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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This is the stable version. Apart from bugfixes, this version does not receive any other updates.

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/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see .
 */

/*
 *    Matrix.java
 *    Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.core;

import java.io.Reader;
import java.io.Serializable;
import java.io.Writer;

/**
 * Class for performing operations on a matrix of floating-point values.
 * 

* Deprecated: Uses internally the code of the sub-package * weka.core.matrix - only for backwards compatibility. * * @author Gabi Schmidberger ([email protected]) * @author Yong Wang ([email protected]) * @author Eibe Frank ([email protected]) * @author Len Trigg ([email protected]) * @author Fracpete (fracpete at waikato dot ac dot nz) * @version $Revision: 10203 $ * @deprecated Use weka.core.matrix.Matrix instead - only for * backwards compatibility. */ @Deprecated public class Matrix implements Cloneable, Serializable, RevisionHandler { /** for serialization */ private static final long serialVersionUID = -3604757095849145838L; /** * The actual matrix */ protected weka.core.matrix.Matrix m_Matrix = null; /** * Constructs a matrix and initializes it with default values. * * @param nr the number of rows * @param nc the number of columns */ public Matrix(int nr, int nc) { m_Matrix = new weka.core.matrix.Matrix(nr, nc); } /** * Constructs a matrix using a given array. * * @param array the values of the matrix */ public Matrix(double[][] array) throws Exception { m_Matrix = new weka.core.matrix.Matrix(array); } /** * Reads a matrix from a reader. The first line in the file should contain the * number of rows and columns. Subsequent lines contain elements of the * matrix. * * @param r the reader containing the matrix * @throws Exception if an error occurs */ public Matrix(Reader r) throws Exception { m_Matrix = new weka.core.matrix.Matrix(r); } /** * Creates and returns a clone of this object. * * @return a clone of this instance. * @throws Exception if an error occurs */ @Override public Object clone() { try { return new Matrix(m_Matrix.getArrayCopy()); } catch (Exception e) { e.printStackTrace(); return null; } } /** * Writes out a matrix. * * @param w the output Writer * @throws Exception if an error occurs */ public void write(Writer w) throws Exception { m_Matrix.write(w); } /** * returns the internal matrix * * @see #m_Matrix */ protected weka.core.matrix.Matrix getMatrix() { return m_Matrix; } /** * Returns the value of a cell in the matrix. * * @param rowIndex the row's index * @param columnIndex the column's index * @return the value of the cell of the matrix */ public final double getElement(int rowIndex, int columnIndex) { return m_Matrix.get(rowIndex, columnIndex); } /** * Add a value to an element. * * @param rowIndex the row's index. * @param columnIndex the column's index. * @param value the value to add. */ public final void addElement(int rowIndex, int columnIndex, double value) { m_Matrix.set(rowIndex, columnIndex, m_Matrix.get(rowIndex, columnIndex) + value); } /** * Returns the number of rows in the matrix. * * @return the number of rows */ public final int numRows() { return m_Matrix.getRowDimension(); } /** * Returns the number of columns in the matrix. * * @return the number of columns */ public final int numColumns() { return m_Matrix.getColumnDimension(); } /** * Sets an element of the matrix to the given value. * * @param rowIndex the row's index * @param columnIndex the column's index * @param value the value */ public final void setElement(int rowIndex, int columnIndex, double value) { m_Matrix.set(rowIndex, columnIndex, value); } /** * Sets a row of the matrix to the given row. Performs a deep copy. * * @param index the row's index * @param newRow an array of doubles */ public final void setRow(int index, double[] newRow) { for (int i = 0; i < newRow.length; i++) { m_Matrix.set(index, i, newRow[i]); } } /** * Gets a row of the matrix and returns it as double array. * * @param index the row's index * @return an array of doubles */ public double[] getRow(int index) { double[] newRow = new double[this.numColumns()]; for (int i = 0; i < newRow.length; i++) { newRow[i] = getElement(index, i); } return newRow; } /** * Gets a