added the estimation package from Mantissa
git-svn-id: https://svn.apache.org/repos/asf/jakarta/commons/proper/math/trunk@512061 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Luc Maisonobe
parent
7f8c5e2562
commit
f3b02ccea3
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@ -15,7 +15,7 @@
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// specific language governing permissions and limitations
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// under the License.
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package org.spaceroots.mantissa.estimation;
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package org.apache.commons.math.estimation;
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import java.io.Serializable;
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@ -15,9 +15,9 @@
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// specific language governing permissions and limitations
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// under the License.
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package org.spaceroots.mantissa.estimation;
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package org.apache.commons.math.estimation;
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import org.spaceroots.mantissa.MantissaException;
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import org.apache.commons.math.MathException;
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/** This class represents exceptions thrown by the estimation solvers.
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@ -27,33 +27,26 @@ import org.spaceroots.mantissa.MantissaException;
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*/
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public class EstimationException
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extends MantissaException {
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extends MathException {
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/** Simple constructor.
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* Build an exception by translating the specified message
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* @param message message to translate
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*/
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public EstimationException(String message) {
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super(message);
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}
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/** Serializable version identifier. */
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private static final long serialVersionUID = -7414806622114810487L;
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/** Simple constructor.
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* Build an exception by translating and formating a message
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* @param specifier format specifier (to be translated)
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* @param parts to insert in the format (no translation)
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*/
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public EstimationException(String specifier, String[] parts) {
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super(specifier, parts);
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}
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/** Simple constructor.
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* Build an exception by translating and formating a message
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* @param specifier format specifier (to be translated)
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* @param parts to insert in the format (no translation)
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*/
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public EstimationException(String specifier, String[] parts) {
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super(specifier, parts);
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}
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/** Simple constructor.
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* Build an exception from a cause
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* @param cause cause of this exception
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*/
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public EstimationException(Throwable cause) {
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super(cause);
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}
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private static final long serialVersionUID = 1613719630569355278L;
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/** Simple constructor.
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* Build an exception from a cause
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* @param cause cause of this exception
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*/
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public EstimationException(Throwable cause) {
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super(cause);
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}
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}
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@ -15,7 +15,7 @@
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// specific language governing permissions and limitations
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// under the License.
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package org.spaceroots.mantissa.estimation;
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package org.apache.commons.math.estimation;
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/** This interface represents an estimation problem.
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@ -15,7 +15,7 @@
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// specific language governing permissions and limitations
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// under the License.
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package org.spaceroots.mantissa.estimation;
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package org.apache.commons.math.estimation;
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/** This interface represents solvers for estimation problems.
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@ -15,14 +15,13 @@
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// specific language governing permissions and limitations
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// under the License.
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package org.spaceroots.mantissa.estimation;
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package org.apache.commons.math.estimation;
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import java.io.Serializable;
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import org.spaceroots.mantissa.linalg.Matrix;
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import org.spaceroots.mantissa.linalg.GeneralMatrix;
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import org.spaceroots.mantissa.linalg.SymetricalMatrix;
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import org.spaceroots.mantissa.linalg.SingularMatrixException;
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import org.apache.commons.math.linear.InvalidMatrixException;
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import org.apache.commons.math.linear.RealMatrix;
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import org.apache.commons.math.linear.RealMatrixImpl;
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/** This class implements a solver for estimation problems.
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@ -67,18 +66,13 @@ public class GaussNewtonEstimator
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* <code>Jn</code> and <code>Jn-1</code> are the current and
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* preceding criterion value (square sum of the weighted residuals
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* of considered measurements).
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* @param epsilon threshold under which the matrix of the linearized
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* problem is considered singular (see {@link
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* org.spaceroots.mantissa.linalg.SquareMatrix#solve(Matrix,double)
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* SquareMatrix.solve}). */
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*/
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public GaussNewtonEstimator(int maxIterations,
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double convergence,
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double steadyStateThreshold,
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double epsilon) {
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double steadyStateThreshold) {
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this.maxIterations = maxIterations;
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this.steadyStateThreshold = steadyStateThreshold;
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this.convergence = convergence;
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this.epsilon = epsilon;
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}
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/** Solve an estimation problem using a least squares criterion.
