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Commons Math example source code file (AbstractEstimator.java)

This example Commons Math source code file (AbstractEstimator.java) is included in the DevDaily.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Java - Commons Math tags/keywords

abstractestimator, abstractestimator, default_max_cost_evaluations, deprecated, estimatedparameter, estimatedparameter, estimationexception, estimationexception, estimator, invalidmatrixexception, realmatrix, util, weightedmeasurement, weightedmeasurement

The Commons Math AbstractEstimator.java source code

/*
 * 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.math.estimation;

import java.util.Arrays;

import org.apache.commons.math.linear.InvalidMatrixException;
import org.apache.commons.math.linear.LUDecompositionImpl;
import org.apache.commons.math.linear.MatrixUtils;
import org.apache.commons.math.linear.RealMatrix;

/**
 * Base class for implementing estimators.
 * <p>This base class handles the boilerplates methods associated to thresholds
 * settings, jacobian and error estimation.</p>
 * @version $Revision: 825919 $ $Date: 2009-10-16 10:51:55 -0400 (Fri, 16 Oct 2009) $
 * @since 1.2
 * @deprecated as of 2.0, everything in package org.apache.commons.math.estimation has
 * been deprecated and replaced by package org.apache.commons.math.optimization.general
 *
 */
@Deprecated
public abstract class AbstractEstimator implements Estimator {

    /** Default maximal number of cost evaluations allowed. */
    public static final int DEFAULT_MAX_COST_EVALUATIONS = 100;

    /** Array of measurements. */
    protected WeightedMeasurement[] measurements;

    /** Array of parameters. */
    protected EstimatedParameter[] parameters;

    /**
     * Jacobian matrix.
     * <p>This matrix is in canonical form just after the calls to
     * {@link #updateJacobian()}, but may be modified by the solver
     * in the derived class (the {@link LevenbergMarquardtEstimator
     * Levenberg-Marquardt estimator} does this).</p>
     */
    protected double[] jacobian;

    /** Number of columns of the jacobian matrix. */
    protected int cols;

    /** Number of rows of the jacobian matrix. */
    protected int rows;

    /** Residuals array.
     * <p>This array is in canonical form just after the calls to
     * {@link #updateJacobian()}, but may be modified by the solver
     * in the derived class (the {@link LevenbergMarquardtEstimator
     * Levenberg-Marquardt estimator} does this).</p>
     */
    protected double[] residuals;

    /** Cost value (square root of the sum of the residuals). */
    protected double cost;

    /** Maximal allowed number of cost evaluations. */
    private int maxCostEval;

    /** Number of cost evaluations. */
    private int costEvaluations;

    /** Number of jacobian evaluations. */
    private int jacobianEvaluations;

    /**
     * Build an abstract estimator for least squares problems.
     * <p>The maximal number of cost evaluations allowed is set
     * to its default value {@link #DEFAULT_MAX_COST_EVALUATIONS}.</p>
     */
    protected AbstractEstimator() {
        setMaxCostEval(DEFAULT_MAX_COST_EVALUATIONS);
    }

    /**
     * Set the maximal number of cost evaluations allowed.
     *
     * @param maxCostEval maximal number of cost evaluations allowed
     * @see #estimate
     */
    public final void setMaxCostEval(int maxCostEval) {
        this.maxCostEval = maxCostEval;
    }

    /**
     * Get the number of cost evaluations.
     *
     * @return number of cost evaluations
     * */
    public final int getCostEvaluations() {
        return costEvaluations;
    }

    /**
     * Get the number of jacobian evaluations.
     *
     * @return number of jacobian evaluations
     * */
    public final int getJacobianEvaluations() {
        return jacobianEvaluations;
    }

    /**
     * Update the jacobian matrix.
     */
    protected void updateJacobian() {
        incrementJacobianEvaluationsCounter();
        Arrays.fill(jacobian, 0);
        int index = 0;
        for (int i = 0; i < rows; i++) {
            WeightedMeasurement wm = measurements[i];
            double factor = -Math.sqrt(wm.getWeight());
            for (int j = 0; j < cols; ++j) {
                jacobian[index++] = factor * wm.getPartial(parameters[j]);
            }
        }
    }

    /**
     * Increment the jacobian evaluations counter.
     */
    protected final void incrementJacobianEvaluationsCounter() {
      ++jacobianEvaluations;
    }

    /**
     * Update the residuals array and cost function value.
     * @exception EstimationException if the number of cost evaluations
     * exceeds the maximum allowed
     */
    protected void updateResidualsAndCost()
    throws EstimationException {

        if (++costEvaluations > maxCostEval) {
            throw new EstimationException("maximal number of evaluations exceeded ({0})",
                                          maxCostEval);
        }

        cost = 0;
        int index = 0;
        for (int i = 0; i < rows; i++, index += cols) {
            WeightedMeasurement wm = measurements[i];
            double residual = wm.getResidual();
            residuals[i] = Math.sqrt(wm.getWeight()) * residual;
            cost += wm.getWeight() * residual * residual;
        }
        cost = Math.sqrt(cost);

