Make filenaming more consistent

julia/{complexityChecks.jl → CheckConstraints.jl}

@@ -39,4 +39,4 @@ function flagUnaOperatorComplexity(tree::Node, op::Int)::Bool
     else
         return (flagUnaOperatorComplexity(tree.l, op) || flagUnaOperatorComplexity(tree.r, op))
     end
-end
+end

julia/{eval.jl → EvaluateEquation.jl}

@@ -45,38 +45,3 @@ function evalTreeArray(tree::Node, cX::Array{Float32, 2})::Union{Array{Float32,
         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

julia/{errors.jl → LossFunctions.jl}

@@ -34,4 +34,47 @@ end
 # Mean of square error between two arrays
 function MSE(x::Array{Float32}, y::Array{Float32}, w::Array{Float32})::Float32
     return SSE(x, y, w)/sum(w)
-end
+end
+
+if weighted
+    const avgy = sum(y .* weights)/sum(weights)
+    const baselineMSE = MSE(y, convert(Array{Float32, 1}, ones(len) .* avgy), weights)
+else
+    const avgy = sum(y)/len
+    const baselineMSE = MSE(y, convert(Array{Float32, 1}, ones(len) .* avgy))
+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

julia/{simulatedAnnealing.jl → Mutate.jl}

@@ -1,5 +1,4 @@
-# Go through one simulated annealing mutation cycle
-#  exp(-delta/T) defines probability of accepting a change
+# Go through one mutation cycle
 function iterate(member::PopMember, T::Float32, curmaxsize::Integer, frequencyComplexity::Array{Float32, 1})::PopMember
     prev = member.tree
     tree = prev
@@ -122,4 +121,4 @@ function iterate(member::PopMember, T::Float32, curmaxsize::Integer, frequencyCo
         end
     end
     return PopMember(tree, afterLoss)
-end
+end

julia/{utils.jl → Utils.jl}

@@ -29,4 +29,4 @@ function testConfiguration()
         @printf("\n\nYour configuration is invalid - one of your operators is not well-defined over the real line.\n\n\n")
         throw(error)
     end
-end
+end

julia/halloffame.jl

@@ -5,4 +5,4 @@ mutable struct HallOfFame
 
     # Arranged by complexity - store one at each.
     HallOfFame() = new([PopMember(Node(1f0), 1f9) for i=1:actualMaxsize], [false for i=1:actualMaxsize])
-end
+end

julia/sr.jl

@@ -2,48 +2,21 @@ import Optim
 import Printf: @printf
 import Random: shuffle!, randperm
 
-
-include("constants.jl")
-
-include("errors.jl")
-
-if weighted
-    const avgy = sum(y .* weights)/sum(weights)
-    const baselineMSE = MSE(y, convert(Array{Float32, 1}, ones(len) .* avgy), weights)
-else
-    const avgy = sum(y)/len
-    const baselineMSE = MSE(y, convert(Array{Float32, 1}, ones(len) .* avgy))
-end
-
-include("utils.jl")
-
-include("Node.jl")
-
-include("eval.jl")
-
-include("randomMutations.jl")
-
-include("simplification.jl")
-
+include("Equation.jl")
+include("ProgramConstants.jl")
+include("LossFunctions.jl")
+include("Utils.jl")
+include("EvaluateEquation.jl")
+include("MutationFunctions.jl")
+include("SimplifyEquation.jl")
 include("PopMember.jl")
-
-
-include("halloffame.jl")
-
-
-include("complexityChecks.jl")
-
-include("simulatedAnnealing.jl")
-
+include("HallOfFame.jl")
+include("CheckConstraints.jl")
+include("Mutate.jl")
 include("Population.jl")
-
-include("regEvolCycle.jl")
-
-include("run.jl")
-
-include("optimization.jl")
-
-
+include("RegularizedEvolution.jl")
+include("SingleIteration.jl")
+include("ConstantOptimization.jl")
 
 function fullRun(niterations::Integer;
                 npop::Integer=300,