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@ -153,40 +147,52 @@ public class GaussNewtonEstimator
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WeightedMeasurement[] measurements = problem.getMeasurements();
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// build the linear problem
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GeneralMatrix b = new GeneralMatrix(parameters.length, 1);
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SymetricalMatrix a = new SymetricalMatrix(parameters.length);
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RealMatrix b = new RealMatrixImpl(parameters.length, 1);
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RealMatrix a = new RealMatrixImpl(parameters.length, parameters.length);
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double[] grad = new double[parameters.length];
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RealMatrixImpl bDecrement = new RealMatrixImpl(parameters.length, 1);
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double[][] bDecrementData = bDecrement.getDataRef();
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RealMatrixImpl wGradGradT = new RealMatrixImpl(parameters.length, parameters.length);
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double[][] wggData = wGradGradT.getDataRef();
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for (int i = 0; i < measurements.length; ++i) {
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if (! measurements [i].isIgnored()) {
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double weight = measurements[i].getWeight();
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double residual = measurements[i].getResidual();
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if (! measurements [i].isIgnored()) {
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double weight = measurements[i].getWeight();
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double residual = measurements[i].getResidual();
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// compute the normal equation
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for (int j = 0; j < parameters.length; ++j) {
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grad[j] = measurements[i].getPartial(parameters[j]);
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bDecrementData[j][0] = weight * residual * grad[j];
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}
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// build the contribution matrix for measurement i
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for (int k = 0; k < parameters.length; ++k) {
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double[] wggRow = wggData[k];
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double gk = grad[k];
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for (int l = 0; l < parameters.length; ++l) {
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wggRow[l] = weight * gk * grad[l];
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}
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}
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// update the matrices
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a = a.add(wGradGradT);
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b = b.add(bDecrement);
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// compute the normal equation
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double[] grad = new double[parameters.length];
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Matrix bDecrement = new GeneralMatrix(parameters.length, 1);
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for (int j = 0; j < parameters.length; ++j) {
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grad[j] = measurements[i].getPartial(parameters[j]);
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bDecrement.setElement(j, 0, weight * residual * grad[j]);
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}
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// update the matrices
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a.selfAddWAAt(weight, grad);
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b.selfAdd(bDecrement);
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}
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}
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try {
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// solve the linearized least squares problem
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Matrix dX = a.solve(b, epsilon);
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RealMatrix dX = a.solve(b);
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// update the estimated parameters
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for (int i = 0; i < parameters.length; ++i) {
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parameters[i].setEstimate(parameters[i].getEstimate()
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+ dX.getElement(i, 0));
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parameters[i].setEstimate(parameters[i].getEstimate() + dX.getEntry(i, 0));
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}
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} catch(SingularMatrixException e) {
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} catch(InvalidMatrixException e) {
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throw new EstimationException(e);
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}
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@ -223,7 +229,6 @@ public class GaussNewtonEstimator
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private int maxIterations;
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private double steadyStateThreshold;
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private double convergence;
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private double epsilon;
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private static final long serialVersionUID = -7606628156644194170L;
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@ -14,7 +14,7 @@
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.spaceroots.mantissa.estimation;
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package org.apache.commons.math.estimation;
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import java.io.Serializable;
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import java.util.Arrays;
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@ -15,7 +15,7 @@
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// specific language governing permissions and limitations
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// under the License.
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package org.spaceroots.mantissa.estimation;
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package org.apache.commons.math.estimation;
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import java.io.Serializable;
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@ -1,73 +0,0 @@
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// Licensed to the Apache Software Foundation (ASF) under one
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// or more contributor license agreements. See the NOTICE file
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// distributed with this work for additional information
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// regarding copyright ownership. The ASF licenses this file
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// to you under the Apache License, Version 2.0 (the
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// "License"); you may not use this file except in compliance
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// with the License. You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing,
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// software distributed under the License is distributed on an
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// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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// KIND, either express or implied. See the License for the
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// specific language governing permissions and limitations
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// under the License.