    }

    /**
     * Get the Root Mean Square value.
     * Get the Root Mean Square value, i.e. the root of the arithmetic
     * mean of the square of all weighted residuals. This is related to the
     * criterion that is minimized by the estimator as follows: if
     * <em>c if the criterion, and n is the number of
     * measurements, then the RMS is <em>sqrt (c/n).
     *
     * @param problem estimation problem
     * @return RMS value
     */
    public double getRMS(EstimationProblem problem) {
        WeightedMeasurement[] wm = problem.getMeasurements();
        double criterion = 0;
        for (int i = 0; i < wm.length; ++i) {
            double residual = wm[i].getResidual();
            criterion += wm[i].getWeight() * residual * residual;
        }
        return Math.sqrt(criterion / wm.length);
    }

    /**
     * Get the Chi-Square value.
     * @param problem estimation problem
     * @return chi-square value
     */
    public double getChiSquare(EstimationProblem problem) {
        WeightedMeasurement[] wm = problem.getMeasurements();
        double chiSquare = 0;
        for (int i = 0; i < wm.length; ++i) {
            double residual = wm[i].getResidual();
            chiSquare += residual * residual / wm[i].getWeight();
        }
        return chiSquare;
    }

    /**
     * Get the covariance matrix of unbound estimated parameters.
     * @param problem estimation problem
     * @return covariance matrix
     * @exception EstimationException if the covariance matrix
     * cannot be computed (singular problem)
     */
    public double[][] getCovariances(EstimationProblem problem)
      throws EstimationException {

        // set up the jacobian
        updateJacobian();

        // compute transpose(J).J, avoiding building big intermediate matrices
        final int n = problem.getMeasurements().length;
        final int m = problem.getUnboundParameters().length;
        final int max  = m * n;
        double[][] jTj = new double[m][m];
        for (int i = 0; i < m; ++i) {
            for (int j = i; j < m; ++j) {
                double sum = 0;
                for (int k = 0; k < max; k += m) {
                    sum += jacobian[k + i] * jacobian[k + j];
                }
                jTj[i][j] = sum;
                jTj[j][i] = sum;
            }
        }

        try {
            // compute the covariances matrix
            RealMatrix inverse =
                new LUDecompositionImpl(MatrixUtils.createRealMatrix(jTj)).getSolver().getInverse();
            return inverse.getData();
        } catch (InvalidMatrixException ime) {
            throw new EstimationException("unable to compute covariances: singular problem");
        }

    }

    /**
     * Guess the errors in unbound estimated parameters.
     * <p>Guessing is covariance-based, it only gives rough order of magnitude.

* @param problem estimation problem * @return errors in estimated parameters * @exception EstimationException if the covariances matrix cannot be computed * or the number of degrees of freedom is not positive (number of measurements * lesser or equal to number of parameters) */ public double[] guessParametersErrors(EstimationProblem problem) throws EstimationException { int m = problem.getMeasurements().length; int p = problem.getUnboundParameters().length; if (m <= p) { throw new EstimationException( "no degrees of freedom ({0} measurements, {1} parameters)", m, p); } double[] errors = new double[problem.getUnboundParameters().length]; final double c = Math.sqrt(getChiSquare(problem) / (m - p)); double[][] covar = getCovariances(problem); for (int i = 0; i < errors.length; ++i) { errors[i] = Math.sqrt(covar[i][i]) * c; } return errors; } /** * Initialization of the common parts of the estimation. * <p>This method must be called at the start * of the {@link #estimate(EstimationProblem) estimate} * method.</p> * @param problem estimation problem to solve */ protected void initializeEstimate(EstimationProblem problem) { // reset counters costEvaluations = 0; jacobianEvaluations = 0; // retrieve the equations and the parameters measurements = problem.getMeasurements(); parameters = problem.getUnboundParameters(); // arrays shared with the other private methods rows = measurements.length; cols = parameters.length; jacobian = new double[rows * cols]; residuals = new double[rows]; cost = Double.POSITIVE_INFINITY; } /** * Solve an estimation problem. * * <p>The method should set the parameters of the problem to several * trial values until it reaches convergence. If this method returns * normally (i.e. without throwing an exception), then the best * estimate of the parameters can be retrieved from the problem * itself, through the {@link EstimationProblem#getAllParameters * EstimationProblem.getAllParameters} method.</p> * * @param problem estimation problem to solve * @exception EstimationException if the problem cannot be solved * */ public abstract void estimate(EstimationProblem problem) throws EstimationException; }

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