#!/usr/bin/python
# -*- coding: utf-8 -*-
from genetic_rough_sets_classifier import GeneticRoughSetsClassifier
if __name__ == '__main__':
K_FOLD_NUMBER = 4
K = 1
GENERATIONS = 500
MUTATION = 0.3
CROSS_OVER = 0.9
G = 7
POPULATION = 10
grs = GeneticRoughSetsClassifier(debug=DEBUG)
filename = 'datasets/iris.data.txt'
grs.read_data(filepath=filename, label_is_last=False)
grs.prepare_data(k_fold_number=K_FOLD_NUMBER)
grs.k_fold_cross_validation(k=K)
grs.initialize_genetic(generations=GENERATIONS, mutation_prop=MUTATION, crossover_prop=CROSS_OVER)
grs.create_population(population_size=POPULATION, division=G)
# run genetic algorithm
grs.run()
# genetic algorithm found granulation
# now classify patterns
classified = 0
for i in range(len(patterns)):
if labels[i] == grs.classify(patterns[i]):
classified += 1
grs.print_summary()
if __name__ == '__main__':
DEBUG = False
result_file = 'results/hybrid_classifier.csv'
try:
fd = open(result_file, 'w')
except IOError:
print "Wrong path for results file"
sys.exit(1)
for d in range(len(datasets)):
for k in range(K_FOLD_NUMBER):
for r in range(1):
print "STARTED iteration %d for %d-fold %s dataset" % (r, k, datasets[d][0])
fuzzy_logic_classifier = GeneticFuzzyLogicClassifier()
rough_set_classifier = GeneticRoughSetsClassifier()
filename = 'datasets/%s' % datasets[d][0]
result = fuzzy_logic_classifier.read_data(filepath=filename, label_is_last=(bool)(datasets[d][1]))
result = rough_set_classifier.read_data(filepath=filename, label_is_last=(bool)(datasets[d][1]))
if not result:
logging.error("Wrong dataset name")
sys.exit(1)
fuzzy_logic_classifier.prepare_data(k_fold_number=K_FOLD_NUMBER)
rough_set_classifier.prepare_data(k_fold_number=K_FOLD_NUMBER)
fuzzy_logic_classifier.k_fold_cross_validation(k=k)
fuzzy_logic_classifier.generate_membership_functions(divisions=DIVISIONS)
fuzzy_logic_classifier.initialize_genetic(generations=200, mutation=MUTATION_PROP, crossover=CROSS_OVER_PROP)
fuzzy_logic_classifier.create_population(population_size=20)
DEBUG = False
result_file = 'results/hybrid_classifier.csv'
try:
fd = open(result_file, 'w')
except IOError:
print "Wrong path for results file"
sys.exit(1)
for d in range(len(datasets)):
for k in range(K_FOLD_NUMBER):
for r in range(1):
print "STARTED iteration %d for %d-fold %s dataset" % (
r, k, datasets[d][0])
fuzzy_logic_classifier = GeneticFuzzyLogicClassifier()
rough_set_classifier = GeneticRoughSetsClassifier()
filename = 'datasets/%s' % datasets[d][0]
result = fuzzy_logic_classifier.read_data(filepath=filename,
label_is_last=(bool)(
datasets[d][1]))
result = rough_set_classifier.read_data(filepath=filename,
label_is_last=(bool)(
datasets[d][1]))
if not result:
logging.error("Wrong dataset name")
sys.exit(1)
fuzzy_logic_classifier.prepare_data(
k_fold_number=K_FOLD_NUMBER)
rough_set_classifier.prepare_data(k_fold_number=K_FOLD_NUMBER)
#!/usr/bin/python
# -*- coding: utf-8 -*-
from genetic_rough_sets_classifier import GeneticRoughSetsClassifier
if __name__ == '__main__':
grs = GeneticRoughSetsClassifier(debug=DEBUG)
filename = 'datasets/thyroid.data.txt'
grs.read_data(filepath=filename, label_is_last=False)
grs.prepare_data(k_fold_number=K_FOLD_NUMBER)
grs.k_fold_cross_validation(k=K)
grs.initialize_genetic(generations=500, mutation_prop=0.3, crossover_prop=0.9)
grs.create_population(population_size=10, division=7)
grs.run()
for pattern in patterns:
grs.classify(pattern)
