def predict(self, data):
"""
a method used to predict label for give data
:param data: data to predict
:return: predicted label
"""
res = []
if len(self.predictors) == 0:
logging.debug('The Model has not been fitted!')
if len(data.shape) == 1:
data = np.reshape(data, [1, -1])
for i in data:
# find k neighbors from train data
knn_model = neighbors.KNeighborsClassifier(algorithm='ball_tree',
n_neighbors=3).fit(
self.train_data,
self.train_label)
knn_pre_label = knn_model.predict([i])
predicted_vector = self._use_predictors(i) # one row
index = {l: i for i, l in enumerate(np.unique(self.train_label))}
knn_pre_index = index[knn_pre_label[0]]
# make it 0 when the knn predicted class is 0
for j in range(len(self.matrix[0])):
if self.matrix[knn_pre_index][j] == 0:
predicted_vector[j] = 0
self.predicted_vector.append(list(predicted_vector))
value = MT.closet_vector(predicted_vector, self.matrix,
self.distance_measure)
res.append(MT.get_key(self.index, value))
return np.array(res)
def predict(self, data):
res = []
if len(self.predictors) == 0:
logging.debug('The Model has not been fitted!')
if len(data.shape) == 1:
data = np.reshape(data, [1, -1])
for i in data:
predict_res = self._use_predictors(i)
if self.predicted_vector == []:
self.predicted_vector = copy.deepcopy(predict_res)
else:
self.predicted_vector = np.row_stack(
(self.predicted_vector, predict_res))
value = MT.closet_vector(predict_res, self.matrix,
y_euclidean_distance,
np.array(self.predictor_weights))
res.append(MT.get_key(self.index, value))
vector = []
for i in range(self.matrix.shape[1]):
vector.append(list(self.predicted_vector[:, i]))
self.predicted_vector = copy.deepcopy(vector)
return np.array(res)