pi = (cls == pred[:, 5]).nonzero().view(-1)

This line of code is comparing the values in the "cls" tensor with the values in the sixth column (index 5) of the "pred" tensor. It then uses the "nonzero" function to get the indices of the elements in the resulting boolean tensor that are non-zero. This returns a tensor of shape (num_matches, 1). Finally, the "view" function is used to reshape the tensor into a 1D tensor of length "num_matches". The resulting tensor contains the indices of the elements in "pred" that have the same value as the corresponding elements in "cls".

相关问题

加载数据 X = data.iloc[:, :-1].values y = data.iloc[:, -1:].values X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 训练模型 input_dim=13 hidden_dim=25 output_dim=1 nn=NeuralNetwork(input_dim, hidden_dim, output_dim) learning_rate=0.0016 num_epochs=2000 loss_history=nn.train(X, y, learning_rate, num_epochs) plt.plot(loss_history) plt.title('loss') plt.xlabel('Epoch') plt.ylabel('MSE') X_pred = data_pred.iloc[:, :-1].values y_pred = data_pred.iloc[:, -1:].values y_pred = nn.predict(X_pred)报错NameError: name 'data_pred' is not defined解决代码

你需要先定义 `data_pred` 变量并赋值，再执行 `X_pred = data_pred.iloc[:, :-1].values` 和 `y_pred = data_pred.iloc[:, -1:].values` 这两行代码。例如：

import pandas as pd

# 加载数据
data = pd.read_csv('train_data.csv')
data_pred = pd.read_csv('predict_data.csv')

X = data.iloc[:, :-1].values
y = data.iloc[:, -1:].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 训练模型
input_dim = 13
hidden_dim = 25
output_dim = 1
nn = NeuralNetwork(input_dim, hidden_dim, output_dim)
learning_rate = 0.0016
num_epochs = 2000
loss_history = nn.train(X, y, learning_rate, num_epochs)
plt.plot(loss_history)
plt.title('loss')
plt.xlabel('Epoch')
plt.ylabel('MSE')

X_pred = data_pred.iloc[:, :-1].values
y_pred = data_pred.iloc[:, -1:].values
y_pred = nn.predict(X_pred)

> str(predall_df) 'data.frame': 显示 9 obs. of 3 variables: $ pred_elec :List of 9 ..$ lower : Time-Series [1:28, 1:2] from 2021 to 2023: -836 -440 -361 -318 -519 ... .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : NULL .. .. ..$ : chr [1:2] "80%" "95%" ..$ upper : Time-Series [1:28, 1:2] from 2021 to 2023: -143 399 480 528 334 ... .. ..- attr(*, "dimnames")=List of 2

根据你提供的`str(predall_df)`输出结果，可以看出`predall_df`是一个数据框(data frame)类型的对象，其中包含了3个变量(pred_elec、pred_Power和pred_income)以及9个观测值。但是，每个变量的类型似乎不是数值型，而是列表型(List)。

根据你提供的信息，猜测可能是在进行预测时，使用了某些函数或工具，导致输出的预测结果格式不符合预期。建议你检查一下预测的代码，确保输出的结果是一个数值型的矢量或矩阵，而非列表型的数据结构。

如果你确定预测结果中包含了多个列表型变量，可以使用`unlist()`函数将列表型变量转换为数值型变量，例如：

predall_df$pred_elec <- unlist(predall_df$pred_elec)
predall_df$pred_Power <- unlist(predall_df$pred_Power)
predall_df$pred_income <- unlist(predall_df$pred_income)

这样，就可以将`predall_df`中的列表型变量转换为数值型变量了。然后，你可以再次尝试使用`diffinv()`函数进行差分还原操作，例如：

pred_diff2 <- diffinv(diffinv(predall_df, differences = 1, lag = 12), differences = 2)

这样，就可以得到差分还原后的序列`pred_diff2`了。需要注意的是，在进行差分还原操作时，需要确保数据的格式和类型正确，否则可能会导致错误。