Change private least square problem implementation to internal class.
git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1571014 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Luc Maisonobe
parent
f299ecf330
commit
dcdfd7ed9e
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@ -25,6 +25,7 @@ import org.apache.commons.math3.linear.DiagonalMatrix;
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import org.apache.commons.math3.linear.EigenDecomposition;
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import org.apache.commons.math3.linear.EigenDecomposition;
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import org.apache.commons.math3.linear.RealMatrix;
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import org.apache.commons.math3.linear.RealMatrix;
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import org.apache.commons.math3.linear.RealVector;
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import org.apache.commons.math3.linear.RealVector;
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import org.apache.commons.math3.optim.AbstractOptimizationProblem;
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import org.apache.commons.math3.optim.ConvergenceChecker;
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import org.apache.commons.math3.optim.ConvergenceChecker;
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import org.apache.commons.math3.optim.PointVectorValuePair;
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import org.apache.commons.math3.optim.PointVectorValuePair;
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import org.apache.commons.math3.util.FastMath;
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import org.apache.commons.math3.util.FastMath;
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@ -61,7 +62,7 @@ public class LeastSquaresFactory {
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final ConvergenceChecker<Evaluation> checker,
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final ConvergenceChecker<Evaluation> checker,
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final int maxEvaluations,
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final int maxEvaluations,
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final int maxIterations) {
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final int maxIterations) {
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return new LeastSquaresProblemImpl(
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return new LocalLeastSquaresProblem(
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model,
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model,
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observed,
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observed,
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start,
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start,
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@ -269,5 +270,129 @@ public class LeastSquaresFactory {
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}
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}
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};
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};
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}
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}
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/**
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* A private, "field" immutable (not "real" immutable) implementation of {@link
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* LeastSquaresProblem}.
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* @since 3.3
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*/
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private static class LocalLeastSquaresProblem
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extends AbstractOptimizationProblem<Evaluation>
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implements LeastSquaresProblem {
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/** Target values for the model function at optimum. */
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private RealVector target;
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/** Model function. */
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private MultivariateJacobianFunction model;
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/** Initial guess. */
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private RealVector start;
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/**
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* Create a {@link LeastSquaresProblem} from the given data.
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*
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* @param model the model function
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* @param target the observed data
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* @param start the initial guess
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* @param checker the convergence checker
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* @param maxEvaluations the allowed evaluations
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* @param maxIterations the allowed iterations
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*/
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LocalLeastSquaresProblem(final MultivariateJacobianFunction model,
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final RealVector target,
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final RealVector start,
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final ConvergenceChecker<Evaluation> checker,
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final int maxEvaluations,
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final int maxIterations) {
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super(maxEvaluations, maxIterations, checker);
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this.target = target;
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this.model = model;
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this.start = start;
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}
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/** {@inheritDoc} */
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public int getObservationSize() {
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return target.getDimension();
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}
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/** {@inheritDoc} */
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public int getParameterSize() {
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return start.getDimension();
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}
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/** {@inheritDoc} */
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public RealVector getStart() {
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return start == null ? null : start.copy();
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}
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/** {@inheritDoc} */
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public Evaluation evaluate(final RealVector point) {
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//evaluate value and jacobian in one function call
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final Pair<RealVector, RealMatrix> value = this.model.value(point);
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return new UnweightedEvaluation(
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value.getFirst(),
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value.getSecond(),
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this.target,
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point);
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}
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/**
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* Container with the model evaluation at a particular point.
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* <p/>
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* TODO revisit lazy evaluation
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*/
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private static class UnweightedEvaluation extends AbstractEvaluation {
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/** the point of evaluation */
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private final RealVector point;
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/** value at point */
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private final RealVector values;
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/** deriviative at point */
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private final RealMatrix jacobian;
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/** reference to the observed values */
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private final RealVector target;
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/**
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* Create an {@link Evaluation} with no weights.
