/**
*  @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 { TransformerMixin } from '../mixins';
import { Scikit2D, Tensor1D, Tensor2D } from '../types';
/** MaxAbsScaler scales the data by dividing by the max absolute value that it finds per feature.
 * It's a useful scaling if you wish to keep sparsity in your dataset.
 *
 * @example
 * ```js
 * import { MaxAbsScaler } from 'scikitjs'
 *
 * const scaler = new MaxAbsScaler()
   const data = [
     [-1, 5],
     [-0.5, 5],
     [0, 10],
     [1, 10]
   ]

   const expected = scaler.fitTransform(data)
   const above = [
    [-1, 0.5],
    [-0.5, 0.5],
    [0, 1],
    [1, 1]
   ]
 *
 * ```
*/
export declare class MaxAbsScaler extends TransformerMixin {
    /** The per-feature scale that we see in the dataset. We divide by this number. */
    scale: 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();
    /**
     * Fits a MinMaxScaler to the data
     */
    fit(X: Scikit2D): MaxAbsScaler;
    /**
     * Transform the data using the fitted scaler
     */
    transform(X: Scikit2D): Tensor2D;
    /**
     * Inverse transform the data using the fitted scaler
     */
    inverseTransform(X: Scikit2D): Tensor2D;
}