def __init__(self,
genome,
twobit_file=None,
remote_fn=None,
groups=None,
trackdb=None,
genome_file_obj=None,
html_string=None,
**kwargs):
"""
Represents a genome stanza within a "genomes.txt" file for a non-UCSC genome.
The file itself is represented by a :class:`GenomesFile` object.
"""
HubComponent.__init__(self)
Genome.__init__(self, genome, trackdb=trackdb, genome_file_obj=genome_file_obj)
self.local_fn = twobit_file
self.remote_fn = remote_fn
self.html_string = html_string
if groups is not None:
self.add_groups(groups)
else:
self.groups = None
self._orig_kwargs = kwargs
self.add_params(**kwargs)
class Organism(object):
""" Wrapper class that provides fitness metrics. """
def __init__(self, genome):
self.genome = Genome(genome)
self.policy = Network(self.genome)
self.evals = list()
def __cmp__(self, other):
return cmp(self.fitness, other.fitness)
def __str__(self):
return '%.3f' % self.fitness
def crossover(self, other):
""" Return a new organism by recombining the parents. """
return Organism(self.genome.crossover(other.genome))
def mutate(self, frac=0.1, std=1.0, repl=0.25):
""" Mutate the organism by mutating its genome. """
self.genome.mutate(frac, std, repl)
self.policy = Network(self.genome)
def copy(self):
""" Return a deep copy of this organism. """
org = Organism(self.genome)
org.evals = list(self.evals)
return org
@property
def fitness(self):
""" Average return. """
try:
return sum(self.evals, 0.) / len(self.evals)
except ZeroDivisionError:
return 0.
class GenomeHub(object):
"""
Hub for a specific genome such as hg18, mm9
"""
def __init__(self, name):
self._name = name
self._genome = Genome(self._name)
self._dbroot = TrackDbRoot()
self._genome.add_trackdb(self._dbroot)
@property
def name(self):
return self._name
@property
def genome(self):
return self._genome
def add_trackdb(self, name):
"""
create a trackdb with given name for this genome
"""
trackdb = TrackDb()
trackdb.local_fn = os.path.join(self.name, 'trackDb.%s.txt' % name)
datadir = os.path.join(self.name, name)
if not os.path.exists(datadir):
os.mkdir(datadir)
self._dbroot.add_trackdbs(trackdb)
return trackdb
def generate_genome_list(all_possible_genes):
"""Generate a list of all possible networks.
Args:
all_possible_genes (dict): The parameter choices
Returns:
networks (list): A list of network objects
"""
genomes = []
# This is silly.
for nbn in all_possible_genes['nb_neurons']:
for nbl in all_possible_genes['nb_layers']:
for a in all_possible_genes['activation']:
for o in all_possible_genes['optimizer']:
# Set the parameters.
genome = {
'nb_neurons': nbn,
'nb_layers': nbl,
'activation': a,
'optimizer': o,
}
# Instantiate a network object with set parameters.
genome_obj = Genome()
genome_obj.set_genes_to(genome, 0, 0)
genomes.append(genome_obj)
return genomes
def __init__(self, size):
self.size = size
self.cur = 0
self.gen = 0
self.individuals = []
for _ in range(self.size):
g = Genome(10)
g.randomize()
self.individuals.append(g)
def generateGenome(self):
"""Create a partially connected genome and add it to our species."""
max_genome = 0
if self.genomes:
max_genome = max(self.genomes) + 1
genome = Genome(INPUTS,OUTPUTS,self.name,max_genome)
for node1 in genome.nodes:
genome.mutateAddConnection()
self.genomes[max_genome] = genome
def __init__(self, genome=None, ploida=None, ploidb=None, **kw):
if genome:
self.genome = genome
elif ploida and ploidb:
self.genome = Genome(ploida, ploidb)
else:
self.genome = Genome.random(self.traits, self.mutation_rate)
self.phenotype = self.genome.make_phenotype(self.trait_ranges)
self.__dict__.update(self.phenotype)
self.subclass_init( **kw)
def make_random_4genome_process(e1, e2, block_number, chromosome_number, chromosome_constructor):
left_ancestor = Genome(block_number, chromosome_number, chromosome_constructor)
right_ancestor = left_ancestor.clone().perform_random_inversions(e1)
ancestors = dict()
ancestors['Left'] = left_ancestor
ancestors['Right'] = right_ancestor
random_genomes = dict()
topology = random.choice(TOPOLOGIES)
(a, b), (c, d) = topology
random_genomes[a] = left_ancestor.clone().perform_random_inversions(e2)
random_genomes[b] = left_ancestor.clone().perform_random_inversions(e2)
random_genomes[c] = right_ancestor.clone().perform_random_inversions(e2)
random_genomes[d] = right_ancestor.clone().perform_random_inversions(e2)
return random_genomes, topology, ancestors
def crossover(self):
new = []
self.individuals = sorted(self.individuals,
key = lambda ind: ind.score,
reverse = True)
for _ in range(self.size):
p1 = self.select()
p2 = self.select()
g = Genome(10)
g.crossover(p1, p2)
new.append(g)
self.individuals = new
def default_hub(hub_name, genome, short_label, long_label, email):
"""
Returns a fully-connected set of hub components using default filenames.
"""
hub = Hub(
hub=hub_name,
short_label=short_label,
long_label=long_label,
email=email)
genome = Genome(genome)
genomes_file = GenomesFile()
trackdb = TrackDb()
hub.add_genomes_file(genomes_file)
genomes_file.add_genome(genome)
genome.add_trackdb(trackdb)
return hub, genomes_file, genome, trackdb
class Creature(object):
mutation_rate = MutationRate(mutation_rate=0.0, new_gene_chance=0.0, multi_chance=0.0)
trait_ranges={"example_trait":(-1,1)}
def __init__(self, genome=None, ploida=None, ploidb=None, **kw):
if genome:
self.genome = genome
elif ploida and ploidb:
self.genome = Genome(ploida, ploidb)
else:
self.genome = Genome.random(self.traits, self.mutation_rate)
self.phenotype = self.genome.make_phenotype(self.trait_ranges)
self.__dict__.update(self.phenotype)
self.subclass_init( **kw)
def subclass_init(self, **kw):
pass
@property
def traits(self):
if not getattr(self, '_traits', None):
self._traits = self.trait_ranges.keys()
return self._traits
def __str__(self):
if options.show_genes:
return str(self.genome)
else:
return str(self.phenotype)
def breed_pop(self):
"""Breed the current population, a better score rank means probably more children."""
pop_size = len(self.goombas)
num_clones = ceil(pop_size * World.CLONE_BEST_FRACTION)
num_bred = ceil(pop_size * World.BREED_FRACTION)
num_rand = ceil(pop_size * World.NEW_RANDOM_FRACTION)
# Order the population by final score
ordered_pop = sorted([gmba for gmba in self.goombas], key=lambda g: g.score(), reverse=True)
# The top few will be cloned into the next generation unchanged
top_dogs = ordered_pop[:num_clones]
new_goombas = [Goomba.from_sequences(dog.genome.sequences()) for dog in top_dogs]
# The bottom fraction is thrown out entirely
breeders = ordered_pop[:num_bred]
# The remaining goombas breed, with a higher likelihood as they rank higher
breed_weighted = dict(zip(breeders, linspace(World.REPRO_SUCCESS_RAMP, 1, len(breeders))))
breeding_pairs = weighted_choice(breed_weighted, 2 * (pop_size - (num_clones + num_rand)))
for i in range(0, len(breeding_pairs), 2):
new_goombas.append(breed(breeding_pairs[i], breeding_pairs[i + 1]))
for i in range(num_rand):
new_goombas.append(Goomba(Genome.random_coding(self.seed_meta, randrange(*self.gen_len_range))))
starts = self.start_locations(len(new_goombas))
for gmba, pos in zip(new_goombas, starts):
gmba.pos = pos
self.goombas = new_goombas
def create_population(self, count):
"""Create a population of random networks.