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package org.spaceroots.mantissa.estimation;
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import java.io.Serializable;
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/** This class implements a solver for estimation problems.
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* @deprecated this class has been replaced by the {@link
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* org.spaceroots.mantissa.estimation.GaussNewtonEstimator GaussNewtonEstimator}
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* class. It is now a simple wrapper delegating everything to {@link
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* org.spaceroots.mantissa.estimation.GaussNewtonEstimator GaussNewtonEstimator}
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* @version $Id: LeastSquaresEstimator.java 1705 2006-09-17 19:57:39Z luc $
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* @author L. Maisonobe
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*/
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public class LeastSquaresEstimator implements Estimator, Serializable {
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/** Simple constructor.
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* @see org.spaceroots.mantissa.estimation.GaussNewtonEstimator#GaussNewtonEstimator(int,
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* double, double, double)
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*/
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public LeastSquaresEstimator(int maxIterations,
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double convergence,
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double steadyStateThreshold,
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double epsilon) {
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estimator = new GaussNewtonEstimator(maxIterations,
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convergence,
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steadyStateThreshold,
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epsilon);
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}
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/** Solve an estimation problem using a least squares criterion.
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* @see org.spaceroots.mantissa.estimation.GaussNewtonEstimator#estimate
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*/
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public void estimate(EstimationProblem problem)
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throws EstimationException {
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estimator.estimate(problem);
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}
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/** Estimate the solution of a linear least square problem.
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* @see org.spaceroots.mantissa.estimation.GaussNewtonEstimator#linearEstimate
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*/
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public void linearEstimate(EstimationProblem problem)
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throws EstimationException {
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estimator.linearEstimate(problem);
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}
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/** Get the Root Mean Square value.
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* @see org.spaceroots.mantissa.estimation.GaussNewtonEstimator#getRMS
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*/
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public double getRMS(EstimationProblem problem) {
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return estimator.getRMS(problem);
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}
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private GaussNewtonEstimator estimator;
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private static final long serialVersionUID = -7542643494637247770L;
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}
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@ -1,39 +0,0 @@
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// Licensed to the Apache Software Foundation (ASF) under one
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// or more contributor license agreements. See the NOTICE file
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// distributed with this work for additional information
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// regarding copyright ownership. The ASF licenses this file
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// to you under the Apache License, Version 2.0 (the
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// "License"); you may not use this file except in compliance
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// with the License. You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing,
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// software distributed under the License is distributed on an
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// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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// KIND, either express or implied. See the License for the
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// specific language governing permissions and limitations
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// under the License.
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package org.spaceroots.mantissa.estimation;
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import junit.framework.Test;
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import junit.framework.TestSuite;
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public class AllTests {
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public static Test suite() {
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TestSuite suite = new TestSuite("org.spaceroots.mantissa.estimation");
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suite.addTest(EstimatedParameterTest.suite());
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suite.addTest(WeightedMeasurementTest.suite());
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suite.addTest(GaussNewtonEstimatorTest.suite());
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suite.addTest(LevenbergMarquardtEstimatorTest.suite());
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suite.addTest(MinpackTest.suite());
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return suite;
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}
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}
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@ -1,262 +0,0 @@
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// Licensed to the Apache Software Foundation (ASF) under one
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// or more contributor license agreements. See the NOTICE file
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// distributed with this work for additional information
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// regarding copyright ownership. The ASF licenses this file
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// to you under the Apache License, Version 2.0 (the
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// "License"); you may not use this file except in compliance
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// with the License. You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing,
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// software distributed under the License is distributed on an
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// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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// KIND, either express or implied. See the License for the
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// specific language governing permissions and limitations
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// under the License.
