.pt, ... )
}
pnu <-
function( x, y, ... )
{
sdd <- sd( (y-x), na.rm=TRUE )
mnd <- mean( (y-x), na.rm=TRUE )
abline( h=mnd+(-1:1)*2.00*sdd, col=col.LoA )
abline( h=0,col="black" )
points( (x+y)/2, y-x, pch=pch, cex=cex, col=col.pt, ... )
}
pldat <- data[,which]
nvar <- ncol( pldat )
if( missing(var.range) )
rg <- range( pldat, na.rm=TRUE )
else rg <- var.range
if( missing(diff.range) )
dif.rg <- c(-1,1)*diff(rg)/2
else if( length( diff.range )==1 ) dif.rg <- c(-1,1)*abs(diff.range)
else dif.rg <- diff.range
oma <- par("mar")
oldpar <- par( mfrow=c(nvar,nvar), mar=c(0,0,0,0), oma=c(4,4,4,4), las=1 )
on.exit( par ( oldpar ) )
for( ir in 1:nvar ) for( ic in 1:nvar )
{
if( ir == ic )
{
plot( 0:1, 0:1, type="n", xlab="", ylab="", axes=FALSE )
text( 0.5, 0.5, names( pldat )[ir], font=2, cex=cex.name )
box()
}
if( ir < ic )
{
plot( 0:1, 0:1, xlim=rg, ylim=dif.rg,
type="n", xlab="", ylab="", axes=FALSE )
pnu( pldat[,ir], pldat[,ic], ... )
if( plot.names )
{
text( rg[1], dif.rg[2], paste(var.names[ic],"-",var.names[ir]), adj=c(0,1) )
text( rg[2], dif.rg[1], paste("(",var.names[ic],"+",var.names[ir],")/2"), adj=c(1,0) )
}
if( ir == nvar ) axis( side=1 )
if( ic == 1 ) axis( side=2 )
if( ir == 1 ) axis( side=3 )
if( ic == nvar ) axis( side=4 )
box()
}
if( ir > ic )
{
plot( 0:1, 0:1, xlim=rg, ylim=rg,
type="n", xlab="", ylab="", axes=FALSE )
pnl( pldat[,ic], pldat[,ir], ... )
if( plot.names )
{
text( rg[1], rg[2], var.names[ir], adj=c(0,1) )
text( rg[2], rg[1], var.names[ic], adj=c(1,0) )
}
if( ir == nvar ) axis( side=1 )
if( ic == 1 ) axis( side=2 )
if( ir == 1 ) axis( side=3 )
if( ic == nvar ) axis( side=4 )
box()
}
}
}
|
library("freqdom")
library(pcdpca)
RES = c()
for (run in 1:100){
d = 7
n = 100
A = t(t(matrix(rnorm(d*n),ncol=d,nrow=n)))
B = t(t(matrix(rnorm(d*n),ncol=d,nrow=n)))
A = t(t(A) * exp(-(1:d)/(d) ))
B = t(t(B) * exp(-(1:d)/(d) ))
ntotal = 3*n
X = matrix(0,ncol=d,nrow=3*n)
X[3*(1:n) - 1,] = A
X[3*(1:n) - 2,] = B
X[3*(1:n) ,] = 2*A - B
train = 1:(ntotal/2)
test = (1 + ntotal/2) : (ntotal)
PR = prcomp(as.matrix(X[train,]))
Y1 = as.matrix(X) %*% PR$rotation
Y1[,-1] = 0
Xpca.est = Y1 %*% t(PR$rotation)
XI.est = dpca(as.matrix(X[train,]),q=sqn,freq=pi*(-150:150/150),Ndpc = 1)
Y.est = freqdom::filter.process(X, XI.est$filters )
Xdpca.est = freqdom::filter.process(Y.est, t(rev(XI.est$filters)) )
XI.est.pc = pcdpca(as.matrix(X[train,]),q=sqn,freq=pi*(-150:150/150),period=period)
Y.est.pc = pcdpca.scores(X, XI.est.pc)
Y.est.pc[,-1] = 0
Xpcdpca.est = pcdpca.inverse(Y.est.pc, XI.est.pc)
r0 = MSE(X[test,],Xpca.est[test,]) / MSE(X[test,],0)
r1 = MSE(X[test,],Xdpca.est[test,]) / MSE(X[test,],0)
r2 = MSE(X[test,],Xpcdpca.est[test,]) / MSE(X[test,],0)
row = c(r0,r1,r2)
print(row)
RES = rbind(RES,row)
}
colnames(RES) = c("PCA","DPCA","PC-DPCA")
df1 = data.frame(RES,row.names = NULL)
colMeans(df1)
summary(df1)
apply(df1, 2, sd)
t.test(df1$DPCA - df1$PC.DPCA)
par(mfrow=c(1,1),ps = 12, cex = 1.8, cex.main = 1.8)
boxplot(df1, main="Simulation study 1", ylab="Normalized mean squared error",ylim=c(0.25,0.9))
|
init.theta.MSAR.VM <-
function(data,...,M,order,regime_names=NULL,nh.emissions=NULL,nh.transitions=NULL,label=NULL,ncov.emis = 0,ncov.trans=0
) {
if (missing(M) || is.null(M) || M==0) {print("Need at least one regime : M=1"); M <- 1 }
if (missing(order)) { order <- 0 }
if (missing(label)) {label = 'HH'}
d = dim(data)[3]
if (is.null(d) ) {d=1}
else if ( is.na(d)) {d=1}
if (length(dim(data))<3) {d = 1}
else {d = dim(data)[3]}
mu <- matrix(6*runif(M*d),M,d) ;
kappa = matrix(1+2*runif(M*(order+1)),M,order+1)
if (M>1) {
prior <- normalise(runif(M))
transmat <- mk_stochastic(diag(1,M)+matrix(runif(M*M),M))
} else {
prior = 1
transmat = 1
}
prior=matrix(prior,M,1)
transmat=matrix(transmat,M,M)
n_par=order*M+M+M^2
emis.linear = FALSE
if (substr(label,2,2)=="N") {
par.emis <- list()
if (missing(nh.emissions)) {nh.emissions = 'linear'}
if (!is.function(nh.emissions)){
if (nh.emissions == 'linear') {
emis.linear = TRUE
if (ncov.emis<1) {ncov.emis=1}
nh.emissions <- function(covar,par.emis){
d <- dim(par.emis)[1]
if(is.null(d) || is.na(d)){d <- 1}
f <- matrix(0,d,dim(covar)[1])
for(i in 1:d){
f[i,] <- par.emis[i,1:dim(par.emis)[2]]%*%t(covar)
}
return(f)
}
}
}
for(i in 1:M){
par.emis[[i]] <- matrix(0,d,ncov.emis)
}
n_par <- n_par+ncov.emis
}
if (substr(label,1,1)=="N") {
par.trans <- array(1,c(M,max(2,ncov.trans+1)))
if (missing(nh.transitions)) { nh.transition = 'VM'}
if (!is.function(nh.transitions)){
if (nh.transitions=='VM') {
nh.transitions <- function(covar,par.trans,transmat){
T <- dim(covar)[1]
N.samples = dim(covar)[2]
ncov = dim(covar)[3]
M = dim(transmat)[1]
par.trans = repmat(t(par.trans[,2])*exp(1i*par.trans[,1]),M,1)%*%(matrix(1,M,M)-diag(1,nrow=M))
f <- array(0,c(M,M,T))
for (j in 1:M) {
for (i in 1:M) {
f[i,j,] = transmat[i,j]*abs(exp(par.trans[i,j]*exp(-1i*covar))) ;
}
}
f.sum = apply(f , c(1,3), sum)
for (i in 1:M) {f[i,,] = f[i,,]/t(matrix(f.sum[i,],T,M))}
return(f)
}
} else if (nh.transitions=='gauss') {
nh.transitions <- function(covar,par.trans,transmat){
T <- dim(covar)[1]
N.samples = dim(covar)[2]
ncov = dim(covar)[3]
M = dim(transmat)[1]
f <- array(0,c(M,M,T))
for (j in 1:M) {
xx = (covar-array(par.trans[j,1:ncov],c(T,N.samples,ncov)))^2
sxx = apply(xx,1,sum)
temp=exp( -sxx/par.trans[j,ncov+1]^2/2)
for (i in 1:M) {
f[i,j,] = transmat[i,j]*temp ;
}
}
f.sum = apply(f , c(1,3), sum)
for (i in 1:M) {f[i,,] = f[i,,]/t(matrix(f.sum[i,],T,M))}
return(f)
}
} else if (nh.transitions=='logistic') {
nh.transitions <- function(covar,par.trans,transmat){
eps = 1e-10
T <- dim(covar)[1]
nc <- dim(covar)[3]
covar = matrix(covar,T,nc)
M = dim(transmat)[1]
f <- array(0,c(M,M,T))
for (m in 1:M) {
f[m,m,] = eps+(1-2*eps)/(1 + exp(par.trans[m,1] + par.trans[m,2:(nc+1)] %*% t(covar)))
}
f[1,2,] = 1-f[1,1,]
f[2,1,] = 1-f[2,2,]
return(f)
}
}}
n_par <- n_par+max(2,ncov.trans+1)
}
if (label=='HH'){theta=list(mu,kappa,prior,transmat)}
if (label=='HN'){theta=list(mu,kappa,prior,transmat,par.emis)}
if (label=='NH'){theta=list(mu,kappa,prior,transmat,par.trans)}
if (label=='NN'){theta=list(mu,kappa,prior,transmat,par.trans,par.emis)}
attr(theta,'NbComp') <- d
attr(theta,'NbRegimes') <- M
attr(theta,'order') <- order
attr(theta,'label') <- label
attr(theta,'nh.emissions') <- nh.emissions
attr(theta,'nh.transitions') <- nh.transitions
attr(theta,'n_par') <- n_par
attr(theta,'emis.linear') <- emis.linear
theta=as.thetaMSAR.VM(theta,label=label,ncov.emis=ncov.emis,ncov.trans=ncov.trans)
class(theta) <- "MSAR.VM"
return(theta)
}
|
context("`select_best()` and `show_best()`")
rcv_results <- readRDS(test_path("rcv_results.rds"))
knn_results <- readRDS(test_path("knn_results.rds"))
source(test_path("../helper-objects.R"))
test_that("select_best()", {
expect_true(
tibble::is_tibble(select_best(rcv_results, metric = "rmse"))
)
best_rmse <-
tibble::tribble(
~deg_free, ~degree, ~`wt df`, ~`wt degree`,
6L, 2L, 2L, 1L
)
best_rsq <-
tibble::tribble(
~deg_free, ~degree, ~`wt df`, ~`wt degree`,
10L, 2L, 2L, 2L
)
expect_equal(
select_best(rcv_results, metric = "rmse") %>% select(-.config),
best_rmse
)
expect_equal(
select_best(rcv_results, metric = "rsq") %>% select(-.config),
best_rsq
)
expect_warning(
select_best(rcv_results, metric = "rsq", maximize = TRUE),
"The `maximize` argument is no longer"
)
expect_error(
select_best(rcv_results, metric = "random"),
"Please check the value of `metric`"
)
expect_error(
select_best(rcv_results, metric = c("rmse", "rsq")),
"Please specify a single character"
)
expect_warning(
expect_equal(
select_best(rcv_results),
select_best(rcv_results, metric = "rmse")
),
"metric 'rmse' will be used"
)
})
test_that("show_best()", {
rcv_rmse <-
rcv_results %>%
collect_metrics() %>%
dplyr::filter(.metric == "rmse") %>%
dplyr::arrange(mean)
expect_equal(
show_best(rcv_results, metric = "rmse", n = 1),
rcv_rmse %>% slice(1)
)
expect_equal(
show_best(rcv_results, metric = "rmse", n = nrow(rcv_rmse) + 1),
rcv_rmse
)
expect_equal(
show_best(rcv_results, metric = "rmse", n = 1) %>% names(),
rcv_rmse %>% names()
)
expect_warning(
expect_equal(
show_best(rcv_results),
show_best(rcv_results, metric = "rmse")
),
"metric 'rmse' will be used"
)
})
test_that("one-std error rule", {
expect_true(
tibble::is_tibble(select_by_one_std_err(knn_results, metric = "accuracy", K))
)
expect_equal(
select_by_one_std_err(rcv_results, metric = "rmse", deg_free, `wt degree`)$mean,
2.94252798698909
)
expect_equal(
select_by_one_std_err(knn_results, metric = "accuracy", K)$K,
25L
)
expect_warning(
select_by_one_std_err(knn_results, metric = "accuracy", K, maximize = TRUE),
"The `maximize` argument is no longer"
)
expect_error(
select_by_one_std_err(rcv_results, metric = "random", deg_free),
"Please check the value of `metric`"
)
expect_error(
select_by_one_std_err(rcv_results, metric = c("rmse", "rsq"), deg_free),
"Please specify a single character"
)
expect_warning(
expect_equal(
select_by_one_std_err(knn_results, K),
select_by_one_std_err(knn_results, K, metric = "roc_auc")
),
"metric 'roc_auc' will be used"
)
expect_error(
select_by_one_std_err(rcv_results, metric = "random"),
"Please choose at least one tuning parameter to sort"
)
})
test_that("percent loss", {
expect_true(
tibble::is_tibble(select_by_pct_loss(knn_results, metric = "accuracy", K))
)
expect_equal(
select_by_pct_loss(rcv_results, metric = "rmse", deg_free, `wt degree`)$mean,
2.94252798698909
)
expect_equal(
select_by_pct_loss(knn_results, metric = "accuracy", K)$K,
12L
)
expect_warning(
select_by_pct_loss(knn_results, metric = "accuracy", K, maximize = TRUE),
"The `maximize` argument is no longer"
)
expect_error(
select_by_pct_loss(rcv_results, metric = "random", deg_free),
"Please check the value of `metric`"
)
expect_error(
select_by_pct_loss(rcv_results, metric = c("rmse", "rsq"), deg_free),
"Please specify a single character"
)
expect_warning(
expect_equal(
select_by_pct_loss(knn_results, K),
select_by_pct_loss(knn_results, K, metric = "roc_auc")
),
"metric 'roc_auc' will be used"
)
expect_error(
select_by_pct_loss(rcv_results, metric = "random"),
"Please choose at least one tuning parameter to sort"
)
})
|
expected <- eval(parse(text="c(FALSE, FALSE)"));
test(id=0, code={
argv <- eval(parse(text="list(structure(list(1L, 3L), class = structure(\"L\", package = \".GlobalEnv\")))"));
do.call(`is.na`, argv);
}, o=expected);
|
`codominant.snp` <-
function(o)
{
o<-codominant.default(o)
class(o)<-c("snp","factor")
o
}
|
test_that("tx_ml subsets only inactive clients within a period", {
expect_identical(
tx_ml(
new_data = ndr_example,
from = lubridate::ymd("2020-10-01"),
to = lubridate::ymd("2021-01-31")
),
ndr_example %>%
dplyr::filter(dplyr::between(
date_lost,
lubridate::as_date("2020-10-01"),
