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 java.util.Arrays;
  
  import org.apache.spark.ml.regression.GeneralizedLinearRegression;
  import org.apache.spark.ml.regression.GeneralizedLinearRegressionModel;
  import org.apache.spark.ml.regression.GeneralizedLinearRegressionTrainingSummary;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  // $example off$
  import org.apache.spark.sql.SparkSession;
  
  /**
   * An example demonstrating generalized linear regression.
   * Run with
   * <pre>
   * bin/run-example ml.JavaGeneralizedLinearRegressionExample
   * </pre>
   */
  
  public class JavaGeneralizedLinearRegressionExample {
  
    public static void main(String[] args) {
      SparkSession spark = SparkSession
        .builder()
        .appName("JavaGeneralizedLinearRegressionExample")
        .getOrCreate();
  
      // $example on$
      // Load training data
      Dataset<Row> dataset = spark.read().format("libsvm")
        .load("data/mllib/sample_linear_regression_data.txt");
  
      GeneralizedLinearRegression glr = new GeneralizedLinearRegression()
        .setFamily("gaussian")
        .setLink("identity")
        .setMaxIter(10)
        .setRegParam(0.3);
  
      // Fit the model
      GeneralizedLinearRegressionModel model = glr.fit(dataset);
  
      // Print the coefficients and intercept for generalized linear regression model
      System.out.println("Coefficients: " + model.coefficients());
      System.out.println("Intercept: " + model.intercept());
  
      // Summarize the model over the training set and print out some metrics
      GeneralizedLinearRegressionTrainingSummary summary = model.summary();
      System.out.println("Coefficient Standard Errors: "
        + Arrays.toString(summary.coefficientStandardErrors()));
      System.out.println("T Values: " + Arrays.toString(summary.tValues()));
      System.out.println("P Values: " + Arrays.toString(summary.pValues()));
      System.out.println("Dispersion: " + summary.dispersion());
      System.out.println("Null Deviance: " + summary.nullDeviance());
      System.out.println("Residual Degree Of Freedom Null: " + summary.residualDegreeOfFreedomNull());
      System.out.println("Deviance: " + summary.deviance());
      System.out.println("Residual Degree Of Freedom: " + summary.residualDegreeOfFreedom());
      System.out.println("AIC: " + summary.aic());
      System.out.println("Deviance Residuals: ");
      summary.residuals().show();
      // $example off$
  
      spark.stop();
    }
  }
  