Refactored Till Population and moved a part to randomMutations

julia/Population.jl

@@ -0,0 +1,40 @@
+# A list of members of the population, with easy constructors,
+#  which allow for random generation of new populations
+mutable struct Population
+    members::Array{PopMember, 1}
+    n::Integer
+
+    Population(pop::Array{PopMember, 1}) = new(pop, size(pop)[1])
+    Population(npop::Integer) = new([PopMember(genRandomTree(3)) for i=1:npop], npop)
+    Population(npop::Integer, nlength::Integer) = new([PopMember(genRandomTree(nlength)) for i=1:npop], npop)
+
+end
+
+# Sample 10 random members of the population, and make a new one
+function samplePop(pop::Population)::Population
+    idx = rand(1:pop.n, ns)
+    return Population(pop.members[idx])
+end
+
+# Sample the population, and get the best member from that sample
+function bestOfSample(pop::Population)::PopMember
+    sample = samplePop(pop)
+    best_idx = argmin([sample.members[member].score for member=1:sample.n])
+    return sample.members[best_idx]
+end
+
+function finalizeScores(pop::Population)::Population
+    need_recalculate = batching
+    if need_recalculate
+        @inbounds @simd for member=1:pop.n
+            pop.members[member].score = scoreFunc(pop.members[member].tree)
+        end
+    end
+    return pop
+end
+
+# Return best 10 examples
+function bestSubPop(pop::Population; topn::Integer=10)::Population
+    best_idx = sortperm([pop.members[member].score for member=1:pop.n])
+    return Population(pop.members[best_idx[1:topn]])
+end

julia/randomMutations.jl

@@ -227,4 +227,13 @@ function deleteRandomOp(tree::Node)::Node
         end
     end
     return tree
+end
+
+# Create a random equation by appending random operators
+function genRandomTree(length::Integer)::Node
+    tree = Node(1.0f0)
+    for i=1:length
+        tree = appendRandomOp(tree)
+    end
+    return tree
 end

julia/sr.jl

@@ -156,56 +156,10 @@ function iterate(member::PopMember, T::Float32, curmaxsize::Integer, frequencyCo
     return PopMember(tree, afterLoss)
 end
 
-# Create a random equation by appending random operators
-function genRandomTree(length::Integer)::Node
-    tree = Node(1.0f0)
-    for i=1:length
-        tree = appendRandomOp(tree)
-    end
-    return tree
-end
-
-
-# A list of members of the population, with easy constructors,
-#  which allow for random generation of new populations
-mutable struct Population
-    members::Array{PopMember, 1}
-    n::Integer
-
-    Population(pop::Array{PopMember, 1}) = new(pop, size(pop)[1])
-    Population(npop::Integer) = new([PopMember(genRandomTree(3)) for i=1:npop], npop)
-    Population(npop::Integer, nlength::Integer) = new([PopMember(genRandomTree(nlength)) for i=1:npop], npop)
-
-end
 
-# Sample 10 random members of the population, and make a new one
-function samplePop(pop::Population)::Population
-    idx = rand(1:pop.n, ns)
-    return Population(pop.members[idx])
-end
-
-# Sample the population, and get the best member from that sample
-function bestOfSample(pop::Population)::PopMember
-    sample = samplePop(pop)
-    best_idx = argmin([sample.members[member].score for member=1:sample.n])
-    return sample.members[best_idx]
-end
+include("Population.jl")
 
-function finalizeScores(pop::Population)::Population
-    need_recalculate = batching
-    if need_recalculate
-        @inbounds @simd for member=1:pop.n
-            pop.members[member].score = scoreFunc(pop.members[member].tree)
-        end
-    end
-    return pop
-end
 
-# Return best 10 examples
-function bestSubPop(pop::Population; topn::Integer=10)::Population
-    best_idx = sortperm([pop.members[member].score for member=1:pop.n])
-    return Population(pop.members[best_idx[1:topn]])
-end
 
 # Pass through the population several times, replacing the oldest
 # with the fittest of a small subsample