def nudge(self, max_value, max_nudge, nudge_rate):
for i in range(len(self.genes)):
if randdouble() < nudge_rate:
new_value = self.genes[i] + randint_spread(max_nudge)
new_value = max(new_value, 0)
new_value = min(new_value, max_value)
self.genes[i] = new_value
def nudge(self, max_value, max_nudge, nudge_rate):
for i in range(len(self.genes)):
if randdouble() < nudge_rate:
new_value = self.genes[i] + randint_spread(max_nudge)
new_value = max(new_value, 0)
new_value = min(new_value, max_value)
self.genes[i] = new_value
def run_generation(self):
evaluated_chromosomes = self.chromosomes[:self.population_size -
self.turnover_size]
# Crossover works best on two that are both pretty good.
# Currently crossing 0 and 1, 2 and 3, etc. so that none
# get their offspring into the pool more than once.
for i in range(self.turnover_size / 2):
parentA = evaluated_chromosomes[(2 * i)]
parentB = evaluated_chromosomes[(2 * i) + 1]
evaluated_chromosomes += parentA.crossover(parentB)
if len(evaluated_chromosomes) != self.population_size:
raise Exception("Noooo we lost or gained chromosomes")
for i in range(self.population_size):
if randdouble() < self.mutation_rate:
evaluated_chromosomes[i].mutate(
self.master_chromosome.max_gene)
self.chromosomes = evaluated_chromosomes
self.sort_population()
best = self.chromosomes[0]
best_anti_fitness = self.master_chromosome.anti_fitness(best)
return best, best_anti_fitness
def run_generation(self):
evaluated_chromosomes = self.chromosomes[:self.population_size - self.turnover_size]
# Crossover works best on two that are both pretty good.
# Currently crossing 0 and 1, 2 and 3, etc. so that none
# get their offspring into the pool more than once.
for i in range(self.turnover_size / 2):
parentA = evaluated_chromosomes[(2 * i)]
parentB = evaluated_chromosomes[(2 * i) + 1]
evaluated_chromosomes += parentA.crossover(parentB)
if len(evaluated_chromosomes) != self.population_size:
raise Exception("Noooo we lost or gained chromosomes")
for i in range(self.population_size):
if randdouble() < self.mutation_rate:
evaluated_chromosomes[i].mutate(self.master_chromosome.max_gene)
self.chromosomes = evaluated_chromosomes
self.sort_population()
best = self.chromosomes[0]
best_anti_fitness = self.master_chromosome.anti_fitness(best)
return best, best_anti_fitness
from random import randint
from random import random as randdouble
from scikits import audiolab
import numpy as np
import fitness as fitness_metric
num_zero_crossings_per_chunk = ((10,20), (20, 40), (40, 80)) #
fitness_metric.use_rms_centroid()
randint_spread = lambda x: int(round(randdouble()*2*x - x))
class Chromosome:
def __init__(self, genes):
self.genes = genes
def crossover(self, other):
max_crossing_point = min(len(self.genes), len(other.genes))
crossing_point = randint(0, max_crossing_point)
child1 = Chromosome(self.genes[:crossing_point] + other.genes[crossing_point:])
child2 = Chromosome(other.genes[:crossing_point] + self.genes[crossing_point:])
return (child1, child2)
def mutate(self, max_value):
index = randint(0, len(self.genes) - 1)
value = randint(0, max_value)
self.genes[index] = value
def nudge(self, max_value, max_nudge, nudge_rate):
for i in range(len(self.genes)):
if randdouble() < nudge_rate:
new_value = self.genes[i] + randint_spread(max_nudge)
from random import randint
from random import random as randdouble
from scikits import audiolab
import numpy as np
import fitness as fitness_metric
num_zero_crossings_per_chunk = ((10, 20), (20, 40), (40, 80)) #
fitness_metric.use_rms_centroid()
randint_spread = lambda x: int(round(randdouble() * 2 * x - x))
class Chromosome:
def __init__(self, genes):
self.genes = genes
def crossover(self, other):
max_crossing_point = min(len(self.genes), len(other.genes))
crossing_point = randint(0, max_crossing_point)
child1 = Chromosome(self.genes[:crossing_point] +
other.genes[crossing_point:])
child2 = Chromosome(other.genes[:crossing_point] +
self.genes[crossing_point:])
return (child1, child2)
def mutate(self, max_value):
index = randint(0, len(self.genes) - 1)
value = randint(0, max_value)
self.genes[index] = value
def nudge(self, max_value, max_nudge, nudge_rate):