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package org.spaceroots.mantissa.estimation;
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import java.util.Random;
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import junit.framework.*;
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public class GaussNewtonEstimatorTest
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extends TestCase
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implements EstimationProblem {
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public GaussNewtonEstimatorTest(String name) {
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super(name);
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}
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public void testNoMeasurementError()
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throws EstimationException {
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initRandomizedGrid(2.3);
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initProblem(0.0);
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GaussNewtonEstimator estimator =
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new GaussNewtonEstimator(100, 1.0e-7, 1.0e-10, 1.0e-10);
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estimator.estimate(this);
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checkGrid(0.01);
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}
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public void testSmallMeasurementError()
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throws EstimationException {
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initRandomizedGrid(2.3);
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initProblem(0.02);
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GaussNewtonEstimator estimator =
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new GaussNewtonEstimator(100, 1.0e-7, 1.0e-10, 1.0e-10);
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estimator.estimate(this);
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checkGrid(0.1);
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}
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public void testNoError()
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throws EstimationException {
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initRandomizedGrid(0.0);
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initProblem(0.0);
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GaussNewtonEstimator estimator =
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new GaussNewtonEstimator(100, 1.0e-7, 1.0e-10, 1.0e-10);
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estimator.estimate(this);
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checkGrid(1.0e-10);
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}
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public void testUnsolvableProblem() {
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initRandomizedGrid(2.3);
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initProblem(0.0);
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// reduce the number of measurements below the limit threshold
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int unknowns = unboundPars.length;
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WeightedMeasurement[] reducedSet = new WeightedMeasurement[unknowns - 1];
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for (int i = 0; i < reducedSet.length; ++i) {
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reducedSet[i] = measurements[i];
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}
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measurements = reducedSet;
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boolean gotIt = false;
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try {
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GaussNewtonEstimator estimator =
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new GaussNewtonEstimator(100, 1.0e-7, 1.0e-10, 1.0e-10);
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estimator.estimate(this);
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} catch(EstimationException e) {
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gotIt = true;
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}
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assertTrue(gotIt);
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}
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public static Test suite() {
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return new TestSuite(GaussNewtonEstimatorTest.class);
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}
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public void setUp() {
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initPerfectGrid(5);
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}
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public void tearDown() {
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perfectPars = null;
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randomizedPars = null;
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unboundPars = null;
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measurements = null;
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}
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private void initPerfectGrid(int gridSize) {
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perfectPars = new EstimatedParameter[gridSize * gridSize * 2];
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int k = 0;
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for (int i = 0; i < gridSize; ++i) {
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for (int j = 0; j < gridSize; ++j) {
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String name = Integer.toString(k);
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perfectPars[2 * k] = new EstimatedParameter("x" + name, i);
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perfectPars[2 * k + 1] = new EstimatedParameter("y" + name, j);
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++k;
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}
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}
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}
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private void initRandomizedGrid(double initialGuessError) {
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Random randomizer = new Random(2353995334l);
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randomizedPars = new EstimatedParameter[perfectPars.length];
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// add an error to every point coordinate
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for (int k = 0; k < randomizedPars.length; ++k) {
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String name = perfectPars[k].getName();
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double value = perfectPars[k].getEstimate();
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double error = randomizer.nextGaussian() * initialGuessError;
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randomizedPars[k] = new EstimatedParameter(name, value + error);
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}
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}
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private void initProblem(double measurementError) {
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int pointsNumber = randomizedPars.length / 2;
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int measurementsNumber = pointsNumber * (pointsNumber - 1) / 2;
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measurements = new WeightedMeasurement[measurementsNumber];
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Random randomizer = new Random(5785631926l);
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// for the test, we consider that the perfect grid is the reality
|
||||
// and that the randomized grid is the first (wrong) estimate.
