/**
*  @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 { SGDRegressor } from './SgdRegressor';
import { ModelFitArgs } from '../types';
/**
 * LinearRegression implementation using gradient descent
 * We aim to mimic the API of scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html
 *
 * The heavy lifting is done in the SGD class.
 * This simply provides sane defaults for a Linear Regression.
 *
 * Potentially we eventually make this class do the "exact" solution using the normal equations
 * https://eli.thegreenplace.net/2014/derivation-of-the-normal-equation-for-linear-regression/
 *
 * In order to do that though, we'd need to do a matrix inversion and tensorflow.js doesn't currently support it.
 * Moreover, for big input / output combinations SGD is faster than doing the matrix inversion anyway.
 * So even if we do eventually do the exact solution, we should then call whichever version is faster (SGD vs Exact)
 * depending on the size of the input.
 */
export interface LinearRegressionParams {
    /**
     * Whether to calculate the intercept for this model.
     * If set to False, no intercept will be used in calculations.
     * **default = true**
     */
    fitIntercept?: boolean;
    modelFitOptions?: Partial<ModelFitArgs>;
}
/** Linear Least Squares
 * @example
 * ```js
 * import { LinearRegression } from 'scikitjs'
 *
 * let X = [
 *  [1, 2],
 *  [1, 4],
 *  [2, 6],
 *  [3, 5],
 *  [10, 20]
 * ]
 * let y = [3, 5, 8, 8, 30]
 * const lr = new LinearRegression({ fitIntercept: false })
  await lr.fit(X, y)
  lr.coef.print() // probably around [1, 1]
 * ```
 */
export declare class LinearRegression extends SGDRegressor {
    constructor({ fitIntercept, modelFitOptions }?: LinearRegressionParams);
}