/**
*  @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, Transformer } from '../types';
import { TransformerMixin } from '../mixins';
export interface MinMaxScalerParams {
    /** Desired range of transformed data. **default = [0, 1] ** */
    featureRange?: [number, number];
}
/**
 * Transform features by scaling each feature to a given range.
 * This estimator scales and translates each feature individually such
 * that it is in the given range on the training set, e.g. between the maximum and minimum value.
 *
 * @example
 * ```js
 * import { MinMaxScaler } from 'scikitjs'
 *
 * const data = [
      [-1, 2],
      [-0.5, 6],
      [0, 10],
      [1, 18]
    ]
    const scaler = new MinMaxScaler()
    const expected = scaler.fitTransform(data)
    // const expected = [
    //  [0, 0],
    //  [0.25, 0.25],
    //  [0.5, 0.5],
    //  [1, 1]
    //]
    ```
 */
export declare class MinMaxScaler extends TransformerMixin implements Transformer {
    featureRange: [number, number];
    /** The per-feature scale that we see in the dataset. */
    scale: Tensor1D;
    min: Tensor1D;
    /** The per-feature minimum that we see in the dataset. */
    dataMin: Tensor1D;
    /** The per-feature maximum that we see in the dataset. */
    dataMax: Tensor1D;
    /** The per-feature range that we see in the dataset. */
    dataRange: Tensor1D;
    /** The number of features seen during fit */
    nFeaturesIn: number;
    /** The number of samples processed by the Estimator. Will be reset on new calls to fit */
    nSamplesSeen: 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({ featureRange }?: MinMaxScalerParams);
    isNumber(value: any): boolean;
    /**
     * Fits a MinMaxScaler to the data
     */
    fit(X: Scikit2D): MinMaxScaler;
    /**
     * Transform the data using the fitted scaler
     * */
    transform(X: Scikit2D): Tensor2D;
    /**
     * Inverse transform the data using the fitted scaler
     * */
    inverseTransform(X: Scikit2D): Tensor2D;
}