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geneticvehicle.py
526 lines (430 loc) · 32.9 KB
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geneticvehicle.py
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#!/usr/bin/env python
# encoding: utf-8
from __future__ import division
from IPython.lib.latextools import genelatex
from pyopencl import clrandom
from pyopencl import array
from pyopencl import scan
from functools import partial
from itertools import izip_longest
import pyopencl
import time
import numpy
import sys
import os
def load_file(filename):
with open(filename) as file_handler:
return "".join(file_handler.readlines())
def xgrouper(n, iterable, fillvalue=None):
return zip(*[iter(iterable)]*n)
def grouper(n, iterable, fillvalue=None):
"grouper(3, 'abcdefg', 'x') --> ('a','b','c'), ('d','e','f'), ('g','x','x')"
return izip_longest(*[iter(iterable)]*n, fillvalue=fillvalue)
class GeneticVehicle(object):
def __init__(self, number_of_cars=1024, number_of_vertices_per_car=4, number_of_wheels_per_car=3):
self.number_of_vertices_per_car = number_of_vertices_per_car
self.number_of_cars = number_of_cars
self.number_of_wheels_per_car = number_of_wheels_per_car
self.number_of_contact_points = 4
self.density = 0.5
self.counter = 0
self.steps = 1
self.delta = 1/16
self.satisfy_constraints = 4
self.crossover_points = 2
self.point_mutations = 1
self.process = False
self.generation = 1
self.run = False
self.island = SlopeXtreme()
# vehicle parameters, used to build vehicle and for mutation
self.min_radius = 5
self.max_radius = 8
self.angle_displacement=0.5
self.min_magnitude = 0
self.max_magnitude = 15
self.build()
def build(self):
self.compile_source()
self.generate_data_structures()
def compile_source(self):
self.context = pyopencl.create_some_context()
self.queue = pyopencl.CommandQueue(self.context)
self.mf = pyopencl.mem_flags
opencl_source = load_file("geneticvehicle.cl") % {
"vertices_per_car" : self.number_of_vertices_per_car,
"number_of_cars" : self.number_of_cars,
"density" : self.density,
"number_of_wheels" : self.number_of_wheels_per_car,
"number_of_contact_points" : self.number_of_contact_points,
"island_start" : self.island.island_start,
"island_step" : self.island.island_step,
"island_end" : self.island.island_end,
"island_acceleration" : int(self.island.island_acceleration),
"island_range" : self.island.range(),
"crossover_points" : self.crossover_points,
"point_mutations" : self.point_mutations}
self.program = pyopencl.Program(self.context, opencl_source)
try:
self.program.build()
except Exception as why:
print why
print(self.program.get_build_info(self.context.devices[0], pyopencl.program_build_info.LOG))
def simulation_step(self, pre_callback=None, post_callback=None):
if self.process or self.run:
self.process = False
if pre_callback:
pre_callback()
for round in range(self.steps):
self.counter += 1
self.program.calculate_loads(self.queue, (self.number_of_cars,), None, self.vehicle_forces.data,
self.vehicle_momenta.data,
self.vehicle_velocities.data,
self.vehicle_angular_velocities.data)
self.program.integrate(self.queue, (self.number_of_cars,), None, self.vehicle_alive.data,
self.vehicle_positions.data,
self.vehicle_masses.data,
self.vehicle_forces.data,
self.vehicle_velocities.data,
self.vehicle_angular_velocities.data,
self.vehicle_orientations.data,
self.vehicle_momenta.data,
self.vehicle_inertias.data,
numpy.float32(self.delta),
self.wheel_angular_velocities.data,
self.wheel_radii.data,
self.wheel_momenta.data,
self.wheel_masses.data,
self.wheel_inertias.data,
self.wheel_orientations.data,
self.vehicle_vertices.data,
self.wheel_vertex_positions.data)
for _ in range(self.satisfy_constraints):
