/**
*  @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 { Scikit1D, Scikit2D, Tensor1D } from '../types';
/**
 * Interface for cross validation splitting strategies.
 */
export interface CrossValidator {
    /**
     * Returns the number of splits this {@link CrossValidator}
     * would produce for the given dataset.
     *
     * @param X Feature tensor.
     * @param y Target tensor (optional).
     * @param groups A tensor containing grouping information.
     *               Used by some CrossValidators to make sure
     *               that data of the same group does not appear
     *               both in the test and training dataset, e.g.
     *               multiple blood samples from a single individual.
     */
    getNumSplits(X: Scikit2D, y?: Scikit1D, groups?: Scikit1D): number;
    /**
     * Yields the different splits into training and test data.
     *
     * @param X Feature tensor.
     * @param y Target tensor (optional).
     * @param groups A tensor containing grouping information.
     *               Used by some CrossValidators to make sure
     *               that data of the same group does not appear
     *               both in the test and training dataset, e.g.
     *               multiple blood samples from a single individual.
     * @yields Trainings splits, where `trainIndex` represents the
     *         indices belonging to training data and `testIndex`
     *         the ones belonging to the test data.
     */
    split(X: Scikit2D, y?: Scikit1D, groups?: Scikit1D): IterableIterator<{
        trainIndex: Tensor1D;
        testIndex: Tensor1D;
    }>;
}