Args:
count (int): Number of networks to generate, aka the
size of the population
Returns:
(list): Population of network objects
"""
pop = []
i = 0
while i < count:
# Initialize a new genome.
genome = Genome( self.all_possible_genes, {}, self.ids.get_next_ID(), 0, 0, self.ids.get_Gen() )
# Set it to random parameters.
genome.set_genes_random()
if i == 0:
#this is where we will store all genomes
self.master = AllGenomes( genome )
else:
# Make sure it is unique....
while self.master.is_duplicate( genome ):
genome.mutate_one_gene()
# Add the genome to our population.
pop.append(genome)
# and add to the master list
if i > 0:
self.master.add_genome(genome)
i += 1
#self.master.print_all_genomes()
#exit()
return pop
def mate(self, genome1, genome2):
"""
Mate two genomes based on their fitness.
Args:
genome1: the first parent genome
genome2: the second parent genome
Returns:
genome3: the resultant child genome
"""
# we want genome1 to be the most fit
if genome2.fitness > genome1.fitness:
tempGenome = genome1
genome1 = genome2
genome2 = tempGenome
assert genome1.fitness >= genome2.fitness
genome3 = Genome(INPUTS, OUTPUTS, self.name, None)
possible_genes = set(genome1.genes) | set(genome2.genes)
for innov in possible_genes:
if random.random() < GENE_DOMINANCE:
if innov in genome1.genes:
genome3.genes[innov] = genome1.genes[innov]
else:
if innov in genome2.genes:
genome3.genes[innov] = genome2.genes[innov]
# creating the nodes
genome3.generateNodes()
genome3.mutate()
return genome3
def readGFFs(self, gffFileDir):
import os
listGFFdir=os.listdir(gffFileDir)
GFFListToRead=[]
#print listGFFdir
#Save files for analysis in listGFFdir
count=0
for files in listGFFdir: #WTF maybe?
filename=files.replace('.gff','')
if filename in self.getFile2GeneDic().keys():
#print filename
#print len(self.getFile2GeneDic()[filename])
#print filename
#listGFFdir.remove(item)
GFFListToRead.append(files)
#print len(listGFFdir)
#print GFFListToRead
#print listGFFdir #only 30!? TODO
#setGenomeList=GenomeList(iD=self.ID)
setGenomeList=[]
for item in GFFListToRead:
path=gffFileDir+'/'+item
filename=item.replace('.gff','')
genome=Genome(name=filename)
genome.readGff(path) #reads all genes in file, save in genome
#clean genes not present in dictionary
listOfGenes=self.getFile2GeneDic()[filename]
#print listOfGenes
genome.cleanGenome(listOfGenes) #send only the list of values for that file!
#print genome
setGenomeList.append(genome)
self.GenomeList=setGenomeList
def _createInitGenomes(self, seed):
'''
Creates the initial set of Genomes in the population
@return Genome[]
'''
genomes=[]
for i in range(self.size):
genomes.append(Genome.createRandom(self.sys, self.goal ,seed))
#TODO anything else?
return genomes
def __init__(self, origin, caller=None):
if type(origin) is Genome:
self.subs = []
self.genome = origin
self.scored = False
self.scores = {}
elif type(origin) is Tree:
self.genome = Genome(origin.genome)
if caller is not None:
self.subs = [Tree(sub,(origin,self) ) for sub in origin if sub is not caller[0]]
self.subs.append(caller[1])
else:
self.subs = [Tree(sub, (origin,self) ) for sub in origin]
self.scored = False
self.scores = {}
class Creature(object):
traits = ["strength", "size", "ferocity", "anger", "health", "penis_length"]
def __init__(self, genome=None, ploida=None, ploidb=None, **kw):
if genome:
self.genome = genome
elif ploida and ploidb:
self.genome = Genome(ploida, ploidb)
else:
self.genome = Genome.random(self.traits)
self.phenotype = self.genome.make_phenotype()
self.__dict__.update(self.phenotype)
self.subclass_init(**kw)
def subclass_init(self, **kw):
pass
def __str__(self):
if options.show_genes:
return str(self.genome)
else:
return str(self.phenotype)
def __init__(self, tile = None, genome = None, trust_parameter = None, performAction = None):
self.performAction = performAction
if performAction == None:
self.performAction = defaultPerformAction
if trust_parameter == None:
trust_parameter = TRUST_PARAMETER
self.trust_parameter = trust_parameter
if LEARN_TRUST:
self.trust = Trust()
self.genome = genome
self.tile = None
if genome == None:
# then generate your own genome
self.genome = Genome()
# self.statistics = self.genome.statistics
self.name = self.genome.name
self.available_moves = GAMES_PER_ITER
self.fitness = 0
self.ID = returnRandomID()
self.parents = []
self.games_played = 0
if tile != None:
tile.acceptAgent(self)
self.initializeStatistics()
class Organism(Entity):
MAX_HEALTH = 500
def __init__(self, init_pos, init_vel, size, color, genome=None):
self.genome = Genome(**dict(outer_vision_rad=int(size / 2) + 100,
inner_vision_rad=int(size / 2) + 30,
food_pref=1,
poison_pref=-0.25,
max_speed=8))
super().__init__(init_pos, size, self.get_sprite_img(size, color),
init_vel)
self.health = self.MAX_HEALTH
def update(self, screen_bounds, organisms, food):
super().update(screen_bounds)
self.health = max(0, self.health - 1)
self._handle_food_eaten(food)
self._look_for_food(food)
if self.health <= 0:
self.kill()
def _look_for_food(self, food):
def my_collide_circle(left, right):
return pygame.sprite.collide_circle(left, right)
orig_rad = self.radius
self.radius = self.genome.get("inner_vision_rad")
food_inside_inner_rad = pygame.sprite.spritecollide(
self, food, False, my_collide_circle)
self.radius = self.genome.get("outer_vision_rad")
food_inside_outer_rad = pygame.sprite.spritecollide(
self, food, False, my_collide_circle)
food_seen = [
food for food in food_inside_outer_rad
if food not in food_inside_inner_rad
]
self.radius = orig_rad
if food_seen:
food_vectors = [
Vector(*f.rect.center).mult(
-1 if self.genome.get("food_pref") < 0 else 1)
for f in food_seen if not f.poisonous
]
poison_vectors = [
Vector(*f.rect.center).mult(
-1 if self.genome.get("poison_pref") < 0 else 1)
for f in food_seen if f.poisonous
]
if food_vectors:
food_force = Vector.steer_forces(food_vectors,
Vector(*self.rect.center),
self.velocity, self.max_force,
self.genome.get("max_speed"))
self.apply_force(
food_force.mult(abs(self.genome.get("food_pref"))))
if poison_vectors:
poison_force = Vector.steer_forces(
poison_vectors, Vector(*self.rect.center), self.velocity,
self.max_force, self.genome.get("max_speed"))
self.apply_force(
poison_force.mult(abs(self.genome.get("poison_pref"))))
def _handle_food_eaten(self, food):
food_eaten = pygame.sprite.spritecollide(self, food, False,
pygame.sprite.collide_circle)
health_diff = 0
for food_entity in food_eaten:
health_diff += food_entity.value
food_entity.kill()
self.health = min(self.MAX_HEALTH, self.health + health_diff)
new_color = self._calc_health_color()
self.change_color(new_color)
def _calc_health_color(self):
blue = 0
red = int(255 * ((self.MAX_HEALTH - self.health) / self.MAX_HEALTH))
green = int(255 * (self.health / self.MAX_HEALTH))
return max(min(red, 255), 0), max(min(green, 255), 0), blue
def change_color(self, color):
self.image = self.get_sprite_img(self.size(), color)
@classmethod
def get_sprite_img(cls, size, color):
sprite_img = pygame.Surface((size, size), pygame.SRCALPHA)
pygame.gfxdraw.aacircle(sprite_img, int(size / 2), int(size / 2),
int(size / 2), color)
pygame.gfxdraw.filled_circle(sprite_img, int(size / 2), int(size / 2),
int(size / 2), color)
return sprite_img
class Insect:
def __init__(self, genGenome=False):