self.program.collision(self.queue, (self.number_of_cars,), None, self.vehicle_alive.data,
self.vehicle_positions.data,
self.vehicle_masses.data,
self.vehicle_velocities.data,
self.vehicle_orientations.data,
self.geometry.data,
numpy.int32(len(self.geometry)),
self.vehicle_vertices.data,
self.vehicle_inertias.data,
self.vehicle_angular_velocities.data,
self.vehicle_contact_points.data,
self.vehicle_contact_normals.data,
self.vehicle_center_masses.data,
numpy.float32(self.delta),
self.wheel_vertex_positions.data,
self.wheel_radii.data,
self.vehicle_bounding_volumes.data,
self.wheel_momenta.data,
self.wheel_masses.data)
self.program.assign_score(self.queue, (self.number_of_cars,), None, self.vehicle_positions.data, self.vehicle_velocities.data, self.vehicle_score.data)
if not self.counter % 500:
self.program.evaluate_score(self.queue, (self.number_of_cars,), None, self.vehicle_score.data, self.vehicle_old_score.data, self.vehicle_alive.data)
self.vehicle_score, self.vehicle_old_score = self.vehicle_old_score, self.vehicle_score
if post_callback:
post_callback()
def generate_data_structures(self, wheel_vertex_positions=None, wheel_radii=None, magnitudes=None, angles=None, vertex_colors=None):
self.work_items = 64*self.queue.device.max_compute_units
self.vehicle_positions = pyopencl.array.Array(self.queue, (self.number_of_cars, 2), dtype=numpy.float32)
self.vehicle_score = pyopencl.array.Array(self.queue, self.number_of_cars, dtype=numpy.float32)
self.vehicle_old_score = pyopencl.array.Array(self.queue, self.number_of_cars, dtype=numpy.float32)
# for testing purposes
centers = numpy.zeros((self.number_of_cars, 2), dtype=numpy.float32)
for index, x in enumerate(numpy.nditer(centers, op_flags=['readwrite'])):
if index % 2 == 0:
#x[...] = (index-2) * 10
x[...] = 0
else:
x[...] = -20
#x[...] = -20-2*index
#x[...] = -0.5*(index-2)**2
self.vehicle_positions.set(centers)
self.generate_vertices(magnitudes=magnitudes, angles=angles, vertex_colors=vertex_colors)
self.generate_vehicle_properties()
self.generate_wheel_properties(wheel_vertex_positions=wheel_vertex_positions, wheel_radii=wheel_radii)
self.generate_bounding_volumes()
# for testing purposes
#pyopencl.clrandom.RanluxGenerator(queue, 64*queue.device.max_compute_units, seed=time.time()).fill_uniform(self.vehicle_alive, a=0.75, b=1.25)
self.geometry_points = self.island.generate_geometry()
self.geometry = pyopencl.array.zeros(self.queue, (len(self.geometry_points), 2), dtype=numpy.float32)
self.geometry.set(numpy.array(self.geometry_points, dtype=numpy.float32))
def generate_bounding_volumes(self):
self.vehicle_bounding_volumes = pyopencl.array.Array(self.queue, self.number_of_cars, dtype=numpy.float32)
self.program.generate_bounding_volumes(self.queue, (self.number_of_cars,), None, self.vehicle_vertices.data, self.wheel_vertex_positions.data, self.wheel_radii.data, self.vehicle_bounding_volumes.data)
def generate_wheel_properties(self, wheel_vertex_positions=None, wheel_radii=None):
if not wheel_vertex_positions:
self.wheel_vertex_positions = pyopencl.array.zeros(self.queue, (self.number_of_cars*self.number_of_wheels_per_car), dtype=numpy.int32)
pyopencl.clrandom.RanluxGenerator(self.queue, self.work_items, seed=time.time()).fill_uniform(self.wheel_vertex_positions, a=0, b=self.number_of_vertices_per_car)
else:
self.wheel_vertex_positions = wheel_vertex_positions
if not wheel_radii:
self.wheel_radii = pyopencl.array.zeros(self.queue, (self.number_of_cars*self.number_of_wheels_per_car), dtype=numpy.float32)
pyopencl.clrandom.RanluxGenerator(self.queue, self.work_items, seed=time.time()).fill_uniform(self.wheel_radii, a=self.min_radius, b=self.max_radius)
else:
self.wheel_radii = wheel_radii
