def main():
#Get the sequence to be searched using
FitnessCalculator.set_solution(str(raw_input("SEQUENCE: ")))
#Define the population
pop_size = int(raw_input("Enter population size: "))
elite_option = str(raw_input("Elitism enabled?(y/n): "))
#Elite switch
if elite_option == 'y':
elitism_enabled = True
else:
elitism_enabled = False
#Create a starting popultation
population = Population(pop_size, True)
#Generation count
generation = 0
evolution = Evolution(elitism_enabled)
#Loop until we dont have a solution
while population.get_fittest().get_fitness() < FitnessCalculator.get_max_fitness():
generation += 1
print "GENERATION : " + str(generation) + " FITTEST: " + str(population.get_fittest().get_fitness())
#Evolve the population
population = evolution.evolve(population)
print "SOLUTION FOUND..."
print "GENERATION: " + str(generation)
print "GENES: " + population.get_fittest().get_string()
return
def make_new_evolution(name, confusion, mask):
print('Making new evolution')
evolution = Evolution(confusion, mask)
evolution.run_tests()
ranked_preys = evolution.rank_preys()
ranked_predators = evolution.rank_predators()
evolution.update_pools(ranked_predators, ranked_preys)
evolution.save(name)
return evolution
def runParallelEvolutions(threads_number=2):
print(f"Runing {threads_number} paralel evolutions")
evolutions, evolution_threads = [], []
for h in range(threads_number):
evolutions.append(Evolution())
evolution_threads.append(Thread(target=evolutions[h].runEvolutionLoop))
evolution_threads[h].start()
for evolution_thread in evolution_threads:
if evolution_thread.isAlive():
evolution_thread.join()
best_of_them_all = evolutions[0]
for evolution in evolutions:
print(
f"Verifying evolution best fit with fitness score of {evolution.best_fit.fitness_score}"
)
best_of_them_all = evolution if evolution.best_fit.fitness_score > best_of_them_all.best_fit.fitness_score else best_of_them_all
return best_of_them_all
def run_evolution(self):
tronEvo = Evolution()
tronEvo.run()
return tronEvo
#Viz Stuff
viz_row = 3
viz_col = 3
Viz = Vizulization(viz_row, viz_col)
energy = []
force = []
fit_history = []
if showViz:
FitnessGraph = Viz.createGraph()
Viz.labelGraph(FitnessGraph, "FitnessGraph")
Viz.legendGraph(FitnessGraph, "Max Fitness", c="red")
Viz.setStruts(num_bars)
#smaller stru ts, less play but faster convergence.....
GA = Evolution(num_struts = num_bars, strut_length = 10, max_gen=1000,init_size =population, pop_size=population, mutation_rate=m_rate, \
selection_rate = s_rate, selection_pressure = s_pressure,elite_rate=e_rate) #Essentailly just have an autmated hill climber rn, b/c mutation rate is so highself.
GA.load(load)
GA.eps = 1
while GA.alive():
if GA.current_gen % 25 == 0:
GA.save(population)
print("----------------------")
print("Curr Gen: ", GA.current_gen)
print("Pop Sicze", len(GA.pop))
for i in np.arange(len(GA.pop)):
drone_structure = GA.pop[i][2] # loop through current population
Solver.solve(drone_structure,
i) # drones have already been solved so they go very fast
fit = GA.fitness(drone_structure) # evaluate drone
def reward(predictions, _y_train):
score = 0
if len(predictions) != len(_y_train):
raise ValueError('Lengths are not the same')
for pred, correct in zip(predictions, _y_train):
if pred[0] == correct:
score += correct_score
return score
"""THIS FORM IS ALWAYS USED"""
def solve(predict, _X_train):
predictions = list()
for sample in _X_train:
predictions.append(predict(sample))
return predictions
if __name__ == '__main__':
X_train, y_train = read_data('../data/xor_train.txt')
evolution = Evolution(reward, solve)
evolution.start_simulation(X_train, y_train,
int(len(X_train) * correct_score * 0.95))
X_test, y_test = read_data('../data/xor_test.txt')
def run_evolution(self):
tronEvo = Evolution()
tronEvo.run()
return tronEvo
