def get_function_result(self, classifier, index, iter_number):
partial_result = []
for i in xrange(iter_number):
X, y, Xun, testing_X, testing_y = self.random_subsampling(0.2, 0.5, 0.3,classifier)
if(classifier == 'WiSARD'):
wisard = SemiSupervisedWiSARD(ss_confidence = index[0],
ignore_zero_addr= index[1],
confidence_threshold = index[2],
bleaching = index[3],
num_bits_addr = index[4],
retina_size = self.__feature_vector_len,
set_of_classes = self.__set_of_classes)
wisard.fit(X, y, Xun)
partial_result.append(self.SS_WiSARD_eval(wisard.predict(testing_X), testing_y))
if(classifier == 'S3VM'):
svm = qns3vm.QN_S3VM_Sparse(X, y, Xun, random)
svm.parameters['lam'] = index[0]
svm.parameters['lamU'] = index[1]
svm.parameters['sigma'] = index[2]
svm.parameters['kernel_type'] = index[3]
svm.train()
partial_result.append(self.S3VM_eval(svm.getPredictions(testing_X),testing_y))
return np.mean(partial_result)
def validation(self, classifier, tuple_of_params, iter_number):
if(classifier == 'WiSARD'):
w1 = []
partial_result = []
time_training = []
time_predicting = []
for i in xrange(iter_number):
w1 = SemiSupervisedWiSARD(ss_confidence = tuple_of_params[0],
ignore_zero_addr= tuple_of_params[1],
confidence_threshold = tuple_of_params[2],
bleaching = tuple_of_params[3],
num_bits_addr = tuple_of_params[4],
retina_size = self.__feature_vector_len,
set_of_classes = self.__set_of_classes)
X, y, Xun, testing_X, testing_y = exp1.random_subsampling(0.1, 0.6, 0.3,'WiSARD')
time1 = time.time()
w1.fit(X, y, Xun)
time2 = time.time()
partial_result.append(self.SS_WiSARD_eval(w1.predict(testing_X), testing_y))
time3 = time.time()
time_training.append(time2 - time1)
time_predicting.append(time3 - time2)
return (np.mean(partial_result), np.mean(time_training), np.mean(time_predicting))
if(classifier == 'S3VM'):
svm = []
partial_result = []
for i in xrange(iter_number):
X, y, Xun, testing_X, testing_y = exp1.random_subsampling(0.1, 0.6, 0.3,'S3VM')
time1 = time.time()
svm = qns3vm.QN_S3VM_Sparse(X, y, Xun, random)
svm.parameters['lam'] = tuple_of_params[0]
svm.parameters['lamU'] = tuple_of_params[1]
svm.parameters['sigma'] = tuple_of_params[2]
svm.parameters['kernel_type'] = tuple_of_params[3]
svm.train()
time2 = time.time()
partial_result.append(self.S3VM_eval(svm.getPredictions(testing_X),testing_y))
time3 = time.time()
time_training.append(time2 - time1)
time_predicting.append(time3 - time2)
return (np.mean(partial_result), np.mean(time_training), np.mean(time_predicting))
if(classifier == 'EMNB'):
w1 = []
partial_result = []
time_training = []
time_predicting = []
for i in xrange(iter_number):
X, y, Xun, testing_X, testing_y = exp1.random_subsampling(0.1, 0.6, 0.3,'EMNB')
time1 = time.time()
emnb = SemiNB()
emnb.train_semi(np.transpose(X), y, np.transpose(Xun))
time2 = time.time() #training time
summing = 0
for j in xrange(len(testing_X)):
result = emnb.predict(np.transpose(testing_X[j]))
if testing_y[j] == result:
summing += 1
partial_result.append(float(summing)/len(testing_X))
time3 = time.time()
time_training.append(time2 - time1)
time_predicting.append(time3 - time2)
return (np.mean(partial_result), np.mean(time_training), np.mean(time_predicting))
