给我翻译一下 if trained == 0: train_label, train_data = trainingDataSet() else: pk_file = open('data_set/train_set_label.pk1', 'rb') train_label = pickle.load(pk_file) pk_file.close() train_data = np.load('data_set/train_set_data.npy') testFileList = listdir('testDigits') error_sum = 0 test_number = len(testFileList) result_list = []
时间: 2023-06-08 16:04:17 浏览: 49
如果训练等于零,则训练标签和数据为训练数据集,否则打开' data_set / train_set_label.pk1 '文件,加载训练标签,关闭文件,训练数据为' data_set / train_set_data.npy '. 然后,将'testDigits'文件夹中的文件列表转为一个列表。 错误总数设置为0,测试数量设置为测试文件列表长度,结果列表为空。
相关问题
x, y = next(iter(train_loader))
As an AI language model, I do not have access to the context of the code. However, assuming that the variable train_loader contains a DataLoader object in PyTorch, the code line x, y = next(iter(train_loader)) would extract the first batch of data from the train_loader and assign it to the variables x and y.
The DataLoader object in PyTorch is used to load data in batches from a dataset. The next function is used to iterate over the batches in the DataLoader object, and the iter function is used to create an iterator object that can be used to iterate over the batches in the DataLoader object.
The x variable would contain the input data (usually a tensor), and the y variable would contain the target data (usually a tensor). These tensors would be in the format required by the neural network model being trained.
model = DecisionTreeClassifier() model.fit(data_train, label_train) print('Decision Tree Model Trained') y_pred = model.predict(data_test)改成随机森林
将决策树模型改为随机森林模型,只需要将代码中的 `DecisionTreeClassifier()` 修改为 `RandomForestClassifier()` 即可,具体代码如下:
```
model = RandomForestClassifier()
model.fit(data_train, label_train)
print('Random Forest Model Trained')
y_pred = model.predict(data_test)
```
注意,随机森林模型有一些超参数需要调节,例如决策树的数量、每棵树的最大深度等。你可以使用交叉验证等技术来选择最佳的超参数组合。