content: /*
   * Licensed to the Apache Software Foundation (ASF) under one or more
   * contributor license agreements.  See the NOTICE file distributed with
   * this work for additional information regarding copyright ownership.
   * The ASF licenses this file to You 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 org.apache.spark.examples.ml;
  
  // $example on$
  import org.apache.spark.ml.regression.LinearRegression;
  import org.apache.spark.ml.regression.LinearRegressionModel;
  import org.apache.spark.ml.regression.LinearRegressionTrainingSummary;
  import org.apache.spark.ml.linalg.Vectors;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  import org.apache.spark.sql.SparkSession;
  // $example off$
  
  public class JavaLinearRegressionWithElasticNetExample {
    public static void main(String[] args) {
      SparkSession spark = SparkSession
        .builder()
        .appName("JavaLinearRegressionWithElasticNetExample")
        .getOrCreate();
  
      // $example on$
      // Load training data.
      Dataset<Row> training = spark.read().format("libsvm")
        .load("data/mllib/sample_linear_regression_data.txt");
  
      LinearRegression lr = new LinearRegression()
        .setMaxIter(10)
        .setRegParam(0.3)
        .setElasticNetParam(0.8);
  
      // Fit the model.
      LinearRegressionModel lrModel = lr.fit(training);
  
      // Print the coefficients and intercept for linear regression.
      System.out.println("Coefficients: "
        + lrModel.coefficients() + " Intercept: " + lrModel.intercept());
  
      // Summarize the model over the training set and print out some metrics.
      LinearRegressionTrainingSummary trainingSummary = lrModel.summary();
      System.out.println("numIterations: " + trainingSummary.totalIterations());
      System.out.println("objectiveHistory: " + Vectors.dense(trainingSummary.objectiveHistory()));
      trainingSummary.residuals().show();
      System.out.println("RMSE: " + trainingSummary.rootMeanSquaredError());
      System.out.println("r2: " + trainingSummary.r2());
      // $example off$
  
      spark.stop();
    }
  }
  