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import Optim
# Proxy function for optimization
function optFunc(x::Array{Float32, 1}, tree::Node)::Float32
setConstants(tree, x)
return scoreFunc(tree)
end
# Use Nelder-Mead to optimize the constants in an equation
function optimizeConstants(member::PopMember)::PopMember
nconst = countConstants(member.tree)
if nconst == 0
return member
end
x0 = getConstants(member.tree)
f(x::Array{Float32,1})::Float32 = optFunc(x, member.tree)
if size(x0)[1] == 1
algorithm = Optim.Newton
else
algorithm = Optim.NelderMead
end
try
result = Optim.optimize(f, x0, algorithm(), Optim.Options(iterations=100))
# Try other initial conditions:
for i=1:nrestarts
tmpresult = Optim.optimize(f, x0 .* (1f0 .+ 5f-1*randn(Float32, size(x0)[1])), algorithm(), Optim.Options(iterations=100))
if tmpresult.minimum < result.minimum
result = tmpresult
end
end
if Optim.converged(result)
setConstants(member.tree, result.minimizer)
member.score = convert(Float32, result.minimum)
member.birth = getTime()
else
setConstants(member.tree, x0)
end
catch error
# Fine if optimization encountered domain error, just return x0
if isa(error, AssertionError)
setConstants(member.tree, x0)
else
throw(error)
end
end
return member
end
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