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*
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* @param values the computed function values
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* @param jacobian the computed function Jacobian
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* @param target the observed values
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* @param point the abscissa
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*/
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private UnweightedEvaluation(final RealVector values,
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final RealMatrix jacobian,
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final RealVector target,
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final RealVector point) {
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super(target.getDimension());
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this.values = values;
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this.jacobian = jacobian;
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this.target = target;
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this.point = point;
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}
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/** {@inheritDoc} */
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public RealVector getValue() {
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return this.values;
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}
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/** {@inheritDoc} */
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public RealMatrix getJacobian() {
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return this.jacobian;
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}
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/** {@inheritDoc} */
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public RealVector getPoint() {
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return this.point;
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}
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/** {@inheritDoc} */
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public RealVector getResiduals() {
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return target.subtract(this.getValue());
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}
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}
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}
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}
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}
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@ -1,149 +0,0 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* 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, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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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.apache.commons.math3.fitting.leastsquares;
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import org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation;
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import org.apache.commons.math3.linear.RealMatrix;
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import org.apache.commons.math3.linear.RealVector;
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import org.apache.commons.math3.optim.AbstractOptimizationProblem;
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import org.apache.commons.math3.optim.ConvergenceChecker;
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import org.apache.commons.math3.util.Pair;
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/**
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* A private, "field" immutable (not "real" immutable) implementation of {@link
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* LeastSquaresProblem}.
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*
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* @version $Id$
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* @since 3.3
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*/
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class LeastSquaresProblemImpl
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extends AbstractOptimizationProblem<Evaluation>
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implements LeastSquaresProblem {
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/** Target values for the model function at optimum. */
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private RealVector target;
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/** Model function. */
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private MultivariateJacobianFunction model;
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/** Initial guess. */
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private RealVector start;
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/**
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* Create a {@link LeastSquaresProblem} from the given data.
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*
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* @param model the model function
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* @param target the observed data
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* @param start the initial guess
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* @param checker the convergence checker
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* @param maxEvaluations the allowed evaluations
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* @param maxIterations the allowed iterations
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*/
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LeastSquaresProblemImpl(final MultivariateJacobianFunction model,
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final RealVector target,
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final RealVector start,
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final ConvergenceChecker<Evaluation> checker,
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final int maxEvaluations,
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final int maxIterations) {
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super(maxEvaluations, maxIterations, checker);
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this.target = target;
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this.model = model;
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this.start = start;
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}
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/** {@inheritDoc} */
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public int getObservationSize() {
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return target.getDimension();
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}
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/** {@inheritDoc} */
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public int getParameterSize() {
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return start.getDimension();
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}
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/** {@inheritDoc} */
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public RealVector getStart() {
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return start == null ? null : start.copy();
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}
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/** {@inheritDoc} */
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public Evaluation evaluate(final RealVector point) {
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//evaluate value and jacobian in one function call
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final Pair<RealVector, RealMatrix> value = this.model.value(point);
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return new UnweightedEvaluation(
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value.getFirst(),
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value.getSecond(),
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this.target,
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point);
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}
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/**
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* Container with the model evaluation at a particular point.
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* <p/>
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* TODO revisit lazy evaluation
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*/
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private static class UnweightedEvaluation extends AbstractEvaluation {
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/** the point of evaluation */
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private final RealVector point;
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/** value at point */
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private final RealVector values;
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/** deriviative at point */
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private final RealMatrix jacobian;
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/** reference to the observed values */
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private final RealVector target;
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/**
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* Create an {@link Evaluation} with no weights.
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*
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* @param values the computed function values
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* @param jacobian the computed function Jacobian
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* @param target the observed values
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* @param point the abscissa
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*/
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private UnweightedEvaluation(final RealVector values,
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final RealMatrix jacobian,
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final RealVector target,
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final RealVector point) {
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super(target.getDimension());
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this.values = values;
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this.jacobian = jacobian;
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this.target = target;
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this.point = point;
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}
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/** {@inheritDoc} */
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public RealVector getValue() {
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return this.values;
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}
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/** {@inheritDoc} */
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public RealMatrix getJacobian() {
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return this.jacobian;
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}
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/** {@inheritDoc} */
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public RealVector getPoint() {
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return this.point;
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}
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/** {@inheritDoc} */
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public RealVector getResiduals() {
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return target.subtract(this.getValue());
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}
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}
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}
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