Add 'algorytmy_genetyczne.py'
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algorytmy_genetyczne.py
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374
algorytmy_genetyczne.py
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import numpy as np
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from .decay import GeomDecay
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def hill_climb(problem, max_iters=np.inf, restarts=0, init_state=None,
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curve=False, random_state=None):
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"""
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best_state array containing state that optimizes the fitness function.
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best_fitness Value of fitness function at best state.
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fitness_curve array containing the fitness at every iteration.
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"""
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if (not isinstance(max_iters, int) and max_iters != np.inf
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and not max_iters.is_integer()) or (max_iters < 0):
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raise Exception("""max_iters must be a positive integer.""")
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if (not isinstance(restarts, int) and not restarts.is_integer()) \
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or (restarts < 0):
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raise Exception("""restarts must be a positive integer.""")
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if init_state is not None and len(init_state) != problem.get_length():
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raise Exception("""init_state must have same length as problem.""")
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# Set random seed
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if isinstance(random_state, int) and random_state > 0:
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np.random.seed(random_state)
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best_fitness = -1*np.inf
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best_state = None
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if curve:
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fitness_curve = []
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for _ in range(restarts + 1):
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# Initialize optimization problem
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if init_state is None:
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problem.reset()
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else:
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problem.set_state(init_state)
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iters = 0
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while iters < max_iters:
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iters += 1
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# Find neighbors and determine best neighbor
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problem.find_neighbors()
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next_state = problem.best_neighbor()
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next_fitness = problem.eval_fitness(next_state)
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# If best neighbor is an improvement, move to that state
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if next_fitness > problem.get_fitness():
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problem.set_state(next_state)
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else:
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break
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if curve:
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fitness_curve.append(problem.get_fitness())
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# Update best state and best fitness
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if problem.get_fitness() > best_fitness:
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best_fitness = problem.get_fitness()
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best_state = problem.get_state()
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best_fitness = problem.get_maximize()*best_fitness
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if curve:
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return best_state, best_fitness, np.asarray(fitness_curve)
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return best_state, best_fitness
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def random_hill_climb(problem, max_attempts=10, max_iters=np.inf, restarts=0,
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init_state=None, curve=False, random_state=None):
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if (not isinstance(max_attempts, int) and not max_attempts.is_integer()) \
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or (max_attempts < 0):
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raise Exception("""max_attempts must be a positive integer.""")
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if (not isinstance(max_iters, int) and max_iters != np.inf
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and not max_iters.is_integer()) or (max_iters < 0):
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raise Exception("""max_iters must be a positive integer.""")
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if (not isinstance(restarts, int) and not restarts.is_integer()) \
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or (restarts < 0):
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raise Exception("""restarts must be a positive integer.""")
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if init_state is not None and len(init_state) != problem.get_length():
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raise Exception("""init_state must have same length as problem.""")
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# Set random seed
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if isinstance(random_state, int) and random_state > 0:
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np.random.seed(random_state)
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best_fitness = -1*np.inf
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best_state = None
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if curve:
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fitness_curve = []
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for _ in range(restarts + 1):
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# Initialize optimization problem and attempts counter
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if init_state is None:
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problem.reset()
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else:
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problem.set_state(init_state)
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attempts = 0
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iters = 0
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while (attempts < max_attempts) and (iters < max_iters):
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iters += 1
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# Find random neighbor and evaluate fitness
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next_state = problem.random_neighbor()
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next_fitness = problem.eval_fitness(next_state)
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# If best neighbor is an improvement,
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# move to that state and reset attempts counter
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if next_fitness > problem.get_fitness():
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problem.set_state(next_state)
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attempts = 0
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else:
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attempts += 1
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if curve:
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fitness_curve.append(problem.get_fitness())
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# Update best state and best fitness
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if problem.get_fitness() > best_fitness:
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best_fitness = problem.get_fitness()
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best_state = problem.get_state()
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best_fitness = problem.get_maximize()*best_fitness
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if curve:
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return best_state, best_fitness, np.asarray(fitness_curve)
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return best_state, best_fitness
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def simulated_annealing(problem, schedule=GeomDecay(), max_attempts=10,
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max_iters=np.inf, init_state=None, curve=False,
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random_state=None):
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if (not isinstance(max_attempts, int) and not max_attempts.is_integer()) \
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or (max_attempts < 0):
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raise Exception("""max_attempts must be a positive integer.""")
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if (not isinstance(max_iters, int) and max_iters != np.inf
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and not max_iters.is_integer()) or (max_iters < 0):
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raise Exception("""max_iters must be a positive integer.""")
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if init_state is not None and len(init_state) != problem.get_length():
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raise Exception("""init_state must have same length as problem.""")
