/**
*  @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, Tensor2D, Tensor1D } from '../types';
import { ClassifierMixin } from '../mixins';
/**
 * Supported strategies for this classifier.
 */
export interface DummyClassifierParams {
    /**
     * If strategy is "mostFrequent" than the most frequent class label is chosen no matter the input.
     * If "uniform" is chosen than a uniformly random class label is chosen for a given input.
     * If "constant" is chosen than you must supply a constant number and this classifier returns that number
     * a given input. **default = "mostFrequent"**
     */
    strategy?: 'mostFrequent' | 'uniform' | 'constant';
    /**
     * If strategy is "constant" than this number is returned for every input. **default = undefined**
     */
    constant?: number;
}
/**
 * Creates an classifier that guesses a class label based on simple rules.
 * By setting a strategy (ie 'mostFrequent', 'uniform', or 'constant'),
 * you can create a simple classifier which can be helpful in determining
 * if a more complicated classifier is actually more predictive.
 *
 * @example
 * ```js
 * import { DummyClassifier } from 'scikitjs'
 *
 * const clf = new DummyClassifier({ strategy: 'mostFrequent' })
    const X = [
      [-1, 5],
      [-0.5, 5],
      [0, 10]
    ]
    const y = [10, 20, 20] // 20 is the most frequent class label
    clf.fit(X, y) // always predicts 20

    clf.predict([
      [0, 0],
      [1000, 1000]
    ]) // [20, 20]


 * ```
 *
 */
export declare class DummyClassifier extends ClassifierMixin {
    constant: number;
    strategy: string;
    /**These are the unique class labels that are seen during fit. */
    classes: number[] | string[];
    /** Useful for pipelines and column transformers to have a default name for transforms */
    name: string;
    tf: any;
    constructor({ strategy, constant }?: DummyClassifierParams);
    /**
     * Fit a DummyClassifier to the data.
     */
    fit(X: Scikit2D, y: Scikit1D): DummyClassifier;
    predictProba(X: Scikit2D): Tensor2D;
    predict(X: Scikit2D): Tensor1D;
}