self.position = None
self.prevPosition = None
self.sensorInput = [] # should be fixed size
self.vector = None
self.brain: InsectBrain = None
self.fitness = 1
self.canvas_sphere = None
self.canvas_line = None
# create random genome
if genGenome:
self.genome = Genome()
self.genome.new_gene(size=25,
value_bounds=(-1, 1),
name="W_inputs")
self.genome.new_gene(size=5, value_bounds=(-1, 1), name="B_inputs")
self.genome.new_gene(size=25, value_bounds=(-1, 1), name="W_one")
self.genome.new_gene(size=5, value_bounds=(-1, 1), name="B_one")
self.genome.new_gene(size=25, value_bounds=(-1, 1), name="W_two")
self.genome.new_gene(size=5, value_bounds=(-1, 1), name="B_two")
self.genome.new_gene(size=5, value_bounds=(-1, 1), name="W_out")
self.genome.new_gene(size=1, value_bounds=(-1, 1), name="B_out")
self.brain: InsectBrain = InsectBrain(self.genome)
else:
self.genome = None
def setGenome(self, genome):
self.genome = genome
self.brain: InsectBrain = InsectBrain(genome)
def update(self, food_coords: list):
# check if intersecting food coordinates
food_to_remove = []
for food in food_coords:
# approximating insect body as a square, cannot use sympy due to bug
intersects = False
if (int(self.position.x) - 5) <= food.x <= (int(self.position.x) +
5):
if (int(self.position.y) -
5) <= food.y <= (int(self.position.y) + 5):
intersects = True
if intersects:
self.fitness += 1
food_to_remove.append(food)
# for to_remove in food_to_remove:
# food_coords.remove(to_remove)
# sensors
sensor_input = [0, 0, 0, 0, 0]
SENSOR_RANGE = 50
for food in food_coords:
delta_x = food.x - int(self.position.x)
delta_y = food.y - int(self.position.y)
vec_to_food = Vector().setXY(delta_x, delta_y)
if vec_to_food is None:
continue
if vec_to_food.mag > SENSOR_RANGE:
continue
angle = self.vector.clockwiseAngleDeg(vec_to_food)
sensor_val = SENSOR_RANGE - float(vec_to_food.mag) / SENSOR_RANGE
if -90 >= angle < -45.5:
sensor_input[0] += sensor_val
elif -45.5 >= angle < -22.5:
sensor_input[1] += sensor_val
elif -22.5 >= angle < 0 or 0 >= angle < 22.5:
sensor_input[2] += sensor_val
elif 22.5 >= angle < 45:
sensor_input[3] += sensor_val
elif 45 >= angle < 90:
sensor_input[4] += sensor_val
# normalize sensor output
max_sensor_val = None
min_sensor_val = None
for val in sensor_input:
if max_sensor_val is None or val > max_sensor_val:
max_sensor_val = float(val)
if min_sensor_val is None or val < min_sensor_val:
min_sensor_val = float(val)
max_sensor_val -= min_sensor_val
for index, val in enumerate(sensor_input):
if max_sensor_val == 0:
continue
sensor_input[index] = (val - min_sensor_val) / max_sensor_val
steering = self.brain.evaluate(np.array(sensor_input).reshape(1, 5))
delta_angle = 10 * steering
#print("Sensor: {} deltaDeg: {}".format([round(val, 1) for val in sensor_input], delta_angle))
self.vector.addDeg(delta_angle)
# updating position
self.prevPosition = self.position
self.position = Point(
int(self.position.x) + int(self.vector.x),
int(self.position.y) + int(self.vector.y))
return food_to_remove
def draw(self, canvas: tkinter.Canvas):
if self.position is None:
return
x0, y0 = int(self.position.x) - 5, int(self.position.y) - 5
x1, y1 = int(self.position.x) + 5, int(self.position.y) + 5
if self.canvas_sphere is None:
self.canvas_sphere = canvas.create_oval(x0,
y0,
x1,
y1,
fill='blue',
width=0)
if self.canvas_line is not None:
canvas.delete(self.canvas_line)
self.canvas_line = canvas.create_line(
int(self.position.x),
int(self.position.y),
int(self.position.x) + int(self.vector.x),
int(self.position.y) + int(self.vector.y),
fill="yellow")
canvas.coords(self.canvas_sphere, x0, y0, x1, y1)
def __init__(self, genome): self.genome = Genome(genome) self.policy = Network(self.genome) self.evals = list()
from genome import Genome
if __name__ == '__main__':
wanted_species = ['Aedes_albopictus', 'Ahrosomya_megacephala', 'Anopheles_funestus', 'Anopheles_albitarsis', 'Anopheles_darlingi',
'Anopheles_gambiae', 'Chrysomya_megacephala', 'Drosophila_melanogaster', 'Drosophila_santomea', 'Drosophila_simulans',
'Drosophila_yakuba', 'Lucilia_cuprina', 'Musca_domestica']
species_objects = [Genome(r'sequences_2\{}.txt'.format(specie)) for specie in wanted_species]
for specie in species_objects:
specie.print_lengths()
# print(species_objects[0].get_list_of_sequences())
# print(species_objects[0].get_just_nucleotides())
def breed(self, mom, dad):
"""Make two children from parental genes.
Args:
mother (dict): genome parameters
father (dict): genome parameters
Returns:
(list): Two network objects
"""
children = []
#where do we recombine? 0, 1, 2, 3, 4... N?
#with four genes, there are three choices for the recombination
# ___ * ___ * ___ * ___
#0 -> no recombination, and N == length of dictionary -> no recombination
#0 and 4 just (re)create more copies of the parents
#so the range is always 1 to len(all_possible_genes) - 1
pcl = len(self.all_possible_genes)
recomb_loc = random.randint(1,pcl - 1)
#for _ in range(2): #make _two_ children - could also make more
child1 = {}
child2 = {}
#enforce defined genome order using list
#keys = ['nb_neurons', 'nb_layers', 'activation', 'optimizer']
keys = list(self.all_possible_genes)
keys = sorted(keys) #paranoia - just to make sure we do not add unintentional randomization
#*** CORE RECOMBINATION CODE ****
for x in range(0, pcl):
if x < recomb_loc:
child1[keys[x]] = mom.geneparam[keys[x]]
child2[keys[x]] = dad.geneparam[keys[x]]
else:
child1[keys[x]] = dad.geneparam[keys[x]]
child2[keys[x]] = mom.geneparam[keys[x]]
# Initialize a new genome
# Set its parameters to those just determined
# they both have the same mom and dad
genome1 = Genome( self.all_possible_genes, child1, self.ids.get_next_ID(), mom.u_ID, dad.u_ID, self.ids.get_Gen() )
genome2 = Genome( self.all_possible_genes, child2, self.ids.get_next_ID(), mom.u_ID, dad.u_ID, self.ids.get_Gen() )
#at this point, there is zero guarantee that the genome is actually unique
# Randomly mutate one gene
if self.mutate_chance > random.random():
genome1.mutate_one_gene()
if self.mutate_chance > random.random():
genome2.mutate_one_gene()
#do we have a unique child or are we just retraining one we already have anyway?
while self.master.is_duplicate(genome1):
genome1.mutate_one_gene()
self.master.add_genome(genome1)
while self.master.is_duplicate(genome2):
genome2.mutate_one_gene()
self.master.add_genome(genome2)
children.append(genome1)
children.append(genome2)
return children
def validate(self):
Genome.validate(self)
folder = '' #If a folder is specified, no networks will be generated, it will try and take them from there.
mode = 'TRAINING'
nGenerations = 90
LOG_FILENAME = './logs/ui.log'
logging.basicConfig(filename=LOG_FILENAME,level=logging.INFO, format='%(asctime)s, %(message)s',
datefmt='%d/%m/%Y %H:%M:%S')
logger = logging.getLogger('main')
logger.info(f'Programa lanzado. Datos: Número de genes: {numGenes}, '
f'Probabilidad de mutación: {mutationProb}, Genes seleccionados: {selection}')
logger.info(f'Se ha elegido el modo {mode}')
logger.info('Loading genome...')
genome = Genome(numGenes, mutationProb, selection, folder, nGenerations)
logger.info('The genome was successfully loaded')
game = Game(mode, nGenerations, numGenes)
if mode == 'TRAINING':
logger.info(f'Training will last {nGenerations} generations.')