self.wheel_masses = pyopencl.array.Array(self.queue, (self.number_of_cars*self.number_of_wheels_per_car), dtype=numpy.float32)
self.wheel_inertias = pyopencl.array.Array(self.queue, (self.number_of_cars*self.number_of_wheels_per_car), dtype=numpy.float32)
self.wheel_angular_velocities = pyopencl.array.zeros(self.queue, (self.number_of_cars*self.number_of_wheels_per_car), dtype=numpy.float32)
self.wheel_momenta = pyopencl.array.zeros(self.queue, (self.number_of_cars*self.number_of_wheels_per_car), dtype=numpy.float32)
self.wheel_orientations = pyopencl.array.zeros(self.queue, (self.number_of_cars*self.number_of_wheels_per_car), dtype=numpy.float32)
self.program.generate_wheel_properties(self.queue, (self.number_of_cars*self.number_of_wheels_per_car,), None, self.wheel_masses.data, self.wheel_inertias.data, self.wheel_radii.data)
def generate_vehicle_properties(self):
self.vehicle_masses = pyopencl.array.Array(self.queue, self.number_of_cars, dtype=numpy.float32)
self.vehicle_alive = pyopencl.array.Array(self.queue, self.number_of_cars, dtype=numpy.float32)
self.vehicle_alive.fill(1)
self.vehicle_inertias = pyopencl.array.Array(self.queue, self.number_of_cars, dtype=numpy.float32)
self.vehicle_center_masses = pyopencl.array.Array(self.queue, (self.number_of_cars, 2), dtype=numpy.float32)
self.vehicle_velocities = pyopencl.array.zeros(self.queue, (self.number_of_cars, 2), dtype=numpy.float32)
self.vehicle_contact_points = pyopencl.array.zeros(self.queue, (self.number_of_cars*self.number_of_contact_points, 2), dtype=numpy.float32)
self.vehicle_contact_normals = pyopencl.array.zeros(self.queue, (self.number_of_cars*self.number_of_contact_points, 2), dtype=numpy.float32)
self.vehicle_angular_velocities = pyopencl.array.zeros(self.queue, self.number_of_cars, dtype=numpy.float32)
self.vehicle_orientations = pyopencl.array.zeros(self.queue, self.number_of_cars, dtype=numpy.float32)
self.vehicle_forces = pyopencl.array.zeros(self.queue, (self.number_of_cars,2), dtype=numpy.float32)
self.vehicle_momenta = pyopencl.array.zeros(self.queue, self.number_of_cars, dtype=numpy.float32)
pyopencl.clrandom.RanluxGenerator(self.queue, self.work_items, seed=time.time()).fill_uniform(self.vehicle_momenta, a=-25000, b=25000)
self.vehicle_vertices.reshape((self.number_of_cars*self.number_of_vertices_per_car, 2))
self.program.generate_vehicle_properties(self.queue, (self.number_of_cars,), None, self.vehicle_masses.data, self.vehicle_center_masses.data, self.vehicle_inertias.data, self.vehicle_vertices.data)
def generate_vertices(self, chaos=False, magnitudes=None, angles=None, vertex_colors=None):
if not magnitudes:
self.magnitudes = pyopencl.array.Array(self.queue, (self.number_of_cars*self.number_of_vertices_per_car),dtype=numpy.float32)
pyopencl.clrandom.RanluxGenerator(self.queue, self.work_items, seed=time.time()).fill_uniform(self.magnitudes, a=self.min_magnitude, b=self.max_magnitude)
else:
self.magnitudes = magnitudes
if not angles:
offset_angles = pyopencl.array.zeros(self.queue, (self.number_of_cars*self.number_of_vertices_per_car, 1), dtype=numpy.float32)
pyopencl.clrandom.RanluxGenerator(self.queue, self.work_items, seed=time.time()).fill_uniform(offset_angles, a=-self.angle_displacement, b=self.angle_displacement)
self.angles = pyopencl.array.zeros(self.queue, (self.number_of_cars*self.number_of_vertices_per_car, 1), dtype=numpy.float32)
if chaos:
pyopencl.clrandom.RanluxGenerator(self.queue, self.work_items, seed=time.time()).fill_uniform(self.angles, a=0, b=2*numpy.pi)
else:
ordered_angles = pyopencl.array.arange(self.queue, 0, 2*numpy.pi, (2*numpy.pi)/self.number_of_vertices_per_car, dtype=numpy.float32)
self.program.generate_angles(self.queue, self.angles.shape, None, self.angles.data, ordered_angles.data)
