diff --git a/03_euler_gen_alg/main.py b/03_euler_gen_alg/main.py index 4da7821..66f5b45 100644 --- a/03_euler_gen_alg/main.py +++ b/03_euler_gen_alg/main.py @@ -10,7 +10,6 @@ Taylor-Reihe um 0. import numpy as np import random -import struct import time import utils @@ -61,9 +60,9 @@ def eval_fitness(bin_pop_values): # Create polynomial function with current parameters approx = lambda x: a*x**3 + b*x**2 + c*x + d e_func = lambda x: np.e**x - quad_error = quadratic_error(e_func, approx, 3) + quad_error = quadratic_error(e_func, approx, 3) # the bigger the error, the worse the fitness - inverse_fitness = 1 / quad_error # the bigger the error, the worse the fitness + inverse_fitness = 1 / quad_error # using inverse to find small errors easier # print("Fitness: " + str(inverse_fitness)) # debugging fitness_arr.append(inverse_fitness) # save fitness @@ -129,52 +128,41 @@ def mutate(population, mutation_rate): # Convert back to string and update population population[random_num] = ''.join(bits) # will work because lists are passed by reference -def main(): - global gray_pop, bin_pop, bin_pop_params, new_pop, fitness, fitness_arr - - bin_pop_values = generate_random_population(POPULATION_SIZE) - - iteration = 0 - print("Working...") - while not np.any((np.array(fitness_arr)) > 200): # Continue while any fitness value is > 1 - # print("Iteration: " + str(iteration)) # debugging - - # Evaluate fitness - fitness_arr = eval_fitness(bin_pop_values) - - # Selection - new_pop = select(fitness_arr) # assigns - - # Crossover - offspring = xover(gray_pop) - new_pop.extend(offspring) # Add offspring to population - - # Mutation - mutate(new_pop, MUTATION_BITS) - - # Update populations for next generation - gray_pop = new_pop.copy() - bin_pop_values = [] - for gray_bin_string in gray_pop: - bin_str = utils.gray_to_bin(gray_bin_string) - params = [bin_str[i:i+7] for i in range(0, 31, 8)] - bin_pop_values.append(params) - - # time.sleep(0.5) - - iteration += 1 - - max_fitness_index = np.argmax(np.array(fitness_arr)) - a, b, c, d = [utils.bin_to_param(param) for param in bin_pop_values[max_fitness_index]] - - - print("index: " + str(max_fitness_index)) - print("a: " + str(a) + "; b: " + str(b) + "; c: " + str(c) + "; d: " + str(d) ) - - utils.plot_graph(a, b, c, d) - - return 0 +bin_pop_values = generate_random_population(POPULATION_SIZE) -if __name__ == "__main__": - main() - print("found that shit") \ No newline at end of file +print("Working...") +# iteration = 0 # debugging +while not np.any((np.array(fitness_arr)) > 200): # Continue while any fitness value is > 1 + # print("Iteration: " + str(iteration)) # debugging + + # Evaluate fitness + fitness_arr = eval_fitness(bin_pop_values) + + # Selection + new_pop = select(fitness_arr) # assigns + + # Crossover + offspring = xover(gray_pop) + new_pop.extend(offspring) # Add offspring to population + + # Mutation + mutate(new_pop, MUTATION_BITS) + + # Update populations for next generation + gray_pop = new_pop.copy() + bin_pop_values = [] + for gray_bin_string in gray_pop: + bin_str = utils.gray_to_bin(gray_bin_string) + params = [bin_str[i:i+7] for i in range(0, 31, 8)] + bin_pop_values.append(params) + + # time.sleep(0.5) # debugging + # iteration += 1 # debugging + +max_fitness_index = np.argmax(np.array(fitness_arr)) +a, b, c, d = [utils.bin_to_param(param) for param in bin_pop_values[max_fitness_index]] + +# print("index: " + str(max_fitness_index)) # debugging +print("a: " + str(a) + "; b: " + str(b) + "; c: " + str(c) + "; d: " + str(d) ) + +utils.plot_graph(a, b, c, d) diff --git a/03_euler_gen_alg/utils.py b/03_euler_gen_alg/utils.py index 7cbafc6..f92c50a 100644 --- a/03_euler_gen_alg/utils.py +++ b/03_euler_gen_alg/utils.py @@ -6,43 +6,31 @@ def gray_to_bin(gray): Convert Gray code to binary, operating on the integer value directly. :returns: 32-bit String """ - try: - num = int(gray, 2) # Convert string to integer - mask = num - while mask != 0: - mask >>= 1 - num ^= mask - return format(num, '032b') # Always return 32-bit string - except ValueError as e: - print(f"Error in gray_to_bin with input: '{gray}'") - raise e + num = int(gray, 2) # Convert string to integer + mask = num + while mask != 0: + mask >>= 1 + num ^= mask + return format(num, '032b') # Always return 32-bit string def bin_to_gray(binary): """ Convert binary to Gray code using XOR with right shift :returns: 32-bit String """ - try: - num = int(binary, 2) # Convert string to integer - gray = num ^ (num >> 1) # Gray code formula: G = B ^ (B >> 1) - return format(gray, '032b') # Always return 32-bit string - except ValueError as e: - print(f"Error in bin_to_gray with input: '{binary}'") - raise e + num = int(binary, 2) # Convert string to integer + gray = num ^ (num >> 1) # Gray code formula: G = B ^ (B >> 1) + return format(gray, '032b') # Always return 32-bit string def bin_to_param(binary, q_min = 0.0, q_max = 10.0): """ Convert one binary string to float parameter in range [q_min, q_max] :returns: float """ - try: - val = int(binary, 2) / 25.5 * 10 # conversion to 0.0 - 10.0 float - # Scale to range [q_min, q_max] - q = q_min + ((q_max - q_min) / (2**len(binary))) * val - return q - except ValueError as e: - print(f"Error in bin_to_param with input: '{binary}'") - raise e + val = int(binary, 2) / 25.5 * 10 # conversion to 0.0 - 10.0 float + # Scale to range [q_min, q_max] + q = q_min + ((q_max - q_min) / (2**len(binary))) * val + return q def plot_graph(a, b, c, d): x = np.arange(-5., 5., 0.1)