lubridate::as_date("2021-01-31")
))
)
})
|
setSeasonLabel<-function(localAnnualResults){
paStart <- localAnnualResults$PeriodStart[1]
paLong <- localAnnualResults$PeriodLong[1]
index <- seq(paStart, paStart + paLong - 1)
index <- ifelse(index > 12, index - 12, index)
monthList <-
sapply(index[1:paLong], function(x) {
monthInfo[[x]]@monthAbbrev
})
monthList <- paste(monthList, collapse = " ")
temp1 <- c("Year Starting With", monthInfo[[paStart]]@monthFull)
temp1 <- paste(temp1, collapse = " ")
temp2 <- "Water Year"
temp3 <- "Calendar Year"
temp4 <- c("Season Consisting of", monthList)
temp4 <- paste(temp4, collapse = " ")
periodName <- temp4
periodName<-if(paLong==12) temp1 else periodName
periodName<-if(paLong==12 & paStart==10) temp2 else periodName
periodName<-if(paLong==12 & paStart==1) temp3 else periodName
return(periodName)
}
|
histGroup <- function(data,groups, main=paste("Histogram of" , dataname),xlab=dataname,ylab,col=NULL, alpha=0.5,breaks="Sturges",legend=TRUE,legend.x=80,legend.y=80,legend.pch=15,freq=TRUE)
{
out <- list()
dataname <- paste(deparse(substitute(data), 500), collapse="\n")
histo <- hist(data,plot=FALSE,breaks=breaks)
if(!is.factor(groups))
{
groups <- as.factor(groups)
}
lev <- levels(groups)
nlev <- length(lev)
if (is.null(col))
colo <- rainbow(nlev,alpha=alpha)
else
{
if (length(col) != nlev)
stop("length of 'col' must match number of groups")
else if (is.character(col))
colo <- col
else
{
rgbfun <- function(x)
{
alpha <- alpha*255
x <- rgb(x[1],x[2],x[3],maxColorValue = 255,alpha=alpha)
return(x)
}
colo <- apply(col2rgb(col),2,rgbfun)
}
}
testrun <- 0
for( i in 1:nlev)
{if(freq)
testrun[i] <- max(hist(data[groups==lev[i]],breaks=histo$breaks,plot=F)$counts)
else
testrun[i] <- max(hist(data[groups==lev[i]],breaks=histo$breaks,plot=F)$density)
}
ylim <- max(testrun)
ylim <- ylim+0.15*ylim
out[[1]] <- hist(data[groups==lev[1]],breaks=histo$breaks,col=colo[1],main=main,xlab=xlab,ylab=ylab,ylim=c(0,ylim),freq=freq)
for (i in 2:nlev)
{
out[[i]] <- hist(data[groups==lev[i]],breaks=histo$breaks,col=colo[i],add=T,freq=freq)
}
if (legend)
{
tmp <- 0
tmp[1] <- grconvertX(legend.x, 'device')
tmp[2] <- grconvertY(legend.y, 'device')
legend(tmp[1],tmp[2],pch=legend.pch,col=colo,legend=lev,cex=1)
}
invisible(out)
}
|
predictMA <- function(object, new.data){
z <- object
c <- z$candidatmodels
w <- z$optimresults$weights
pmodels <- sapply(z$candidatmodels, predict, newdata = new.data)
MApredict <- w%*%t(sapply(c, predict, newdata = new.data))
res <- list(prediction = MApredict, weights = w)
return(res)
}
|
library(sarima)
context("Fitting Sarima models with estimated unit roots")
test_that("Sarima and Arma models work ok", {
expect_true(TRUE)
sarima(log(AirPassengers) ~ 0 | ma(1, c(-0.3)) + sma(12,1, c(-0.1)) +
i(1) + si(12,1), ss.method = "sarima")
sarima(log(AirPassengers) ~ 0 | ma(1, c(-0.3)) + sma(12,1, c(-0.1)) +
uar(13, c(rep(0,12), 1), fixed = 13, atanh.tr = TRUE), ss.method = "sarima")
sarima(log(AirPassengers) ~ 0 | ma(1, c(-0.3)) + sma(12,1, c(-0.1)) +
i(2) + uar(11, c(rep(0,10), 1), fixed = 11), ss.method = "sarima")
})
|
segment.outside<-function(img,blobsize=1)
{
nimg<-img/max(img)
dims<-dim(img)
thresh<-c()
XX<-dims[1]
YY<-dims[1]
ZZ<-dims[1]
for (i in 1:5)
{
try({
Y<-round(dims[2]*stats::runif(1,.4,.6),0)
Z<-round(dims[3]*stats::runif(1,.4,.6),0)
m<-mean(nimg[1:3,Y,Z])
s<-stats::sd(nimg[1:3,Y,Z])
X<-4
while(((m+3*s)>nimg[X,Y,Z])|(s<1e-4))
{
m<-mean(nimg[1:X,Y,Z])
s<-stats::sd(nimg[1:X,Y,Z])
X<-X+1
}
thresh<-c(thresh,nimg[X+1,Y,Z])
})
try({
Y<-round(dims[2]*stats::runif(1,.4,.6),0)
Z<-round(dims[3]*stats::runif(1,.4,.6),0)
m<-mean(nimg[XX-(0:2),Y,Z])
s<-stats::sd(nimg[XX-(0:2),Y,Z])
X<-3
while(((m+3*s)>nimg[XX-X,Y,Z])|(s<1e-4))
{
m<-mean(nimg[XX-(0:X),Y,Z])
s<-stats::sd(nimg[XX-(0:X),Y,Z])
X<-X+1
}
thresh<-c(thresh,nimg[XX-X-1,Y,Z])
})
try({
X<-round(dims[1]*stats::runif(1,.4,.6),0)
Z<-round(dims[3]*stats::runif(1,.4,.6),0)
m<-mean(nimg[X,1:3,Z])
s<-stats::sd(nimg[X,1:3,Z])
Y<-4
while(((m+3*s)>nimg[X,Y,Z])|(s<1e-4))
{
m<-mean(nimg[X,1:Y,Z])
s<-stats::sd(nimg[X,1:Y,Z])
Y<-Y+1
}
thresh<-c(thresh,nimg[X,Y+1,Z])
})
try({
X<-round(dims[1]*stats::runif(1,.4,.6),0)
Z<-round(dims[3]*stats::runif(1,.4,.6),0)
m<-mean(nimg[X,YY-(0:2),Z])
s<-stats::sd(nimg[X,YY-(0:2),Z])
Y<-3
while(((m+3*s)>nimg[X,YY-Y,Z])|(s<1e-4))
{
m<-mean(nimg[X,YY-(0:Y),Z])
s<-stats::sd(nimg[X,YY-(0:Y),Z])
Y<-Y+1
}
thresh<-c(thresh,nimg[X,YY-Y-1,Z])
})
}
thresh<-stats::quantile(thresh,.1)
cat(paste("Threshold is ",round(thresh*100,1),"%\n",sep=""))
nimg<-array(ifelse(nimg<thresh,0,1),dims)
cat("Starting Segmentation.\n")
return(outside(nimg,0,blobsize))
}
|
context("coord_matrix")
test_that("guess_loncol/guess_latcol work as expected", {
coord_cols <- c("lon", "lat")
x <- matrix(c(1, 2, 3, 4, 5, 6), ncol = 2)
expect_identical(guess_loncol(x), 1L)
expect_identical(guess_latcol(x), 2L)
colnames(x) <- c("lat", "longitude")
expect_identical(guess_loncol(x), 2L)
expect_identical(guess_latcol(x), 1L)
colnames(x) <- c("lon", "long")
expect_warning(guess_loncol(x), class = "multipleCandidateColumnsFoundWarning")
expect_error(guess_latcol(x), class = "cannotGuessColumnError")
colnames(x) <- c("lat", "lon")
expect_identical(guess_loncol(as.data.frame(x)), 2L)
expect_identical(guess_latcol(as.data.frame(x)), 1L)
})
test_that("as_coord_matrix.numeric works as expected", {
expect_identical(
as_coord_matrix(c(16.422524, 48.185686)),
as_coord_matrix(c(lon = 16.422524, lat = 48.185686))
)
expect_identical(
as_coord_matrix(c(16.422524, 48.185686)),
as_coord_matrix(c(LAT = 48.185686, LoNgItuDE = 16.422524))
)
})
test_that("as_coord_matrix.data.frame works as expected", {
x <- matrix(c(1, 2, 3, 4, 5, 6), ncol = 2)
y <- as.data.frame(x)
expect_error(as_coord_matrix(y), class = "cannotGuessColumnError")
colnames(x) <- c("lon", "lat")
colnames(y) <- c("lat", "lon")
y <- as_coord_matrix(y)
expect_identical(colnames(y), c("lon", "lat"))
expect_identical(x[, "lat"], y[, "lon"])
expect_identical(x[, "lon"], y[, "lat"])
expect_identical(colnames(x), colnames(y))
})
test_that("as_coord_matrix.default fails nicely on unspported objects", {
expect_error(as_coord_matrix("blubb"), class = "objectNotSupportedError")
})
|
.fit.param.fj.endcensoring <- function(counting, s, kmax, distr, cens.beg) {
theta0 <- matrix(data = NA, nrow = 2, ncol = s)
for (j in 1:s) {
if (distr[j] == "dweibull") {
theta0[, j] <- .fit.param.fj.dweibull(counting, j, kmax, cens.beg = FALSE)
} else if (distr[j] == "geom") {
theta0[, j] <- .fit.param.fj.geom(counting, j, kmax, cens.beg = FALSE)
} else if (distr[j] == "nbinom") {
theta0[, j] <- .fit.param.fj.nbinom(counting, j, kmax, cens.beg = FALSE)
} else if (distr[j] == "pois") {
theta0[, j] <- .fit.param.fj.pois(counting, j, kmax, cens.beg = FALSE)
}
}
theta0 <- as.vector(theta0[!(is.na(theta0))])
if (s == 2) {
ptrans <- matrix(c(0, 1, 1, 0), nrow = s, byrow = TRUE)
param <- matrix(data = NA, nrow = s, ncol = 2)
if ((distr[1] == "unif") & (distr[2] == "unif")) {
for (j in 1:s) {
param[j, ] <- .fit.param.fj.unif(counting, j, kmax)
}
} else if ("unif" %in% distr) {
for (j in 1:s) {
if (distr[j] == "unif") {
param[j, ] <- .fit.param.fj.unif(counting, j, kmax)
} else {
if (cens.beg) {
loglik <- function(par) {
fv <- matrix(data = 0, nrow = s, ncol = kmax)
Fv <- matrix(data = 0, nrow = s, ncol = kmax)
skipindex <- 1
for (j in 1:s) {
maskNjk <- counting$Njk[j, ] != 0
maskNeNb <- (counting$Nbjk[j, ] + counting$Neik[abs(j - 3), ]) != 0
if (distr[j] == "dweibull") {
fv[j, maskNjk] <- log(ddweibull(x = (1:kmax)[maskNjk], q = par[skipindex], beta = par[skipindex + 1], zero = FALSE))
Fv[j, maskNeNb] <- log(1 - pdweibull(x = (1:kmax)[maskNeNb], q = par[skipindex], beta = par[skipindex + 1], zero = FALSE) + .Machine$double.xmin)
skipindex <- skipindex + 2
} else if (distr[j] == "geom") {
fv[j, maskNjk] <- dgeom(x = (0:(kmax - 1))[maskNjk], prob = par[skipindex], log = TRUE)
Fv[j, maskNeNb] <- pgeom(q = (0:(kmax - 1))[maskNeNb], prob = par[skipindex], lower.tail = FALSE, log.p = TRUE)
skipindex <- skipindex + 1
} else if (distr[j] == "nbinom") {
fv[j, maskNjk] <- dnbinom(x = (0:(kmax - 1))[maskNjk], size = par[skipindex], prob = par[skipindex + 1], log = TRUE)
Fv[j, maskNeNb] <- pnbinom(q = (0:(kmax - 1))[maskNeNb], size = par[skipindex], prob = par[skipindex + 1], lower.tail = FALSE, log.p = TRUE)
skipindex <- skipindex + 2
} else if (distr[j] == "pois") {
fv[j, maskNjk] <- dpois(x = (0:(kmax - 1))[maskNjk], lambda = par[skipindex], log = TRUE)
Fv[j, maskNeNb] <- ppois(q = (0:(kmax - 1))[maskNeNb], lambda = par[skipindex], lower.tail = FALSE, log.p = TRUE)
skipindex <- skipindex + 1
}
}
return(
-(sum(counting$Njk[1, ] * fv[1, ]) + sum(counting$Njk[2, ] * fv[2, ])
+ sum((counting$Nbjk[1, ] + counting$Neik[2, ]) * Fv[1, ])
+ sum((counting$Nbjk[2, ] + counting$Neik[1, ]) * Fv[2, ]))
)
}
} else {
loglik <- function(par) {
fv <- matrix(data = 0, nrow = s, ncol = kmax)
Fv <- matrix(data = 0, nrow = s, ncol = kmax)
skipindex <- 1
for (j in 1:s) {
maskNjk <- counting$Njk[j, ] != 0
maskNeik <- counting$Neik[abs(j - 3), ] != 0
if (distr[j] == "dweibull") {
fv[j, maskNjk] <- log(ddweibull(x = (1:kmax)[maskNjk], q = par[skipindex], beta = par[skipindex + 1], zero = FALSE))
Fv[j, maskNeik] <- log(1 - pdweibull(x = (1:kmax)[maskNeik], q = par[skipindex], beta = par[skipindex + 1], zero = FALSE) + .Machine$double.xmin)
skipindex <- skipindex + 2
} else if (distr[j] == "geom") {
fv[j, maskNjk] <- dgeom(x = (0:(kmax - 1))[maskNjk], prob = par[skipindex], log = TRUE)
Fv[j, maskNeik] <- pgeom(q = (0:(kmax - 1))[maskNeik], prob = par[skipindex], lower.tail = FALSE, log.p = TRUE)
skipindex <- skipindex + 1
} else if (distr[j] == "nbinom") {
fv[j, maskNjk] <- dnbinom(x = (0:(kmax - 1))[maskNjk], size = par[skipindex], prob = par[skipindex + 1], log = TRUE)
Fv[j, maskNeik] <- pnbinom(q = (0:(kmax - 1))[maskNeik], size = par[skipindex], prob = par[skipindex + 1], lower.tail = FALSE, log.p = TRUE)
skipindex <- skipindex + 2
} else if (distr[j] == "pois") {
fv[j, maskNjk] <- dpois(x = (0:(kmax - 1))[maskNjk], lambda = par[skipindex], log = TRUE)
Fv[j, maskNeik] <- ppois(q = (0:(kmax - 1))[maskNeik], lambda = par[skipindex], lower.tail = FALSE, log.p = TRUE)
skipindex <- skipindex + 1
}
}
return(
-(sum(counting$Njk[1, ] * fv[1, ]) + sum(counting$Njk[2, ] * fv[2, ])
+ sum(counting$Neik[1, ] * Fv[2, ]) + sum(counting$Neik[2, ] * Fv[1, ]))
)
}
}
if (distr[j] == "dweibull") {
u0 <- diag(x = 1, nrow = 2)
c0 <- c(0, 0)
u1 <- matrix(data = c(-1, 0), nrow = 1, ncol = 2)
c1 <- c(-1)
mle <- constrOptim(
theta = theta0,
f = loglik,
ui = rbind(u0, u1),
ci = c(c0, c1),
method = "Nelder-Mead"
)
param[j, ] <- mle$par
} else if (distr[j] == "geom") {
