/**
*  @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 { Scikit2D, Tensor2D } from '../types';
import { TransformerMixin } from '../mixins';
export interface NormalizerParams {
    /** What kind of norm we wish to scale by. **default = "l2" ** */
    norm?: 'l2' | 'l1' | 'max';
}
/**
 * A Normalizer scales each *sample* by the $l_1$, $l_2$ or $max$ value in that sample.
 * If you imagine the input matrix as a 2D grid, then this is effectively a "horizontal" scaling (per-sample scaling)
 * as opposed to a StandardScaler which is a "vertical" scaling (per-feature scaling).
 *
 * The only input is what kind of norm you wish to scale by.
 *
 * @example
 * ```js
 * import { Normalizer } from 'scikitjs'
 *
 * const data = [
      [-1, 1],
      [-6, 6],
      [0, 10],
      [10, 20]
    ]
    const scaler = new Normalizer({ norm: 'l1' })
    const expected = scaler.fitTransform(scaler)
    const expectedValueAbove = [
      [-0.5, 0.5],
      [-0.5, 0.5],
      [0, 1],
      [0.33, 0.66]
    ]
 * ```
 */
export declare class Normalizer extends TransformerMixin {
    norm: string;
    /** The number of features seen during fit */
    nFeaturesIn: number;
    /** Names of features seen during fit. Only stores feature names if input is a DataFrame */
    featureNamesIn: Array<string>;
    /** Useful for pipelines and column transformers to have a default name for transforms */
    name: string;
    constructor({ norm }?: NormalizerParams);
    /**
     * Fits a Normalizer to the data
     */
    fit(X: Scikit2D): Normalizer;
    /**
     * Transform the data using the Normalizer
     * */
    transform(X: Scikit2D): Tensor2D;
}