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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,
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* See the License for the specific language governing permissions and
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package org.apache.ignite.examples.ml.math.decompositions;
import org.apache.ignite.ml.math.Tracer;
import org.apache.ignite.ml.math.decompositions.SingularValueDecomposition;
import org.apache.ignite.ml.math.impls.matrix.DenseLocalOnHeapMatrix;
/**
* Example of using {@link SingularValueDecomposition}.
*/
public class SingularValueDecompositionExample {
/**
* Executes example.
*
* @param args Command line arguments, none required.
*/
public static void main(String[] args) {
System.out.println(">>> Singular value decomposition (SVD) example started.");
// Let's compute a SVD of (l x k) matrix m. This decomposition can be thought as extension of EigenDecomposition to
// rectangular matrices. The factorization we get is following:
// m = u * s * v^{*}, where
// u is a real or complex unitary matrix
// s is a rectangular diagonal matrix with non-negative real numbers on diagonal (this numbers are singular values of m)
// v is a real or complex unitary matrix
// If m is real then u and v are also real.
// Complex case is not supported for the moment.
DenseLocalOnHeapMatrix m = new DenseLocalOnHeapMatrix(new double[][] {
{1.0d, 0.0d, 0.0d, 0.0d, 2.0d},
{0.0d, 0.0d, 3.0d, 0.0d, 0.0d},
{0.0d, 0.0d, 0.0d, 0.0d, 0.0d},
{0.0d, 2.0d, 0.0d, 0.0d, 0.0d}
});
System.out.println("\n>>> Matrix m for decomposition: ");
Tracer.showAscii(m);
SingularValueDecomposition dec = new SingularValueDecomposition(m);
System.out.println("\n>>> Made decomposition m = u * s * v^{*}.");
System.out.println(">>> Matrix u is ");
Tracer.showAscii(dec.getU());
System.out.println(">>> Matrix s is ");
Tracer.showAscii(dec.getS());
System.out.println(">>> Matrix v is ");
Tracer.showAscii(dec.getV());
// This decomposition can in particular help with solving problem of finding x minimizing 2-norm of m x such
// that 2-norm of x is 1. It appears that it is the right singular vector corresponding to minimal singular
// value, which is always last.
System.out.println("\n>>> Vector x minimizing 2-norm of m x such that 2 norm of x is 1: ");
Tracer.showAscii(dec.getV().viewColumn(dec.getSingularValues().length - 1));
System.out.println("\n>>> Singular value decomposition (SVD) example completed.");
}
}