class ES(object):
def __init__(self,knapsack_file="weing1.pkl"):
super(ES, self).__init__()
# GA stuff
self.generations = 100
self.knapsack = pickle.load(open(knapsack_file))
print "k:",self.knapsack
self.N = int(self.knapsack.items)
# RMB stuff
self.RBM = RBM(n_visible=self.N,n_hidden=50)
self.sample_RBM()
# Stats stuff
self.population_snapshots = []
self.genotypes_history = Genotypes(min=False)
def create_individual(self,N):
I = Individual()
I.genotype = [random.choice([0,1]) for i in range(N)]
I.fitness = 0
return I
def fitness_function(self,individual,knapsack=None):
weights = []
for i,c in enumerate(knapsack.capacities):
weights.append(np.sum(np.array(knapsack.constraints[i])*individual.genotype))
over = 0
for i,w in enumerate(weights):
if w > knapsack.capacities[i]:
over += (w - knapsack.capacities[i])
if over > 0:
return -over
else:
return np.sum(np.array(knapsack.values)*individual.genotype)
def evaluate_population(self,population,params=None):
for p in population:
p.fitness = self.fitness_function(p,params)
def normalise_fitnesses(self,population):
max_fitness = np.max([p.fitness for p in population])
min_fitness = np.min([p.fitness for p in population])
for p in population:
p.normalised_fitness = (p.fitness + min_fitness)/(min_fitness+max_fitness)
def offspring_from_sample(self,individual_to_copy):
individual = copy.deepcopy(individual_to_copy)
individual_genome = np.array(individual.genotype).reshape(1,-1)
output = self.sample_from_RBM(np.array(individual_genome))
# print "output:",output
individual.genotype[:] = output[0][:]
return individual
def train_RBM(self,k=20,lr=0.1):
train_data = self.genotypes_history.top_x_percent()
train_set = SequenceDataset(train_data,batch_size=20,number_batches=None)
inputs,params,cost,monitor,updates,consider_constant = self.RBM.build_RBM(k=k)
sgd_optimizer(params,[inputs],cost,train_set,updates_old=updates,monitor=monitor,
consider_constant=[consider_constant],lr=0.1,num_epochs=10)
def sample_RBM(self,k=20):
v,v_sample,updates = self.RBM.sample_RBM(k=k)
self.sample_from_RBM = theano.function([v],v_sample,updates=updates)
def run_1_plus_1(self, path= "", experiment = 0):
random.seed(random.uniform(0,1000000))
print("Start of evolution")
parent = self.create_individual(self.N)
# Evaluate the parent
parent.fitness = self.fitness_function(parent,self.knapsack)
self.genotypes_history.add_genotypes([parent])
self.genotypes_history.get_and_save_top_x(1.0)
self.train_RBM()
# Begin the evolution
for g in range(self.generations):
print("-- Generation %i --" % (g + 1))
offspring = self.offspring_from_sample(parent)
offspring.fitness = self.fitness_function(parent,self.knapsack)
print "parent_fitness:",parent.fitness
print "offspring_fitness:",offspring.fitness
if offspring.fitness > parent.fitness:
print "offspring replacing parent"
parent = offspring
self.genotypes_history.add_genotypes([offspring])
self.genotypes_history.get_and_save_top_x(1.0)
self.train_RBM()
print("-- End of (successful) evolution --")
return parent
def run_mu_plus_lambda(self, path= "", experiment = 0):
population_size = 50
random.seed(random.uniform(0,1000000))
print("Start of evolution")
population = [self.create_individual(self.N) for i in range(population_size)]
# Evaluate the population
self.evaluate_population(population,self.knapsack)
self.genotypes_history.add_genotypes(population)
self.genotypes_history.get_and_save_top_x(1.0)
self.train_RBM()
# Begin the evolution
for g in range(self.generations):
print("-- Generation %i --" % (g + 1))
offspring = []
for ind in population:
offspring.append(self.offspring_from_sample(ind))
self.evaluate_population(offspring,self.knapsack)
self.genotypes_history.add_genotypes(offspring)
self.genotypes_history.get_and_save_top_x(1.0)
self.train_RBM()
new_population = []
population = population + offspring
while len(new_population) < population_size:
# tournament selection on combined population
a = int(len(population) * random.random())
b = int(len(population) * random.random())
while a == b:
b = int(len(population) * random.random())
if population[a].fitness > population[b].fitness:
new_population.append(population.pop(a))
else:
new_population.append(population.pop(b))
population = new_population
print "average fitness:",np.mean([p.fitness for p in population])
print "max fitness:",np.max([p.fitness for p in population])
print "min fitness:",np.min([p.fitness for p in population])
print("-- End of (successful) evolution --")
return parent
