Remove extra code

eureqa.jl

@@ -16,12 +16,10 @@ const y = ((cx,)->cx^2).(X[:, 2]) + cos.(X[:, 3]) .+ 5.0;
 const parsimony = 0.01
 # How much to scale temperature by (T between 0 and 1)
 const alpha = 10.0
-
 const maxsize = 20
 
 
 
-
 id = (x,) -> x
 const nuna = size(unaops)[1]
 const nbin = size(binops)[1]

paralleleureqa.jl

@@ -13,15 +13,14 @@ allPops = [Population(npop, 3) for j=1:nthreads]
 bestScore = Inf
 # Repeat this many evolutions; we collect and migrate the best
 # each time.
-for k=1:4
-    # Spawn independent evolutions
+for k=1:niterations
 
-    # Gather them
+    # Spawn threads to run indepdent evolutions, then gather them
     @inbounds Threads.@threads for i=1:nthreads
         allPops[i] = run(allPops[i], ncyclesperiteration, annealing)
     end
 
-    # Get best 10 models for each processes. Copy because we re-assign later.
+    # Get best 10 models from each evolution. Copy because we re-assign later.
     bestPops = deepcopy(Population([member for pop in allPops for member in bestSubPop(pop).members]))
     bestCurScoreIdx = argmin([bestPops.members[member].score for member=1:bestPops.n])
     bestCurScore = bestPops.members[bestCurScoreIdx].score
@@ -36,116 +35,3 @@ for k=1:4
     end
 end
 
-## Possibly calls once for every thread? But works.
-
-# using Distributed
-# addprocs(8)
-# @everywhere const nthreads = 8
-
-# @everywhere include("eureqa.jl")
-
-# println("Lets try to learn (x2^2 + cos(x3) + 5) using regularized evolution from scratch")
-# @everywhere const npop = 100
-# @everywhere const annealing = false
-# @everywhere const niterations = 30
-# @everywhere const ncyclesperiteration = 10000
-
-# # Generate random initial populations
-
-# # Create a mapping for running the algorithm on all processes
-# @everywhere f = (pop,)->run(pop, ncyclesperiteration, annealing)
-
-
-# @everywhere allPops = [Population(npop, 3) for j=1:nthreads]
-# @everywhere bestScore = Inf
-# # Repeat this many evolutions; we collect and migrate the best
-# # each time.
-# for k=1:4
-    # # Spawn independent evolutions
-    # @everywhere futures = [@spawnat :any f(allPops[i]) for i=1:nthreads]
-
-    # # Gather them
-    # for i=1:nthreads
-        # @everywhere allPops[i] = fetch(futures[i])
-    # end
-    # # Get best 10 models for each processes. Copy because we re-assign later.
-    # @everywhere bestPops = deepcopy(Population([member for pop in allPops for member in bestSubPop(pop).members]))
-    # @everywhere bestCurScoreIdx = argmin([bestPops.members[member].score for member=1:bestPops.n])
-    # @everywhere bestCurScore = bestPops.members[bestCurScoreIdx].score
-    # println(bestCurScore, " is the score for ", stringTree(bestPops.members[bestCurScoreIdx].tree))
-
-    # # Migration
-    # for j=1:nthreads
-        # for k in rand(1:npop, 50)
-            # # Copy in case one gets copied twice
-            # @everywhere allPops[j].members[k] = deepcopy(bestPops.members[rand(1:size(bestPops.members)[1])])
-        # end
-    # end
-# end
-
-
-
-
-
-# julia> @everywhere include_string(Main, $(read("count_heads.jl", String)), "count_heads.jl")
-
-# julia> a = @spawnat :any count_heads(100000000)
-# Future(2, 1, 6, nothing)
-
-# julia> b = @spawnat :any count_heads(100000000)
-# Future(3, 1, 7, nothing)
-
-# julia> fetch(a)+fetch(b)
-# 100001564
-
-# allPops = [Population(npop, 3) for j=1:nthreads]
-# bestScore = Inf
-# for i=1:10
-    # tmpPops = fetch(pmap(f, allPops))
-    # allPops[1:nthreads] = tmpPops[1:nthreads]
-    # # Get best 11 models for each processes
-    # bestPops = Population([member for pop in allPops for member in bestSubPop(pop).members])
-    # bestCurScoreIdx = argmin([bestPops.members[member].score for member=1:bestPops.n])
-    # bestCurScore = bestPops.members[bestCurScoreIdx].score
-    # println(bestCurScore, " is the score for ", stringTree(bestPops.members[bestCurScoreIdx].tree))
-# end
-
-
-# function update(allPops::Array{Population, 1}, bestScore::Float64)
-    # # Map it over our workers
-    # #global allPops = deepcopy(pmap(f, deepcopy(allPops)))
-    # #curAllPops = deepcopy(pmap(f, allPops))
-    # curAllPops = pmap(f, allPops)
-    # for j=1:nthreads
-        # allPops[j] = curAllPops[j]
-    # end
-
-    # # Get best 10 models for each processes
-    # bestPops = Population([member for pop in allPops for member in bestSubPop(pop).members])
-    # bestCurScoreIdx = argmin([bestPops.members[member].score for member=1:bestPops.n])
-    # bestCurScore = bestPops.members[bestCurScoreIdx].score
-    # if bestCurScore < bestScore
-        # bestScore = bestCurScore
-        # println(bestScore, " is the score for ", stringTree(bestPops.members[bestCurScoreIdx].tree))
-    # end
-
-    # # Migration
-    # for j=1:nthreads
-        # allPops[j].members[1:50] = deepcopy(bestPops.members[rand(1:bestPops.n, 50)])
-    # end
-    # return allPops, bestScore
-# end
-
-
-# function runExperiment()
-    # # Do niterations cycles
-    # allPops = [Population(npop, 3) for j=1:nthreads]
-    # bestScore = Inf
-    # for i=1:niterations
-        # allPops, bestScore = update(allPops, bestScore)
-    # end
-
-    # return bestScore
-# end
-
-# runExperiment()