/**
*  @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, Tensor1D, Tensor2D } from '../types';
import { TransformerMixin } from '../mixins';
export interface RobustScalerParams {
    /**Quantile range used to calculate scale_. By default this is equal to the IQR, i.e.,
     * q_min is the first quantile and q_max is the third quantile.
     * Numbers must be between 0, and 100. **default [25.0, 75.0]** */
    quantileRange?: [number, number];
    /** Whether or not we should scale the data. **default = true** */
    withScaling?: boolean;
    /** Whether or not we should center the data. **default = true** */
    withCentering?: boolean;
}
/**
 * Scales the data but is robust to outliers. While StandardScaler will subtract the mean, and
 * divide by the variance, both of those measures are not robust to outliers. So instead of the mean
 * we use the median, and instead of the variance we use the Interquartile Range (which is the distance
 * between the quantile .25, and quantile .75).
 *
 * @example
 * ```js
 * import { RobustScaler } from 'scikitjs'
 *
    const X = [
      [1, -2, 2],
      [-2, 1, 3],
      [4, 1, -2]
    ]

    const scaler = new RobustScaler()
    scaler.fitTransform(X)

    const result = [
      [0, -2, 0],
      [-1, 0, 0.4],
      [1, 0, -1.6]
    ]
 * ```
 */
export declare class RobustScaler extends TransformerMixin {
    /** The per-feature scale that we see in the dataset. We divide by this number. */
    scale: Tensor1D;
    /** The per-feature median that we see in the dataset. We subtrace this number. */
    center: Tensor1D;
    /** 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>;
    quantileRange: [number, number];
    withScaling: boolean;
    withCentering: boolean;
    /** Useful for pipelines and column transformers to have a default name for transforms */
    name: string;
    constructor({ quantileRange, withCentering, withScaling }?: RobustScalerParams);
    isNumber(value: any): boolean;
    fit(X: Scikit2D): RobustScaler;
    transform(X: Scikit2D): Tensor2D;
    inverseTransform(X: Scikit2D): Tensor2D;
}