connga_sin.py

# The function to create the initial population
organism_creator = lambda : Organism([1, 16, 16, 16, 1], output='linear')
# The function we are trying to learn. numpy doesn't have tau...
true_function = lambda x : np.sin(2 * np.pi * x) #
# The loss function, mean squared error, will serve as the negative fitness
loss_function = lambda y_true, y_estimate : np.mean((y_true - y_estimate)**2)

def simulate_and_evaluate(organism, replicates=1):
    """
    Randomly generate `replicates` samples in [0,1],
    use the organism to predict their corresponding value,
    and return the fitness score of the organism
    """
    X = np.random.random((replicates, 1))
    predictions = organism.predict(X)
    loss = loss_function(true_function(X), predictions)
    return -loss

# Ecosystem requires a function that maps an organism to a real number fitness
scoring_function = lambda organism : simulate_and_evaluate(organism, replicates=100)
# Create the ecosystem
ecosystem = Ecosystem(organism_creator, scoring_function, 
                      population_size=100, holdout=0.1, mating=True)
# Save the fitness score of the best organism in each generation
best_organism_scores = [ecosystem.get_best_organism(include_reward=True)[1]]
generations = 201
for i in range(generations):
    ecosystem.generation()
    this_generation_best = ecosystem.get_best_organism(include_reward=True)
    best_organism_scores.append(this_generation_best[1])
    # [Visualization code omitted...]