def mutate(xman, mutateValue):
xmanJr = Parameters()
for i in range(ANN.NODES_PER_LAYER):
for j in range(19):
xmanJr.ih[i][j] = xman.ih[i][j]
xmanJr.c[i][j] = xman.c[i][j]
for j in range(ANN.NODES_PER_LAYER):
xmanJr.hh[i][j] = xman.hh[i][j]
xmanJr.c2[i][j] = xman.c2[i][j]
xmanJr.w[i] = xman.w[i]
xmanJr.w2[i] = xman.w2[i]
xmanJr.ho[i] = xman.ho[i]
node = random.randint(0, ANN.NODES_PER_LAYER-1)
if random.choice([True, False]):
for i in range(19):
xmanJr.ih[node][i] += getMutationValue(mutateValue)
xmanJr.c[node][i] += getMutationValue(mutateValue)
xmanJr.w[node] += getMutationValue(mutateValue)
for i in range(ANN.NODES_PER_LAYER):
xmanJr.hh[i][node] += getMutationValue(mutateValue)
else:
for i in range(ANN.NODES_PER_LAYER):
xmanJr.hh[i][node] += getMutationValue(mutateValue)
xmanJr.c2[i][node] += getMutationValue(mutateValue)
xmanJr.w2[node] += getMutationValue(mutateValue)
xmanJr.ho[node] += getMutationValue(mutateValue)
return xmanJr
def generatePop(generation):
for i in range(POPSIZE):
nextMember = Parameters()
for j in range(ANN.NODES_PER_LAYER):
for k in range(19):
nextMember.ih[j][k] = getInitialFloat()
nextMember.c[j][k] = getInitialFloat()
for n in range(ANN.NODES_PER_LAYER):
nextMember.hh[j][n] = getInitialFloat()
nextMember.c2[j][n] = getInitialFloat()
nextMember.w[j] = getInitialFloat()
nextMember.w2[j] = getInitialFloat()
nextMember.ho[j] = getInitialFloat()
generation.append(nextMember)
def mate(parent1, parent2):
parentList = [parent1, parent2]
child = Parameters()
for i in range(ANN.NODES_PER_LAYER):
for j in range(19):
child.ih[i][j] = parentList[random.randint(0,1)].ih[i][j]
child.c[i][j] = parentList[random.randint(0,1)].c[i][j]
for j in range(ANN.NODES_PER_LAYER):
child.hh[i][j] = parentList[random.randint(0,1)].hh[i][j]
child.c2[i][j] = parentList[random.randint(0,1)].c2[i][j]
child.w[i] = parentList[random.randint(0,1)].w[i]
child.w2[i] = parentList[random.randint(0,1)].w2[i]
child.ho[i] = parentList[random.randint(0,1)].ho[i]
return child
def main(annfile):
randSample = random.Random(input.SAMPLE_SEED)
inp = input.Input("train3-std.tsv", randSample)
print "Test set:",
inp.testSet.show()
param = Parameters.from_file(annfile)
tester = SampleTester()
tester.prepare(inp.testSet, randSample)
tester.showSampleSets()
avg, std = tester.test(param)
print "Lift:", "avg: %.03f" % avg, "std: %.03f" % std
