python - numpy:如何在np数组中选择特定索引以进行k倍交叉验证？

我有一个尺寸为5000 x 3027的矩阵形式的训练数据集(CIFAR-10数据集)。在numpy中使用array_split，我将其划分为5个不同的部分，我只选择其中一部分作为交叉验证折叠。但是，当我使用类似的东西时，我的问题来了
XTrain [[Indexes]]，其中index是一个像[0,1,2,3]的数组，因为这样做给了我3D张量，尺寸为4 x 1000 x 3027，而不是矩阵。如何将“4 x 1000”折叠成4000行，以获得4000 x 3027的矩阵？

for fold in range(len(X_train_folds)):
    indexes = np.delete(np.arange(len(X_train_folds)), fold)
    XTrain = X_train_folds[indexes]
    X_cv = X_train_folds[fold]
    yTrain = y_train_folds[indexes]
    y_cv = y_train_folds[fold]

    classifier.train(XTrain, yTrain)
    dists = classifier.compute_distances_no_loops(X_cv)
    y_test_pred = classifier.predict_labels(dists, k)

    num_correct = np.sum(y_test_pred == y_test)
    accuracy = float(num_correct/num_test)
    k_to_accuracy[k] = accuracy

最佳答案

我建议使用scikit-learn包。它已经带有许多常见的机器学习工具，例如K-fold cross-validation generator:

>>> from sklearn.cross_validation import KFold
>>> X = # your data [samples x features]
>>> y = # gt labels
>>> kf = KFold(X.shape[0], n_folds=5)

然后，遍历kf:

>>> for train_index, test_index in kf:
        X_train, X_test = X[train_index], X[test_index]
        y_train, y_test = y[train_index], y[test_index]
        # do something

上面的循环将执行n_folds次，每次使用不同的训练和测试索引。