|
||||
int i = 0;
|
||||
for (int l = 0; l < (pointsNumber - 1); ++l) {
|
||||
for (int m = l + 1; m < pointsNumber; ++m) {
|
||||
// perfect measurements on the real data
|
||||
double dx = perfectPars[2 * l].getEstimate()
|
||||
- perfectPars[2 * m].getEstimate();
|
||||
double dy = perfectPars[2 * l + 1].getEstimate()
|
||||
- perfectPars[2 * m + 1].getEstimate();
|
||||
double d = Math.sqrt(dx * dx + dy * dy);
|
||||
|
||||
// adding a noise to the measurements
|
||||
d += randomizer.nextGaussian() * measurementError;
|
||||
|
||||
// add the measurement to the current problem
|
||||
measurements[i++] = new Distance(1.0, d,
|
||||
randomizedPars[2 * l],
|
||||
randomizedPars[2 * l + 1],
|
||||
randomizedPars[2 * m],
|
||||
randomizedPars[2 * m + 1]);
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
// fix three values in the randomized grid and bind them (there
|
||||
// are two abscissas and one ordinate, so if there were no error
|
||||
// at all, the estimated grid should be correctly centered on the
|
||||
// perfect grid)
|
||||
int oddNumber = 2 * (randomizedPars.length / 4) - 1;
|
||||
for (int k = 0; k < 2 * oddNumber + 1; k += oddNumber) {
|
||||
randomizedPars[k].setEstimate(perfectPars[k].getEstimate());
|
||||
randomizedPars[k].setBound(true);
|
||||
}
|
||||
|
||||
// store the unbound parameters in a specific table
|
||||
unboundPars = new EstimatedParameter[randomizedPars.length - 3];
|
||||
for (int src = 0, dst = 0; src < randomizedPars.length; ++src) {
|
||||
if (! randomizedPars[src].isBound()) {
|
||||
unboundPars[dst++] = randomizedPars[src];
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
private void checkGrid(double threshold) {
|
||||
|
||||
double rms = 0;
|
||||
for (int i = 0; i < perfectPars.length; ++i) {
|
||||
rms += perfectPars[i].getEstimate() - randomizedPars[i].getEstimate();
|
||||
}
|
||||
rms = Math.sqrt(rms / perfectPars.length);
|
||||
|
||||
assertTrue(rms <= threshold);
|
||||
|
||||
}
|
||||
|
||||
private static class Distance extends WeightedMeasurement {
|
||||
|
||||
public Distance(double weight, double measuredValue,
|
||||
EstimatedParameter x1, EstimatedParameter y1,
|
||||
EstimatedParameter x2, EstimatedParameter y2) {
|
||||
super(weight, measuredValue);
|
||||
this.x1 = x1;
|
||||
this.y1 = y1;
|
||||
this.x2 = x2;
|
||||
this.y2 = y2;
|
||||
}
|
||||
|
||||
public double getTheoreticalValue() {
|
||||
double dx = x2.getEstimate() - x1.getEstimate();
|
||||
double dy = y2.getEstimate() - y1.getEstimate();
|
||||
return Math.sqrt(dx * dx + dy * dy);
|
||||
}
|
||||
|
||||
public double getPartial(EstimatedParameter p) {
|
||||
|
||||
// first quick answer for most parameters
|
||||
if ((p != x1) && (p != y1) && (p != x2) && (p != y2)) {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
// compute the value now as we know we depend on the specified parameter
|
||||
double distance = getTheoreticalValue();
|
||||
|
||||
if (p == x1) {
|
||||
return (x1.getEstimate() - x2.getEstimate()) / distance;
|
||||
} else if (p == x2) {
|
||||
return (x2.getEstimate() - x1.getEstimate()) / distance;
|
||||
} else if (p == y1) {
|
||||
return (y1.getEstimate() - y2.getEstimate()) / distance;
|
||||
} else {
|
||||
return (y2.getEstimate() - y1.getEstimate()) / distance;
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
private EstimatedParameter x1;
|
||||
private EstimatedParameter y1;
|
||||
private EstimatedParameter x2;
|
||||
private EstimatedParameter y2;
|
||||
private static final long serialVersionUID = 4090004243280980746L;
|
||||
|
||||
}
|
||||
|
||||
public WeightedMeasurement[] getMeasurements() {
|
||||
return (WeightedMeasurement[]) measurements.clone();
|
||||
}
|
||||
|
||||
public EstimatedParameter[] getUnboundParameters() {
|
||||
return (EstimatedParameter[]) unboundPars.clone();
|
||||
}
|
||||
|
||||
public EstimatedParameter[] getAllParameters() {
|
||||
return (EstimatedParameter[]) randomizedPars.clone();
|
||||
}
|
||||
|
||||
private EstimatedParameter[] perfectPars;
|
||||
private EstimatedParameter[] randomizedPars;
|
||||
private EstimatedParameter[] unboundPars;
|
||||
private WeightedMeasurement[] measurements;
|
||||
|
||||
}
|
||||
|
|
@ -15,7 +15,9 @@
|
|||
// specific language governing permissions and limitations
|
||||
// under the License.