self.angles += offset_angles
else:
self.angles = angles
if not vertex_colors:
vehicle_colors = pyopencl.array.Array(self.queue, (self.number_of_cars, 4) , dtype=numpy.float32)
pyopencl.clrandom.RanluxGenerator(self.queue, self.work_items, seed=time.time()).fill_uniform(vehicle_colors, a=0, b=255)
self.vertex_colors = pyopencl.array.Array(self.queue, (self.number_of_cars*self.number_of_vertices_per_car, 4) , dtype=numpy.float32)
self.program.generate_colors(self.queue, (self.number_of_cars, 1), None, self.vertex_colors.data, vehicle_colors.data)
else:
self.vertex_colors = vertex_colors
self.vehicle_vertices = pyopencl.array.Array(self.queue, (self.number_of_cars*self.number_of_vertices_per_car, 2), dtype=numpy.float32)
self.program.generate_vertices(self.queue, self.vehicle_vertices.shape, None, self.magnitudes.data, self.angles.data, self.vehicle_vertices.data)
def get_sorted_score_ids(self):
return self.vehicle_score.get().argsort()[::-1]
def get_vehicle_information(self, vehicle_id):
center = self.vehicle_positions.get()[vehicle_id]
vertices = xgrouper(self.number_of_vertices_per_car, self.vehicle_vertices.get())[vehicle_id]
bounding_radius = self.vehicle_bounding_volumes.get()[vehicle_id]
vertex_colors = xgrouper(self.number_of_vertices_per_car, self.vertex_colors.get())[vehicle_id]
wheel_radii = xgrouper(self.number_of_wheels_per_car, self.wheel_radii.get())[vehicle_id]
wheel_vertex = xgrouper(self.number_of_wheels_per_car, self.wheel_vertex_positions.get())[vehicle_id]
return center, vertices, bounding_radius, vertex_colors, wheel_radii, wheel_vertex
def get_chromosome(self, vehicle_id):
columns = ""
values = ""
magnitudes = xgrouper(self.number_of_vertices_per_car, self.magnitudes.get())[vehicle_id]
angles = xgrouper(self.number_of_vertices_per_car, self.angles.get())[vehicle_id]
wheel_radii = xgrouper(self.number_of_wheels_per_car, self.wheel_radii.get())[vehicle_id]
wheel_vertex = xgrouper(self.number_of_wheels_per_car, self.wheel_vertex_positions.get())[vehicle_id]
data = {"magnitudes": magnitudes,
"angles": angles,
"radii": wheel_radii,
"wheel vertex": wheel_vertex}
for column_name, values in data.items():
for index, value in enumerate(values):
columns += column_name+str(index) + "\t"
values += str(value) + "\t"
#columns += "\t"
#valuess += "\t"
return columns + "\n" + values
def evolve(self):
# TODO dependancies are clearly wrong here
from visualization import display_vehicles
def generate(wheel_vertex_positions, wheel_radii, magnitudes, angles, vertex_colors):
self.generate_data_structures(wheel_vertex_positions=wheel_vertex_positions,
wheel_radii=wheel_radii,
magnitudes=magnitudes,
angles=angles,
vertex_colors=vertex_colors)
if self.number_of_cars > 2:
display_vehicles(genetic_vehicle, range(self.number_of_cars), filename="%s_1_pool" % genetic_vehicle.generation)
display_vehicles(genetic_vehicle, genetic_vehicle.get_sorted_score_ids()[0:10], filename="%s_2_top" % genetic_vehicle.generation)
#selection = self.roulette_wheel_selection(population_size=int(self.number_of_cars/2))
selection = self.roulette_wheel_selection(population_size=int(self.number_of_cars))
print "Chromosome\n", genetic_vehicle.get_chromosome(genetic_vehicle.get_sorted_score_ids()[0])
if selection:
display_vehicles(genetic_vehicle, selection.get(), filename="%s_3_selection" % genetic_vehicle.generation)
crossover = self.crossover(selection)
generate(*crossover)
display_vehicles(genetic_vehicle, range(self.number_of_cars), filename="%s_4_crossover" % genetic_vehicle.generation)
mutation = self.mutate(*crossover)
generate(*mutation)
display_vehicles(genetic_vehicle, range(self.number_of_cars), filename="%s_5_mutation" % genetic_vehicle.generation)
