/*
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* This software is a cooperative product of The MathWorks and the National
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* Institute of Standards and Technology (NIST) which has been released to the
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* public domain. Neither The MathWorks nor NIST assumes any responsibility
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* whatsoever for its use by other parties, and makes no guarantees, expressed
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* or implied, about its quality, reliability, or any other characteristic.
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*/
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/*
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* LinearRegression.java
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* Copyright (C) 2005 University of Waikato, Hamilton, New Zealand
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*
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*/
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package //weka.core.
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matrix;
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import weka.core.RevisionHandler;
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import weka.core.RevisionUtils;
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import weka.core.Utils;
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/**
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* Class for performing (ridged) linear regression.
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*
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* @author Fracpete (fracpete at waikato dot ac dot nz)
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* @version $Revision: 1.4 $
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*/
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public class LinearRegression
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implements RevisionHandler {
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/** the coefficients */
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protected double[] m_Coefficients = null;
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/**
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* Performs a (ridged) linear regression.
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*
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* @param a the matrix to perform the regression on
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* @param y the dependent variable vector
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* @param ridge the ridge parameter
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* @throws IllegalArgumentException if not successful
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*/
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public LinearRegression(Matrix a, Matrix y, double ridge) {
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calculate(a, y, ridge);
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}
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/**
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* Performs a weighted (ridged) linear regression.
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*
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* @param a the matrix to perform the regression on
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* @param y the dependent variable vector
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* @param w the array of data point weights
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* @param ridge the ridge parameter
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* @throws IllegalArgumentException if the wrong number of weights were
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* provided.
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*/
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public LinearRegression(Matrix a, Matrix y, double[] w, double ridge) {
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if (w.length != a.getRowDimension())
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throw new IllegalArgumentException("Incorrect number of weights provided");
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Matrix weightedThis = new Matrix(
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a.getRowDimension(), a.getColumnDimension());
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Matrix weightedDep = new Matrix(a.getRowDimension(), 1);
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for (int i = 0; i < w.length; i++) {
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double sqrt_weight = Math.sqrt(w[i]);
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for (int j = 0; j < a.getColumnDimension(); j++)
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weightedThis.set(i, j, a.get(i, j) * sqrt_weight);
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weightedDep.set(i, 0, y.get(i, 0) * sqrt_weight);
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}
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calculate(weightedThis, weightedDep, ridge);
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}
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/**
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* performs the actual regression.
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*
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* @param a the matrix to perform the regression on
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* @param y the dependent variable vector
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* @param ridge the ridge parameter
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* @throws IllegalArgumentException if not successful
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*/
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protected void calculate(Matrix a, Matrix y, double ridge) {
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if (y.getColumnDimension() > 1)
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throw new IllegalArgumentException("Only one dependent variable allowed");
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int nc = a.getColumnDimension();
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m_Coefficients = new double[nc];
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Matrix xt = a.transpose();
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Matrix solution;
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boolean success = true;
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do {
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Matrix ss = xt.times(a);
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// Set ridge regression adjustment
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for (int i = 0; i < nc; i++)
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ss.set(i, i, ss.get(i, i) + ridge);
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// Carry out the regression
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Matrix bb = xt.times(y);
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for(int i = 0; i < nc; i++)
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m_Coefficients[i] = bb.get(i, 0);
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try {
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solution = ss.solve(new Matrix(m_Coefficients, m_Coefficients.length));
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for (int i = 0; i < nc; i++)
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m_Coefficients[i] = solution.get(i, 0);
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success = true;
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}
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catch (Exception ex) {
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ridge *= 10;
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success = false;
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}
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} while (!success);
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}
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/**
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* returns the calculated coefficients
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*
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* @return the coefficients
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*/
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public final double[] getCoefficients() {
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return m_Coefficients;
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}
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/**
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* returns the coefficients in a string representation
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*/
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public String toString() {
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return Utils.arrayToString(getCoefficients());
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}
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/**
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* Returns the revision string.
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*
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* @return the revision
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*/
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public String getRevision() {
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return RevisionUtils.extract("$Revision: 1.4 $");
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}
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}
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