LSTM循环神经网络_预测股价

import os
import json
import time
import math
import matplotlib.pyplot as plt
import numpy as np
from keras.utils import plot_model
import pandas as pd
import warnings
from keras.models import Sequential,load_model
import datetime as dt
from keras.layers import Dense,Activation,Dropout,LSTM
from keras.utils import plot_model
from keras.callbacks import EarlyStopping, ModelCheckpoint
from numpy import newaxis
warnings.filterwarnings("ignore")
%matplotlib inline

1.数据处理类

'''
从配置文件加载数据
'''
class DataLoader():
    '''
    file_name: 数据文件路径
    split_rate:训练数据占(训练数据+测试数据)的比例
    feature_cols: 特征的列集合
    '''
    def __init__(self,file_name, split_rate, feature_cols):
        dataframe = pd.read_csv(file_name)
        count_split_train = int(len(dataframe) * split_rate)
        self.data_train = dataframe.get(feature_cols).values[:count_split_train]
        self.data_test = dataframe.get(feature_cols).values[count_split_train:]
        self.len_train = len(self.data_train)
        self.len_test = len(self.data_test)
        self.len_train_windows = None
        
    def get_train_data(self, seq_len, normalise):
        data_x = []
        data_y = []
        for i in range(self.len_train - seq_len):
            x, y = self._next_window(i, seq_len, normalise)
            data_x.append(x)
            data_y.append(y)
        return np.array(data_x), np.array(data_y)
    
    def _next_window(self, i, seq_len, normalise):
        window = self.data_train[i: i + seq_len]
        window = self.normalise_windows(window, single_window = True)[0] if normalise else window
        x = window[:-1]
        y = window[-1, [0]]
        return x, y 
        
        
    def normalise_windows(self, window_data, single_window = False):
        normalised_data = []
        window_data = [window_data] if single_window else window_data
        # 都计算和第一条数据的同比涨幅
        for window in window_data:
            normalised_window = []
            for col_i in range(window.shape[1]):
                normalised_col = [((float