content: /*******************************************************************************
   * Copyright (c) 2010 Haifeng Li
   *   
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *  
   *     http://www.apache.org/licenses/LICENSE-2.0
   *
   * 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.
   *******************************************************************************/
  
  package smile.regression;
  
  import java.io.Serializable;
  
  /**
   * Regression analysis includes any techniques for modeling and analyzing
   * the relationship between a dependent variable and one or more independent
   * variables. Most commonly, regression analysis estimates the conditional
   * expectation of the dependent variable given the independent variables.
   * Regression analysis is widely used for prediction and forecasting, where
   * its use has substantial overlap with the field of machine learning. 
   * 
   * @author Haifeng Li
   */
  public interface Regression<T> extends Serializable {
      /**
       * Predicts the dependent variable of an instance.
       * @param x the instance.
       * @return the predicted value of dependent variable.
       */
      double predict(T x);
  
      /**
       * Predicts the dependent variables of an array of instances.
       *
       * @param x the instances.
       * @return the predicted values.
       */
      default double[] predict(T[] x) {
          double[] y = new double[x.length];
          for (int i = 0; i < y.length; i++) {
              y[i] = predict(x[i]);
          }
          return y;
      }
  }
  