def run_swarm_process(self):
# First we find the combination of creature that cover the space the more thoroughly.
# To achieve that, we use KMEANS with k=2 on the list of creature position.
kmeans = KMeans(n_clusters=2)
swarm_positions = self._swarm.get_list_position() # Get the list of point in the space for KMeans
# Normalize the dimension of each point so the point chosen is irrelevant of the possible different unities
# of each dimensions
normalized_swarm_position = np.array(swarm_positions) / np.array(self._bound_distance)
kmeans.fit(normalized_swarm_position) # Train KMeans
centers = kmeans.cluster_centers_ # Get the centers
centers *= self._bound_distance # Go back to the original dimension
print "Centers: ", centers
# Add two new creatures with their position corresponding to the centers of kmeans.
creature0_position = centers[0]
creature0_fitness = FitnessFunction.calculate_fitness(self._fitness_function, creature0_position,
self._number_of_dimensions)
self._number_of_real_evaluation -= 1 # Did a real evaluation
creature1_position = centers[1]
creature1_fitness = FitnessFunction.calculate_fitness(self._fitness_function, creature1_position,
self._number_of_dimensions)
self._number_of_real_evaluation -= 1 # Did a real evaluation
self._swarm.add_creature_to_swarm(position=creature0_position)
self._swarm.add_creature_to_swarm(position=creature1_position)
# Add the creatures position and fitness to the list of position and fitness evaluated
self._list_real_evaluation_position.append(creature0_position)
self._list_real_evaluation_fitness.append(creature0_fitness)
self._list_real_evaluation_position.append(creature1_position)
self._list_real_evaluation_fitness.append(creature1_fitness)
# Train the regressor
self._regressor.train_regressor(self._list_real_evaluation_position, self._list_real_evaluation_fitness)
# From here, we alternate between exploration and exploitation randomly based on an heuristic except for the
# Very first pass where we for the algorithm to be in exploration mode for one more evaluation
# (3 evaluations total)
self.exploration()
self._number_of_real_evaluation -= 1 # Did a real evaluation
# Now that we have three points evaluated, we are ready to start the algorithm for the requested amount of real
# evaluations. Or until the user stop the program
for generation in range(self._number_of_real_evaluation):
print self._list_real_evaluation_position
print self._list_real_evaluation_fitness
print self._fitness_before_real_evalutation
# Decide if we explore or exploite.
exploitation_threshold = max(0.2, 1/math.sqrt((generation+2)/2))
exploitation_threshold = 0.0
if self._random.rand() < exploitation_threshold:
best_creature_ever = self.exploration()
# TODO once the exploitation algorithm is finish. Remove the if/else and move this block after
# We are almost done with this generation, get the real value of the point of interest found
new_point_to_add_fitness = FitnessFunction.calculate_fitness(self._fitness_function,
best_creature_ever.get_position(),
self._number_of_dimensions)
# Finish the generation by adding the new creature to the list and updating the regressor
self._list_real_evaluation_position.append(best_creature_ever.get_position())
self._list_real_evaluation_fitness.append(new_point_to_add_fitness)
self._fitness_before_real_evalutation.append(self._regressor.get_estimation_fitness_value(best_creature_ever.get_position())[0])
choose_kernel = False
if len(self._list_real_evaluation_fitness) % 10 == 0:
choose_kernel = True
self._regressor.train_regressor(self._list_real_evaluation_position,
self._list_real_evaluation_fitness, choose_kernel=choose_kernel)
print "Smallest point found: ", new_point_to_add_fitness, "Fitness found by the PSO:", \
best_creature_ever.get_fitness(), " At position: ", best_creature_ever.get_position()
# Reset swarm fitness
self._swarm.reset_swarm()
else:
best_creature_ever = self.exploitation()
index = self._list_real_evaluation_fitness.index(min(self._list_real_evaluation_fitness))
print "Smallest point found: ", self._list_real_evaluation_fitness[index], " At position: ", \
self._list_real_evaluation_position[index]