for i in range(1, nGenerations):
with keyboard.Listener(on_press=keys.on_press) as listener:
if not keys.break_program: #End key to stop a running program by the end of the generation.
genome.execute_generation(game)
if (i % 5 == 0 and i != 0) or i == nGenerations:
genome.save_all()
genome.kill_and_reproduce()
def test_genome(self):
genome = Genome()
genome.mutate()
def find_closest_specie(self, genome: Genome,
specie_list: List[Specie]) -> Specie:
return min([(self.distance_metric.measure_distance(
genome.get_position(), b.centroid), b) for b in specie_list],
key=lambda x: x[0])[1]
def validate(self):
Genome.validate(self)
def __init__(self,
n,
input_size,
output_size,
evaluate,
parent_selection,
train,
cross_over=crossover,
name=None,
elitism_rate=0.1,
min_species_size=5,
n_generations_no_change=5,
tol=1e-5,
mutate_speed=1,
min_species=1,
max_species=10,
epochs=2,
reward_epochs=10,
load=None,
save_mode="elites",
monitor=None,
load_params=True):
# Evolution parameters
self.evaluate = evaluate
self.parent_selection = parent_selection
self.crossover = cross_over
self.train = train
self.epochs = epochs
self.reward_epochs = reward_epochs
self.min_species_size = min_species_size
self.min_species = min_species
self.max_species = max_species
self.elitism_rate = elitism_rate
self.mutate_speed = mutate_speed
self.n_generations_no_change = n_generations_no_change
self.tol = tol
# Plotting and tracking training progress
self.monitor = monitor
self.polygons = dict()
# Species centers calculated after first clustering
self.species_repr = None
self.converged = False
# What to save: save_genomes =1 saves elites =2 saves all genomes
self.save_genomes, self.save_genes = {
"all": [2, True],
"elites": [1, True],
"genomes": [2, False],
"bare": [1, False],
"None": [0, False]
}[save_mode]
# Load if a checkpoint is given
if load is not None:
self.load_checkpoint(*load, load_params=load_params)
else:
# Historical markers starting at 5
self.id_generator = itertools.count(5)
self.species_id_generator = itertools.count(1)
self.input_size = input_size
self.output_size = output_size
# Begin with min species of nearly same size
self.n = n
self.species = {
i: [
Genome(self)
for _ in range(n //
min_species if i > 0 else (n //
min_species) +
(n % min_species))
]
for i in range(min_species)
}
self.best_genome = self.species[0][0].copy()
self.top_acc = 0
self.history = []
# Metadata
self.generation = 1
self.checkpoint_name = name or time.strftime("%d.%m-%H:%M")
self.check_args()
def combine_genomes(self, genome1, genome2):
# print("Empezando prro")
matching_edges = []
disjoint_edges_fitter = []
excess_edges_fitter = []
g_fitter = None
g_other = None
print("parent1: " + str(genome1.calculated_fitness))
print("parent2: " + str(genome2.calculated_fitness))
if genome1.calculated_fitness >= genome2.calculated_fitness:
g_fitter = genome1
g_other = genome2
else:
g_fitter = genome2
g_other = genome1
rr = rnd.random()
r2 = rnd.random()
for u, vs in g_fitter.get_edge_iter():
matching_count = 0
for v, eatt1 in vs.items():
data1 = eatt1['inN']
## find in the
for u2, vs2 in g_other.get_edge_iter():
for v2, eatt2 in vs2.items():
data2 = eatt2['inN']
if data2 == data1:
matching_count = matching_count + 1
# print("matching!!")
## tiene el mismo numero de inovacion, elegimos aleatoriamente un edge
#rr = rnd.random()
r2 = rnd.random()
atts_inh = None
#print("atts p1:")
#print(eatt1)
#print("atts p2:")
#print(eatt2)
if r2 < 0.5:
atts_inh = eatt1
else:
atts_inh = eatt2
#print("heredado")
#print(atts_inh)
matching_edges.append((u, v, atts_inh))
#else:
#print("no matching prro")
if matching_count == 0:
# no hubo conteos positivos
#print( eatt1)
"""
if g_fitter.inovationNumber <= g_other.inovationNumber:
# disjoint
disjoint_edges_fitter.append( ( u ,v , eatt1 ) )
else:
# exess
excess_edges_fitter.append( (u,v, eatt1) )
## no coinciden
"""
excess_edges_fitter.append((u, v, eatt1))
"""
print(matching_edges)
print(len(matching_edges ))
print(len( excess_edges_fitter ) )
print(len( disjoint_edges_fitter ) )
"""
Gnew = Genome()
Gnew.clear()
Gnew.add_nodes_from(g_fitter.get_input_genes(), is_input=True)
Gnew.add_nodes_from(g_fitter.get_output_genes(), is_output=True)
Gnew.add_edges_from(matching_edges)
Gnew.add_edges_from(disjoint_edges_fitter)
Gnew.add_edges_from(excess_edges_fitter)
Gnew.set_inovation_number(g_fitter.get_inovation_number())
#Gnew.set_specie( g_fitter.specie )
Gnew.HLayers = g_fitter.HLayers
#Gnew.re_add()
return Gnew
def new(cls, genome_length):
return cls(Genome.generate(genome_length))
def eval_genomes(genomes, config):
for _, genome in genomes:
genome.fitness = 0.
net = neat.nn.FeedForwardNetwork.create(genome, config)
gen = Genome(net)
genome.fitness = gen.get_fitness()
def main():
POP_SIZE = 100
generations = 500 # No of generations
Model = list()
best_individual = Genome()
best_fitness = -1 # Initially the best score is 0
# There are 2 types of score to maintain
# 1- The current_best score
# 2- The global best score
# Hence there will be corrensponding individuals for this score
global_best_fitness = -1
current_best_fitness = -1
global_best = Genome() # Best individual of all generations
current_best = Genome() # Current best individual of the lot
c1 = 2 #np.random.randint(0,2)
c2 = 2 #np.random.randint(0,2)
initPygame(" AI Learning Flappy ")
IP = list()
for i in range(POP_SIZE): # Training the first set of batch
genome = Genome()
#genome.mutate()
genome = playGame(genome)
# here also the intial population has to start playing the game
IP.append(genome)
P = IP # Setting Population to initial population
print(" Training of initial Population is successfull ")
#time.sleep(10)
maximum_fitness = -1
average_fitness = -1
average_fitness_list = np.array([])
maximum_fitness_list = np.array([])
all_generations = np.array([])
#****************************STARTING THE GENERATION LOOP*************************************#
for g in range(generations): # The iteration for each generation
#print(" Printing generation number ",g)
#time.sleep(10)
metric = list()
for m in range(POP_SIZE): # training each model from the population
#print(type(m))
#print(type(P[m]))
P[m] = playGame(P[m]) #instead of train , the model has to play
metric.append([m, P[m].fitness
]) # instead of test_error , append its fitness
if current_best_fitness < P[m].fitness:
current_best_fitness = P[m].fitness
current_best_weights = P[m].get_weights()
current_best.set_weights(current_best_weights)
informationforscreen = {
'generation': g,
'birdnumber': m,
'lastfitness': P[m].fitness,
'lastgenerationaveragefitness': average_fitness,
'bestfitness': current_best_fitness
}
updateScreen(informationforscreen)
values = np.array(metric)
results = values.reshape(values.shape[0],
values.shape[1]) # setting up all the results
average_fitness = np.sum(
results[:, 1], axis=0) / 100 # calculating the average fitness
#maximum_fitness = np.amax(results[:,1],axis = 0) # calcualting the maximum fitness
individuals = results
if global_best_fitness < current_best_fitness:
global_best_fitness = current_best_fitness
global_best_weights = current_best.get_weights()
global_best.set_weights(global_best_weights)
average_fitness_list = np.append(average_fitness_list, average_fitness)
maximum_fitness_list = np.append(maximum_fitness_list,
current_best_fitness)
all_generations = np.append(all_generations, g)
print(" The score of all people is ")
print(individuals)
progeny = list()
#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ UPDATE THE FLOCK OF BIRDS IN PSO ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#
for pop in P:
#new_guy = Genome()
ran1 = random.randrange(0, 2)
ran2 = random.randrange(0, 2)