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# Set random seed
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if isinstance(random_state, int) and random_state > 0:
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np.random.seed(random_state)
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# Initialize problem, time and attempts counter
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if init_state is None:
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problem.reset()
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else:
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problem.set_state(init_state)
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if curve:
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fitness_curve = []
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attempts = 0
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iters = 0
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while (attempts < max_attempts) and (iters < max_iters):
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temp = schedule.evaluate(iters)
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iters += 1
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if temp == 0:
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break
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else:
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# Find random neighbor and evaluate fitness
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next_state = problem.random_neighbor()
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next_fitness = problem.eval_fitness(next_state)
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# Calculate delta E and change prob
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delta_e = next_fitness - problem.get_fitness()
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prob = np.exp(delta_e/temp)
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# If best neighbor is an improvement or random value is less
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# than prob, move to that state and reset attempts counter
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if (delta_e > 0) or (np.random.uniform() < prob):
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problem.set_state(next_state)
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attempts = 0
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else:
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attempts += 1
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if curve:
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fitness_curve.append(problem.get_fitness())
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best_fitness = problem.get_maximize()*problem.get_fitness()
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best_state = problem.get_state()
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if curve:
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return best_state, best_fitness, np.asarray(fitness_curve)
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return best_state, best_fitness
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def genetic_alg(problem, pop_size=200, mutation_prob=0.1, max_attempts=10,
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max_iters=np.inf, curve=False, random_state=None):
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if pop_size < 0:
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raise Exception("""pop_size must be a positive integer.""")
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elif not isinstance(pop_size, int):
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if pop_size.is_integer():
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pop_size = int(pop_size)
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else:
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raise Exception("""pop_size must be a positive integer.""")
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if (mutation_prob < 0) or (mutation_prob > 1):
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raise Exception("""mutation_prob must be between 0 and 1.""")
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if (not isinstance(max_attempts, int) and not max_attempts.is_integer()) \
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or (max_attempts < 0):
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raise Exception("""max_attempts must be a positive integer.""")
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if (not isinstance(max_iters, int) and max_iters != np.inf
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and not max_iters.is_integer()) or (max_iters < 0):
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raise Exception("""max_iters must be a positive integer.""")
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# Set random seed
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if isinstance(random_state, int) and random_state > 0:
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np.random.seed(random_state)
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if curve:
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fitness_curve = []
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# Initialize problem, population and attempts counter
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problem.reset()
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problem.random_pop(pop_size)
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attempts = 0
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iters = 0
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while (attempts < max_attempts) and (iters < max_iters):
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iters += 1
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# Calculate breeding probabilities
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problem.eval_mate_probs()
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# Create next generation of population
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next_gen = []
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for _ in range(pop_size):
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# Select parents
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selected = np.random.choice(pop_size, size=2,
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p=problem.get_mate_probs())
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parent_1 = problem.get_population()[selected[0]]
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parent_2 = problem.get_population()[selected[1]]
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# Create offspring
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child = problem.reproduce(parent_1, parent_2, mutation_prob)
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next_gen.append(child)
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next_gen = np.array(next_gen)
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problem.set_population(next_gen)
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next_state = problem.best_child()
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next_fitness = problem.eval_fitness(next_state)
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# If best child is an improvement,
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# move to that state and reset attempts counter
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if next_fitness > problem.get_fitness():
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problem.set_state(next_state)
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attempts = 0
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else:
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attempts += 1
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if curve:
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fitness_curve.append(problem.get_fitness())
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best_fitness = problem.get_maximize()*problem.get_fitness()
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best_state = problem.get_state()
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if curve:
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return best_state, best_fitness, np.asarray(fitness_curve)
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return best_state, best_fitness
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def mimic(problem, pop_size=200, keep_pct=0.2, max_attempts=10,
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max_iters=np.inf, curve=False, random_state=None, fast_mimic=False):
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"""Use MIMIC to find the optimum for a given optimization problem.
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"""
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if problem.get_prob_type() == 'continuous':
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raise Exception("""problem type must be discrete or tsp.""")
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if pop_size < 0:
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raise Exception("""pop_size must be a positive integer.""")
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elif not isinstance(pop_size, int):
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if pop_size.is_integer():
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pop_size = int(pop_size)
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else:
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raise Exception("""pop_size must be a positive integer.""")
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if (keep_pct < 0) or (keep_pct > 1):
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raise Exception("""keep_pct must be between 0 and 1.""")
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if (not isinstance(max_attempts, int) and not max_attempts.is_integer()) \
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or (max_attempts < 0):
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raise Exception("""max_attempts must be a positive integer.""")
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if (not isinstance(max_iters, int) and max_iters != np.inf
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and not max_iters.is_integer()) or (max_iters < 0):
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raise Exception("""max_iters must be a positive integer.""")
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# Set random seed
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if isinstance(random_state, int) and random_state > 0:
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np.random.seed(random_state)
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if curve:
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fitness_curve = []
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if fast_mimic not in (True, False):
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raise Exception("""fast_mimic mode must be a boolean.""")
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else:
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problem.mimic_speed = fast_mimic
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# Initialize problem, population and attempts counter
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problem.reset()
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problem.random_pop(pop_size)
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attempts = 0
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iters = 0
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while (attempts < max_attempts) and (iters < max_iters):
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iters += 1
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# Get top n percent of population
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problem.find_top_pct(keep_pct)
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# Update probability estimates
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problem.eval_node_probs()
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# Generate new sample
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new_sample = problem.sample_pop(pop_size)
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problem.set_population(new_sample)
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next_state = problem.best_child()
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next_fitness = problem.eval_fitness(next_state)
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# If best child is an improvement,
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# move to that state and reset attempts counter
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if next_fitness > problem.get_fitness():
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problem.set_state(next_state)
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attempts = 0
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else:
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attempts += 1
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if curve:
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fitness_curve.append(problem.get_fitness())
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best_fitness = problem.get_maximize()*problem.get_fitness()
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best_state = problem.get_state().astype(int)
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if curve:
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return best_state, best_fitness, np.asarray(fitness_curve)
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return best_state, best_fitness
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