mle <- optim(par = theta0, loglik, method = "Brent", lower = 0, upper = 1)
param[j, ] <- mle$par
} else if (distr[j] == "nbinom") {
u0 <- diag(x = 1, nrow = 2)
c0 <- c(0, 0)
u1 <- matrix(data = c(0, -1), nrow = 1, ncol = 2)
c1 <- c(-1)
mle <- constrOptim(
theta = theta0,
f = loglik,
ui = rbind(u0, u1),
ci = c(c0, c1),
method = "Nelder-Mead"
)
param[j, ] <- mle$par
} else if (distr[j] == "pois") {
mle <- optim(par = theta0, loglik, method = "Brent", lower = 0, upper = kmax - 1)
param[j, ] <- mle$par
}
}
}
} else {
if (cens.beg) {
loglik <- function(par) {
fv <- matrix(data = 0, nrow = s, ncol = kmax)
Fv <- matrix(data = 0, nrow = s, ncol = kmax)
skipindex <- 1
for (j in 1:s) {
maskNjk <- counting$Njk[j, ] != 0
maskNeNb <- (counting$Nbjk[j, ] + counting$Neik[abs(j - 3), ]) != 0
if (distr[j] == "dweibull") {
fv[j, maskNjk] <- log(ddweibull(x = (1:kmax)[maskNjk], q = par[skipindex], beta = par[skipindex + 1], zero = FALSE))
Fv[j, maskNeNb] <- log(1 - pdweibull(x = (1:kmax)[maskNeNb], q = par[skipindex], beta = par[skipindex + 1], zero = FALSE) + .Machine$double.xmin)
skipindex <- skipindex + 2
} else if (distr[j] == "geom") {
fv[j, maskNjk] <- dgeom(x = (0:(kmax - 1))[maskNjk], prob = par[skipindex], log = TRUE)
Fv[j, maskNeNb] <- pgeom(q = (0:(kmax - 1))[maskNeNb], prob = par[skipindex], lower.tail = FALSE, log.p = TRUE)
skipindex <- skipindex + 1
} else if (distr[j] == "nbinom") {
fv[j, maskNjk] <- dnbinom(x = (0:(kmax - 1))[maskNjk], size = par[skipindex], prob = par[skipindex + 1], log = TRUE)
Fv[j, maskNeNb] <- pnbinom(q = (0:(kmax - 1))[maskNeNb], size = par[skipindex], prob = par[skipindex + 1], lower.tail = FALSE, log.p = TRUE)
skipindex <- skipindex + 2
} else if (distr[j] == "pois") {
fv[j, maskNjk] <- dpois(x = (0:(kmax - 1))[maskNjk], lambda = par[skipindex], log = TRUE)
Fv[j, maskNeNb] <- ppois(q = (0:(kmax - 1))[maskNeNb], lambda = par[skipindex], lower.tail = FALSE, log.p = TRUE)
skipindex <- skipindex + 1
}
}
return(
-(sum(counting$Njk[1, ] * fv[1, ]) + sum(counting$Njk[2, ] * fv[2, ])
+ sum((counting$Nbjk[1, ] + counting$Neik[2, ]) * Fv[1, ])
+ sum((counting$Nbjk[2, ] + counting$Neik[1, ]) * Fv[2, ])
)
)
}
} else {
loglik <- function(par) {
fv <- matrix(data = 0, nrow = s, ncol = kmax)
Fv <- matrix(data = 0, nrow = s, ncol = kmax)
skipindex <- 1
for (j in 1:s) {
maskNjk <- counting$Njk[j, ] != 0
maskNeik <- counting$Neik[abs(j - 3), ] != 0
if (distr[j] == "dweibull") {
fv[j, maskNjk] <- log(ddweibull(x = (1:kmax)[maskNjk], q = par[skipindex], beta = par[skipindex + 1], zero = FALSE))
Fv[j, maskNeik] <- log(1 - pdweibull(x = (1:kmax)[maskNeik], q = par[skipindex], beta = par[skipindex + 1], zero = FALSE) + .Machine$double.xmin)
skipindex <- skipindex + 2
} else if (distr[j] == "geom") {
fv[j, maskNjk] <- dgeom(x = (0:(kmax - 1))[maskNjk], prob = par[skipindex], log = TRUE)
Fv[j, maskNeik] <- pgeom(q = (0:(kmax - 1))[maskNeik], prob = par[skipindex], lower.tail = FALSE, log.p = TRUE)
skipindex <- skipindex + 1
} else if (distr[j] == "nbinom") {
fv[j, maskNjk] <- dnbinom(x = (0:(kmax - 1))[maskNjk], size = par[skipindex], prob = par[skipindex + 1], log = TRUE)
Fv[j, maskNeik] <- pnbinom(q = (0:(kmax - 1))[maskNeik], size = par[skipindex], prob = par[skipindex + 1], lower.tail = FALSE, log.p = TRUE)
skipindex <- skipindex + 2
} else if (distr[j] == "pois") {
fv[j, maskNjk] <- dpois(x = (0:(kmax - 1))[maskNjk], lambda = par[skipindex], log = TRUE)
Fv[j, maskNeik] <- ppois(q = (0:(kmax - 1))[maskNeik], lambda = par[skipindex], lower.tail = FALSE, log.p = TRUE)
skipindex <- skipindex + 1
}
}
return(
-(sum(counting$Njk[1, ] * fv[1, ]) + sum(counting$Njk[2, ] * fv[2, ])
+ sum(counting$Neik[1, ] * Fv[2, ]) + sum(counting$Neik[2, ] * Fv[1, ]))
)
}
}
u0 <- diag(x = 1, nrow = length(theta0), ncol = length(theta0))
c0 <- rep(0, length(theta0))
u1 <- matrix(0, nrow = length(theta0), ncol = length(theta0))
skipindex <- 1
rowstoremove <- c()
for (j in 1:s) {
if (distr[j] == "dweibull") {
u1[skipindex, skipindex] <- -1
rowstoremove <- c(rowstoremove, skipindex + 1)
skipindex <- skipindex + 2
} else if (distr[j] == "geom") {
u1[skipindex, skipindex] <- -1
skipindex <- skipindex + 1
} else if (distr[j] == "nbinom") {
u1[skipindex + 1, skipindex + 1] <- -1
rowstoremove <- c(rowstoremove, skipindex)
skipindex <- skipindex + 2
} else if (distr[j] == "pois") {
rowstoremove <- c(rowstoremove, skipindex)
skipindex <- skipindex + 1
}
}
if (!is.null(rowstoremove)) {
u1 <- u1[-rowstoremove, ]
}
if (!is.null(nrow(u1))) {
c1 <- rep(-1, nrow(u1))
} else {
c1 <- c(-1)
}
if (length(u1) != 0) {
u2 <- rbind(u0, u1)
c2 <- c(c0, c1)
} else {
u2 <- u0
c2 <- c0
}
mle <-
constrOptim(
theta = theta0,
f = loglik,
ui = u2,
ci = c2,
method = "Nelder-Mead"
)
skipindex <- 1
for (j in 1:s) {
if (distr[j] %in% c("dweibull", "nbinom")) {
param[j, ] <- mle$par[skipindex:(skipindex + 1)]
skipindex <- skipindex + 2
} else if (distr[j] %in% c("geom", "pois")) {
param[j, 1] <- mle$par[skipindex]
skipindex <- skipindex + 1
} else if (distr[j] == "unif") {
param[j, ] <- .fit.param.fj.unif(counting, j, kmax)
}
}
}
} else {
if (cens.beg) {
loglik <- function(par) {
parpuv <- matrix(par[1:(s * (s - 2))], nrow = s, ncol = s - 2, byrow = T)
parpuv <- cbind(parpuv, 1 - apply(parpuv, 1, sum))
parp <- matrix(data = 0, nrow = s, ncol = s)
parp[row(parp) != col(parp)] <- t(parpuv)
parp <- t(parp)
fv <- matrix(data = 0, nrow = s, ncol = kmax)
Fv <- matrix(data = 0, nrow = s, ncol = kmax)
Fv2 <- matrix(data = 0, nrow = s, ncol = kmax)
skipindex <- s * (s - 2) + 1
for (j in 1:s) {
maskNjk <- counting$Njk[j, ] != 0
maskNbjk <- counting$Nbjk[j, ] != 0
if (distr[j] == "dweibull") {
fv[j, maskNjk] <- log(ddweibull(x = (1:kmax)[maskNjk], q = par[skipindex], beta = par[skipindex + 1], zero = FALSE))
Fv[j, maskNbjk] <- log(1 - pdweibull(x = (1:kmax)[maskNbjk], q = par[skipindex], beta = par[skipindex + 1], zero = FALSE) + .Machine$double.xmin)
Fv2[j, ] <- 1 - pdweibull(x = (1:kmax), q = par[skipindex], beta = par[skipindex + 1], zero = FALSE)
skipindex <- skipindex + 2
} else if (distr[j] == "geom") {
fv[j, maskNjk] <- dgeom(x = (0:(kmax - 1))[maskNjk], prob = par[skipindex], log = TRUE)
Fv[j, maskNbjk] <- pgeom(q = (0:(kmax - 1))[maskNbjk], prob = par[skipindex], lower.tail = FALSE, log.p = TRUE)
Fv2[j, ] <- pgeom(q = 0:(kmax - 1), prob = par[skipindex], lower.tail = FALSE)
skipindex <- skipindex + 1
} else if (distr[j] == "nbinom") {
fv[j, maskNjk] <- dnbinom(x = (0:(kmax - 1))[maskNjk], size = par[skipindex], prob = par[skipindex + 1], log = TRUE)
Fv[j, maskNbjk] <- pnbinom(q = (0:(kmax - 1))[maskNbjk], size = par[skipindex], prob = par[skipindex + 1], lower.tail = FALSE, log.p = TRUE)
Fv2[j, ] <- pnbinom(q = 0:(kmax - 1), size = par[skipindex], prob = par[skipindex + 1], lower.tail = FALSE)
skipindex <- skipindex + 2
} else if (distr[j] == "pois") {
fv[j, maskNjk] <- dpois(x = (0:(kmax - 1))[maskNjk], lambda = par[skipindex], log = TRUE)
Fv[j, maskNbjk] <- ppois(q = (0:(kmax - 1))[maskNbjk], lambda = par[skipindex], lower.tail = FALSE, log.p = TRUE)
Fv2[j, ] <- ppois(q = 0:(kmax - 1), lambda = par[skipindex], lower.tail = FALSE)
skipindex <- skipindex + 1
}
}
Fv2 <- parp %*% Fv2
Fv2[Fv2 != 0] <- log(Fv2[Fv2 != 0])
return(-(
sum(counting$Nij[row(counting$Nij) != col(counting$Nij)] * log(parp[row(parp) != col(parp)])) +
sum(counting$Njk * fv) + sum(counting$Nbjk * Fv) + sum(counting$Neik * Fv2)
))
}
} else {
loglik <- function(par) {
parpuv <- matrix(par[1:(s * (s - 2))], nrow = s, ncol = s - 2, byrow = T)
parpuv <- cbind(parpuv, 1 - apply(parpuv, 1, sum))
parp <- matrix(data = 0, nrow = s, ncol = s)
parp[row(parp) != col(parp)] <- t(parpuv)
parp <- t(parp)
fv <- matrix(data = 0, nrow = s, ncol = kmax)
Fv2 <- matrix(data = 0, nrow = s, ncol = kmax)
skipindex <- s * (s - 2) + 1
for (j in 1:s) {
maskNjk <- counting$Njk[j, ] != 0
if (distr[j] == "dweibull") {
fv[j, maskNjk] <- log(ddweibull(x = (1:kmax)[maskNjk], q = par[skipindex], beta = par[skipindex + 1], zero = FALSE))
Fv2[j, ] <- 1 - pdweibull(x = 1:kmax, q = par[skipindex], beta = par[skipindex + 1], zero = FALSE)
skipindex <- skipindex + 2
} else if (distr[j] == "geom") {
fv[j, maskNjk] <- dgeom(x = (0:(kmax - 1))[maskNjk], prob = par[skipindex], log = TRUE)
Fv2[j, ] <- pgeom(q = 0:(kmax - 1), prob = par[skipindex], lower.tail = FALSE)
skipindex <- skipindex + 1
} else if (distr[j] == "nbinom") {
fv[j, maskNjk] <- dnbinom(x = (0:(kmax - 1))[maskNjk], size = par[skipindex], prob = par[skipindex + 1], log = TRUE)
Fv2[j, ] <- pnbinom(q = 0:(kmax - 1), size = par[skipindex], prob = par[skipindex + 1], lower.tail = FALSE)
skipindex <- skipindex + 2
} else if (distr[j] == "pois") {
fv[j, maskNjk] <- dpois(x = (0:(kmax - 1))[maskNjk], lambda = par[skipindex], log = TRUE)
Fv2[j, ] <- ppois(q = 0:(kmax - 1), lambda = par[skipindex], lower.tail = FALSE)
skipindex <- skipindex + 1
}
}
Fv2 <- parp %*% Fv2
Fv2[Fv2 != 0] <- log(Fv2[Fv2 != 0])
return(-(
sum(counting$Nij[row(counting$Nij) != col(counting$Nij)] * log(parp[row(parp) != col(parp)])) +
sum(counting$Njk * fv) + sum(counting$Neik * Fv2)
))
}
}
u0 <- diag(x = 1, nrow = s * (s - 2) + length(theta0), ncol = s * (s - 2) + length(theta0))
c0 <- rep(0, s * (s - 2) + length(theta0))
u1 <- matrix(0, nrow = s * (s - 2), ncol = s * (s - 2) + length(theta0))
diag(u1) <- -1
c1 <- rep(-1, s * (s - 2))
u2 <- matrix(0, nrow = s, ncol = s * (s - 2) + length(theta0))
u2[1, 1:(s - 2)] <- -1
for (l in 1:(s - 1)) {
u2[l + 1, (l * (s - 2) + 1):(l * (s - 2) + (s - 2))] <- -1
}
c2 <- rep(-1, s)
u3 <- matrix(0, nrow = length(theta0), ncol = s * (s - 2) + length(theta0))
skipindex <- 1
rowstoremove <- c()
for (j in 1:s) {
if (distr[j] == "dweibull") {
u3[skipindex, skipindex + s * (s - 2)] <- -1
rowstoremove <- c(rowstoremove, skipindex + 1)
skipindex <- skipindex + 2
} else if (distr[j] == "geom") {
u3[skipindex, skipindex + s * (s - 2)] <- -1
skipindex <- skipindex + 1
} else if (distr[j] == "nbinom") {
u3[skipindex + 1, skipindex + 1 + s * (s - 2)] <- -1
rowstoremove <- c(rowstoremove, skipindex)
skipindex <- skipindex + 2
} else if (distr[j] == "pois") {
rowstoremove <- c(rowstoremove, skipindex)
skipindex <- skipindex + 1
}
}
if (!is.null(rowstoremove)) {
u3 <- u3[-rowstoremove, ]
}
if (!is.null(nrow(u3))) {
c3 <- rep(-1, nrow(u3))
} else {
c3 <- c(-1)
}
if (length(u3) != 0) {
u4 <- rbind(u0, u1, u2, u3)
c4 <- c(c0, c1, c2, c3)
} else {
u4 <- rbind(u0, u1, u2)
c4 <- c(c0, c1, c2)
}
mle <-
constrOptim(
theta = c(rep(1 / (s - 1), s * (s - 2)), theta0),
f = loglik,
ui = u4,
ci = c4,
method = "Nelder-Mead"
)
parpuv <- matrix(mle$par[1:(s * (s - 2))], nrow = s, ncol = s - 2, byrow = T)
parpuv <- cbind(parpuv, 1 - apply(parpuv, 1, sum))
ptrans <- matrix(data = 0, nrow = s, ncol = s)
ptrans[row(ptrans) != col(ptrans)] <- t(parpuv)
