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# Evaluate an equation over an array of datapoints
function evalTreeArray(tree::Node)::Union{Array{Float32, 1}, Nothing}
return evalTreeArray(tree, X)
end
# Evaluate an equation over an array of datapoints
function evalTreeArray(tree::Node, cX::Array{Float32, 2})::Union{Array{Float32, 1}, Nothing}
clen = size(cX)[1]
if tree.degree == 0
if tree.constant
return fill(tree.val, clen)
else
return copy(cX[:, tree.val])
end
elseif tree.degree == 1
cumulator = evalTreeArray(tree.l, cX)
if cumulator === nothing
return nothing
end
op_idx = tree.op
UNAOP!(cumulator, op_idx, clen)
@inbounds for i=1:clen
if isinf(cumulator[i]) || isnan(cumulator[i])
return nothing
end
end
return cumulator
else
cumulator = evalTreeArray(tree.l, cX)
if cumulator === nothing
return nothing
end
array2 = evalTreeArray(tree.r, cX)
if array2 === nothing
return nothing
end
op_idx = tree.op
BINOP!(cumulator, array2, op_idx, clen)
@inbounds for i=1:clen
if isinf(cumulator[i]) || isnan(cumulator[i])
return nothing
end
end
return cumulator
end
end
# Score an equation
function scoreFunc(tree::Node)::Float32
prediction = evalTreeArray(tree)
if prediction === nothing
return 1f9
end
if weighted
mse = MSE(prediction, y, weights)
else
mse = MSE(prediction, y)
end
return mse / baselineMSE + countNodes(tree)*parsimony
end
# Score an equation with a small batch
function scoreFuncBatch(tree::Node)::Float32
# batchSize
batch_idx = randperm(len)[1:batchSize]
batch_X = X[batch_idx, :]
prediction = evalTreeArray(tree, batch_X)
if prediction === nothing
return 1f9
end
size_adjustment = 1f0
batch_y = y[batch_idx]
if weighted
batch_w = weights[batch_idx]
mse = MSE(prediction, batch_y, batch_w)
size_adjustment = 1f0 * len / batchSize
else
mse = MSE(prediction, batch_y)
end
return size_adjustment * mse / baselineMSE + countNodes(tree)*parsimony
end |