/**
*  @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, Tensor } from '../types';
import { TransformerMixin } from '../mixins';
export interface StandardScalerParams {
    /** Whether or not we should subtract the mean. **default = true** */
    withMean?: boolean;
    /** Whether or not we should divide by the standard deviation. **default = true** */
    withStd?: boolean;
}
/**
 * Standardize features by removing the mean and scaling to unit variance.
 * The standard score of a sample x is calculated as: $z = (x - u) / s$,
 * where $u$ is the mean of the training samples, and $s$ is the standard deviation of the training samples.
 *
 * @example
 * ```js
 * import { StandardScaler } from 'scikitjs'
 *
 * const data = [
      [0, 0],
      [0, 0],
      [1, 1],
      [1, 1]
    ]

    const scaler = new StandardScaler()
    const expected = scaler.fitTransform(data)
    // const expected = [
    //  [-1, -1],
    //  [-1, -1],
    //  [1, 1],
    //  [1, 1]
    // ]
 * ```
 */
export declare class StandardScaler extends TransformerMixin {
    /** The per-feature scale that we see in the dataset. We divide by this number. */
    scale: Tensor;
    /** The per-feature mean that we see in the dataset. We subtract by this number. */
    mean: Tensor;
    /** Whether or not we should subtract the mean */
    withMean: boolean;
    /** Whether or not we should divide by the standard deviation */
    withStd: boolean;
    /** 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({ withMean, withStd }?: StandardScalerParams);
    /**
     * Fit a StandardScaler to the data.
     */
    fit(X: Scikit2D): StandardScaler;
    /**
     * Transform the data using the fitted scaler
     */
    transform(X: Scikit2D): Tensor2D;
    /**
     * Inverse transform the data using the fitted scaler
     */
    inverseTransform(X: Scikit2D): Tensor2D;
}