def __init__(self,
session,
npy_convol_path=None,
npy_ae_path=None,
npy_ae_class_paths=None,
normal_max_path=None,
trainable=False,
num_class=0,
k_classes=1,
threshold=0):
if npy_convol_path is not None:
self.data_dict = np.load(npy_convol_path, encoding='latin1').item()
print("npy file loaded")
else:
self.data_dict = None
print("random weight")
if normal_max_path is not None:
self.normalization_max = utils.load_max_csvData(normal_max_path)
else:
self.normalization_max = 1
print("Data no normalization")
self.var_dict = {}
self.trainable = trainable
self.weight_ae_path = npy_ae_path
self.weight_ae_class_paths = npy_ae_class_paths
self.num_class = num_class
self.AEclass = []
self.sess = session
self.k = k_classes
self.threshold = threshold
for i in range(num_class):
path_weight_ae.append(path_weight + 'vggAE_class' + str(i) + '.npy')
assert os.path.exists(path), print('No existe el directorio de datos ' + path)
assert os.path.exists(path_weight), print('No existe el directorio de pesos ' +
path_weight)
if __name__ == '__main__':
# Datos de valor maximo
# data_normal = Dataset_csv(path_data=[path_data_train_all[0], path_data_test_all[0]], random=False)
# Damax = data_normal.amax
# del data_normal
# utils.generate_max_csvData([path_data_train_all[0], path_data_test_all[0]], path+'maximo.csv', has_label=True)
Damax = utils.load_max_csvData(path + 'maximo.csv')
c = tf.ConfigProto()
c.gpu_options.visible_device_list = "1,2"
print('SEARCH SAMPLES')
print('--------------')
data = Dataset_csv(path_data=path_data_test_all,
minibatch=1,
max_value=Damax,
restrict=False,
random=False)
with tf.device('/cpu:0'):
with tf.Session(config=c) as sess:
X_test, y_test = data_all.generate_batch()
return X_train, X_test, y_train, y_test, len(y_train), len(y_test)
if __name__ == '__main__':
path_logs = xpath + 'resultClasify2.csv'
f = open(path_logs, 'a')
for i in range(0, 3):
path_data_train_csv, path_data_test_csv, path_max_csv, name = path_datasets(
i)
print('\n[NAME:', name, ']')
Damax = utils.load_max_csvData(path_max_csv)
# Metodo 1
# X_train, X_test, y_train, y_test, total_train, total_test = get_data_split(path_data_test_csv, Damax, 0.3)
# Metodo 2
X_train, X_test, y_train, y_test, total_train, total_test = get_data_all(
path_data_train_csv, path_data_test_csv, Damax)
print(np.shape(X_train), np.shape(X_test))
knn = neighbors.KNeighborsClassifier()
print(" Train model...")
knn.fit(X_train, y_train)
print(" Test model...")
Z = knn.predict(X_test)
acc = utils.metrics_multiclass(y_test, Z)