ptrans <- t(ptrans)
param <- matrix(data = NA, nrow = s, ncol = 2)
skipindex <- s * (s - 2) + 1
for (j in 1:s) {
if (distr[j] %in% c("dweibull", "nbinom")) {
param[j, ] <- mle$par[skipindex:(skipindex + 1)]
skipindex <- skipindex + 2
} else if (distr[j] %in% c("geom", "pois")) {
param[j, 1] <- mle$par[skipindex]
skipindex <- skipindex + 1
} else if (distr[j] == "unif") {
param[j, ] <- .fit.param.fj.unif(counting, j, kmax)
}
}
}
return(list(ptrans = ptrans, param = param))
}
|
as.edgelist <- function(x, ...){
UseMethod("as.edgelist")
}
as.edgelist.rankings <- function(x, ...){
x <- unclass(x)
maxRank <- max(x)
nm <- colnames(x)
res <- list()
for (i in seq_len(maxRank)){
res[[i]] <- list()
for(j in seq_len(nrow(x))){
if (!is.null(nm)) {
res[[i]][[j]] <- nm[which(x[j, ] == i)]
} else res[[i]][[j]] <- which(x[j, ] == i)
}
}
res <- unlist(res, recursive = FALSE)
rep <- lengths(res)
n <- nrow(x)
m <- length(res)
cbind(from = unlist(rep(res[seq(m - n)], rep[-seq(n)])),
to = unlist(rep(res[-seq(n)], rep[seq(m - n)])))
}
|
library("httr")
world <- "https://raw.githubusercontent.com/johan/world.geo.json/master/countries.geo.json" %>%
GET() %>%
content() %>%
jsonlite::fromJSON(simplifyVector = FALSE)
marine <- "http://cedeusdata.geosteiniger.cl/geoserver/wfs?srsName=EPSG%3A4326&typename=geonode%3Amundo_corrientes_maritimas&outputFormat=json&version=1.0.0&service=WFS&request=GetFeature" %>%
GET() %>%
content()
plates <- "http://cedeusdata.geosteiniger.cl/geoserver/wfs?srsName=EPSG%3A4326&typename=geonode%3Amundo_limites_placas&outputFormat=json&version=1.0.0&service=WFS&request=GetFeature" %>%
GET() %>%
content()
volcano <- "http://cedeusdata.geosteiniger.cl/geoserver/wfs?srsName=EPSG%3A4326&typename=geonode%3Amundo_volcanes&outputFormat=json&version=1.0.0&service=WFS&request=GetFeature" %>%
GET() %>%
content()
highchart(type = "map") %>%
hc_title(text = "Marine Currents, Plates & Volcanos") %>%
hc_chart(backgroundColor = "
hc_add_series(mapData = world, showInLegend = FALSE,
nullColor = "
hc_add_series(data = marine, type = "mapline", lineWidth = 2,
name = "Marine currents", color = 'rgba(0, 0, 80, 0.33)',
states = list(hover = list(color = "
tooltip = list(pointFormat = "{point.properties.NOMBRE}")) %>%
hc_add_series(data = plates, type = "mapline",
name = "Plates", color = 'rgba(5, 5, 5, 0.5)',
tooltip = list(pointFormat = "{point.properties.TIPO}")) %>%
hc_add_series(data = volcano, type = "mappoint",
name = "Volcanos", color = 'rgba(200, 10, 80, 0.5)',
tooltip = list(pointFormat = "{point.properties.NOMBRE}")) %>%
hc_mapNavigation(enabled = TRUE)
|
print.rocTree <- function(x, digits = 5, tree = NULL, ...) {
if (!is.rocTree(x)) stop("Response must be a \"rocTree\" object.")
if (!x$ensemble) {
printTree(x$Frame, x$vNames, digits)
} else {
if (!is.null(tree)) {
if (!is.wholenumber(tree)) stop("Tree number must be an integer.")
if (tree > length(x$trees)) stop("Tree number exceeded the number of trees in forest.")
printTree(x$Frame[[tree]], vNames = x$vNames, digits = digits)
} else {
cat("ROC-guided ensembles\n\n")
cat("Call:\n", deparse(x$call), "\n\n")
cat("Sample size: ", ncol(x$xlist[[1]]), "\n")
cat("Number of independent variables: ", length(unique(x$data$.id)),"\n")
cat("Number of trees: ", x$control$numTree, "\n")
cat("Split rule: ", x$splitBy, "\n")
cat("Number of variables tried at each split: ", x$control$mtry, "\n")
cat("Number of time points to evaluate CON: ", x$control$K, "\n")
cat("Min. number of baseline obs. in a splittable node: ", x$control$minSplitNode, "\n")
cat("Min. number of baseline obs. in a terminal node: ", x$control$minSplitTerm, "\n")
}
}
}
tree.split.names <- function(nd0, nd, p, cut, xname, digits = getOption("digits")) {
if (nd0 == 1) return("root")
ind <- which(nd == nd0 %/% 2)
if (nd0 %% 2 == 0) {
return(paste(xname[p[ind]], "<=", formatC(cut[ind], digits = digits, flag = "
} else {
return(paste(xname[p[ind]], ">", formatC(cut[ind], digits = digits, flag = "
}
}
printTree <- function(Frame, vNames, digits) {
root <- Node$new("Root", type = "root", decision = "", nd = 1)
if (nrow(Frame) > 1) {
for (i in 2:nrow(Frame)) {
if (i <= 3) parent <- "root"
if (i > 3) parent <- paste0("Node", Frame$nd[i] %/% 2)
if (Frame$is.terminal[i] > 0) {
type <- "terminal"
display <- with(Frame, paste0(nd[i], ") ", tree.split.names(nd[i], nd, p, cutVal, vNames, digits), "*"))
} else {
type <- "interior"
display <- with(Frame, paste0(nd[i], ") ", tree.split.names(nd[i], nd, p, cutVal, vNames, digits)))
}
eval(parse(text = paste0("Node", Frame$nd[i], "<-", parent,
"$AddChild(display, type = type, nd = Frame$nd[i])")))
}
}
toPrint <- ToDataFrameTree(root)[[1]]
cat(" ROC-guided survival tree\n")
if (nrow(Frame) > 1) {
cat("\n")
cat(" node), split\n")
cat(" * denotes terminal node\n")
cat(" ", toPrint, sep = "\n")
} else {
cat(" Decision tree found no splits.")
}
cat("\n")
}
print.predict.rocTree <- function(x, ...) {
if (!is.predict.rocTree(x)) stop("Response must be a 'predict.rocTree' object")
if (names(x$pred)[[2]] == "Survival") {
cat(" Fitted survival probabilities:\n")
}
if (names(x$pred)[[2]] == "hazard") {
cat(" Fitted cumulative hazard:\n")
}
print(head(x$pred, 5))
cat("\n")
}
|
test_that("normal print method works", {
x <- as_sys_time(year_month_day(2019, 1:5, 1))
expect_snapshot(x)
})
test_that("can limit with `max`", {
x <- as_sys_time(year_month_day(2019, 1:5, 1))
expect_snapshot(print(x, max = 2))
expect_snapshot(print(x, max = 4))
expect_snapshot(print(x, max = 5))
expect_snapshot(print(x, max = 6))
})
test_that("`max` defaults to `getOption('max.print')` but can be overridden", {
local_options(max.print = 3)
x <- as_naive_time(year_month_day(2019, 1:5, 1))
expect_snapshot(x)
expect_snapshot(print(x, max = 4))
expect_snapshot(print(x, max = 5))
})
test_that("can round to less precise precision", {
x <- naive_seconds(c(-86401, -86400, -86399, 0, 86399, 86400, 86401))
floor <- naive_days(c(-2, -1, -1, 0, 0, 1, 1))
ceiling <- naive_days(c(-1, -1, 0, 0, 1, 1, 2))
round <- naive_days(c(-1, -1, -1, 0, 1, 1, 1))
expect_identical(time_point_floor(x, "day"), floor)
expect_identical(time_point_ceiling(x, "day"), ceiling)
expect_identical(time_point_round(x, "day"), round)
floor <- naive_days(c(-2, -2, -2, 0, 0, 0, 0))
ceiling <- naive_days(c(0, 0, 0, 0, 2, 2, 2))
round <- naive_days(c(-2, 0, 0, 0, 0, 2, 2))
expect_identical(time_point_floor(x, "day", n = 2), floor)
expect_identical(time_point_ceiling(x, "day", n = 2), ceiling)
expect_identical(time_point_round(x, "day", n = 2), round)
})
test_that("can round with `origin` altering starting point", {
x <- sys_seconds(c(-86401, -86400, -86399, 0, 86399, 86400, 86401))
origin <- sys_days(-1)
floor <- sys_days(c(-3, -1, -1, -1, -1, 1, 1))
ceiling <- sys_days(c(-1, -1, 1, 1, 1, 1, 3))
round <- sys_days(c(-1, -1, -1, 1, 1, 1, 1))
expect_identical(time_point_floor(x, "day", origin = origin, n = 2), floor)
expect_identical(time_point_ceiling(x, "day", origin = origin, n = 2), ceiling)
expect_identical(time_point_round(x, "day", origin = origin, n = 2), round)
})
test_that("cannot floor to more precise precision", {
expect_snapshot_error(time_point_floor(naive_days(), "second"))
})
test_that("rounding with `origin` requires same clock", {
origin <- sys_days(0)
x <- naive_days(0)
expect_snapshot_error(time_point_floor(x, "day", origin = origin))
})
test_that("`origin` can be cast to a more precise `precision`, but not to a less precise one", {
origin1 <- as_naive_time(duration_days(1))
origin2 <- as_naive_time(duration_milliseconds(0))
x <- naive_seconds(0)
expect_identical(
time_point_floor(x, "hour", origin = origin1, n = 5),
time_point_floor(x - as_duration(origin1), "hour", n = 5) + as_duration(origin1)
)
expect_snapshot_error(time_point_floor(x, "hour", origin = origin2))
})
test_that("`origin` must be size 1", {
origin <- naive_days(0:1)
x <- naive_days(0)
expect_snapshot_error(time_point_floor(x, "day", origin = origin))
})
test_that("`origin` must not be `NA`", {
origin <- naive_days(NA)
x <- naive_days(0)
expect_snapshot_error(time_point_floor(x, "day", origin = origin))
})
test_that("`origin` can't be Date or POSIXt", {
origin1 <- new_date(0)
origin2 <- new_datetime(0, "America/New_York")
x <- naive_days(0)
expect_snapshot_error(time_point_floor(x, "day", origin = origin1))
expect_snapshot_error(time_point_floor(x, "day", origin = origin2))
})
test_that("can shift to next weekday", {
expect_identical(
time_point_shift(
naive_days(0:1),
weekday(clock_weekdays$sunday)
),
naive_days(c(3, 3))
)
})
test_that("can shift to next if on the boundary", {
naive_sunday <- naive_days(3)
sunday <- weekday(clock_weekdays$sunday)
expect_identical(
time_point_shift(naive_sunday, sunday),
naive_sunday
)
expect_identical(
time_point_shift(naive_sunday, sunday, boundary = "advance"),
naive_sunday + 7
)
})
test_that("can shift to previous weekday", {
expect_identical(
time_point_shift(
naive_days(0:1),
weekday(clock_weekdays$sunday),
which = "previous"
),
naive_days(c(-4, -4))
)
})
test_that("can shift to previous weekday if on boundary", {
naive_sunday <- naive_days(3)
sunday <- weekday(clock_weekdays$sunday)
expect_identical(
time_point_shift(naive_sunday, sunday, which = "previous"),
naive_sunday
)
expect_identical(
time_point_shift(naive_sunday, sunday, which = "previous", boundary = "advance"),
naive_sunday - 7
)
})
test_that("`target` is recycled to size of `x`", {
expect_identical(
time_point_shift(
sys_days(0:1),
weekday(1:2)
),
sys_days(3:4)
)
expect_snapshot_error(time_point_shift(sys_days(0), weekday(1:2)))
})
test_that("`x` is validated", {
expect_snapshot_error(time_point_shift(1))
})
test_that("`target` is validated", {
expect_snapshot_error(time_point_shift(sys_days(0), 1))
})
test_that("`which` is validated", {
expect_snapshot_error(time_point_shift(sys_days(), weekday(), which = 1))
expect_snapshot_error(time_point_shift(sys_days(), weekday(), which = "foo"))
expect_snapshot_error(time_point_shift(sys_days(), weekday(), which = c("next", "previous")))
})
test_that("`boundary` is validated", {
expect_snapshot_error(time_point_shift(sys_days(), weekday(), boundary = 1))
expect_snapshot_error(time_point_shift(sys_days(), weekday(), boundary = "foo"))
expect_snapshot_error(time_point_shift(sys_days(), weekday(), boundary = c("keep", "advance")))
})
test_that("can count units between", {
x <- as_naive_time(year_month_day(1990, 02, 03, 04))
y <- as_naive_time(year_month_day(1995, 04, 05, 03))
expect_identical(time_point_count_between(x, y, "day"), 1886L)
expect_identical(time_point_count_between(x, y, "hour"), 45287L)
})
test_that("'week' is an allowed precision", {
x <- sys_days(0)
y <- sys_days(13:15)
