/**
*  @license
* Copyright 2022, JsData. All rights reserved.
*
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.

* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* ==========================================================================
*/
import { CrossValidator } from './CrossValidator';
import { Scikit1D, Scikit2D, Tensor1D } from '../types';
export interface KFoldParams {
    /**
     * Number of ways in which the dataset is to be split.
     * Defaults to `5`. Larger numbers of splits will result
     * in increased computational cost, since the model is
     * trained `nSplits` time during cross validation. In
     * return, more training data is available in each split,
     * which allows utilize smaller datasets more efficiently.
     */
    nSplits?: number;
    /**
     * If set to `true`, indices are shuffled before train/test
     * splitting. Defaults to `false`.
     */
    shuffle?: boolean;
    /**
     * Random seed to be used for shuffling. Ignored if `nSplits = false`.
     */
    randomState?: number;
}
/**
 * K-Fold cross-validator
 *
 * Generates train and test indices to split data in train/test subsets.
 * To generate these subsets, the dataset is split into k (about) evenly
 * sized chunks of consecutive elements. Each split takes another chunk
 * as test data and the remaining chunks are combined to be the training
 * data.
 *
 * Optionally, the indices can be shuffled before splitting it into chunks
 * (disabled by default).
 *
 * @example
 * ```js
 * import { KFold } from 'scikitjs'
 *
 * const kf = new KFold({ nSplits: 3 })
 *
 * const X = tf.range(0, 7).reshape([7, 1]) as Tensor2D
 *
 * console.log( 'nSplits:', kf.getNumSplits(X) )
 *
 * for (const { trainIndex, testIndex } of kf.split(X) )
 * {
 *   try {
 *     console.log( 'train:', trainIndex.toString() )
 *     console.log( 'test:',   testIndex.toString() )
 *   }
 *   finally {
 *     trainIndex.dispose()
 *      testIndex.dispose()
 *   }
 * }
 * ```
 */
export declare class KFold implements CrossValidator {
    nSplits: number;
    shuffle: boolean;
    randomState?: number;
    name: string;
    tf: any;
    constructor({ nSplits, shuffle, randomState }?: KFoldParams);
    getNumSplits(): number;
    split(X: Scikit2D, y?: Scikit1D, groups?: Scikit1D): IterableIterator<{
        trainIndex: Tensor1D;
        testIndex: Tensor1D;
    }>;
}