error.append(function_vector(np.array(train_inputs[len(train_inputs)-1-i]))* hidden_layers_weights[len(train_outputs)-i].T.dot(error[i-1]))

这段代码中有一个错误，应该是 `train_outputs` 而不是 `train_inputs`。修改后的代码如下：

error.append(function_vector(np.array(train_outputs[len(train_outputs)-1-i]))* hidden_layers_weights[len(train_outputs)-i].T.dot(error[i-1]))

相关问题

for i in range(len(train_data) - seq_len - pre_len): train_X.append(np.array(train_data[i : i + seq_len])) train_Y.append(np.array(train_data[i + seq_len : i + seq_len + pre_len]))

对于这段代码，它是指对训练数据进行切片处理，其中train_data是数据集，seq_len是序列长度，pre_len是预测长度。代码通过循环遍历数据集，每次提取一个序列（长度为seq_len），并将其作为训练输入（train_X），同时提取下一个序列（从i+seq_len开始，长度为pre_len），将其作为训练标签（train_Y）。这样就可以将数据处理为模型所需的输入和输出。

train_x, train_y = [], [] for i in range(look_back, len(train_data)): x = [] for j in range(len(svr_models)): x.append(svr_models[j].predict(np.reshape(low_freq_imfs.iloc[i-look_back:i, j].values, (1, -1)))[0]) x.append(lstm_model.predict(np.reshape(high_freq_imfs.iloc[i-look_back:i, :].values, (1, look_back, -1)))[0]) train_x.append(x) train_y.append(train_data.iloc[i, 1]) train_x, train_y = np.array(train_x), np.array(train_y) stack_model = SVR(kernel='rbf', C=100, gamma=0.1, epsilon=.1) stack_model.fit(train_x, train_y) 将以上代码按规范排列，突出显示缩进情况

train_x, train_y = [], []
for i in range(look_back, len(train_data)):
    x = []
    for j in range(lenvr_models)):
        x.append(svr_models[j].predict(np.reshape(low_freq_imfs.iloc[i-look_back:i, j].values, (1, -1)))[0])
    x.append(lstm_model.predict(np.reshape(high_freq_imfs.iloc[i-look_back:i, :].values, (1, look_back, -1)))[0])
    train_x.append(x)
    train_y.append(train_data.iloc[i, 1])

train_x, train_y = np.array(train_x), np.array(train_y)

stack_model = SVR(kernel='rbf', C=100, gamma=0.1, epsilon=.1)
stack_model.fit(train_x, train_y)