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min_value) / (max_value - min_value) -# dataset_y = (dataset_y - min_value) / (max_value - min_value) -# -# print(max_value,min_value) -# # 划分训练集和测试集 -# train_size = int(len(dataset_x)*0.7) -# train_x = dataset_x[:train_size] -# train_y = dataset_y[:train_size] -# -# # 将数据改变形状，RNN 读入的数据维度是 (seq_size, batch_size, feature_size) -# train_x = train_x.reshape(-1, 1, DAYS_FOR_TRAIN) -# train_y = train_y.reshape(-1, 1, 5) -# -# # 转为pytorch的tensor对象 -# train_x = torch.from_numpy(train_x).to(device).type(torch.float32) -# train_y = torch.from_numpy(train_y).to(device).type(torch.float32) +# 标准化到0~1 +max_value = np.max(dataset_x) +min_value = np.min(dataset_x) +dataset_x = (dataset_x - min_value) / (max_value - min_value) +dataset_y = (dataset_y - min_value) / (max_value - min_value) + +print(max_value,min_value) +# 划分训练集和测试集 +train_size = int(len(dataset_x)*0.7) +train_x = dataset_x[:train_size] +train_y = dataset_y[:train_size] + +# 将数据改变形状，RNN 读入的数据维度是 (seq_size, batch_size, feature_size) +train_x = train_x.reshape(-1, 1, DAYS_FOR_TRAIN) +train_y = train_y.reshape(-1, 1, 5) + +# 转为pytorch的tensor对象 +train_x = torch.from_numpy(train_x).to(device).type(torch.float32) +train_y = torch.from_numpy(train_y).to(device).type(torch.float32) model = LSTM_Regression(DAYS_FOR_TRAIN, 32, output_size=5, num_layers=2).to(device) # 导入模型并设置模型的参数输入输出层、隐藏层等