Refactored optimization too, performance normal

julia/optimization.jl

@@ -0,0 +1,47 @@
+# 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

julia/sr.jl

@@ -42,53 +42,7 @@ include("regEvolCycle.jl")
 include("run.jl")
 
 include("optimization.jl")
-# 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
 
 
 function fullRun(niterations::Integer;