init_weights = pop.get_weights()
global_weights = global_best.get_weights()
current_weights = current_best.get_weights()
weight_diff_from_global = list(
map(sub, global_weights, init_weights))
weight_diff_from_current = list(
map(sub, current_weights, init_weights))
z1 = c1 * ran1
z2 = c2 * ran2
new_guy_weights_additive = list(
map(add, map(lambda x: x * z1, weight_diff_from_global),
map(lambda x: x * z2, weight_diff_from_current)))
new_guy_weights = list(
map(add, init_weights, new_guy_weights_additive))
#new_guy.set_weights(new_guy_weights)
pop.set_weights(new_guy_weights)
#progeny.append(new_guy)
#children = progeny
# NO MUTATION IN PSO
#r1 = np.random.randint(1,1000)
#r2 = np.random.randint(0,5)
#if(r1 < 5) :
# children[r2].mutate() # call a random genome of child and mutate it
#P = children
metric_final = list()
Model = P
############################# END OF GENERATION LOOP ##################################################
for m in range(len(P)):
P[m] = playGame(P[m]) #instead of train , the model has to play
metric_final.append([m, P[m].fitness
]) # instead of test_error , append its fitness
values_final = np.array(metric_final)
results_final = values_final.reshape(values_final.shape[0],
values_final.shape[1])
individuals_final = results_final
#ranked_individuals_final = individuals_final[individuals_final[:,1].argsort()]
#print(" Final ranked individuals ")
#print(ranked_individuals_final)
#print(type(ranked_individuals_final))
#best_individual = int(ranked_individuals_final[0][0])
#`````````````````````````````` PLOTTING THE CONVERGENCE OF ALL THE BIRDS ```````````````````````
f_handle = open('max_pso.txt', 'w')
np.save('max_pso.npy', maximum_fitness_list)
f_handle.close()
f_handle = open('avg_pso.txt', 'w')
np.save('avg_pso.npy', average_fitness_list)
f_handle.close()
f_handle = open('generations.txt', 'w')
np.save('gen.npy', all_generations)
f_handle.close()
plt.figure(2)
#plt.subplot(211)
plt.plot(all_generations,
maximum_fitness_list,
color='red',
label='maximum fitness')
plt.plot(all_generations,
average_fitness_list,
color='blue',
label='average fitness')
plt.xlabel("Generation number")
red_patch = mpatches.Patch(color='red', label='minimum fitness')
blue_patch = mpatches.Patch(color='blue', label='average fitness')
plt.legend(handles=[red_patch, blue_patch])
plt.ylabel("Fitness")
#plt.subplot(212)
#plt.plot(X,Y,color = 'green')
#plt.xlabel("X-value")
#plt.ylabel("Y = exp(-3x) + sin(6*pi*x)")
plt.show()
def write_to_genome(self):
gene = Genome(self.model.get_weights())
return gene
from genome import Genome
# TODO: train "iseq" model
MODELS = ["novaseq", "miseq", "hiseq"]
# We're committed to uniform.
UNIFORM_ABUNDANCE = "uniform"
# 2x this for a pair
READ_SIZE = 150
TOP_6_ID_GENOMES = [
Genome("bacteria", "klebsiella_pneumoniae_HS11286",
[("subspecies", 1125630), ("species", 573), ("genus", 570),
("family", 543)], ["NC_016845.1"]),
Genome("fungi", "aspergillus_fumigatus", [("subspecies", 330879),
("species", 746128),
("genus", 5052),
("family", 1131492)],
[
"NC_007194.1", "NC_007195.1", "NC_007196.1", "NC_007197.1",
"NC_007198.1", "NC_007199.1", "NC_007200.1", "NC_007201.1"
],
"https://www.ncbi.nlm.nih.gov/genome/18?genome_assembly_id=22576"),
Genome("viruses", "rhinovirus_c", [("subspecies", 0), ("species", 463676),
("genus", 12059), ("family", 12058)],
["MG148341.1"]),
Genome("viruses", "chikungunya", [("subspecies", 0), ("species", 37124),
("genus", 11019), ("family", 11018)],
["MG049915.1"]),
Genome(
def get_new_population(self, adjusted_species_sizes, remaining_species,
species_set, generation_tracker, backprop_mutation):
"""
Creates the dictionary of the new genomes for the next generation population
:param: genetation_tracker:
:param adjusted_species_sizes:
:param remaining_species:
:param species_set:
:param new_population:
:return:
"""
new_population = {}
for species_size, species in zip(adjusted_species_sizes,
remaining_species):
# TODO: Uncomment if you removed min_species_size
# assert (species_size > 0)
if species_size > 0:
# List of old species members
old_species_members = list(species.members.values())
# Reset the members for the current species
species.members = {}
# Save the species in the species set object
species_set.species[species.key] = species
# Sort the members into the descending fitness
old_species_members.sort(reverse=True, key=lambda x: x.fitness)
# Double check that it is descending
if len(old_species_members) > 1:
assert (old_species_members[0].fitness >=
old_species_members[1].fitness)
# If we have specified a number of genomes to carry over, carry them over to the new population
num_genomes_without_crossover = int(
round(species_size *
self.config.chance_for_mutation_without_crossover))
if num_genomes_without_crossover > 0:
for member in old_species_members[:
num_genomes_without_crossover]:
# Check if we should carry over a member un-mutated or not
if not self.config.keep_unmutated_top_percentage:
child = copy.deepcopy(member)
child.mutate(
reproduction_instance=self,
innovation_tracker=self.innovation_tracker,
config=self.config,
backprop_mutation=backprop_mutation)
if not child.check_connection_enabled_amount(
) and not child.check_num_paths(
only_add_enabled_connections=True):
raise Exception(
'This child has no enabled connections')
new_population[child.key] = child
self.ancestors[child.key] = ()
# new_population[member.key] = member
species_size -= 1
assert (species_size >= 0)
else:
# Else we just add the current member to the new population
new_population[member.key] = member
species_size -= 1
assert (species_size >= 0)
# If there are no more genomes for the current species, then restart the loop for the next species
if species_size <= 0:
continue
# Only use the survival threshold fraction to use as parents for the next generation.
reproduction_cutoff = int(
math.ceil(
(1 - self.config.chance_for_mutation_without_crossover)
* len(old_species_members)))
# Need at least two parents no matter what the previous result
reproduction_cutoff = max(reproduction_cutoff, 2)
old_species_members = old_species_members[:reproduction_cutoff]
# Randomly choose parents and choose whilst there can still be additional genomes for the given species
while species_size > 0:
species_size -= 1
# TODO: If you don't allow them to mate with themselves then it's a problem because if the species previous
# TODO: size is 1, then how can you do with or without crossover?
parent_1 = copy.deepcopy(
random.choice(old_species_members))
parent_2 = copy.deepcopy(
random.choice(old_species_members))
# Has to be a deep copy otherwise the connections which are crossed over are also modified if mutation
# occurs on the child.
parent_1 = copy.deepcopy(parent_1)
parent_2 = copy.deepcopy(parent_2)
self.genome_indexer += 1
genome_id = self.genome_indexer
child = Genome(key=genome_id)
# TODO: Save the parent_1 and parent_2 mutation history as well as what connections they had
# Create the genome from the parents
num_connections_enabled = child.crossover(
genome_1=parent_1,
genome_2=parent_2,
config=self.config)
# If there are no connections enabled we forget about this child and don't add it to the existing
# population
if num_connections_enabled:
child.mutate(
reproduction_instance=self,
innovation_tracker=self.innovation_tracker,
config=self.config,
generation_tracker=generation_tracker,
backprop_mutation=backprop_mutation)
if not child.check_connection_enabled_amount(
) and not child.check_num_paths(
only_add_enabled_connections=True):
raise Exception(
'This child has no enabled connections')
new_population[child.key] = child
self.ancestors[child.key] = (parent_1.key,
parent_2.key)
else:
