Partition sample indices from zero through N−1 into k folds for cross-validation. When N is not divisible by k, the first Nmodk validation folds receive one extra index.
{0,1,…,N−1}=F1∪F2∪⋯∪Fk Fi∩Fj=∅for i=jFor fold i, validation indices are Fi and training indices are all remaining folds. If shuffle is true, shuffle indices with np.random.default_rng(seed).permutation before partitioning. Return a list of k dictionaries, each containing train_idx and val_idx as one-dimensional integer NumPy arrays.
Input: N = 5, k = 2, shuffle = false, seed = 0
Output: [{"train_idx": [3, 4], "val_idx": [0, 1, 2]}, {"train_idx": [0, 1, 2], "val_idx": [3, 4]}]
Explanation: Five indices produce validation folds of sizes three and two, and each fold is held out once.
Input: N = 7, k = 3, shuffle = false, seed = 0
Output: [{"train_idx": [3, 4, 5, 6], "val_idx": [0, 1, 2]}, {"train_idx": [0, 1, 2, 5, 6], "val_idx": [3, 4]}, {"train_idx": [0, 1, 2, 3, 4], "val_idx": [5, 6]}]
Use np.array_split(indices, k) to create balanced validation folds.
Concatenate every fold except the current validation fold for train_idx.
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Partition sample indices from zero through N−1 into k folds for cross-validation. When N is not divisible by k, the first Nmodk validation folds receive one extra index.
{0,1,…,N−1}=F1∪F2∪⋯∪Fk Fi∩Fj=∅for i=jFor fold i, validation indices are Fi and training indices are all remaining folds. If shuffle is true, shuffle indices with np.random.default_rng(seed).permutation before partitioning. Return a list of k dictionaries, each containing train_idx and val_idx as one-dimensional integer NumPy arrays.
Input: N = 5, k = 2, shuffle = false, seed = 0
Output: [{"train_idx": [3, 4], "val_idx": [0, 1, 2]}, {"train_idx": [0, 1, 2], "val_idx": [3, 4]}]
Explanation: Five indices produce validation folds of sizes three and two, and each fold is held out once.
Input: N = 7, k = 3, shuffle = false, seed = 0
Output: [{"train_idx": [3, 4, 5, 6], "val_idx": [0, 1, 2]}, {"train_idx": [0, 1, 2, 5, 6], "val_idx": [3, 4]}, {"train_idx": [0, 1, 2, 3, 4], "val_idx": [5, 6]}]
Use np.array_split(indices, k) to create balanced validation folds.
Concatenate every fold except the current validation fold for train_idx.
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Accepts: number
Accepts: number
Accepts: number