|
||||
|
||||
package org.spaceroots.mantissa.estimation;
|
||||
package org.apache.commons.math.estimation;
|
||||
|
||||
import org.apache.commons.math.estimation.EstimatedParameter;
|
||||
|
||||
import junit.framework.*;
|
||||
|
||||
|
|
@ -15,13 +15,19 @@
|
|||
* limitations under the License.
|
||||
*/
|
||||
|
||||
package org.spaceroots.mantissa.estimation;
|
||||
package org.apache.commons.math.estimation;
|
||||
|
||||
import java.util.ArrayList;
|
||||
import java.util.IdentityHashMap;
|
||||
import java.util.HashMap;
|
||||
import java.util.Iterator;
|
||||
import java.util.Set;
|
||||
|
||||
import org.apache.commons.math.estimation.EstimatedParameter;
|
||||
import org.apache.commons.math.estimation.EstimationException;
|
||||
import org.apache.commons.math.estimation.EstimationProblem;
|
||||
import org.apache.commons.math.estimation.LevenbergMarquardtEstimator;
|
||||
import org.apache.commons.math.estimation.WeightedMeasurement;
|
||||
|
||||
import junit.framework.*;
|
||||
|
||||
/**
|
||||
|
|
@ -519,7 +525,7 @@ public class LevenbergMarquardtEstimatorTest
|
|||
}
|
||||
|
||||
public EstimatedParameter[] getAllParameters() {
|
||||
IdentityHashMap map = new IdentityHashMap();
|
||||
HashMap map = new HashMap();
|
||||
for (int i = 0; i < measurements.length; ++i) {
|
||||
EstimatedParameter[] parameters = measurements[i].getParameters();
|
||||
for (int j = 0; j < parameters.length; ++j) {
|
||||
|
|
@ -1,7 +1,13 @@
|
|||
package org.spaceroots.mantissa.estimation;
|
||||
package org.apache.commons.math.estimation;
|
||||
|
||||
import java.util.Arrays;
|
||||
|
||||
import org.apache.commons.math.estimation.EstimatedParameter;
|
||||
import org.apache.commons.math.estimation.EstimationException;
|
||||
import org.apache.commons.math.estimation.EstimationProblem;
|
||||
import org.apache.commons.math.estimation.LevenbergMarquardtEstimator;
|
||||
import org.apache.commons.math.estimation.WeightedMeasurement;
|
||||
|
||||
import junit.framework.*;
|
||||
|
||||
/**
|
||||
|
|
@ -15,7 +15,10 @@
|
|||
// specific language governing permissions and limitations
|
||||
// under the License.
|
||||
|
||||
package org.spaceroots.mantissa.estimation;
|
||||
package org.apache.commons.math.estimation;
|
||||
|
||||
import org.apache.commons.math.estimation.EstimatedParameter;
|
||||
import org.apache.commons.math.estimation.WeightedMeasurement;
|
||||
|
||||
import junit.framework.*;
|
||||
|
||||
Loading…
Reference in New Issue