self.generation += 1
return True
def mutate(self, wheel_vertex_positions, wheel_radii, magnitudes, angles, vertex_colors):
# TODO the code below needs some serious refactorization
if self.point_mutations > 0:
# mutate magnitude, angle, vehicle_radii and wheel vertex position separately
mutation_colors = pyopencl.array.Array(self.queue, ((self.number_of_cars*self.point_mutations), 4), dtype=numpy.float32)
pyopencl.clrandom.RanluxGenerator(self.queue, self.work_items, seed=time.time()).fill_uniform(mutation_colors, a=0, b=255)
mutated_magnitudes = pyopencl.array.Array(self.queue, (self.number_of_cars*self.point_mutations), dtype=numpy.float32)
pyopencl.clrandom.RanluxGenerator(self.queue, self.work_items, seed=time.time()).fill_uniform(mutated_magnitudes, a=self.min_magnitude, b=self.max_magnitude)
mutation_indexes = pyopencl.array.Array(self.queue, (self.number_of_cars*self.point_mutations), dtype=numpy.int32)
pyopencl.clrandom.RanluxGenerator(self.queue, self.work_items, seed=time.time()).fill_uniform(mutation_indexes, a=0, b=self.number_of_vertices_per_car)
self.program.mutate(self.queue, (self.number_of_cars, 1), None, magnitudes.data, mutation_indexes.data, mutated_magnitudes.data, mutation_colors.data, vertex_colors.data, numpy.int32(self.number_of_vertices_per_car), numpy.int32(0))
mutation_colors = pyopencl.array.Array(self.queue, ((self.number_of_cars*self.point_mutations), 4), dtype=numpy.float32)
pyopencl.clrandom.RanluxGenerator(self.queue, self.work_items, seed=time.time()).fill_uniform(mutation_colors, a=0, b=255)
mutated_angles = pyopencl.array.Array(self.queue, (self.number_of_cars*self.point_mutations), dtype=numpy.float32)
pyopencl.clrandom.RanluxGenerator(self.queue, self.work_items, seed=time.time()).fill_uniform(mutated_angles, a=-self.angle_displacement, b=self.angle_displacement)
mutation_indexes = pyopencl.array.Array(self.queue, (self.number_of_cars*self.point_mutations), dtype=numpy.int32)
pyopencl.clrandom.RanluxGenerator(self.queue, self.work_items, seed=time.time()).fill_uniform(mutation_indexes, a=0, b=self.number_of_vertices_per_car)
self.program.mutate(self.queue, (self.number_of_cars, 1), None, angles.data, mutation_indexes.data, mutated_angles.data, mutation_colors.data, vertex_colors.data, numpy.int32(self.number_of_vertices_per_car), numpy.int32(1))
mutation_colors = pyopencl.array.Array(self.queue, ((self.number_of_cars*self.point_mutations), 4), dtype=numpy.float32)
pyopencl.clrandom.RanluxGenerator(self.queue, self.work_items, seed=time.time()).fill_uniform(mutation_colors, a=0, b=255)
mutated_radii = pyopencl.array.Array(self.queue, (self.number_of_cars*self.point_mutations), dtype=numpy.float32)
pyopencl.clrandom.RanluxGenerator(self.queue, self.work_items, seed=time.time()).fill_uniform(mutated_radii, a=self.min_radius, b=self.max_radius)
mutation_indexes = pyopencl.array.Array(self.queue, (self.number_of_cars*self.point_mutations), dtype=numpy.int32)
pyopencl.clrandom.RanluxGenerator(self.queue, self.work_items, seed=time.time()).fill_uniform(mutation_indexes, a=0, b=self.number_of_wheels_per_car)
self.program.mutate(self.queue, (self.number_of_cars, 1), None, wheel_radii.data, mutation_indexes.data, mutated_radii.data, mutation_colors.data, vertex_colors.data, numpy.int32(self.number_of_wheels_per_car), numpy.int32(0))
mutation_colors = pyopencl.array.Array(self.queue, ((self.number_of_cars*self.point_mutations), 4), dtype=numpy.float32)
pyopencl.clrandom.RanluxGenerator(self.queue, self.work_items, seed=time.time()).fill_uniform(mutation_colors, a=0, b=255)
mutated_wheel_vertex_positions = pyopencl.array.Array(self.queue, (self.number_of_cars*self.point_mutations), dtype=numpy.int32)