expect_identical(time_point_count_between(x, y, "week"), c(1L, 2L, 2L))
})
test_that("`n` affects the result", {
x <- sys_days(0)
y <- sys_days(10)
expect_identical(time_point_count_between(x, y, "day", n = 2L), 5L)
expect_identical(time_point_count_between(x, y, "day", n = 3L), 3L)
})
test_that("negative vs positive differences are handled correctly", {
one_hour <- duration_hours(1)
x <- sys_days(0)
y <- sys_days(1)
z <- sys_days(-1)
expect_identical(time_point_count_between(x, y - one_hour, "day"), 0L)
expect_identical(time_point_count_between(x, y, "day"), 1L)
expect_identical(time_point_count_between(x, y + one_hour, "day"), 1L)
expect_identical(time_point_count_between(x, z - one_hour, "day"), -1L)
expect_identical(time_point_count_between(x, z, "day"), -1L)
expect_identical(time_point_count_between(x, z + one_hour, "day"), 0L)
})
test_that("common precision of inputs and `precision` is taken", {
expect_identical(
time_point_count_between(sys_days(0), sys_days(2) + duration_hours(1), "second"),
176400L
)
expect_identical(
time_point_count_between(sys_seconds(0), sys_seconds(86401), "day"),
1L
)
})
test_that("OOB results return a warning and NA", {
expect_snapshot({
out <- time_point_count_between(sys_days(0), sys_days(1000), "nanosecond")
})
expect_identical(out, NA_integer_)
})
test_that("both inputs must be time points", {
expect_snapshot({
(expect_error(time_point_count_between(sys_days(1), 1)))
(expect_error(time_point_count_between(1, sys_days(1))))
})
})
test_that("both inputs must be compatible", {
x <- sys_days(1)
y <- naive_days(1)
expect_snapshot((expect_error(
time_point_count_between(x, y)
)))
})
test_that("`n` is validated", {
x <- sys_days(1)
expect_snapshot({
(expect_error(time_point_count_between(x, x, "day", n = NA_integer_)))
(expect_error(time_point_count_between(x, x, "day", n = -1)))
(expect_error(time_point_count_between(x, x, "day", n = 1.5)))
(expect_error(time_point_count_between(x, x, "day", n = "x")))
(expect_error(time_point_count_between(x, x, "day", n = c(1L, 2L))))
})
})
test_that("`precision` must be a time point precision", {
x <- sys_days(1)
expect_snapshot((expect_error(
time_point_count_between(x, x, "year")
)))
})
test_that("seq(to, by) works", {
expect_identical(seq(sys_days(0L), to = sys_days(4L), by = 2), sys_days(c(0L, 2L, 4L)))
expect_identical(seq(sys_days(0L), to = sys_days(5L), by = 2), sys_days(c(0L, 2L, 4L)))
expect_identical(seq(sys_seconds(0L), to = sys_seconds(-4L), by = -2), sys_seconds(c(0L, -2L, -4L)))
expect_identical(seq(sys_seconds(0L), to = sys_seconds(-5L), by = -2), sys_seconds(c(0L, -2L, -4L)))
})
test_that("seq(to, length.out) works", {
expect_identical(seq(naive_days(0L), to = naive_days(4L), length.out = 2), naive_days(c(0L, 4L)))
expect_identical(seq(naive_days(0L), to = naive_days(4L), length.out = 1), naive_days(c(0L)))
expect_identical(seq(naive_days(0L), to = naive_days(4L), length.out = 5), naive_days(c(0:4)))
expect_identical(seq(naive_seconds(0L), to = naive_seconds(4L), along.with = 1:2), naive_seconds(c(0L, 4L)))
})
test_that("seq(by, length.out) works", {
expect_identical(seq(naive_seconds(0L), by = 2, length.out = 3), naive_seconds(c(0L, 2L, 4L)))
expect_identical(seq(naive_seconds(0L), by = -2, length.out = 3), naive_seconds(c(0L, -2L, -4L)))
expect_identical(seq(naive_seconds(0L), by = 2, along.with = 1:3), naive_seconds(c(0L, 2L, 4L)))
})
test_that("`by` can be a duration", {
expect_identical(
seq(naive_seconds(0), to = naive_seconds(1000), by = duration_minutes(1)),
seq(naive_seconds(0), to = naive_seconds(1000), by = 60)
)
expect_identical(
seq(as_naive_time(duration_nanoseconds(0)), to = as_naive_time(duration_nanoseconds(2e9)), by = duration_seconds(1)),
seq(as_naive_time(duration_nanoseconds(0)), to = as_naive_time(duration_nanoseconds(2e9)), by = 1e9)
)
})
test_that("can't mix chronological time points and calendrical durations", {
expect_snapshot_error(seq(naive_seconds(0), by = duration_years(1), length.out = 2))
})
test_that("can't mix clocks in seq()", {
expect_snapshot_error(seq(sys_seconds(0), to = naive_seconds(5), by = 1))
})
test_that("`to` is always cast to `from`", {
expect_identical(
seq(naive_seconds(0), to = naive_days(12), by = duration_days(2)),
seq(naive_seconds(0), to = naive_seconds(12 * 86400), by = 86400 * 2)
)
expect_snapshot_error(seq(naive_days(0), to = naive_seconds(5), by = 2))
})
test_that("can make nanosecond precision seqs", {
x <- as_naive_time(duration_nanoseconds(0))
y <- as_naive_time(duration_nanoseconds(10))
expect_identical(seq(x, by = 2, length.out = 5), x + c(0, 2, 4, 6, 8))
expect_identical(seq(x, y, by = 3), x + c(0, 3, 6, 9))
})
test_that("duration to add to a time-point must have at least week precision (
expect_snapshot_error(naive_seconds(0) + duration_years(1))
})
test_that("precision: can get the precision", {
expect_identical(time_point_precision(as_naive_time(duration_days(2:5))), "day")
expect_identical(time_point_precision(as_naive_time(duration_nanoseconds(2:5))), "nanosecond")
})
test_that("precision: can only be called on time points", {
expect_snapshot_error(time_point_precision(duration_days()))
})
|
renv_ci_dependencies <- function() {
ensure_directory("ci")
saveRDS(R.version, file = "ci/version.rds", version = 2L)
records <- renv_project_remotes(project = getwd())
packages <- extract_chr(records, "Package")
revdeps <- tools::package_dependencies(packages, recursive = TRUE)
all <- sort(unique(unlist(revdeps)))
db <- as.data.frame(
available.packages(filters = c("OS_type", "duplicates")),
stringsAsFactors = FALSE
)
resolved <- db[db$Package %in% all, c("Package", "Version")]
rownames(resolved) <- NULL
envvar <- case(
renv_platform_linux() ~ "RENV_CI_CACHE_VERSION_LINUX",
renv_platform_macos() ~ "RENV_CI_CACHE_VERSION_MACOS",
renv_platform_windows() ~ "RENV_CI_CACHE_VERSION_WINDOWS"
)
version <- Sys.getenv(envvar, unset = NA)
if (!is.na(version))
attr(resolved, "cache") <- version
print(resolved)
saveRDS(resolved, file = "ci/dependencies.rds", version = 2L)
}
renv_ci_repair <- function() {
library <- renv_libpaths_default()
db <- renv_installed_packages(lib.loc = library)
packages <- db$Package
names(packages) <- packages
ok <- map_lgl(packages, requireNamespace, quietly = TRUE)
broken <- packages[!ok]
if (empty(broken)) {
vwritef("* All installed packages can be successfully loaded.")
return(broken)
}
renv_pretty_print(
values = paste("-", packages),
preamble = "The following package(s) could not be successfully loaded:",
postamble = "These packages will be removed and later reinstalled.",
wrap = FALSE
)
paths <- file.path(library, broken)
unlink(paths, recursive = TRUE)
return(broken)
}
|
expected <- c(1L, 2L, 3L, 0L, 1L, 4L, 5L)
test(id=1, code={
argv <- structure(list(x = 1:5, values = 0:1, after = 3), .Names = c("x",
"values", "after"))
do.call('append', argv);
}, o = expected);
|
Wald1 <-
function(y,k)
{
y <- as.matrix(y)
n <- nrow(y)
m <- mean(y)
vr1 <- sum( (y-m)^2 )/n
flt = filter(y, rep(1,k), method = "convolution")
flt = flt[!is.na(flt)]
summ = sum((flt - k * m)^2)
vr2 <- summ/(n*k)
vr <- vr2/vr1 -1
return(vr)
}
|
SUMIF <-
function(range,criteria, sum_range) {
if(is.na(as.numeric(criteria)) == FALSE){
c1 <- "=="
} else if (str_detect(criteria,"^>") == TRUE){
c1 <- ">"
criteria <- extract_numeric(criteria)
} else if (str_detect(criteria,"^<") == TRUE){
c1 <- "<"
criteria <- extract_numeric(criteria)
} else if (str_detect(criteria,"^>=")){
c1 <- ">="
criteria <- extract_numeric(criteria)
} else if (str_detect(criteria,"^<=")){
c1 <- "<="
criteria <- extract_numeric(criteria)
} else if (is.character(criteria) == TRUE){
c1 <- "=="
}
ret <- sum(sum_range[get(c1)(range,criteria)])
ret
}
|
simulate_clustered_phylogeny<-function(v_sizeclusts,joining_branchlengths=NULL,f_simclustphyl="sim.bd.taxa_Yule1",joiningphyl=NULL,b_change_joining_branches=FALSE,...){
if (is.null(v_sizeclusts)){stop("Size clusters not provided!")}
numclusts<-length(v_sizeclusts)
if ((inherits(joiningphyl,"phylo"))&&(numclusts!=(joiningphyl$Nnode+1))){stop("Number of tips of connecting phylogeny does not equal the number of clusters.")}
if (numclusts==1){
if ((is.character(f_simclustphyl))&&(f_simclustphyl=="sim.bd.taxa_Yule1")){
p1<-TreeSim::sim.bd.taxa(n=v_sizeclusts[1],lambda=1,mu=0,numbsim=1)[[1]]
p_joined<-p1;p_joined$root.edge<-joining_branchlengths[1];return(p_joined)
}
else if(is.function(f_simclustphyl)){
p1<-f_simclustphyl(n=v_sizeclusts[1],...)
if (!inherits(p1,"phylo")){
stop("The simulation function passed through f_simclustphyl does not return a phylo object!")
}
p_joined<-p1;p_joined$root.edge<-joining_branchlengths[1];return(p_joined)
}else{stop("Cannot simulate the phylogeny with the provided f_simclustphyl.")}
}
if (!is.null(joining_branchlengths)){
internal_joining_branchlength<-NA
if (length(joining_branchlengths)==2){
internal_joining_branchlength<-joining_branchlengths[[2]]
joining_branchlengths<-joining_branchlengths[[1]]
}else{
if (length(joining_branchlengths)==1){internal_joining_branchlength<-joining_branchlengths}
else{stop("At the moment joining_branchlengths cannot have length more than 2!")}
}
}
if (is.null(joiningphyl)){
if (is.null(joining_branchlengths)){stop("If joining phylogeny is not provided, joining_branchlengths is required!")}
joiningphyl<-list(Nnode=numclusts-1,edge=matrix(NA,nrow=2*numclusts-2,ncol=2),edge.length=rep(NA,2*numclusts-2),tip.label=paste(paste0("t",1:numclusts)))
if (numclusts>2){
joiningphyl$edge[1,]<-c(numclusts+2,1)
joiningphyl$edge.length[1]<-joining_branchlengths
curredge<-2
currintnode<-numclusts+2
for (i in 2:(numclusts-1)){
joiningphyl$edge[curredge,]<-c(currintnode,i)
joiningphyl$edge.length[curredge]<-joining_branchlengths
joiningphyl$edge[(curredge+1),]<-c(currintnode+1,currintnode)
if (is.na(internal_joining_branchlength)){
stop("The second element of joining_branchlengths cannot be NA if the joining phylogeny is NOT provided.")
}
joiningphyl$edge.length[curredge+1]<-internal_joining_branchlength
curredge<-curredge+2
currintnode<-currintnode+1
}
joiningphyl$edge[curredge,]<-c(numclusts+1,numclusts)
joiningphyl$edge[which(joiningphyl$edge==(2*numclusts))]<-numclusts+1
joiningphyl$edge.length[curredge]<-joining_branchlengths
}else{
joiningphyl$edge<-rbind(c(3,1),c(3,2))
joiningphyl$edge.length<-rep(joining_branchlengths,2)
}
class(joiningphyl)<-"phylo"
}else{
if (!inherits(joiningphyl,"phylo")){
if((inherits(joiningphyl,"character")) && (joiningphyl=="sim.bd.taxa_Yule1")){
joiningphyl<-TreeSim::sim.bd.taxa(n=numclusts,lambda=1,mu=0,numbsim=1)[[1]]
}
else if(is.function(joiningphyl)){
joiningphyl<-joiningphyl(n=numclusts,...)
if (!inherits(joiningphyl,"phylo")){
stop("The simulation function passed through joiningphyl does not return a phylo object!")
