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A Java's Collaborative Filtering library to carry out experiments in research of Collaborative Filtering based Recommender Systems. The library has been designed from researchers to researchers.
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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.commons.math3.linear;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.util.Precision;
/**
* Class transforming a general real matrix to Hessenberg form.
* A m × m matrix A can be written as the product of three matrices: A = P
* × H × PT with P an orthogonal matrix and H a Hessenberg
* matrix. Both P and H are m × m matrices.
* Transformation to Hessenberg form is often not a goal by itself, but it is an
* intermediate step in more general decomposition algorithms like
* {@link EigenDecomposition eigen decomposition}. This class is therefore
* intended for internal use by the library and is not public. As a consequence
* of this explicitly limited scope, many methods directly returns references to
* internal arrays, not copies.
* This class is based on the method orthes in class EigenvalueDecomposition
* from the JAMA library.
*
* @see MathWorld
* @see Householder Transformations
* @since 3.1
*/
class HessenbergTransformer {
/** Householder vectors. */
private final double householderVectors[][];
/** Temporary storage vector. */
private final double ort[];
/** Cached value of P. */
private RealMatrix cachedP;
/** Cached value of Pt. */
private RealMatrix cachedPt;
/** Cached value of H. */
private RealMatrix cachedH;
/**
* Build the transformation to Hessenberg form of a general matrix.
*
* @param matrix matrix to transform
* @throws NonSquareMatrixException if the matrix is not square
*/
HessenbergTransformer(final RealMatrix matrix) {
if (!matrix.isSquare()) {
throw new NonSquareMatrixException(matrix.getRowDimension(),
matrix.getColumnDimension());
}
final int m = matrix.getRowDimension();
householderVectors = matrix.getData();
ort = new double[m];
cachedP = null;
cachedPt = null;
cachedH = null;
// transform matrix
transform();
}
/**
* Returns the matrix P of the transform.
* P is an orthogonal matrix, i.e. its inverse is also its transpose.
*
* @return the P matrix
*/
public RealMatrix getP() {
if (cachedP == null) {
final int n = householderVectors.length;
final int high = n - 1;
final double[][] pa = new double[n][n];
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
pa[i][j] = (i == j) ? 1 : 0;
}
}
for (int m = high - 1; m >= 1; m--) {
if (householderVectors[m][m - 1] != 0.0) {
for (int i = m + 1; i <= high; i++) {
ort[i] = householderVectors[i][m - 1];
}
for (int j = m; j <= high; j++) {
double g = 0.0;
for (int i = m; i <= high; i++) {
g += ort[i] * pa[i][j];
}
// Double division avoids possible underflow
g = (g / ort[m]) / householderVectors[m][m - 1];
for (int i = m; i <= high; i++) {
pa[i][j] += g * ort[i];
}
}
}
}
cachedP = MatrixUtils.createRealMatrix(pa);
}
return cachedP;
}
/**
* Returns the transpose of the matrix P of the transform.
* P is an orthogonal matrix, i.e. its inverse is also its transpose.
*
* @return the transpose of the P matrix
*/
public RealMatrix getPT() {
if (cachedPt == null) {
cachedPt = getP().transpose();
}
// return the cached matrix
return cachedPt;
}
/**
* Returns the Hessenberg matrix H of the transform.
*
* @return the H matrix
*/
public RealMatrix getH() {
if (cachedH == null) {
final int m = householderVectors.length;
final double[][] h = new double[m][m];
for (int i = 0; i < m; ++i) {
if (i > 0) {
// copy the entry of the lower sub-diagonal
h[i][i - 1] = householderVectors[i][i - 1];
}
// copy upper triangular part of the matrix
for (int j = i; j < m; ++j) {
h[i][j] = householderVectors[i][j];
}
}
cachedH = MatrixUtils.createRealMatrix(h);
}
// return the cached matrix
return cachedH;
}
/**
* Get the Householder vectors of the transform.
* Note that since this class is only intended for internal use, it returns
* directly a reference to its internal arrays, not a copy.
*
* @return the main diagonal elements of the B matrix
*/
double[][] getHouseholderVectorsRef() {
return householderVectors;
}
/**
* Transform original matrix to Hessenberg form.
* Transformation is done using Householder transforms.
*/
private void transform() {
final int n = householderVectors.length;
final int high = n - 1;
for (int m = 1; m <= high - 1; m++) {
// Scale column.
double scale = 0;
for (int i = m; i <= high; i++) {
scale += FastMath.abs(householderVectors[i][m - 1]);
}
if (!Precision.equals(scale, 0)) {
// Compute Householder transformation.
double h = 0;
for (int i = high; i >= m; i--) {
ort[i] = householderVectors[i][m - 1] / scale;
h += ort[i] * ort[i];
}
final double g = (ort[m] > 0) ? -FastMath.sqrt(h) : FastMath.sqrt(h);
h -= ort[m] * g;
ort[m] -= g;
// Apply Householder similarity transformation
// H = (I - u*u' / h) * H * (I - u*u' / h)
for (int j = m; j < n; j++) {
double f = 0;
for (int i = high; i >= m; i--) {
f += ort[i] * householderVectors[i][j];
}
f /= h;
for (int i = m; i <= high; i++) {
householderVectors[i][j] -= f * ort[i];
}
}
for (int i = 0; i <= high; i++) {
double f = 0;
for (int j = high; j >= m; j--) {
f += ort[j] * householderVectors[i][j];
}
f /= h;
for (int j = m; j <= high; j++) {
householderVectors[i][j] -= f * ort[j];
}
}
ort[m] = scale * ort[m];
householderVectors[m][m - 1] = scale * g;
}
}
}
}
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