pyopencl.clrandom.RanluxGenerator(self.queue, self.work_items, seed=time.time()).fill_uniform(mutated_wheel_vertex_positions, a=0, b=self.number_of_vertices_per_car)
mutation_indexes = pyopencl.array.Array(self.queue, (self.number_of_cars*self.point_mutations), dtype=numpy.int32)
pyopencl.clrandom.RanluxGenerator(self.queue, self.work_items, seed=time.time()).fill_uniform(mutation_indexes, a=0, b=self.number_of_wheels_per_car)
self.program.mutate(self.queue, (self.number_of_cars, 1), None, wheel_vertex_positions.data, mutation_indexes.data, mutated_wheel_vertex_positions.data, mutation_colors.data, vertex_colors.data, numpy.int32(self.number_of_wheels_per_car), numpy.int32(0))
return wheel_vertex_positions, wheel_radii, magnitudes, angles, vertex_colors
def roulette_wheel_selection(self, population_size=32):
# calculate sum over all scores
total_score = pyopencl.array.sum(self.vehicle_score).get()
if total_score > 0:
from pyopencl.elementwise import ElementwiseKernel
roulette_wheel_probabilities = ElementwiseKernel(self.context,
"float total_score, float *scores, "
"float *probabilities",
"probabilities[i] = scores[i]/total_score",
"roulette_wheel_probabilities")
probabilities = pyopencl.array.empty_like(self.vehicle_score)
roulette_wheel_probabilities(total_score, self.vehicle_score, probabilities)
accumulated_probabilities_kernel = pyopencl.scan.InclusiveScanKernel(self.context, numpy.float32, "a+b")
accumulated_probabilities_kernel(probabilities)
selection_probabilities = pyopencl.array.Array(self.queue, population_size, dtype=numpy.float32)
pyopencl.clrandom.RanluxGenerator(self.queue, self.work_items, seed=time.time()).fill_uniform(selection_probabilities)
population_indexes = pyopencl.array.Array(self.queue, population_size, dtype=numpy.uint32)
self.program.roulette_wheel_selection(self.queue, (population_size,), None, selection_probabilities.data, probabilities.data, population_indexes.data, numpy.uint32(self.number_of_cars))
associated_scores = array.take(self.vehicle_score, population_indexes, queue=self.queue)
return population_indexes
def crossover(self, indexes):
# remember old magnitudes and angles
old_magnitudes = self.magnitudes
old_angles = self.angles
old_number_of_cars = self.number_of_cars
old_wheel_vertex_positions = self.wheel_vertex_positions
old_wheel_radii = self.wheel_radii
old_vertex_colors = self.vertex_colors
self.number_of_cars = len(indexes)
# create magnitude and angle arrays for offspring
magnitudes = pyopencl.array.Array(self.queue, (self.number_of_cars*self.number_of_vertices_per_car), dtype=numpy.float32)
angles = pyopencl.array.Array(self.queue, (self.number_of_cars*self.number_of_vertices_per_car), dtype=numpy.float32)
vertex_colors = pyopencl.array.Array(self.queue, (self.number_of_cars*self.number_of_vertices_per_car, 4), dtype=numpy.float32)
# create wheel vertex position and wheel radii for offspring
wheel_vertex_positions = pyopencl.array.Array(self.queue, (self.number_of_cars*self.number_of_wheels_per_car), dtype=numpy.int32)
wheel_radii = pyopencl.array.Array(self.queue, (self.number_of_cars*self.number_of_wheels_per_car), dtype=numpy.float32)
crossover_magnitude_array = pyopencl.array.Array(self.queue, (self.number_of_cars*self.crossover_points), dtype=numpy.int32)
pyopencl.clrandom.RanluxGenerator(self.queue, self.work_items, seed=time.time()).fill_uniform(crossover_magnitude_array, a=0, b=self.number_of_vertices_per_car)
crossover_angle_array = pyopencl.array.Array(self.queue, (self.number_of_cars*self.crossover_points), dtype=numpy.int32)
pyopencl.clrandom.RanluxGenerator(self.queue, self.work_items, seed=time.time()).fill_uniform(crossover_angle_array, a=0, b=self.number_of_vertices_per_car)
