main.py

from NEAT import NEAT
import time
from config import Config
from data_storage import get_circle_data, get_spiral_data
from neural_network import create_data
import numpy as np

from read_mat_files import get_shm_two_class_data


def main():
    # Keep a consistent seed to make debugging easier TODO: Check if this work's across files
    np.random.seed(1)

    algorithm_options = {0: 'xor_full', 1: 'xor_small_noise', 2: 'circle_data', 3: 'shm_two_class', 4: 'spiral_data'}
    # Choose which algorithm is running using keys
    algorithm_running = algorithm_options[3]

    if algorithm_running == algorithm_options[0]:
        num_data_to_generate = 6250

        # Create data
        x_data, y_data = create_data(n_generated=num_data_to_generate, add_noise=False)
    elif algorithm_running == algorithm_options[1]:
        num_data_to_generate = 300

        # Create data
        x_data, y_data = create_data(n_generated=num_data_to_generate, add_noise=True)
    elif algorithm_running == algorithm_options[2]:
        x_data, y_data = get_circle_data()
        x_data = x_data[:, 0:2]
        y_data.shape = (len(x_data), 1)
        for row in range(y_data.shape[0]):
            if y_data[row, 0] == -1:
                y_data[row, 0] = 0
        num_data_to_generate = len(x_data)
    elif algorithm_running == algorithm_options[3]:
        x_data, y_data = get_shm_two_class_data()
        num_data_to_generate = len(x_data)
    elif algorithm_running == algorithm_options[4]:
        x_data, y_data = get_spiral_data()
        x_data = x_data[:, 0:2]
        y_data.shape = (len(x_data), 1)
        for row in range(y_data.shape[0]):
            if y_data[row, 0] == -1:
                y_data[row, 0] = 0
        num_data_to_generate = len(x_data)

    # Training data
    training_percentage = 0.8
    training_upper_limit_index = round(num_data_to_generate * training_percentage)
    x_training = x_data[0:training_upper_limit_index]
    y_training = y_data[0:training_upper_limit_index]

    # Test data
    x_test = x_data[training_upper_limit_index:]
    y_test = y_data[training_upper_limit_index:]

    neat = NEAT(x_training_data=x_training, y_training_data=y_training, x_test_data=x_test, y_test_data=y_test,
                config=Config, fitness_threshold=-0.000001, f1_score_threshold=0.95, algorithm_running=algorithm_running)

    start_evaluate_time = time.time()
    neat.run(max_num_generations=10000, use_backprop=True, print_generation_information=True,
             show_population_weight_distribution=False)
    end_evaluate_time = time.time()
    total_time = end_evaluate_time - start_evaluate_time
    print(total_time)


if __name__ == "__main__":
    main()