# Else if the crossover resulted in an invalid genome.
assert num_connections_enabled == 0
species_size += 1
self.genome_indexer -= 1
return new_population
def generateRandomGenome():
net = Network(Config.networkArchitecture)
genome = Genome(net)
return genome
def test_combine_two_constant(self):
g1 = Constant(15.1)
g2 = Constant(20.2)
self.assertEqual(20.2, Genome.combine(g1, g2))
lenght_result = ((len(genome) - window_lenght) / stride) + 1
for i in range(int(lenght_result)):
nb_motif_checked = 0
for s in seq:
if s in genome[i:window_lenght + i]:
nb_motif_checked += 1
if thersehold == nb_motif_checked:
list_result.append((i, window_lenght + i))
return list_result
if __name__ == "__main__":
g = Genome('rmark3.fa')
g.read_fasta()
genome = g.dict_genome['rmark1'].seq
sequence = ["AUG"]
t1 = time()
for pattern in sequence:
print(scan_genome_given_seq(genome, pattern))
t2 = time()
print('Elapsed time is %f seconds.' % (t2 - t1))
def test_combine_two_multiply(self):
g1 = Multiplier(0.23)
g2 = Multiplier(4.2)
self.assertEqual(1, Genome.combine(g1, g2))
class Player:
MAX_ROTATION = 25
IMGS = [pygame.transform.scale2x(pygame.image.load(
os.path.join("imgs", "bird" + str(x) + ".png"))) for x in range(1, 4)]
ROT_VEL = 20
ANIMATION_TIME = 5
genome_inputs = 3 # Flappy-Bird Vision! If you change vision parameter in look(), change here too!
genome_outputs = 1 # To jump or not to jump!
def __init__(self, x, y):
self.dead = False
self.x = x
self.y = y
self.tilt = 0
self.tick_count = 0
self.vel = 0
self.height = self.y
self.img_count = 0
self.img = self.IMGS[0]
self.fitness = 1
self.unadjusted_fitness = 0
self.lifespan = 0
self.best_score = 0
#self.replay = False
self.score = 1
#self.gen = 0
self.vision = [0 for _ in range(self.genome_inputs)]
self.decision = 0
self.brain = Genome(self.genome_inputs, self.genome_outputs)
def draw(self, win):
self.img_count += 1
if self.img_count <= self.ANIMATION_TIME:
self.img = self.IMGS[0]
elif self.img_count <= self.ANIMATION_TIME*2:
self.img = self.IMGS[1]
elif self.img_count <= self.ANIMATION_TIME*3:
self.img = self.IMGS[2]
elif self.img_count <= self.ANIMATION_TIME*4:
self.img = self.IMGS[1]
elif self.img_count == self.ANIMATION_TIME*4 + 1:
self.img = self.IMGS[0]
self.img_count = 0
if self.tilt <= -80:
self.img = self.IMGS[1]
self.img_count = self.ANIMATION_TIME*2
self.blitRotateCenter(win, self.img, (self.x, self.y), self.tilt)
def blitRotateCenter(self, surf, image, topleft, angle):
rotated_image = pygame.transform.rotate(image, angle)
new_rect = rotated_image.get_rect(
center=image.get_rect(topleft=topleft).center)
surf.blit(rotated_image, new_rect.topleft)
def get_mask(self):
return pygame.mask.from_surface(self.img)
def jump(self):
self.vel = -10.5
self.tick_count = 0
self.height = self.y
def move(self):
self.tick_count += 1
displacement = self.vel*(self.tick_count) + 0.5 * \
(3)*(self.tick_count)**2
if displacement >= 16:
displacement = (displacement/abs(displacement)) * 16
if displacement < 0:
displacement -= 2
self.y = self.y + displacement
if displacement < 0 or self.y < self.height + 50:
if self.tilt < self.MAX_ROTATION:
self.tilt = self.MAX_ROTATION
else:
if self.tilt > -90:
self.tilt -= self.ROT_VEL
def clone(self):
clone = Player(230, randint(200,400))
clone.brain = self.brain.clone()
clone.fitness = self.fitness
clone.unadjusted_fitness = self.unadjusted_fitness
clone.brain.generate_network()
clone.best_score = self.best_score
return clone
def calculate_fitness(self):
self.unadjusted_fitness = self.score*self.score
self.fitness = self.unadjusted_fitness
def crossover(self, parent_2):
child = Player(230, randint(200,400))
child.brain = self.brain.crossover(parent_2.brain)
child.brain.generate_network()
return child
def look(self, pipes, pipe_ind):
self.vision[0] = self.y
self.vision[1] = abs(self.y - pipes[pipe_ind].height)
self.vision[2] = abs(self.y - pipes[pipe_ind].bottom)
def think_and_act(self):
decision = self.brain.feed_forward(self.vision)[0]
if decision > 0:
self.jump()
#def increment_counters(self):
# self.lifespan += 1
#
# if self.lifespan % 5 == 0:
# self.score += 1
def increment_score_by(self, number):
self.score += number
def main():
"""Parse option"""
if len(sys.argv) != 1:
#Evaluate a single genome
if str(sys.argv[1]) == "-evaluate":
initPygame("FlapAI: Evaluating")
print str(sys.argv[2])
net = load(str(sys.argv[2]))
genome = Genome(net)
global savestat
savestat = False
fitness = []
for i in xrange(100):
fit = playGame(genome)
fitness.append(fit.fitness)
print "fitness : %s " % fit.fitness
average = sum(fitness) / float(len(fitness))
printc("Average fitness : %s" % average, "red")
pygame.quit()
sys.exit()
#Show the stat of an experiment
if str(sys.argv[1]) == "-stats":
showStat(str(sys.argv[2]))
#No argument, starting FlapAI
initPygame("FlapAI: Learning")
"""Init the population"""
bestFitness = 0
population = Population()
population.generateRandomPopulation()
generation = 1
maxgeneration = Config.maxGeneration
lastgenerationaveragefitness = 0
#Main Loop
while generation <= maxgeneration:
birdnmbr = 1
for i in xrange(population.size()):
genome = playGame(population.getGenome(i))
population.setGenomeFitness(i, genome.fitness)
informationforscreen = {
'generation': generation,
'birdnumber': birdnmbr,
'lastfitness': genome.fitness,
'lastgenerationaveragefitness': lastgenerationaveragefitness,
'bestfitness': bestFitness
}
updateScreen(informationforscreen)
if genome.fitness > bestFitness:
global bestFitness
bestFitness = genome.fitness
genome.network.save(today + "/bestfitness.json")
birdnmbr += 1
global fitnessovergeneration
fitnessovergeneration.append(population.averageFitness())
lastgenerationaveragefitness = population.averageFitness()
global fittestovergeneration
fittestovergeneration.append(population.findFittest().fitness)
#Evolve the population
population.evolvePopulation()
generation += 1
def random_goombas(cls, dimensions, num_goombas, seed_meta, gen_len_range, gen_time=200):
"""Generate a world containing a number of goombas with random coding genomes."""
gens = [Genome.random_coding(seed_meta, randrange(*gen_len_range)) for _ in range(num_goombas)]
gen_seqs = [genome.sequences() for genome in gens]
return cls(dimensions, gen_seqs, seed_meta, gen_len_range, gen_time)
def SetFromFilename(self, filename):
self.winnergen = 0
self.highest_fitness = 0.0
self.highest_last_changed = 0
self.cur_node_id = 0
self.cur_innov_num = 0.0
curwordnum = 0
dprint(DEBUG_FILEINPUT, "Opening population file %s." % (filename, ))
iFile = open(filename, "r")
if not iFile:
dprint(DEBUG_ERROR, "Can't open genomes file for input %s." % (filename, ))
return
md = False
metadata = ""
#//Loop until file is finished, parsing each line
while True:
curline = iFile.readline()
if not curline:
break
dprint(DEBUG_FILEINPUT, "line: %s." % (curline.strip(), ))
words = curline.strip().split()
dprint(DEBUG_FILEINPUT, "words: %s." % (":".join(words), ))
if len(words) == 0:
continue
if re.search('^\s*#', words[0]):
continue
if words[0] == "genomestart":
dprint(DEBUG_FILEINPUT, "genomestart")
idcheck = int(words[1])
if not md:
metadata = ""
md = False
new_genome = Genome()
new_genome.SetFromFile(idcheck, iFile)
new_organism = Organism()
new_organism.SetFromGenome(0., new_genome, 1, metadata)
self.organisms.append(new_organism)
if self.cur_node_id < new_genome.get_last_node_id():
self.cur_node_id = new_genome.get_last_node_id()
if self.cur_innov_num < new_genome.get_last_gene_innovnum():
self.cur_innov_num = new_genome.get_last_gene_innovnum()
elif words[0] == "/*":
#// New metadata possibly, so clear out the metadata
metadata = ""
word_i = 1
while words[word_i] != "*/":
#// If we've started to form the metadata, put a space in the front
if md:
metadata += " "
metadata += words[word_i]
md = True
word_i += 1
# end of conditional processing
# end of while True
dprint(DEBUG_FILEINPUT, "End of file.")