}
}else{stop("Cannot simulate the phylogeny with the provided joiningphyl.")}
}
joiningphyl$root.edge<-NULL
if (b_change_joining_branches){
if ((!is.null(joining_branchlengths))&&(is.numeric(joining_branchlengths))){
v_tip_branches<-which(joiningphyl$edge[,2]<(numclusts+1))
joiningphyl$edge.length[v_tip_branches]<-joining_branchlengths[[1]]
if (!is.na(internal_joining_branchlength)){
v_non_tip_branches<-setdiff(1:length(joiningphyl$edge.length),v_tip_branches)
if (length(v_non_tip_branches)>0){
joiningphyl$edge.length[v_non_tip_branches]<-internal_joining_branchlength
}
}
}else{message("Cannot change branches leading to clusters as joining_branchlengths not provided")}
}
}
numtipscurr<-0
p_joined<-joiningphyl
numalltips<-sum(v_sizeclusts)
p_joined$edge[which(p_joined$edge>(numclusts+1))]<-p_joined$edge[which(p_joined$edge>(numclusts+1))]+numalltips
p_joined$edge[which(p_joined$edge==(numclusts+1))]<-numalltips+1
p_joined$edge[which(p_joined$edge<=numclusts)]<-p_joined$edge[which(p_joined$edge<=numclusts)]+numalltips+1
labelfirstnodeinsubtree<-numalltips+2*numclusts
p_joined$edges_clusters<-vector("list",numclusts+1)
p_joined$tips_clusters<-vector("list",numclusts)
names(p_joined$edges_clusters)<-c("joining_tree",paste0("cluster_",1:numclusts))
names(p_joined$tips_clusters)<-paste0("cluster_",1:numclusts)
p_joined$edges_clusters$joining_tree<-1:nrow(p_joined$edge)
for (i in 1:numclusts){
if ((is.character(f_simclustphyl))&&(f_simclustphyl=="sim.bd.taxa_Yule1")){
pnext<-TreeSim::sim.bd.taxa(n=v_sizeclusts[i],lambda=1,mu=0,numbsim=1)[[1]]
}
else if(is.function(f_simclustphyl)){
pnext<-f_simclustphyl(n=v_sizeclusts[i],...)
if (!inherits(pnext,"phylo")){
stop("The simulation function passed through f_simclustphyl does not return a phylo object!")
}
}else{stop("Cannot simulate the phylogeny with the provided f_simclustphyl.")}
pnext$root.edge<-NULL
intnodes_torelabel<-which(pnext$edge>(v_sizeclusts[i]+1))
if (length(intnodes_torelabel)>0){
pnext$edge[intnodes_torelabel]<-pnext$edge[intnodes_torelabel]+labelfirstnodeinsubtree-(v_sizeclusts[i]+2)
labelfirstnodeinsubtree<-labelfirstnodeinsubtree+v_sizeclusts[i]-2
}
pnext$edge[which(pnext$edge==(v_sizeclusts[i]+1))]<-numalltips+i+1
p_joined$tips_clusters[[i]]<-sort(unique(pnext$edge[which(pnext$edge<=v_sizeclusts[i])]))+numtipscurr
pnext$edge[which(pnext$edge<=v_sizeclusts[i])]<-pnext$edge[which(pnext$edge<=v_sizeclusts[i])]+numtipscurr
numtipscurr<-numtipscurr+v_sizeclusts[i]
start_edge_cluster<-nrow(p_joined$edge)+1
p_joined$edge<-rbind(p_joined$edge,pnext$edge)
end_edge_cluster<-nrow(p_joined$edge)
p_joined$edges_clusters[[i+1]]<-start_edge_cluster:end_edge_cluster
p_joined$edge.length<-c(p_joined$edge.length,pnext$edge.length)
}
p_joined$Nnode<-numalltips-1
p_joined$tip.label<-paste0("t",1:numalltips)
class(p_joined)<-c("clustered_phylo","phylo")
p_joined
}
plot.clustered_phylo<-function(x,clust_cols=NULL,clust_edge.width=NULL,clust_edge.lty=NULL,clust_tip.color="black",joiningphylo_col="black",joiningphylo_edge.width=1,joiningphylo_edge.lty=1,...){
if (is.null(joiningphylo_col)){joiningphylo_col<-"black"}
vedgecol<-rep(joiningphylo_col,length(x$edge.length))
if (!is.null(clust_cols)){
if (length(clust_cols)!=(length(x$edges_clusters)-1)){
opt_warn<-options("warn")
options(warn=-1)
new_clust_cols<-rep(NA,length(x$edges_clusters)-1)
new_clust_cols[]<-clust_cols
clust_cols<-new_clust_cols
options(warn=opt_warn$warn)
}
for (i in 2:length(x$edges_clusters)){
vedgecol[x$edges_clusters[[i]]]<-clust_cols[i-1]
}
}
if (is.null(joiningphylo_edge.width)){joiningphylo_edge.width<-1}
vedgewidth<-rep(joiningphylo_edge.width,length(x$edge.length))
if (!is.null(clust_edge.width)){
if (length(clust_edge.width)!=(length(x$edges_clusters)-1)){
opt_warn<-options("warn")
options(warn=-1)
new_clust_edge.width<-rep(NA,length(x$edges_clusters)-1)
new_clust_edge.width[]<-clust_edge.width
clust_edge.width<-new_clust_edge.width
options(warn=opt_warn$warn)
}
for (i in 2:length(x$edges_clusters)){
vedgewidth[x$edges_clusters[[i]]]<-clust_edge.width[i-1]
}
}
if (is.null(joiningphylo_edge.lty)){joiningphylo_edge.lty<-1}
vedgelty<-rep(joiningphylo_edge.lty,length(x$edge.length))
if (!is.null(clust_edge.lty)){
if (length(clust_edge.lty)!=(length(x$edges_clusters)-1)){
opt_warn<-options("warn")
options(warn=-1)
new_clust_edge.lty<-rep(NA,length(x$edges_clusters)-1)
new_clust_edge.lty[]<-clust_edge.lty
clust_edge.lty<-new_clust_edge.lty
options(warn=opt_warn$warn)
}
for (i in 2:length(x$edges_clusters)){
vedgelty[x$edges_clusters[[i]]]<-clust_edge.lty[i-1]
}
}
if (is.null(clust_tip.color)){clust_tip.color<-"black"}
if (length(clust_tip.color)!=length(x$tips_clusters)){
opt_warn<-options("warn")
options(warn=-1)
new_clust_tip.color<-rep(NA,length(x$tips_clusters))
new_clust_tip.color[]<-clust_tip.color
clust_tip.color<-new_clust_tip.color
options(warn=opt_warn$warn)
}
vtipcolor<-rep(NA,length(x$tip.label))
for (i in 1:length(x$tips_clusters)){
vtipcolor[x$tips_clusters[[i]]]<-clust_tip.color[i]
}
class(x)<-"phylo"
ape::plot.phylo(x,edge.color=vedgecol,edge.width=vedgewidth,edge.lty=vedgelty,tip.color=vtipcolor,...)
}
|
WriteMCL <- function(mtrx, filename) {
write.csv(mtrx, filename, quote = F, row.names = F)
wd <- getwd()
file <- paste(wd, filename, sep = "/")
cat("wrote matrix to", file, "\n")
}
|
iBMA.bicreg<- function(x, ...)
UseMethod("iBMA.bicreg")
iBMA.bicreg.data.frame<- function(x, Y, wt = rep(1, nrow(X)), thresProbne0 = 5, maxNvar = 30, nIter=100, verbose = FALSE, sorted = FALSE, ...)
{
printCGen<- function(printYN)
{
printYN<- printYN
return(function(x) if (printYN) cat(paste(paste(x,sep="", collapse = " "),"\n", sep="")))
}
sortX<- function(Y, X, wt)
{
r2vec<- rep(NA, times = ncol(X))
for (i in 1:ncol(X))
r2vec[i]<- summary(lm(Y~X[,i], weights = wt))$r.squared
initial.order<- order(abs(r2vec),decreasing = TRUE)
sortedX<- X[, initial.order]
return(list(sortedX = sortedX, initial.order = initial.order))
}
cl <- match.call()
printC<- printCGen(verbose)
X<- x
if (!sorted)
{
printC("sorting X")
sorted<- sortX(Y,X, wt = wt)
sortedX<- sorted$sortedX
initial.order<- sorted$initial.order
}
else
{
sortedX<- X
initial.order<- 1:ncol(sortedX)
}
nVar<- ncol(sortedX)
maxNvar <- min (maxNvar, nVar)
stopVar <- 0
nextVar <- maxNvar + 1
current.probne0<- rep(0, maxNvar)
maxProbne0<- rep(0, times = nVar)
nTimes<- rep(0, times = nVar)
currIter <- 0
first.in.model<- rep(NA, times = nVar)
new.vars<- 1:maxNvar
first.in.model[new.vars]<- currIter + 1
iter.dropped<- rep(NA, times = nVar)
currentSet<- NULL
current_state<- list(Y = Y,
sortedX = sortedX,
wt = wt,
call = cl,
initial.order = initial.order,
thresProbne0 = thresProbne0,
maxNvar = maxNvar,
nIter = nIter,
verbose = verbose,
nVar = nVar,
currentSet = currentSet,
new.vars= new.vars,
stopVar = stopVar,
nextVar = nextVar,
current.probne0 = current.probne0,
maxProbne0 = maxProbne0,
nTimes = nTimes,
currIter = currIter,
first.in.model = first.in.model,
iter.dropped = iter.dropped)
class(current_state)<- "iBMA.intermediate.bicreg"
result<- iBMA.bicreg.iBMA.intermediate.bicreg(current_state, ...)
result
}
iBMA.bicreg.iBMA.intermediate.bicreg<- function (x, nIter = NULL, verbose = NULL, ...)
{
printCGen<- function(printYN)
{
printYN<- printYN
return(function(x) if (printYN) cat(paste(paste(x,sep="", collapse = " "),"\n", sep="")))
}
cs<- x
if (!is.null(nIter)) cs$nIter<- nIter
if (!is.null(verbose)) cs$verbose<- verbose
printC<- printCGen(cs$verbose)
finalIter<- cs$currIter + cs$nIter
while (cs$stopVar == 0 && cs$currIter < finalIter)
{
nextSet<- c(cs$currentSet, cs$new.vars)
cs$currIter<- cs$currIter + 1
printC(paste("starting iteration ",cs$currIter," nextVar =",cs$nextVar))
printC("applying bicreg now")
currentX<- cs$sortedX[,nextSet]
colnames(currentX)<- colnames(cs$sortedX)[nextSet]
ret.bicreg <- bicreg (x = currentX, y = cs$Y, wt = cs$wt, maxCol = cs$maxNvar + 1, ...)
printC(ret.bicreg$probne0)
cs$maxProbne0[nextSet]<- pmax(ret.bicreg$probne0, cs$maxProbne0[nextSet])
cs$nTimes[nextSet]<- cs$nTimes[nextSet] + 1
cs$rmVector <- ret.bicreg$probne0 < cs$thresProbne0
if (any(cs$rmVector) == FALSE)
{
currMin <- min (ret.bicreg$probne0)
printC (paste("no var to swap! Min probne0 = ", currMin, sep=""))
newThresProbne0 <- currMin + 1
printC(paste("new probne0 threshold = ", newThresProbne0, sep=""))
cs$rmVector <- ret.bicreg$probne0 < newThresProbne0
if (all(cs$rmVector))
cs$rmVector<- c(rep(FALSE, times = length(cs$rmVector)-1), TRUE)
}
cs$iter.dropped[nextSet[cs$rmVector]]<- cs$currIter
cs$currentSet<- nextSet[!cs$rmVector]
if ( cs$nextVar <= cs$nVar)
{
printC ("generating next set of variables")
lastVar<- sum(cs$rmVector) + cs$nextVar - 1
if (lastVar <= cs$nVar)
{
cs$new.vars<- cs$nextVar:lastVar
cs$first.in.model[cs$new.vars]<- cs$currIter + 1
cs$nextVar <- lastVar + 1
}
else
{
cs$new.vars<- NULL
for (i in length(cs$rmVector):1)
{
if (cs$rmVector[i] == TRUE && cs$nextVar <= cs$nVar)
{
cs$new.vars<- c(cs$new.vars, cs$nextVar)
cs$first.in.model[cs$nextVar]<- cs$currIter + 1
cs$nextVar <- cs$nextVar + 1
}
}
}
}
else
{
cs$stopVar <- 1
cs$new.vars = NULL
}
}
if (cs$stopVar == 1)
{
printC("finished iterating")
currentX<- cs$sortedX[,cs$currentSet]
colnames(currentX)<- colnames(cs$sortedX)[cs$currentSet]
ret.bicreg <- bicreg (x = currentX, y = cs$Y, wt = cs$wt, maxCol = cs$maxNvar + 1, ...)
