X_train, X_test, y_train, y_test = train_test_split( inputs, mises_stress, test_size=0.2, random_state=42

train_test_split是一个用于将数据集划分为训练集和测试集的函数。它的参数包括inputs（输入数据），mises_stress（目标变量），test_size（测试集占总数据集的比例），random_state（随机种子）。

函数的返回值是四个数组：X_train、X_test、y_train和y_test。其中，X_train包含了训练集的输入数据，X_test包含了测试集的输入数据，y_train包含了训练集的目标变量，y_test包含了测试集的目标变量。

这个函数的作用是将输入数据和目标变量按照指定的比例划分为训练集和测试集，以便进行机器学习模型的训练和评估。

相关问题

dataset = new_data.values train= dataset #valid = dataset[2187:,:] #converting dataset into x_train and y_train scaler = MinMaxScaler(feature_range=(0, 1)) scaled_data = scaler.fit_transform(dataset) x_train, y_train = [], [] for i in range(60,len(train)): x_train.append(scaled_data[i-60:i,0]) y_train.append(scaled_data[i,0]) x_train, y_train = np.array(x_train), np.array(y_train) x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1)) # 重塑训练数据格式为三维形式 x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1)) # 创建 LSTM 模型 model = Sequential() model.add(LSTM(units=50, activation='relu', input_shape=(x_train.shape[1], 1))) model.add(Dense(1)) # 编译并拟合模型 model.compile(optimizer='adam', loss='mse') model.fit(x_train, y_train, epochs=10, batch_size=32, verbose=1) #predicting 246 values, using past 60 from the train data inputs = new_data[len(new_data) - 30 - 60:].values inputs = inputs.reshape(-1,1) inputs = scaler.transform(inputs) X_test = [] for i in range(60,inputs.shape[0]): X_test.append(inputs[i-60:i,0]) X_test = np.array(X_test) X_test = np.reshape(X_test, (X_test.shape[0],X_test.shape[1],1)) closing_price = model.predict(X_test)模型预测值为nan

根据代码，可能的原因有以下几种：

1. 数据集中可能存在缺失值或异常值。可以通过检查数据集来解决此问题。

2. 在进行数据归一化时，MinMaxScaler可能没有正确地处理数据。可以尝试使用其他归一化方法来处理数据。

3. 模型可能存在训练不充分的问题，需要增加训练次数或增加训练数据量。

4. 模型中的某些参数可能需要调整，例如LSTM单元数、学习率等。

建议进行逐个排查，找出问题所在。

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

This code snippet is splitting the dataset into training and testing sets using the train_test_split function from the scikit-learn library.

The inputs are X (the features) and y (the target variable), which are the two arrays that contain the data points. The test_size parameter specifies the proportion of the dataset that should be used for testing, and random_state ensures that the same split is obtained each time the code is run.

The outputs are four arrays: X_train and y_train are the training sets (used to train the machine learning model), while X_test and y_test are the testing sets (used to evaluate the performance of the model).