FukuML/MLBase.py
def prediction(self, input_data='', mode='test_data'):

        '''
        Make prediction
        input test data
        output the prediction
        '''

        prediction = {}

        if (self.status != 'train'):
            print("Please load train data and init W then train the W first.")
            return prediction

        if (input_data == ''):
            print("Please input test data for prediction.")
            return prediction

        if mode == 'future_data':
            data = input_data.split()
            input_data_x = [float(v) for v in data]
            input_data_x = utility.DatasetLoader.feature_transform(
                np.array(input_data_x).reshape(1, -1),
                self.feature_transform_mode,
                self.feature_transform_degree
            )
            input_data_x = np.ravel(input_data_x)
            prediction = self.score_function(input_data_x, self.W)
            return {"input_data_x": input_data_x, "input_data_y": None, "prediction": prediction}
        else:
            data = input_data.split()
            input_data_x = [float(v) for v in data[:-1]]
            input_data_x = utility.DatasetLoader.feature_transform(
                np.array(input_data_x).reshape(1, -1),
                self.feature_transform_mode,
                self.feature_transform_degree
            )
            input_data_x = np.ravel(input_data_x)
            input_data_y = float(data[-1])
            prediction = self.score_function(input_data_x, self.W)
            return {"input_data_x": input_data_x, "input_data_y": input_data_y, "prediction": prediction}
def prediction(self, input_data='', mode='test_data'):

        '''
        Make prediction
        input test data
        output the prediction
        '''

        prediction = {}

        if (self.status != 'train'):
            print("Please load train data and init W then train the W first.")
            return prediction

        if (input_data == ''):
            print("Please input test data for prediction.")
            return prediction

        if mode == 'future_data':
            data = input_data.split()
            input_data_x = [float(v) for v in data]
            input_data_x = utility.DatasetLoader.feature_transform(
                np.array(input_data_x).reshape(1, -1),
                self.feature_transform_mode,
                self.feature_transform_degree
            )
            input_data_x = np.ravel(input_data_x)
            prediction = self.score_function(input_data_x, self.W)
            return {"input_data_x": input_data_x, "input_data_y": None, "prediction": prediction}
        else:
            data = input_data.split()
            input_data_x = [float(v) for v in data[:-1]]
            input_data_x = utility.DatasetLoader.feature_transform(
                np.array(input_data_x).reshape(1, -1),
                self.feature_transform_mode,
                self.feature_transform_degree
            )
            input_data_x = np.ravel(input_data_x)
            input_data_y = float(data[-1])
            prediction = self.score_function(input_data_x, self.W)
            return {"input_data_x": input_data_x, "input_data_y": input_data_y, "prediction": prediction}
Make prediction
        input test data
        output the prediction