output<- cs
output$bma<- ret.bicreg
output$selected<- cs$currentSet
output$nIterations<- cs$currIter
class(output)<- "iBMA.bicreg"
}
else
{
output<- cs
class(output)<- "iBMA.intermediate.bicreg"
}
output
}
iBMA.bicreg.matrix<- iBMA.bicreg.data.frame
|
NULL
embedding_glove6b <- function(dir = NULL,
dimensions = c(50, 100, 200, 300),
delete = FALSE,
return_path = FALSE,
clean = FALSE,
manual_download = FALSE) {
this_glove <- "6b"
available_dims <- c(50, 100, 200, 300)
all_names <- construct_glove_name(this_glove, available_dims)
dimensions <- as.character(dimensions)
dimensions <- match.arg(dimensions, as.character(available_dims))
name <- construct_glove_name(this_glove, dimensions)
load_dataset(data_name = "glove6b", name = name, dir = dir,
delete = delete, return_path = return_path, clean = clean,
clean_manual = all_names,
manual_download = manual_download)
}
construct_glove_name <- function(tokens = c("6b", "27b"),
dimensions = c(25, 50, 100, 200, 300)) {
tokens <- match.arg(tokens)
dimensions <- as.character(dimensions)
dimensions <- match.arg(
dimensions,
choices = as.character(c(25, 50, 100, 200, 300)),
several.ok = TRUE
)
paste0(
paste(
"glove",
tokens,
dimensions,
sep = "_"
),
".rds"
)
}
embedding_glove27b <- function(dir = NULL,
dimensions = c(25, 50, 100, 200),
delete = FALSE,
return_path = FALSE,
clean = FALSE,
manual_download = FALSE) {
this_glove <- "27b"
available_dims <- c(25, 50, 100, 200)
all_names <- construct_glove_name(this_glove, available_dims)
dimensions <- as.character(dimensions)
dimensions <- match.arg(dimensions, as.character(available_dims))
name <- construct_glove_name(this_glove, dimensions)
load_dataset(data_name = "glove27b", name = name, dir = dir,
delete = delete, return_path = return_path, clean = clean,
clean_manual = all_names,
manual_download = manual_download)
}
embedding_glove42b <- function(dir = NULL,
delete = FALSE,
return_path = FALSE,
clean = FALSE,
manual_download = FALSE) {
name <- "glove_42b.rds"
load_dataset(data_name = "glove42b", name = name, dir = dir,
delete = delete, return_path = return_path, clean = clean,
manual_download = manual_download)
}
embedding_glove840b <- function(dir = NULL,
delete = FALSE,
return_path = FALSE,
clean = FALSE,
manual_download = FALSE) {
name <- "glove_840b.rds"
load_dataset(data_name = "glove840b", name = name, dir = dir,
delete = delete, return_path = return_path, clean = clean,
manual_download = manual_download)
}
download_glove6b <- function(folder_path) {
file_path <- path(folder_path, "glove.6B.zip")
if (file_exists(file_path)) {
return(invisible())
}
download.file(url = "http://nlp.stanford.edu/data/glove.6B.zip",
destfile = file_path)
}
download_glove42b <- function(folder_path) {
file_path <- path(folder_path, "glove.42B.300d.zip")
if (file_exists(file_path)) {
return(invisible())
}
download.file(url = "http://nlp.stanford.edu/data/glove.42B.300d.zip",
destfile = file_path)
}
download_glove840b <- function(folder_path) {
file_path <- path(folder_path, "glove.840B.300d.zip")
if (file_exists(file_path)) {
return(invisible())
}
download.file(url = "http://nlp.stanford.edu/data/glove.840B.300d.zip",
destfile = file_path)
}
download_glove27b <- function(folder_path) {
file_path <- path(folder_path, "glove.twitter.27B.zip")
if (file_exists(file_path)) {
return(invisible())
}
download.file(url = "http://nlp.stanford.edu/data/glove.twitter.27B.zip",
destfile = file_path)
}
process_glove6b <- function(folder_path, name_path) {
filename <- gsub(folder_path, "", name_path)
dimensions <- unlist(strsplit(filename, "_|\\."))[[3]]
raw_name <- paste0("glove.6B.", dimensions, "d.txt")
file <- unz(path(folder_path, "glove.6B.zip"), raw_name)
write_glove(file, name_path, dimensions)
}
process_glove42b <- function(folder_path, name_path) {
dimensions <- 300
raw_name <- "glove.42B.300d.txt"
file <- unz(path(folder_path, "glove.42B.300d.zip"), raw_name)
write_glove(file, name_path, dimensions)
}
process_glove840b <- function(folder_path, name_path) {
dimensions <- 300
raw_name <- "glove.840B.300d.txt"
file <- unz(path(folder_path, "glove.840B.300d.zip"), raw_name)
write_glove(file, name_path, dimensions)
}
process_glove27b <- function(folder_path, name_path) {
filename <- gsub(folder_path, "", name_path)
dimensions <- unlist(strsplit(filename, "_|\\."))[[3]]
raw_name <- paste0("glove.twitter.27B.", dimensions, "d.txt")
file <- unz(path(folder_path, "glove.twitter.27B.zip"), raw_name)
write_glove(file, name_path, dimensions)
}
write_glove <- function(file, name_path, dimensions) {
embeddings <- read_delim(
file,
delim = " ",
quote = "",
col_names = c(
"token",
paste0("d", seq_len(dimensions))
),
col_types = paste0(
c(
"c",
rep("d", dimensions)
),
collapse = ""
)
)
write_rds(embeddings, name_path)
}
|
emgm = function(X, init, maxiter=100,verb=0){
initialization = function(X, init){
d = nrow(X) ; n = ncol(X);
if (is.list(init))
{R = expectation(X,init);}
else {
if (length(init) == 1)
{k = init;
idx = sample(1:n,k,replace=FALSE);
m = X[,idx,drop=FALSE];
label = max.col(t(sweep(t(m)%*%X,1,colSums(m^2)/2,"-")));
u = sort(unique(label));
count=0;
while (k != length(u) && count<20){
count=count+1;
k=length(u);
idx = sample(1:n,k,replace=FALSE);
m = X[,idx];
m = as.matrix(m);
label = max.col(t(sweep(t(m)%*%X,1,colSums(m^2)/2,"-")));
u = sort(unique(label));
}
k=length(u);
R = as.matrix(sparseMatrix(i=1:n,j=label,x=rep(1,n),dims=c(n,k)))
}
else {
if (nrow(init) == n)
{R = init;}
else {
if (nrow(init) == d)
{k = ncol(init);
m = init;
m = as.matrix(m);
label = max.col(t(sweep(t(m)%*%X,2,colSums(m^2)/2,"-")));
R = as.matrix(sparseMatrix(i=1:n,j=label,x=rep(1,n),dims=c(n,k))) ;
}
else {stop('ERROR: init is not valid.');}
}
}
}
return(R)
}
expectation = function(X, model){
mu = model$mu;
Sigma = model$Sigma;
w = model$weight;
n = ncol(X);
k = ncol(mu);
logRho = matrix(0,n,k);
for (i in 1:k){
logRho[,i] = loggausspdf(X,mu[,i,drop=FALSE],Sigma[,,i]);
}
logRho = sweep(logRho,2,log(w),"+")
TT = logsumexp(logRho,2);
llh = sum(TT)/n;
logR= sweep(logRho,1,TT,"-")
R = exp(logR);
return(list(R=R, llh=llh))
}
maximization = function(X, R){
d = nrow(X) ; n = ncol(X)
k = ncol(R) ;
nk = colSums(R);
w = nk/n;
mu = sweep(X%*%R,2,1/nk,"*")
Sigma = array(0,dim=c(d,d,k));
sqrtR = sqrt(R);
for (i in 1:k){
Xo = sweep(X,1,mu[,i],"-")
Xo = sweep(Xo,2,sqrtR[,i],"*")
Sigma[,,i] = tcrossprod(Xo)/nk[i];
Sigma[,,i] = Sigma[,,i]+diag(d)*(1e-08);
}
return(list(mu=mu,Sigma=Sigma,weight=w))
}
if(verb>=1) print(' EM for Gaussian mixture: running ... ');
R = initialization(X,init);
label = max.col(R)
R = R[,sort(unique(label))];
tol = 1e-14;
llh = rep(-Inf, maxiter)
converged = FALSE;
t = 0;
while (!converged & t < maxiter){
t = t+1;
if(verb>=1) print(paste(' Step ',t,sep=""));
model = maximization(X,as.matrix(R));
tmp = expectation(X,model);
R=tmp$R; llh[t]=tmp$llh
label = max.col(R)
u = unique(label);
if (ncol(R) != length(u))
{ R = R[,u]; } else {converged = ((llh[t+1]-llh[t]) < tol*abs(llh[t+1]));}
}
if(verb>=1) {
if (converged)
{print(paste('Converged in ',t,' steps.',sep=""));}
else
{print(paste('Did not converge in ',maxiter,' steps.',sep=""));}
}
return(list(label= label, model= model, llh= llh[1:t], R=R))
}
|
context("alignTransect")
data("sierraTransect")
g <- subset(sierraTransect, transect == 'Granite')
a <- subset(sierraTransect, transect == 'Andesite')
test_that("alignTransect works as expected", {
p <- alignTransect(g$elev, 1, length(g), fix = FALSE)
expect_true(inherits(p, 'list'))
expect_true(length(p) == 3)
expect_true(all(p$order == 1:7))
})
test_that("more complex input", {
p <- alignTransect(a$elev, 1, length(a), fix = FALSE)
expect_true(inherits(p, 'list'))
expect_true(length(p) == 3)
expect_true(all(p$order == c(7, 6, 5, 4, 3, 2, 1)))
})
|
plot.new_ezmmek_act_group <- function(x, ...) {
columns <- rlang::enquos(...)
if("act_raw_data_ibc" %in% colnames(x)) {
df <- x %>% dplyr::rename(act_raw_data = act_raw_data_ibc)
unnest_act_df <- tidyr::unnest(df, act_raw_data)
act_plot <- ggplot2::ggplot(data = unnest_act_df,
mapping = ggplot2::aes(x = substrate_conc,
y = signal,
color = as.factor(replicate))) +
ggplot2::geom_point() +
ggplot2::theme_bw() +
ggplot2::scale_color_discrete(name = "replicate") +
ggplot2::facet_wrap(columns)
}
if("act_raw_data_isc" %in% colnames(x)) {
df <- x %>% dplyr::rename(act_raw_data = act_raw_data_isc)
unnest_act_df <- tidyr::unnest(df, act_raw_data)
act_plot <- ggplot2::ggplot(data = unnest_act_df,
mapping = ggplot2::aes(x = time,
y = signal,
color = as.factor(replicate))) +
ggplot2::geom_point() +
ggplot2::geom_smooth(method = "lm") +
ggplot2::theme_bw() +
ggplot2::scale_color_discrete(name = "replicate") +
ggplot2::facet_wrap(columns)
}
act_plot
}
|
question_radio <- function(
text,
...,
correct = "Correct!",
incorrect = "Incorrect",
try_again = incorrect,
allow_retry = FALSE,
random_answer_order = FALSE
) {
learnr::question(
text = text,
...,
type = "learnr_radio",
correct = correct,
incorrect = incorrect,
allow_retry = allow_retry,
random_answer_order = random_answer_order
)
}
question_ui_initialize.learnr_radio <- function(question, value, ...) {
choice_names <- answer_labels(question)
choice_values <- answer_values(question)
radioButtons(
question$ids$answer,
label = question$question,
choiceNames = choice_names,
choiceValues = choice_values,
selected = value %||% FALSE
)
}
question_is_correct.learnr_radio <- function(question, value, ...) {
for (ans in question$answers) {
if (as.character(ans$option) == value) {
return(mark_as(
ans$correct,
ans$message
))
}
}
mark_as(FALSE, NULL)
}
question_ui_completed.learnr_radio <- function(question, value, ...) {
choice_values <- answer_values(question)
choice_names_final <- lapply(question$answers, function(ans) {
if (ans$correct) {
tag <- " &
tagClass <- "correct"
} else {
tag <- " &
tagClass <- "incorrect"
}
tags$span(ans$label, HTML(tag), class = tagClass)
})
disable_all_tags(
radioButtons(
question$ids$answer,
label = question$question,
choiceValues = choice_values,
choiceNames = choice_names_final,
selected = value
)
)
}
|
gtm_api_request <- function(
creds,
request,
scope = gtm_scopes["read_only"],
base_url = "https://www.googleapis.com/tagmanager/v1",
req_type = "GET",
body_list = NULL,
fields = NULL,
max_results = NULL
) {
stopifnot(scope %in% gtm_scopes)
google_api_request(
creds = creds,
scope = scope,
request = request,
base_url = base_url,
req_type = req_type,
body_list = body_list,
fields = fields
)
}
.gtmManagementApi <- R6Class(
".gtmManagementApi",
inherit = .googleApi,
private = list(
scope = gtm_scopes['read_only'],
write_scope = gtm_scopes["edit_containers"],
api_req_func = gtm_api_request,
base_url = "https://www.googleapis.com/tagmanager/v1"
)
)
.gtmResource <- R6Class(
".gtmResource",
inherit = .googleApiResource,
private = c(
get_privates(.gtmManagementApi),
list(field_corrections = function(field_list) {
names(field_list)[names(field_list) == paste0(private$resource_name, 'Id')] <- "id"
super$field_corrections(field_list)
})
)
)
.gtmCollection <- R6Class(
".gtmCollection",
inherit = .googleApiCollection,
private = c(
get_privates(.gtmResource),
list(collection_name = NULL)
)
)
gtmAccount <- R6Class(
"gtmAccount",
inherit = .gtmResource,
public = list(
shareData = NA,
fingerprint = NA
),
active = list(
containers = function() {self$.child_nodes(gtmContainers)},
permissions = function() {
tryCatch(
self$.child_nodes(gtmPermissions),
error = function(e) {
e$message
}
)
}
),
private = list(
parent_class_name = "NULL",
request = "accounts",
resource_name = "account"
)
)
GtmAccount <- function(id = NULL, creds = get_creds()){
gtmAccount$new(id = id, creds = creds)
}
gtmAccounts <- R6Class(
"gtmAccounts",
inherit = .gtmCollection,
private = list(
entity_class = gtmAccount
)
)
GtmAccounts <- function(creds = get_creds()){
gtmAccounts$new(creds = creds)
}
gtmPermission <- R6Class(
"gtmPermission",
inherit = .gtmResource,
public = list(
emailAddress = NA,
accountAccess = NA,
containerAccess = NA
),
private = list(
parent_class_name = "gtmAccount",
request = "permissions",
scope = gtm_scopes[c('read_only', 'manage_users')],
resource_name = "permission"
)
)
gtmPermissions <- R6Class(
"gtmPermissions",
inherit = .gtmCollection,
private = list(
entity_class = gtmPermission,
collection_name = "userAccess",
scope = gtmPermission$private_fields$scope
)
)
gtmContainer <- R6Class(
"gtmContainer",
inherit = .gtmResource,
public = list(
domainName = NA,
publicId = NA,
timeZoneCountryId = NA,
timeZoneId = NA,
notes = NA,
usageContext = NA,
enabledBuiltInVariable = NA,
fingerprint = NA
),
active = list(
tags = function() {self$.child_nodes(gtmTags)},
rules = function() {self$.child_nodes(gtmRules)},
macros = function() {self$.child_nodes(gtmMacros)},
versions = function() {self$.child_nodes(gtmContainerVersions)},
variables = function() {self$.child_nodes(gtmVariables)},
triggers = function() {
tryCatch(
self$.child_nodes(gtmTriggers),
error = function(e) {
e$message
}
)
}
),
private = list(
parent_class_name = "gtmAccount",
request = "containers",
resource_name = "container"
)
)
gtmContainers <- R6Class(
"gtmContainers",
inherit = .gtmCollection,
private = list(
entity_class = gtmContainer
)
)
gtmTag <- R6Class(
"gtmTag",
inherit = .gtmResource,
public = list(
type = NA,
firingRuleId = NA,
blockingRuleId = NA,
firingTriggerId = NA,
blockingTriggerId = NA,
liveOnly = NA,
priority = NA,
notes = NA,
scheduleStartMs = NA,
scheduleEndMs = NA,
parameter = NA,
fingerprint = NA
),
private = list(
parent_class_name = "gtmContainer",
request = "tags",
resource_name = "tag"
)
)
gtmTags <- R6Class(
"gtmTags",
inherit = .gtmCollection,
private = list(
entity_class = gtmTag
)
)
gtmRule <- R6Class(
"gtmRule",
inherit = .gtmResource,
public = list(
notes = NA,
condition = NA,
fingerprint = NA
),
private = list(
parent_class_name = "gtmContainer",
request = "rules",
resource_name = "rule"
)
)
gtmRules <- R6Class(
"gtmRules",
inherit = .gtmCollection,
private = list(
entity_class = gtmRule
)
)
gtmTrigger <- R6Class(
"gtmTrigger",
inherit = .gtmResource,
public = list(
type = NA,
customEventFilter = NA,
filter = NA,
autoEventFilter = NA,
waitForTags = NA,
checkValidation = NA,
waitForTagsTimeout = NA,
uniqueTriggerId = NA,
eventName = NA,
interval = NA,
limit = NA,
enableAllVideos = NA,
videoPercentageList = NA,
fingerprint = NA
),
private = list(
parent_class_name = "gtmContainer",
request = "triggers",
resource_name = "trigger"
)
)
gtmTriggers <- R6Class(
"gtmTriggers",
inherit = .gtmCollection,
private = list(
entity_class = gtmTrigger
)
)
gtmMacro <- R6Class(
"gtmMacro",
public = list(
type = NA,
notes = NA,
scheduleStartMs = NA,
scheduleEndMs = NA,
parameter = NA,
enablingRuleId = NA,
disablingRuleId = NA,
fingerprint = NA
),
inherit = .gtmResource,
private = list(
parent_class_name = "gtmContainer",
request = "macros",
resource_name = "macro"
)
)
gtmMacros <- R6Class(
"gtmMacros",
inherit = .gtmCollection,
private = list(
entity_class = gtmMacro
)
)
gtmVariable <- R6Class(
"gtmVariable",
inherit = .gtmResource,
public = list(
type = NA,
notes = NA,
scheduleStartMs = NA,
scheduleEndMs = NA,
parameter = NA,
enablingTriggerId = NA,
disablingTriggerId = NA,
fingerprint = NA
),
private = list(
parent_class_name = "gtmContainer",
request = "variables",
resource_name = "variable"
)
)
gtmVariables <- R6Class(
"gtmVariables",
inherit = .gtmCollection,
private = list(
entity_class = gtmVariable
)
)
gtmContainerVersion <- R6Class(
"gtmContainerVersion",
inherit = .gtmResource,
public = list(
deleted = NA,
notes = NA,
container = NA,
macro = NA,
rule = NA,
tag = NA,
trigger = NA,
variable = NA,
fingerprint = NA
),
private = list(
parent_class_name = "gtmContainer",
request = "versions",
resource_name = "containerVersion"
)
)
gtmContainerVersions <- R6Class(
"gtmContainerVersions",
inherit = .gtmCollection,
private = list(
entity_class = gtmContainerVersion,
collection_name = "containerVersion"
)
)
|
load_nrrd <- function(filename, mask_filename = NULL, keep_mask_values = 1, switch_z = TRUE,
crop_in = TRUE, replace_in = TRUE, center_in = FALSE, zero_value = NULL, min_to = -1024,
verbose_in = TRUE,
origin_in = NULL, ReadByteAsRaw_in = "unsigned", ...
