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from pyevolve import GSimpleGA
from pyevolve import GTree
from pyevolve import Consts
from pyevolve import Selectors
from pyevolve import Mutators
from math import sqrt
import pydot
import random
def gp_add(a, b):
assert len(a)==len(b)
new_list = [x+y for x,y in zip(a,b)]
return new_list
#def gp_sub(a, b):
# assert len(a)==len(b)
# new_list = [x-y for x,y in zip(a,b)]
# return new_list
def prot_div(a, b):
if b==0:
return b
else:
return a/b
#def gp_div(a,b):
# assert len(a)==len(b)
# new_list = [prot_div(x,float(y)) for x,y in zip(a,b)]
# return new_list
def gp_mul(a,b):
assert len(a)==len(b)
new_list = [x*y for x,y in zip(a,b)]
return new_list
def random_lists(size):
list_a = [random.randint(1,20) for i in xrange(size)]
list_b = [random.randint(1,20) for i in xrange(size)]
return (list_a, list_b)
def eval_func(chromosome):
sz = 20
code_comp = chromosome.getCompiledCode()
square_accum = 0.0
for j in xrange(sz):
a, b = random_lists(5)
target_list = gp_add(gp_mul(a,b),gp_mul(a,b))
ret_list = eval(code_comp)
square_accum += (sum(target_list)-sum(ret_list))**2
RMSE = sqrt(square_accum / float(sz))
score = (1.0 / (RMSE+1.0))
return score
def main_run():
genome = GTree.GTreeGP()
root = GTree.GTreeNodeGP('a', Consts.nodeType["TERMINAL"])
genome.setRoot(root)
genome.setParams(max_depth=2, method="ramped")
genome.evaluator += eval_func
genome.mutator.set(Mutators.GTreeGPMutatorSubtree)
ga = GSimpleGA.GSimpleGA(genome)
ga.setParams(gp_terminals = ['a', 'b'],
gp_function_prefix = "gp")
ga.setMinimax(Consts.minimaxType["maximize"])
ga.setGenerations(500)
ga.setCrossoverRate(1.0)
ga.setMutationRate(0.08)
ga.setPopulationSize(80)
ga(freq_stats=1)
print ga.bestIndividual()
graph = pydot.Dot()
ga.bestIndividual().writeDotGraph(graph)
graph.write_jpeg('tree.png', prog='dot')
if __name__ == "__main__":
main_run()
#import hotshot, hotshot.stats
#prof = hotshot.Profile("ev.prof")
#prof.runcall(main_run)
#prof.close()
#stats = hotshot.stats.load("ev.prof")
#stats.strip_dirs()
#stats.sort_stats('time', 'calls')
#stats.print_stats(20)
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