105 lines
4.1 KiB
Python
105 lines
4.1 KiB
Python
import random
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import copy
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from tqdm import tqdm
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from experiment import *
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from network import *
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def prng():
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return random.random()
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class Soup:
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def __init__(self, size, generator, **kwargs):
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self.size = size
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self.generator = generator
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self.particles = []
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self.params = dict(meeting_rate=0.1, train_other_rate=0.1, train=0)
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self.params.update(kwargs)
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def with_params(self, **kwargs):
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self.params.update(kwargs)
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return self
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def seed(self):
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self.particles = []
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for _ in range(self.size):
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self.particles += [self.generator()]
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return self
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def evolve(self, iterations=1):
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for _ in range(iterations):
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for particle_id, particle in enumerate(self.particles):
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if prng() < self.params.get('meeting_rate'):
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other_particle_id = int(prng() * len(self.particles))
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other_particle = self.particles[other_particle_id]
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particle.attack(other_particle)
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if prng() < self.params.get('train_other_rate'):
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other_particle_id = int(prng() * len(self.particles))
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other_particle = self.particles[other_particle_id]
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particle.train_other(other_particle)
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try:
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for _ in range(self.params.get('train', 0)):
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particle.compiled().train()
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except AttributeError:
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pass
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if self.params.get('remove_divergent') and particle.is_diverged():
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self.particles[particle_id] = self.generator()
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if self.params.get('remove_zero') and particle.is_zero():
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self.particles[particle_id] = self.generator()
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def count(self):
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counters = dict(divergent=0, fix_zero=0, fix_other=0, fix_sec=0, other=0)
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for particle in self.particles:
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if particle.is_diverged():
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counters['divergent'] += 1
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elif particle.is_fixpoint():
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if particle.is_zero():
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counters['fix_zero'] += 1
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else:
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counters['fix_other'] += 1
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elif particle.is_fixpoint(2):
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counters['fix_sec'] += 1
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else:
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counters['other'] += 1
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return counters
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class LearningSoup(Soup):
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def __init__(self, *args, **kwargs):
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super(LearningSoup, self).__init__(**kwargs)
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if __name__ == '__main__':
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if False:
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with SoupExperiment() as exp:
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for run_id in range(1):
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net_generator = lambda: WeightwiseNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
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# net_generator = lambda: AggregatingNeuralNetwork(4, 2, 2).with_keras_params(activation='sigmoid')\
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# .with_params(shuffler=AggregatingNeuralNetwork.shuffle_random)
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# net_generator = lambda: RecurrentNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
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soup = Soup(100, net_generator).with_params(remove_divergent=True, remove_zero=True)
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soup.seed()
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for _ in tqdm(range(100)):
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soup.evolve()
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exp.log(soup.count())
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if True:
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with SoupExperiment() as exp:
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for run_id in range(1):
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net_generator = lambda: TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(2, 2)).with_keras_params(
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activation='linear')
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# net_generator = lambda: AggregatingNeuralNetwork(4, 2, 2).with_keras_params(activation='sigmoid')\
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# .with_params(shuffler=AggregatingNeuralNetwork.shuffle_random)
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# net_generator = lambda: RecurrentNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
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soup = Soup(10, net_generator).with_params(remove_divergent=True, remove_zero=True).with_params(train=500)
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soup.seed()
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for _ in tqdm(range(10)):
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soup.evolve()
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exp.log(soup.count())
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