RileyLazarou

class Ecosystem():
    # [Some code removed here]
    
    def generation(self, repeats=1, keep_best=True):
        rewards = rewards = [np.mean([self.scoring_function(x) for _ in range(repeats)]) for x in self.population]
        self.population = [self.population[x] for x in np.argsort(rewards)[::-1]]
        new_population = []
        for i in range(self.population_size):
            parent_1_idx = i % self.holdout
            if self.mating:

RileyLazarou

class Organism():
    # [Some code removed here]

    def mate(self, other, mutate=True):
        if self.use_bias != other.use_bias:
            raise ValueError('Both parents must use bias or not use bias')
        if not len(self.layers) == len(other.layers):
            raise ValueError('Both parents must have same number of layers')
        if not all(self.layers[x].shape == other.layers[x].shape for x in range(len(self.layers))):
            raise ValueError('Both parents must have same shape')

RileyLazarou

class Organism():
    def __init__(self, dimensions, use_bias=True, output='softmax'):
        self.layers = []
        self.biases = []
        self.use_bias = use_bias
        self.output = self._activation(output)
        for i in range(len(dimensions)-1):
            shape = (dimensions[i], dimensions[i+1])
            std = np.sqrt(2 / sum(shape))
            layer = np.random.normal(0, std, shape)