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 smile.data.Attribute;
  
  /**
   * Abstract regression model trainer.
   * 
   * @param <T> the type of input object.
   * 
   * @author Haifeng Li
   */
  public abstract class RegressionTrainer <T> {
      /**
       * The feature attributes. This is optional since most classifiers can only
       * work on real-valued attributes.
       */
      Attribute[] attributes;
      
      /**
       * Constructor.
       */
      public RegressionTrainer() {
          
      }
      
      /**
       * Constructor.
       * @param attributes the attributes of independent variable.
       */
      public RegressionTrainer(Attribute[] attributes) {
          this.attributes = attributes;
      }
      
      /**
       * Sets feature attributes. This is optional since most regression models
       * can only work on real-valued attributes.
       * 
       * @param attributes the feature attributes.
       */
      public void setAttributes(Attribute[] attributes) {
          this.attributes = attributes;
      }
      
      /**
       * Learns a regression model with given training data.
       * 
       * @param x the training instances.
       * @param y the training response values.
       * @return a trained regression model.
       */
      public abstract Regression<T> train(T[] x, double[] y);
  }
  