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.classification.LogisticRegression;
  import org.apache.spark.ml.classification.LogisticRegressionModel;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  import org.apache.spark.sql.SparkSession;
  // $example off$
  
  public class JavaMulticlassLogisticRegressionWithElasticNetExample {
      public static void main(String[] args) {
          SparkSession spark = SparkSession
                  .builder()
                  .appName("JavaMulticlassLogisticRegressionWithElasticNetExample")
                  .getOrCreate();
  
          // $example on$
          // Load training data
          Dataset<Row> training = spark.read().format("libsvm")
                  .load("data/mllib/sample_multiclass_classification_data.txt");
  
          LogisticRegression lr = new LogisticRegression()
                  .setMaxIter(10)
                  .setRegParam(0.3)
                  .setElasticNetParam(0.8);
  
          // Fit the model
          LogisticRegressionModel lrModel = lr.fit(training);
  
          // Print the coefficients and intercept for multinomial logistic regression
          System.out.println("Coefficients: \n"
                  + lrModel.coefficientMatrix() + " \nIntercept: " + lrModel.interceptVector());
          // $example off$
  
          spark.stop();
      }
  }
  