iFile.close()
self.speciate()
dprint(DEBUG_FILEINPUT, "Done.")
return
def __init__(self, name):
self._name = name
self._genome = Genome(self._name)
self._dbroot = TrackDbRoot()
self._genome.add_trackdb(self._dbroot)
import sys
from genome import Genome
from organism import Organism
PREFIX = '/home/koppejan/projects/helicopter/ghh09/policies/'
POLICIES = [Genome.open(PREFIX + 'mdp%i.net' % i) for i in range(10) if i != 2]
class Agent:
""" Model-Free agent, chooses best policy from a set of pre-trained policies. """
def __init__(self):
""" Initialize agent. """
self.pool = [Organism(genome) for genome in POLICIES]
self.episode = 0
def start(self):
""" Start a new episode """
self.set_policy()
self.episode += 1
self.reward = 0
self.steps = 0
def step(self, state, reward):
""" Choose an action based on the current state. """
self.steps += 1
self.reward += reward
return self.org.policy.propagate(state, 1)
def end(self, reward):
""" Ends the current episode """
def test_make_gamete_calls_reproduce(self):
flagger = Mock(**{'reproduce.return_value': "FLAG"})
test_genome = Genome({"a": flagger}, {"a": flagger})
test_gamete = test_genome.make_gamete()
self.assertEqual(test_gamete.values()[0], "FLAG")
def breed(self, mom, dad):
"""Make two children from parental genes.
Args:
mother (dict): genome parameters
father (dict): genome parameters
Returns:
(list): Two network objects
"""
children = []
# where do we recombine? 0, 1, 2, 3, 4... N?
# with four genes, there are three choices for the recombination
# ___ * ___ * ___ * ___
# 0 -> no recombination, and N == length of dictionary -> no recombination
# 0 and 4 just (re)create more copies of the parents
# so the range is always 1 to len(all_possible_genes) - 1
pcl = len(self.all_possible_genes)
recomb_loc = random.randint(1, pcl - 1)
# for _ in range(2): #make _two_ children - could also make more
child1 = {}
child2 = {}
# enforce defined genome order using list
#keys = ['nb_neurons', 'nb_layers', 'activation', 'optimizer']
keys = list(self.all_possible_genes)
# paranoia - just to make sure we do not add unintentional randomization
keys = sorted(keys)
# *** CORE RECOMBINATION CODE ****
for x in range(0, pcl):
if x < recomb_loc:
child1[keys[x]] = mom.geneparam[keys[x]]
child2[keys[x]] = dad.geneparam[keys[x]]
else:
child1[keys[x]] = dad.geneparam[keys[x]]
child2[keys[x]] = mom.geneparam[keys[x]]
# Initialize a new genome
# Set its parameters to those just determined
# they both have the same mom and dad
genome1 = Genome(self.all_possible_genes, child1, self.ids.get_next_ID(
), mom.u_ID, dad.u_ID, self.ids.get_Gen())
genome2 = Genome(self.all_possible_genes, child2, self.ids.get_next_ID(
), mom.u_ID, dad.u_ID, self.ids.get_Gen())
# at this point, there is zero guarantee that the genome is actually unique
# Randomly mutate one gene
if self.mutate_chance > random.random():
genome1.mutate_one_gene()
if self.mutate_chance > random.random():
genome2.mutate_one_gene()
# do we have a unique child or are we just retraining one we already have anyway?
while self.master.is_duplicate(genome1):
genome1.mutate_one_gene()
self.master.add_genome(genome1)
while self.master.is_duplicate(genome2):
genome2.mutate_one_gene()
self.master.add_genome(genome2)
children.append(genome1)
children.append(genome2)
return children
def test_combine_half_and_half(self):
g1 = Multiplier(0.333)
g2 = Constant(20.2)
self.assertEqual(6.7266, Genome.combine(g1, g2))
# order shouldn't matter
self.assertEqual(6.7266, Genome.combine(g2, g1))
def test_crossover():
a = Genome()
for i in range(3):
a.add_node(Node(NodeType.inp, i + 1))
a.add_node(Node(NodeType.out, 4))
a.add_node(Node(NodeType.hidden, 5))
a.add_connection(Connection(1, 4, 1.0, True, 1))
a.add_connection(Connection(2, 4, 1.0, False, 2))
a.add_connection(Connection(3, 4, 1.0, True, 3))
a.add_connection(Connection(2, 5, 1.0, True, 4))
a.add_connection(Connection(5, 4, 1.0, True, 5))
a.add_connection(Connection(1, 5, 1.0, True, 8))
a.conn_innovation = 9
a.node_innovation = 6
b = Genome()
for i in range(3):
b.add_node(Node(NodeType.inp, i + 1))
b.add_node(Node(NodeType.out, 4))
b.add_node(Node(NodeType.hidden, 5))
b.add_node(Node(NodeType.hidden, 6))
b.add_connection(Connection(1, 4, 1.0, True, 1))
b.add_connection(Connection(2, 4, 1.0, False, 2))
b.add_connection(Connection(3, 4, 1.0, True, 3))
b.add_connection(Connection(2, 5, 1.0, True, 4))
b.add_connection(Connection(5, 4, 1.0, False, 5))
b.add_connection(Connection(5, 6, 1.0, True, 6))
b.add_connection(Connection(6, 4, 1.0, True, 7))
b.add_connection(Connection(3, 5, 1.0, True, 9))
b.add_connection(Connection(1, 6, 1.0, True, 10))
a.conn_innovation = 11
a.node_innovation = 7
c = Genome.crossover(b, a)
a.render("render/crossover_a.gv")
b.render("render/crossover_b.gv")
c.render("render/crossover_c.gv")
class Tree( object ):
def __init__(self, origin, caller=None):
if type(origin) is Genome:
self.subs = []
self.genome = origin
self.scored = False
self.scores = {}
elif type(origin) is Tree:
self.genome = Genome(origin.genome)
if caller is not None:
self.subs = [Tree(sub,(origin,self) ) for sub in origin if sub is not caller[0]]
self.subs.append(caller[1])
else:
self.subs = [Tree(sub, (origin,self) ) for sub in origin]
self.scored = False
self.scores = {}
def __iter__( self ):
return iter( self.subs )
def __getitem__( self, key):
return self.subs[key]
def setGenome(self, g):
self.scored = False
self.scores = {}
self.genome = g
def isBinary( self, caller=None):
return True
def allNodes( self, caller=None):
s = self.genome.getName()
s += " | "
for sub in self:
s += sub.genome.getName()
s += ", "
s += "\n"
for sub in self:
if sub is not caller:
s += sub.allNodes(self)
return s
def isLeaf(self):
return len(self.subs) in (0,1)
def addConnection(self, other, caller=None ):
self.scored = False
self.scores = {}
if type(other) is Tree:
self.subs.append( other )
if other is not caller:
other.addConnection( self, self)
elif type(other) is Genome:
t = Tree( other )
self.addConnection( t )
else:
raise Exception("Tried to add an invalid connection to a tree.")
