/**
*  @license
* Copyright 2021, 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';
import { RegressorMixin } from '../mixins';
export interface DummyRegressorParams {
    /**
     * The strategy that this DummyRegressor will use to make a prediction.
     * Accepted values are 'mean', 'median', 'constant', and 'quantile'.
     *
     * If 'mean' is chosen then the DummyRegressor will just return the 'mean'
     * of the target variable as it's prediction.
     *
     * Likewise with 'median'.
     *
     * If "constant" is chosen, you will have to supply the constant number, and this regressor will always
     * return that value.
     *
     * If "quantile" is chosen, you'll have to chosen the quantile value between 0 < `quantile` < 1.
     * And that value will be returned always. **default = mean**
     */
    strategy?: 'mean' | 'median' | 'constant' | 'quantile';
    /**
     * In the case where you chose 'constant' as your strategy, this number
     * will be the number that is predicted for any input.
     *
     * Every constructor parameter is used as a class variable as well.
     * If "mean", "median", or "quantile" are chosen the class variable "constant" will be
     * set with the "mean", "median", or "quantile" after fit.
     */
    constant?: number;
    /**
     * The quantile to predict in the quantile strategy.
     * 0.5 is the median. 0.0 is the min. 1.0 is the max
     */
    quantile?: number;
}
/** Builds a regressor with simple rules.
 *
 * @example
 * ```js
 * import { DummyRegressor } from 'scikitjs'
 * const reg = new DummyRegressor({ strategy: 'mean' })

    const X = [
      [-1, 5],
      [-0.5, 5],
      [0, 10]
    ]
    const y = [10, 20, 30] // The mean is 20
    reg.fit(X, y) // This regressor will return 20 for any input
 * ```
 */
export declare class DummyRegressor extends RegressorMixin {
    strategy: string;
    constant?: number;
    quantile?: number;
    /** Useful for pipelines and column transformers to have a default name for transforms */
    name: string;
    tf: any;
    constructor({ strategy, constant, quantile }?: DummyRegressorParams);
    fit(X: Scikit2D, y: Scikit1D): DummyRegressor;
    predict(X: Scikit2D): Tensor1D;
}