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/*
 * 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
 * 
 * bin/run-example ml.JavaGeneralizedLinearRegressionExample
 * 
*/ public class JavaGeneralizedLinearRegressionExample { public static void main(String[] args) { SparkSession spark = SparkSession .builder() .appName("JavaGeneralizedLinearRegressionExample") .getOrCreate(); // $example on$ // Load training data Dataset 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(); } }




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