tflearn_regression.py 文件源码

def make_x_y(self, data, code):
        data_x = []
        data_y = []
        data.loc[:, 'month'] = data.loc[:, '??']%10000/100
        data = data.drop(['??', '????'], axis=1)

        # normalization
        data = np.array(data)
        if len(data) <= 0 :
            return np.array([]), np.array([])

        if code not in self.scaler:
            self.scaler[code] = StandardScaler()
            data = self.scaler[code].fit_transform(data)
        elif code not in self.scaler:
            return np.array([]), np.array([])
        else:
            data = self.scaler[code].transform(data)

        for i in range(self.frame_len, len(data)-self.predict_dist+1):
            data_x.extend(np.array(data[i-self.frame_len:i, :]))
            data_y.append(data[i+self.predict_dist-1][0])
        np_x = np.array(data_x).reshape(-1, 23*self.frame_len)
        np_y = np.array(data_y)
        return np_x, np_y