column of the matrix and returns it as a double array. * * @param index the column's index * @return an array of doubles */ public double[] getColumn(int index) { double[] newColumn = new double[this.numRows()]; for (int i = 0; i < newColumn.length; i++) { newColumn[i] = getElement(i, index); } return newColumn; } /** * Sets a column of the matrix to the given column. Performs a deep copy. * * @param index the column's index * @param newColumn an array of doubles */ public final void setColumn(int index, double[] newColumn) { for (int i = 0; i < numRows(); i++) { m_Matrix.set(i, index, newColumn[i]); } } /** * Converts a matrix to a string * * @return the converted string */ @Override public String toString() { return m_Matrix.toString(); } /** * Returns the sum of this matrix with another. * * @return a matrix containing the sum. */ public final Matrix add(Matrix other) { try { return new Matrix(m_Matrix.plus(other.getMatrix()).getArrayCopy()); } catch (Exception e) { e.printStackTrace(); return null; } } /** * Returns the transpose of a matrix. * * @return the transposition of this instance. */ public final Matrix transpose() { try { return new Matrix(m_Matrix.transpose().getArrayCopy()); } catch (Exception e) { e.printStackTrace(); return null; } } /** * Returns true if the matrix is symmetric. * * @return boolean true if matrix is symmetric. */ public boolean isSymmetric() { return m_Matrix.isSymmetric(); } /** * Returns the multiplication of two matrices * * @param b the multiplication matrix * @return the product matrix */ public final Matrix multiply(Matrix b) { try { return new Matrix(getMatrix().times(b.getMatrix()).getArrayCopy()); } catch (Exception e) { e.printStackTrace(); return null; } } /** * Performs a (ridged) linear regression. * * @param y the dependent variable vector * @param ridge the ridge parameter * @return the coefficients * @throws IllegalArgumentException if not successful */ public final double[] regression(Matrix y, double ridge) { return getMatrix().regression(y.getMatrix(), ridge).getCoefficients(); } /** * Performs a weighted (ridged) linear regression. * * @param y the dependent variable vector * @param w the array of data point weights * @param ridge the ridge parameter * @return the coefficients * @throws IllegalArgumentException if the wrong number of weights were * provided. */ public final double[] regression(Matrix y, double[] w, double ridge) { return getMatrix().regression(y.getMatrix(), w, ridge).getCoefficients(); } /** * Returns the L part of the matrix. This does only make sense after LU * decomposition. * * @return matrix with the L part of the matrix; * @see #LUDecomposition() */ public Matrix getL() throws Exception { int nr = numRows(); // num of rows int nc = numColumns(); // num of columns double[][] ld = new double[nr][nc]; for (int i = 0; i < nr; i++) { for (int j = 0; (j < i) && (j < nc); j++) { ld[i][j] = getElement(i, j); } if (i < nc) { ld[i][i] = 1; } } Matrix l = new Matrix(ld); return l; } /** * Returns the U part of the matrix. This does only make sense after LU * decomposition. * * @return matrix with the U part of a matrix; * @see #LUDecomposition() */ public Matrix getU() throws Exception { int nr = numRows(); // num of rows int nc = numColumns(); // num of columns double[][] ud = new double[nr][nc]; for (int i = 0; i < nr; i++) { for (int j = i; j < nc; j++) { ud[i][j] = getElement(i, j); } } Matrix u = new Matrix(ud); return u; } /** * Performs a LUDecomposition on the matrix. It changes the matrix into its LU * decomposition. * * @return the indices of the row permutation */ public int[] LUDecomposition() throws Exception { // decompose weka.core.matrix.LUDecomposition lu = m_Matrix.lu(); // singular? old class throws Exception! if (!lu.isNonsingular()) { throw new Exception("Matrix is singular"); } weka.core.matrix.Matrix u = lu.getU(); weka.core.matrix.Matrix l = lu.getL(); // modify internal matrix int nr = numRows(); int nc = numColumns(); for (int i = 0; i < nr; i++) { for (int j = 0; j < nc; j++) { if (j < i) { setElement(i, j, l.get(i, j)); } else { setElement(i, j, u.get(i, j)); } } } u = null; l = null; return lu.getPivot(); } /** * Solve A*X = B using backward substitution. A is current object (this). Note * that this matrix will be changed! B parameter bb. X returned in parameter * bb. * * @param bb first vector B in above