def localSearch(self, solution, solution_fitness, materials_changed):
new_concept_coverage = solution
new_best_fitness = solution_fitness
for j in range(self.local_search_size):
fitness_improved = False
for material in materials_changed:
for concept in range(num_concepts):
step_concept_coverage = new_concept_coverage.copy()
step_concept_coverage[
material,
concept] = ~step_concept_coverage[material, concept]
step_fitness = sum([
Fitness.get_fitnessConcepts(student_id,
step_concept_coverage.T)
for student_id in range(num_students)
]) / num_students
if new_best_fitness > step_fitness:
new_best_fitness = step_fitness
new_concept_coverage = step_concept_coverage
fitness_improved = True
if not fitness_improved:
break
return new_concept_coverage, new_best_fitness
def run(self, concept_coverage, fitnessConcepts_total, DATFILE=None):
best_solution = concept_coverage
best_fitness = fitnessConcepts_total
current_temperature = self.initial_temperature
current_solution = concept_coverage
current_fitness = fitnessConcepts_total
self.get_orderOfModifiedMaterials()
fitness_progress = np.empty(self.max_iterations)
for i in range(self.max_iterations):
for l in range(self.cycle):
next_solution = self.get_randNeighbor(best_solution)
next_fitness = sum([
Fitness.get_fitnessConcepts(student_id, current_solution.T)
for student_id in range(num_students)
]) / num_students
deltaE = next_fitness - current_fitness
if (deltaE < 0.0):
current_solution = next_solution
current_fitness = next_fitness
if (next_fitness < best_fitness):
best_solution = next_solution
best_fitness = next_fitness
else:
if (random.uniform(0, 1) < math.exp(
-deltaE / current_temperature)):
current_solution = next_solution
current_fitness = next_fitness
current_temperature = self.decreaseTemperature(current_temperature)
fitness_progress[i] = best_fitness
# print("current_temperature: ", current_temperature)
if (DATFILE):
with open(DATFILE, 'w') as f:
f.write(str(best_fitness))
else:
with open('results_SA.pickle', 'wb') as file:
pickle.dump(
{
"fitness_progress": fitness_progress,
"sa_concept_coverage": best_solution,
"sa_fitness": best_fitness
}, file)
return best_solution, best_fitness
def greadyRandomizedConstruction(self):
student_yes = np.matmul(
recommendation.astype(int), objectives.astype(int)
) #quantos alunos receberam o material E o tem como objetivo
student_no = np.matmul(
recommendation.astype(int), (~objectives).astype(int)
) #quantos alunos receberam o material E não o tem como objetivo
student_difference = student_no - student_yes #(quantos alunos querem ter o conveito adicionado) - (quantos alunos querem ter o conceito removido)
scaled_coverage = (concept_coverage * 2 - 1
) # concept_coverage, onde False é -1 e True é 1
conflicts = student_difference * scaled_coverage
change_potential = np.maximum(0, conflicts).sum(axis=1)
materials_changed = []
new_concept_coverage = concept_coverage.copy()
change_potential_order = np.argsort(-change_potential).tolist()
for j in range(int(self.max_materials_changes * num_materials)):
# Select a material and remove from the list
selected_material_index = random.randrange(
int(len(change_potential_order) * self.alpha_m))
material = change_potential_order[selected_material_index]
materials_changed.append(material)
del change_potential_order[selected_material_index]
# print('[', material, change_potential[material], ']')
# print(conflicts[material])
conflicts_order = np.argsort(-conflicts[material]).tolist()
for k in range(int(self.max_concepts_changes * num_concepts)):
# Select a concept from the material and remove from the list
selected_concept_index = random.randrange(
int(len(conflicts_order) * self.alpha_c))
concept = conflicts_order[selected_concept_index]
del conflicts_order[selected_concept_index]
# print(material, concept)
if conflicts[material, concept] > 0:
new_concept_coverage[
material,
concept] = ~new_concept_coverage[material, concept]
new_best_fitness = sum([
Fitness.get_fitnessConcepts(student_id, new_concept_coverage.T)
for student_id in range(num_students)
]) / num_students
return new_concept_coverage, new_best_fitness, materials_changed
type=float,
help='Final temperature')
ap.add_argument('-a', '--alpha', type=float, default=0.90, help='alpha')
ap.add_argument('-b', '--beta', type=str, default=0.2, help='beta')
ap.add_argument('--datfile',
dest='datfile',
type=str,
help='File where it will be save the score (result)')
args = ap.parse_args()
if args.verbose:
logging.basicConfig(level=logging.DEBUG)
logging.debug(args)
student_results_before = sum([
Fitness.get_fitnessConcepts(student_id, concept_coverage.T)
for student_id in range(num_students)
]) / num_students
simulatedAnnealing = SimulatedAnnealing(args.max_iterations, args.cycle,
args.initial_temperature,
args.final_temperature, args.alpha,
args.beta, 0.0356, 0.1667,
98092891)
simulatedAnnealing.run(concept_coverage, student_results_before,
args.datfile)
# annealing_concept_coverage, student_results_annealing = simulatedAnnealing.run(concept_coverage, student_results_before)
# print(f'student_results_annealing: {student_results_annealing}')
import pickle
import numpy as np
import matplotlib.pyplot as plt
import config
from fitness import Fitness
old_student_results = [Fitness.get_fitnessConcepts(student_id, config.concept_coverage.T) for student_id in range(config.num_students)]
student_results_before = sum(old_student_results)/config.num_students
print(f'student_results_before: {student_results_before}')
grasp_results = pickle.load( open( "results_grasp.pickle", "rb" ) )
grasp_concept_coverage = grasp_results["grasp_concept_coverage"]
grasp_progress = grasp_results["fitness_progress"]
grasp_student_results = [Fitness.get_fitnessConcepts(student_id, grasp_concept_coverage.T) for student_id in range(config.num_students)]
sa_results = pickle.load( open( "results_SA.pickle", "rb" ) )
sa_concept_coverage = sa_results["sa_concept_coverage"]
sa_progress = sa_results["fitness_progress"]
annealing_student_results = [Fitness.get_fitnessConcepts(student_id, sa_concept_coverage.T) for student_id in range(config.num_students)]
fig = plt.figure()
plt.bar(np.arange(config.num_students)-0.2, old_student_results, width=0.2, align='center', label='None')
plt.bar(np.arange(config.num_students) , annealing_student_results, width=0.2, align='center', label='SA')
