def get_k_fold_data(k, i, X, y): assert k > 1 fold_size = X.shape[0] // k X_train, y_train = None, None for j in range(k): idx = slice(j * fold_size, (j + 1) * fold_size) X_part, y_part = X[idx,:], y[idx] if j == i: X_valid, y_valid = X_part, y_part elif X_train is None: X_train, y_train = X_part, y_part else: X_train = nd.concat(X_train, X_part, dim=0) y_train = nd.concat(y_train, y_part, dim=0) return X_train, y_train, X_valid, y_valid 对代码进行注释

# 定义一个函数，用于生成 k 折交叉验证数据集
# k: 折数
# i: 当前为第 i 折作为验证集
# X: 特征数据
# y: 标签数据
def get_k_fold_data(k, i, X, y):
# 断言 k 的值必须大于 1
assert k > 1
# 计算每一折数据集的大小
fold_size = X.shape[0] // k
# 初始化训练集和验证集的特征数据和标签数据
X_train, y_train = None, None
# 遍历每一折数据集
for j in range(k):
# 计算当前折数据集的索引范围
idx = slice(j * fold_size, (j + 1) * fold_size)
# 划分出当前折的特征数据和标签数据作为验证集
X_part, y_part = X[idx,:], y[idx]
if j == i:
# 如果当前折是验证集，则将其作为验证集
X_valid, y_valid = X_part, y_part
elif X_train is None:
# 如果当前训练集为空，则将当前折的特征数据和标签数据作为训练集
X_train, y_train = X_part, y_part
else:
# 如果当前训练集不为空，则在训练集的特征数据和标签数据后面拼接上当前折的特征数据和标签数据
X_train = nd.concat(X_train, X_part, dim=0)
y_train = nd.concat(y_train, y_part, dim=0)
# 返回训练集和验证集的特征数据和标签数据
return X_train, y_train, X_valid, y_valid