線性迴歸預測

pom

<dependencies>
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-core_2.12</artifactId>
        <version>2.4.0</version>
    </dependency>

    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-streaming_2.12</artifactId>
        <version>2.4.0</version>
    </dependency>

    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-mllib_2.12</artifactId>
        <version>2.4.0</version>
    </dependency>

    <dependency>
        <groupId>com.thoughtworks.paranamer</groupId>
        <artifactId>paranamer</artifactId>
        <version>2.8</version>
    </dependency>
</dependencies>
<!--打可執行jar包-->
<build>
    <plugins>
        <plugin>
            <groupId>org.apache.maven.plugins</groupId>
            <artifactId>maven-compiler-plugin</artifactId>
            <version>3.3</version>
            <configuration>
                <source>1.8</source>
                <target>1.8</target>
                <encoding>UTF-8</encoding>
            </configuration>
        </plugin>
    </plugins>
    <resources>
        <resource>
            <directory>src/main/resources</directory>
            <includes>
                <include>**/*.*</include>
            </includes>
        </resource>
    </resources>
</build>

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.DoubleFunction;
import org.apache.spark.mllib.linalg.Vector;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.mllib.regression.*;
import scala.Tuple2;
import java.util.Arrays;

public class Logistic {
    public static void main(String[] args) {
        SparkConf sparkConf = new SparkConf().setAppName("Regression").setMaster("local[1]");
        JavaSparkContext sc = new JavaSparkContext(sparkConf);
        sc.setLogLevel("ERROR");
        //加載數據
        JavaRDD<String> data = sc.textFile("D:\\IdeaProjects\\SparkMLlib\\src\\test\\java\\data4");
        JavaRDD<LabeledPoint> parsedData = data.map(line -> {
            String[] parts = line.split(",");
            double[] ds = Arrays.stream(parts[1].split(" "))
                    .mapToDouble(Double::parseDouble)
                    .toArray();
            return new LabeledPoint(Double.parseDouble(parts[0]), Vectors.dense(ds));//賦值f(x),Vectors.dense(Array(x))
        }).cache();

        int numIterations = 1000; //循環迭代修改參數的次數
        //三種訓練方式
        LinearRegressionModel model1 = new LinearRegressionWithSGD(1.0D, numIterations, 0.0D, 1.0D).setIntercept(true).run(parsedData.rdd());//使用截距.setIntercept(true)
        //LinearRegressionModel model1 = LinearRegressionWithSGD.train(parsedData.rdd(), numIterations);//不使用截距的方式
        RidgeRegressionModel model2 = new RidgeRegressionWithSGD(1.0D, numIterations, 0.0D, 1.0D).run(parsedData.rdd());
        LassoModel model3 = LassoWithSGD.train(parsedData.rdd(), numIterations);//使用默認的參數
        //統計預測原始數據的方差
        print(parsedData, model1);
        print(parsedData, model2);
        print(parsedData, model3);
        //預測一條新數據方法
        double[] d = new double[]{0};
        Vector v = Vectors.dense(d);
        System.out.println("預測結果爲:" + model1.predict(v));
        System.out.println("預測結果爲:" + model2.predict(v));
        System.out.println("預測結果爲:" + model3.predict(v));
    }

    //用模型預測訓練數據,並計算模型的預測誤差
    public static void print(JavaRDD<LabeledPoint> parsedData, GeneralizedLinearModel model) {
        JavaPairRDD<Double, Double> valuesAndPreds = parsedData.mapToPair(point -> {
            double prediction = model.predict(point.features());
            return new Tuple2<>(point.label(), prediction);
        });
        Double MSE = valuesAndPreds.mapToDouble(new DoubleFunction<Tuple2<Double, Double>>() {
            @Override
            public double call(Tuple2<Double, Double> doubleDoubleTuple2) throws Exception {//計算預測值與實際值差值的平方值的均值
                System.out.println("實際值:" + doubleDoubleTuple2._1() + ", 預測值:" + doubleDoubleTuple2._2());
                return Math.pow(doubleDoubleTuple2._1() - doubleDoubleTuple2._2(), 2);
            }
        }).mean();
        System.out.println(model.getClass().getName() + " 方差 = " + MSE);
    }
}