equation then X in same equation. */ public void solve(double[] bb) throws Exception { // solve weka.core.matrix.Matrix x = m_Matrix.solve(new weka.core.matrix.Matrix(bb, bb.length)); // move X into bb int nr = x.getRowDimension(); for (int i = 0; i < nr; i++) { bb[i] = x.get(i, 0); } } /** * Performs Eigenvalue Decomposition using Householder QR Factorization * * Matrix must be symmetrical. Eigenvectors are return in parameter V, as * columns of the 2D array. (Real parts of) Eigenvalues are returned in * parameter d. * * @param V double array in which the eigenvectors are returned * @param d array in which the eigenvalues are returned * @throws Exception if matrix is not symmetric */ public void eigenvalueDecomposition(double[][] V, double[] d) throws Exception { // old class only worked with symmetric matrices! if (!this.isSymmetric()) { throw new Exception("EigenvalueDecomposition: Matrix must be symmetric."); } // perform eigenvalue decomposition weka.core.matrix.EigenvalueDecomposition eig = m_Matrix.eig(); weka.core.matrix.Matrix v = eig.getV(); double[] d2 = eig.getRealEigenvalues(); // transfer data int nr = numRows(); int nc = numColumns(); for (int i = 0; i < nr; i++) { for (int j = 0; j < nc; j++) { V[i][j] = v.get(i, j); } } for (int i = 0; i < d2.length; i++) { d[i] = d2[i]; } } /** * Returns sqrt(a^2 + b^2) without under/overflow. * * @param a length of one side of rectangular triangle * @param b length of other side of rectangular triangle * @return lenght of third side */ protected static double hypot(double a, double b) { return weka.core.matrix.Maths.hypot(a, b); } /** * converts the Matrix into a single line Matlab string: matrix is enclosed by * parentheses, rows are separated by semicolon and single cells by blanks, * e.g., [1 2; 3 4]. * * @return the matrix in Matlab single line format */ public String toMatlab() { return getMatrix().toMatlab(); } /** * creates a matrix from the given Matlab string. * * @param matlab the matrix in matlab format * @return the matrix represented by the given string * @see #toMatlab() */ public static Matrix parseMatlab(String matlab) throws Exception { return new Matrix(weka.core.matrix.Matrix.parseMatlab(matlab).getArray()); } /** * Returns the revision string. * * @return the revision */ @Override public String getRevision() { return RevisionUtils.extract("$Revision: 10203 $"); } /** * Main method for testing this class. */ public static void main(String[] ops) { double[] first = { 2.3, 1.2, 5 }; double[] second = { 5.2, 1.4, 9 }; double[] response = { 4, 7, 8 }; double[] weights = { 1, 2, 3 }; try { // test eigenvaluedecomposition double[][] m = { { 1, 2, 3 }, { 2, 5, 6 }, { 3, 6, 9 } }; Matrix M = new Matrix(m); int n = M.numRows(); double[][] V = new double[n][n]; double[] d = new double[n]; M.eigenvalueDecomposition(V, d); Matrix a = new Matrix(2, 3); Matrix b = new Matrix(3, 2); System.out.println("Number of columns for a: " + a.numColumns()); System.out.println("Number of rows for a: " + a.numRows()); a.setRow(0, first); a.setRow(1, second); b.setColumn(0, first); b.setColumn(1, second); System.out.println("a:\n " + a); System.out.println("b:\n " + b); System.out.println("a (0, 0): " + a.getElement(0, 0)); System.out.println("a transposed:\n " + a.transpose()); System.out.println("a * b:\n " + a.multiply(b)); Matrix r = new Matrix(3, 1); r.setColumn(0, response); System.out.println("r:\n " + r); System.out.println("Coefficients of regression of b on r: "); double[] coefficients = b.regression(r, 1.0e-8); for (double coefficient : coefficients) { System.out.print(coefficient + " "); } System.out.println(); System.out.println("Weights: "); for (double weight : weights) { System.out.print(weight + " "); } System.out.println(); System.out.println("Coefficients of weighted regression of b on r: "); coefficients = b.regression(r, weights, 1.0e-8); for (double coefficient : coefficients) { System.out.print(coefficient + " "); } System.out.println(); a.setElement(0, 0, 6); System.out.println("a with (0, 0) set to 6:\n " + a); a.write(new java.io.FileWriter("main.matrix")); System.out.println("wrote matrix to \"main.matrix\"\n" + a); a = new Matrix(new java.io.FileReader("main.matrix")); System.out.println("read matrix from \"main.matrix\"\n" + a); } catch (Exception e) { e.printStackTrace(); } } }





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