)
{
if(verbose_in) {message(paste0("LOADING nrrd FILES FROM: ", filename, "\n"))}
dcmImages <- nat::read.nrrd(filename, Verbose = verbose_in, origin = origin_in, ReadByteAsRaw = ReadByteAsRaw_in, AttachFullHeader = FALSE)
data <- dcmImages
RIA_image <- list(data = NULL, header = list(), log = list())
if(length(dim(data)) == 3 | length(dim(data)) == 2) {class(RIA_image) <- append(class(RIA_image), "RIA_image")
} else {stop(paste0("nrrd LOADED IS ", length(dim(data)), " DIMENSIONAL. ONLY 2D AND 3D DATA ARE SUPPORTED!"))}
if(is.null(zero_value)) zero_value <- min(data, na.rm = TRUE)
if(!is.null(mask_filename)) {
if(identical(filename, mask_filename)) {
if(verbose_in) {message(paste0("CANCELING OUT VALUES OTHER THAN THOSE SPECIFIED IN 'keep_mask_values' PARAMETER \n"))}
if(suppressWarnings(any(is.na(as.numeric(keep_mask_values))))) {
data[!eval(parse(text = paste0("data", keep_mask_values)))] <- zero_value
} else{
data[!data %in% keep_mask_values] <- zero_value
}
} else {
for(i in 1:length(mask_filename)) {
mask_filename_i <- mask_filename[i]
if(verbose_in) {message(paste0("LOADING nrrd IMAGES OF MASK IMAGE FROM: ", mask_filename_i, "\n"))}
dcmImages_mask <- nat::read.nrrd(mask_filename_i, Verbose = verbose_in, origin = origin_in, ReadByteAsRaw = ReadByteAsRaw_in, AttachFullHeader = FALSE)
data_mask <- dcmImages_mask
if(!all(dim(data) == dim(data_mask))) {
stop(paste0("DIMENSIONS OF THE IMAGE AND THE MASK ARE NOT EQUAL!\n",
"DIMENSION OF IMAGE: ", dim(data)[1], " ", dim(data)[2], " ", dim(data)[3], "\n",
"DIMENSION OF MASK: ", dim(data_mask)[1], " ", dim(data_mask)[2], " ", dim(data_mask)[3], "\n"))
} else {
if(switch_z) {data_mask[,,dim(data_mask)[3]:1] <- data_mask
message("MASK IMAGE WAS TRANSFORMED TO ACHIEVE PROPER ORIENTATION OF THE ORIGINAL AND THE MASK IMAGE.\n")
}
if(suppressWarnings(any(is.na(as.numeric(keep_mask_values))))) {
data[!eval(parse(text = paste0("data_mask", keep_mask_values)))] <- zero_value
} else{
data_mask[!(data_mask %in% keep_mask_values)] <- NA
if(i == 1) {
data_mask_all <- data_mask
}else {
data_mask_all[is.na(data_mask_all)] <- data_mask[is.na(data_mask_all)]
}
}
}
}
if(suppressWarnings(!any(is.na(as.numeric(keep_mask_values))))) {
data[!(data_mask_all %in% keep_mask_values)] <- zero_value
}
}
}
RIA_image$data$orig <- data
RIA_image$data$modif <- NULL
class(RIA_image$header) <- append(class(RIA_image$header), "RIA_header")
class(RIA_image$data) <- append(class(RIA_image$data), "RIA_data")
class(RIA_image$log) <- append(class(RIA_image$log), "RIA_log")
RIA_image$log$events <- "Created"
RIA_image$log$orig_dim <- dim(data)
RIA_image$log$directory <- filename
if(!is.null(mask_filename)) {
if(identical(filename, mask_filename)) {
RIA_image$log$events <- paste0("Filtered_using_values_", paste0(keep_mask_values, collapse = "_"))
} else {
RIA_image$log$events <- paste0("Filtered_using_mask_values_", paste0(keep_mask_values, collapse = "_"))
}
}
if(crop_in)
{
if(verbose_in) {message(paste0("SMALLEST VALUES IS ", zero_value, ", AND WILL BE CONSIDERED AS REFERENCE POINT TO IDENTIFY VOXELS WITHOUT ANY SIGNAL\n"))}
if(verbose_in & center_in == FALSE) message(paste0("MIGHT CONSIDER RESCALING, SINCE SMALLEST VALUE IS NOT -1024, AND THUS VOXEL VALUES MIGHT NOT BE CORRECT\n"))
RIA_image <- crop(RIA_image, zero_value, write_orig = TRUE, verbose_in = verbose_in)
}
if(replace_in)
{
if(verbose_in) {message(paste0("SMALLEST VALUES IS ", zero_value, ", AND WILL CHANGE TO NA\n"))}
RIA_image <- change_to(RIA_image, zero_value_in = zero_value, verbose_in = verbose_in)
}
if(center_in & (min(data, na.rm = T) != min_to))
{
if(verbose_in) {message(paste0("SMALLEST VALUES IS not ", min_to, " THEREFORE SHIFTING VALUES TO ACHIVE THIS\n"))}
RIA_image <- shift_to(RIA_image, to = min_to, min_value_in = zero_value, verbose_in = verbose_in)
}
header <- create_header_nrrd(filename)
RIA_image$header <- header
xy_dim <- as.numeric(RIA_image$header$PixelSpacing)
z_dim <- as.numeric(RIA_image$header$SpacingBetweenSlices)
RIA_image$log$orig_vol_mm <- volume(RIA_image$data$orig, xy_dim = xy_dim, z_dim = z_dim)
RIA_image$log$orig_surf_mm <- surface(RIA_image$data$orig, xy_dim = xy_dim, z_dim = z_dim)
RIA_image$log$surface_volume_r <- ifelse(RIA_image$log$orig_vol_mm != 0, RIA_image$log$orig_surf_mm/RIA_image$log$orig_vol_mm, 0)
RIA_image$log$orig_xy_dim <- xy_dim
RIA_image$log$orig_z_dim <- z_dim
if(verbose_in) {message(paste0("SUCCESSFULLY LOADED ", RIA_image$header$PatientsName, "'s nrrd IMAGES TO RIA IMAGE CLASS\n"))}
data_NA <- as.vector(RIA_image$data$orig)
data_NA <- data_NA[!is.na(data_NA)]
if(length(data_NA) == 0) {message("WARNING: RIA_image$data DOES NOT CONTAIN ANY DATA!!!\n")}
return(RIA_image)
}
|
cxr_competitive_response <- function(pair_matrix){
if(all(pair_matrix>=0)){
return(sqrt((pair_matrix[2,1]/pair_matrix[1,1]) * (pair_matrix[2,2]/pair_matrix[1,2])))
}else{
return(NA_real_)
}
}
|
plot_mir_new <- function(df,
threshold = 1,
start = NULL,
end = NULL,
colour = "steelblue3",
col.mir = miRNA,
col.year = Year,
title = NULL) {
if(!threshold >= 1) {
stop("'threshold' must be >= 1.")
}
if(is.null(title)) {
title <- "Number of new miRNAs per year"
}
if(is.null(start)) {
start <- df %>%
dplyr::select({{col.year}}) %>%
dplyr::pull() %>%
min()
}
if(is.null(end)) {
end <- df %>%
dplyr::select({{col.year}}) %>%
dplyr::pull() %>%
max()
}
df_ <- df %>%
dplyr::add_count({{col.year}}, {{col.mir}}) %>%
dplyr::filter(n >= threshold) %>%
dplyr::group_by({{col.mir}}) %>%
dplyr::summarise(first_mentioned = min({{col.year}})) %>%
dplyr::add_count(first_mentioned, name = "new_miRNAs") %>%
dplyr::select(first_mentioned, new_miRNAs) %>%
dplyr::distinct() %>%
dplyr::filter(dplyr::between(first_mentioned, start, end))
df_empty <- data.frame(first_mentioned = seq(start, end, by = 1),
stringsAsFactors = FALSE) %>%
dplyr::as_tibble()
df_plot <- df_empty %>%
dplyr::left_join(df_, by = "first_mentioned") %>%
dplyr::mutate(new_miRNAs = ifelse(is.na(new_miRNAs), 0, new_miRNAs))
plot <- ggplot(df_plot, aes(x = factor(first_mentioned), y = new_miRNAs)) +
geom_col(fill = colour) +
theme_classic() +
xlab("Year") +
ylab("
labs(caption = paste("Threshold: ", threshold)) +
ggtitle(title)
plot <- pretty_breaks_miretrieve(plot, df_plot$new_miRNAs)
return(plot)
}
plot_mir_development <- function(df,
mir,
start = NULL,
end = NULL,
linetype = "miRNA",
alpha = 0.8,
width = 0.3,
col.mir = miRNA,
col.year = Year,
title = NULL) {
if(is.null(start)) {
start <- df %>%
dplyr::select({{col.year}}) %>%
dplyr::pull() %>%
min()
}
if(is.null(end)) {
end <- df %>%
dplyr::select({{col.year}}) %>%
dplyr::pull() %>%
max()
}
if(!is.numeric(start) | !is.numeric(end)) {
stop("'start' and 'end' must be numeric.")
}
if(is.null(title)) {
title <- paste0("Development of ", paste(mir, collapse = ", "), " in PubMed abstracts")
}
df_empty <- expand.grid(mir,
seq(start, end, by = 1),
0,
stringsAsFactors = FALSE) %>%
dplyr::as_tibble() %>%
dplyr::rename({{col.mir}} := Var1, {{col.year}} := Var2)
df_ <- df %>%
dplyr::select({{col.mir}}, {{col.year}}) %>%
dplyr::filter({{col.mir}} %in% mir) %>%
dplyr::add_count({{col.mir}}, {{col.year}}, name = "n")
df_plot <- dplyr::left_join(df_empty, df_) %>%
dplyr::mutate(n = ifelse(is.na(n), 0, n))
plot <- ggplot(df_plot, aes(x = factor({{col.year}}),
y = n,
col = {{col.mir}},
group = {{col.mir}})) +
theme_classic() +
scale_y_continuous(limits = c(0, as.integer(max(df_$n) + 2)),
expand = c(0.01,0.01),
breaks = function(x)
unique(floor(pretty(seq(0, (max(x) + 1) * 1.1))))) +
xlab("Year") +
ylab("Mentioned in
labs(color = "microRNA") +
ggtitle(title)
if(linetype == "miRNA") {
plot <- plot + geom_line(alpha = alpha,
position=position_dodge(width=width),
aes(linetype = {{col.mir}})) +
scale_linetype(guide = FALSE)
} else {
plot <- plot + geom_line(alpha = alpha,
position=position_dodge(width=width),
linetype = linetype)
}
return(plot)
}
|
context("test-other_helpers")
set.seed(1234)
x <- c(1, 5, -1, 2)
G1 <- matrix(nrow = 1, ncol = 2)
G2 <- matrix(nrow = 1, ncol = 1)
G3 <- matrix(nrow = 3, ncol = 3)
test_that("unif works", {
expect_equal(unif(x), c(2/5, 4/5, 1/5, 3/5))
})
test_that("dim_Gamma works", {
expect_error(dim_Gamma(G1))
expect_equal(dim_Gamma(G2), 1)
expect_equal(dim_Gamma(G3), 3)
})
test_that("select_edges works", {
small_graph <- igraph::make_empty_graph(n = 4, directed = FALSE)
small_graph <- igraph::add_edges(small_graph, c(1, 2, 2, 3, 2, 4))
expect_equal(select_edges(small_graph), rbind(c(1, 3), c(1, 4), c(3, 4)))
})
|
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