def breakConnection(self, other, caller=None):
self.scored = False
self.scores = {}
self.subs.remove( other )
if other is not caller:
other.breakConnection( self, self )
def getScore( self, caller=None):
if self.scored and caller in self.scores:
return self.scores[caller]
else:
if self.isLeaf() and caller is not None:
return 0
elif self.isLeaf() and caller is None:
return self.subs[0].getScore()
else:
grimm = Grimm()
s = 0
for sub in self:
if sub is not caller:
s += sub.getScore( self )
s += grimm.getDistance( self.genome, sub.genome)
self.scores[caller] = s
self.scored = True
return s
def getTips(self):
tips = []
def rTipFinder( t, tips, caller=None ):
if t.isLeaf() and caller is not None:
tips.append( t )
elif t.isLeaf() and caller is None:
tips.append(t)
if not len(t.subs) == 0:
rTipFinder( t.subs[0],tips, t)
else:
for sub in t:
if sub is not caller:
rTipFinder( sub , tips, t)
rTipFinder(self , tips)
return tips
def __len__( self ):
size = [0,]
def rSizeFinder( t, size, caller=None ):
size[0] += 1
for sub in t:
if sub is not caller:
rSizeFinder( sub, size, t)
rSizeFinder(self ,size)
return size[0]
def toTuple( self, caller=None):
if self.isLeaf() and caller is not None:
return self
elif self.isLeaf() and caller is None:
return self.subs[0].toTuple()
elif caller is None:
return ( self.subs[0].toTuple(self), self.toTuple( self.subs[0] ))
else:
return tuple([sub.toTuple(self) for sub in self if sub is not caller])
def __str__( self ):
def rStringFinder( t, caller=None):
if t.isLeaf() and caller is not None:
return t.genome.getName()
elif t.isLeaf() and caller is None:
return rStringFinder( t.subs[0])
elif caller is None:
return "(" + rStringFinder(self.subs[0], self) + ", " + rStringFinder(self, self.subs[0]) + ")"
else:
s = "("
for sub in t:
if sub is not caller:
s += rStringFinder( sub, t )
s += ", "
s = s[:-2]
s += ")"
return s
return "Tree: " + rStringFinder(self )
def genomeHash(self):
return hash( self.genome )
def getName(self):
return self.genome.getName()
def test_weight_mut():
a = Genome()
a.add_node(Node(NodeType.inp, 1))
a.add_node(Node(NodeType.inp, 2))
a.add_node(Node(NodeType.out, 3))
a.add_connection(Connection(1, 3, 0.5, True, 1))
a.add_connection(Connection(2, 3, 1.0, True, 2))
a.conn_innovation = 3
a.node_innovation = 4
a.render("render/weight_begin.gv")
a.mutate_weights()
a.render("render/weight_mutated.gv")
def reproduce_with_species(self, species_set, pop_size, generation):
'''
Creates and speciates genomes.
species_set -- set of current species
pop_size -- population size
generation -- current generation
'''
all_fitnesses = []
remaining_species = []
# Traverse species and grab fitnesses from non-stagnated species
for sid, species, species_is_stagnant in self.stagnation.update(
species_set, generation):
if species_is_stagnant:
print("!!! Species {} Stagnated !!!".format(sid))
# self.reporters.species_stagnant(sid, species)
pass
else:
# Add fitnesses of members of current species
all_fitnesses.extend(member.fitness
for member in itervalues(species.members))
remaining_species.append(species)
# No species
if not remaining_species:
species_set.species = {}
return {}
# Find min/max fitness across entire population
min_population_fitness = min(all_fitnesses)
max_population_fitness = max(all_fitnesses)
# Do not allow the fitness range to be zero, as we divide by it below.
population_fitness_range = max(
1.0, max_population_fitness - min_population_fitness)
# Compute adjusted fitness and record minimum species size
for species in remaining_species:
# Determine current species average fitness
mean_species_fitness = mean(
[member.fitness for member in itervalues(species.members)])
max_species_fitness = max(
[member.fitness for member in itervalues(species.members)])
# Determine current species adjusted fitness and update it
species_adjusted_fitness = (
mean_species_fitness -
min_population_fitness) / population_fitness_range
species.adjusted_fitness = species_adjusted_fitness
species.max_fitness = max_species_fitness
adjusted_fitnesses = [
species.adjusted_fitness for species in remaining_species
]
avg_adjusted_fitness = mean(adjusted_fitnesses)
# Compute the number of new members for each species in the new generation.
previous_sizes = [
len(species.members) for species in remaining_species
]
min_species_size = max(2, self.species_elitism)
spawn_amounts = self.compute_species_sizes(adjusted_fitnesses,
previous_sizes, pop_size,
min_species_size)
new_population = {}
species_set.species = {}
for spawn, species in zip(spawn_amounts, remaining_species):
# If elitism is enabled, each species always at least gets to retain its elites.
spawn = max(spawn, self.species_elitism)
assert spawn > 0
# The species has at least one member for the next generation, so retain it.
old_species_members = list(iteritems(species.members))
# Update species with blank slate
species.members = {}
# Update species in species set accordingly
species_set.species[species.key] = species
# Sort old species members in order of descending fitness.
old_species_members.sort(reverse=True, key=lambda x: x[1].fitness)
# Clone elites to new generation.
if self.species_elitism > 0:
for member_key, member in old_species_members[:self.
species_elitism]:
new_population[member_key] = member
spawn -= 1
# If the species only has room for the elites, move onto next species
if spawn <= 0: continue
# Only allow fraction of species members to reproduce
reproduction_cutoff = int(
ceil(self.species_reproduction_threshold *
len(old_species_members)))
# Use at least two parents no matter what the threshold fraction result is.
reproduction_cutoff = max(reproduction_cutoff, 2)
old_species_members = old_species_members[:reproduction_cutoff]
# Randomly choose parents and produce the number of offspring allotted to the species.
# NOTE: Asexual reproduction for now
while spawn > 0:
spawn -= 1
parent1_key, parent1 = random.choice(old_species_members)
# parent2_key, parent2 = random.choice(old_species_members)
child_key = next(self.genome_indexer)
child = Genome(child_key)
# child.crossover(parent1, parent2)
child.copy(parent1, generation)
child.mutate(generation)
new_population[child_key] = child
return new_population
def _get_genome(self):
if os.path.isfile(self.genome_filename):
return Genome(self.genome_filename)
else:
return None
import sys
from genome import Genome
from organism import Organism
PREFIX = '/home/koppejan/projects/helicopter/ghh09/policies/'
POLICIES = [Genome.open(PREFIX + 'mdp%i.net' % i) for i in range(10) if i != 2]
class Agent:
""" Model-Free agent, chooses best policy from a set of pre-trained policies. """
def __init__(self):
""" Initialize agent. """
self.pool = [Organism(genome) for genome in POLICIES]
self.episode = 0
def start(self):
""" Start a new episode """
self.set_policy()
self.episode += 1
self.reward = 0
self.steps = 0
def step(self, state, reward):
""" Choose an action based on the current state. """
self.steps += 1
self.reward += reward
return self.org.policy.propagate(state, 1)
def end(self, reward):
def genome_template(self):
"""
Get a template of the genome, all chromosomes will have empty genes
:return:
"""
genome = Genome()
chromosome = Chromosome(chromosome_id='SingleTrue',
encoding_type=EncodingType.BOOLEAN,
n_genes=1)
chromosome.init_genes()
genome.add_chromosome(chromosome)
chromosome = Chromosome(chromosome_id='SingleFalse',
encoding_type=EncodingType.BOOLEAN,
n_genes=1)
chromosome.init_genes()
genome.add_chromosome(chromosome)
chromosome = Chromosome(chromosome_id='Fixed10True',
encoding_type=EncodingType.BOOLEAN,
n_genes=10)
chromosome.init_genes()
genome.add_chromosome(chromosome)
chromosome = Chromosome(chromosome_id='Fixed10False',
encoding_type=EncodingType.BOOLEAN,
n_genes=10)
chromosome.init_genes()
genome.add_chromosome(chromosome)
chromosome = Chromosome(chromosome_id='Variable10True',
encoding_type=EncodingType.BOOLEAN)
chromosome.init_genes()
genome.add_chromosome(chromosome)
chromosome = Chromosome(chromosome_id='Variable10False',
encoding_type=EncodingType.BOOLEAN)
chromosome.init_genes()
genome.add_chromosome(chromosome)
chromosome = Chromosome(chromosome_id='Alternating',
encoding_type=EncodingType.BOOLEAN)
chromosome.init_genes()
genome.add_chromosome(chromosome)
return genome