crossover_wheel_array = pyopencl.array.Array(self.queue, (self.number_of_cars*self.crossover_points), dtype=numpy.int32)
pyopencl.clrandom.RanluxGenerator(self.queue, self.work_items, seed=time.time()).fill_uniform(crossover_wheel_array, a=0, b=self.number_of_wheels_per_car)
self.program.crossover(self.queue, (int(self.number_of_cars/2),), None, magnitudes.data,
angles.data,
vertex_colors.data,
old_magnitudes.data,
old_angles.data,
old_vertex_colors.data,
wheel_vertex_positions.data,
wheel_radii.data,
old_wheel_vertex_positions.data,
old_wheel_radii.data,
indexes.data,
crossover_magnitude_array.data,
crossover_angle_array.data,
crossover_wheel_array.data)
return wheel_vertex_positions, wheel_radii, magnitudes, angles, vertex_colors
class Island(object):
def __init__(self, start=-20000, end=20000, step=50):
self.island_acceleration = True
self.island_start = start
self.island_end = end
self.island_step = step
self.x_values = numpy.linspace(self.island_start, self.island_end, self.range()/self.island_step)
self.y_values = numpy.random.random_sample(self.range()/self.island_step)*10
def range(self):
return numpy.abs(self.island_start)+numpy.abs(self.island_end)
def generate_geometry(self):
return numpy.array(zip(self.x_values, self.y_values), dtype=numpy.float32)
class Rocky(Island):
def __init__(self):
Island.__init__(self, step=5)
self.y_values = numpy.random.random_sample(self.range()/self.island_step)*90
class Slope(Island):
def __init__(self):
Island.__init__(self, step=20)
right_slope = -5*numpy.arange(self.range()/self.island_step/2)
self.y_values = 15*numpy.random.random_sample(self.range()/self.island_step)+(numpy.concatenate((right_slope[::-1], right_slope)))
class SlopeXtreme(Island):
def __init__(self):
Island.__init__(self, step=10)
right_slope = -5*numpy.arange(self.range()/self.island_step/2)
right_multiplier = 0.0025*numpy.arange(self.range()/self.island_step/2)**2
left_multiplier = numpy.zeros(self.range()/self.island_step/2)+0
self.y_values = numpy.concatenate((left_multiplier, right_multiplier))*numpy.random.random_sample(self.range()/self.island_step)+(numpy.concatenate((-right_slope[::-1], right_slope)))
class Parabola(Island):
def __init__(self):
Island.__init__(self)
right_side = -0.05*numpy.arange(self.range()/self.island_step/2)**2
self.y_values = numpy.concatenate((right_side[::-1], right_side))
def show_histogram(genetic_vehicle):
import matplotlib.pyplot as plt
bins = 64
plt.clf()
plt.hist(genetic_vehicle.vehicle_score.get(), bins)
plt.title("Score Distribution (population size: %s, generation: %s)" % (genetic_vehicle.number_of_cars, genetic_vehicle.generation))
plt.xlabel("Scores (%s bins)" % bins)
plt.ylabel("Count")
plt.ion()
plt.draw()
plt.show()
return plt
def save_histogram(plt):
results_directory = "results"
if not os.path.isdir(results_directory):
os.mkdir(results_directory)
plt.savefig(os.path.join(results_directory, "figure%s.png" % genetic_vehicle.generation))
if __name__ == '__main__':
genetic_vehicle = GeneticVehicle()
genetic_vehicle.steps = 50
genetic_vehicle.run = True
genetic_vehicle.number_of_cars = 1024
def callback(counter):
if genetic_vehicle.number_of_cars < 4:
print "number of cars too low"
sys.exit()
if counter % 4 == 0:
alive_total_ratio = pyopencl.array.sum(genetic_vehicle.vehicle_alive).get()/genetic_vehicle.number_of_cars*100
print "alive/total ratio", alive_total_ratio
if alive_total_ratio < genetic_vehicle.number_of_cars/64 or counter > 10**5:
print "evolving"
save_histogram(show_histogram(genetic_vehicle))
if not genetic_vehicle.evolve():
print "evolving failed"
sys.exit()
counter = 0
while True:
genetic_vehicle.simulation_step(post_callback=partial(callback, counter))
counter += 1