1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
|
/*
* 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.mllib;
// $example on$
import java.util.LinkedList;
// $example off$
import org.apache.spark.SparkConf;
import org.apache.spark.SparkContext;
// $example on$
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.mllib.linalg.Matrix;
import org.apache.spark.mllib.linalg.SingularValueDecomposition;
import org.apache.spark.mllib.linalg.Vector;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.mllib.linalg.distributed.RowMatrix;
// $example off$
/**
* Example for SingularValueDecomposition.
*/
public class JavaSVDExample {
public static void main(String[] args) {
SparkConf conf = new SparkConf().setAppName("SVD Example");
SparkContext sc = new SparkContext(conf);
JavaSparkContext jsc = JavaSparkContext.fromSparkContext(sc);
// $example on$
double[][] array = {{1.12, 2.05, 3.12}, {5.56, 6.28, 8.94}, {10.2, 8.0, 20.5}};
LinkedList<Vector> rowsList = new LinkedList<>();
for (int i = 0; i < array.length; i++) {
Vector currentRow = Vectors.dense(array[i]);
rowsList.add(currentRow);
}
JavaRDD<Vector> rows = jsc.parallelize(rowsList);
// Create a RowMatrix from JavaRDD<Vector>.
RowMatrix mat = new RowMatrix(rows.rdd());
// Compute the top 3 singular values and corresponding singular vectors.
SingularValueDecomposition<RowMatrix, Matrix> svd = mat.computeSVD(3, true, 1.0E-9d);
RowMatrix U = svd.U();
Vector s = svd.s();
Matrix V = svd.V();
// $example off$
Vector[] collectPartitions = (Vector[]) U.rows().collect();
System.out.println("U factor is:");
for (Vector vector : collectPartitions) {
System.out.println("\t" + vector);
}
System.out.println("Singular values are: " + s);
System.out.println("V factor is:\n" + V);
jsc.stop();
}
}
|
