001 /* 002 * Licensed to the Apache Software Foundation (ASF) under one or more 003 * contributor license agreements. See the NOTICE file distributed with 004 * this work for additional information regarding copyright ownership. 005 * The ASF licenses this file to You under the Apache License, Version 2.0 006 * (the "License"); you may not use this file except in compliance with 007 * the License. You may obtain a copy of the License at 008 * 009 * http://www.apache.org/licenses/LICENSE-2.0 010 * 011 * Unless required by applicable law or agreed to in writing, software 012 * distributed under the License is distributed on an "AS IS" BASIS, 013 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 014 * See the License for the specific language governing permissions and 015 * limitations under the License. 016 */ 017 018 package org.apache.commons.math3.optim.nonlinear.vector; 019 020 import org.apache.commons.math3.exception.TooManyEvaluationsException; 021 import org.apache.commons.math3.exception.DimensionMismatchException; 022 import org.apache.commons.math3.analysis.MultivariateVectorFunction; 023 import org.apache.commons.math3.optim.OptimizationData; 024 import org.apache.commons.math3.optim.BaseMultivariateOptimizer; 025 import org.apache.commons.math3.optim.ConvergenceChecker; 026 import org.apache.commons.math3.optim.PointVectorValuePair; 027 import org.apache.commons.math3.linear.RealMatrix; 028 029 /** 030 * Base class for a multivariate vector function optimizer. 031 * 032 * @version $Id$ 033 * @since 3.1 034 */ 035 public abstract class MultivariateVectorOptimizer 036 extends BaseMultivariateOptimizer<PointVectorValuePair> { 037 /** Target values for the model function at optimum. */ 038 private double[] target; 039 /** Weight matrix. */ 040 private RealMatrix weightMatrix; 041 /** Model function. */ 042 private MultivariateVectorFunction model; 043 044 /** 045 * @param checker Convergence checker. 046 */ 047 protected MultivariateVectorOptimizer(ConvergenceChecker<PointVectorValuePair> checker) { 048 super(checker); 049 } 050 051 /** 052 * Computes the objective function value. 053 * This method <em>must</em> be called by subclasses to enforce the 054 * evaluation counter limit. 055 * 056 * @param params Point at which the objective function must be evaluated. 057 * @return the objective function value at the specified point. 058 * @throws TooManyEvaluationsException if the maximal number of evaluations 059 * (of the model vector function) is exceeded. 060 */ 061 protected double[] computeObjectiveValue(double[] params) { 062 super.incrementEvaluationCount(); 063 return model.value(params); 064 } 065 066 /** 067 * {@inheritDoc} 068 * 069 * @param optData Optimization data. The following data will be looked for: 070 * <ul> 071 * <li>{@link org.apache.commons.math3.optim.MaxEval}</li> 072 * <li>{@link org.apache.commons.math3.optim.InitialGuess}</li> 073 * <li>{@link org.apache.commons.math3.optim.SimpleBounds}</li> 074 * <li>{@link Target}</li> 075 * <li>{@link Weight}</li> 076 * <li>{@link ModelFunction}</li> 077 * </ul> 078 * @return {@inheritDoc} 079 * @throws TooManyEvaluationsException if the maximal number of 080 * evaluations is exceeded. 081 * @throws DimensionMismatchException if the initial guess, target, and weight 082 * arguments have inconsistent dimensions. 083 */ 084 public PointVectorValuePair optimize(OptimizationData... optData) 085 throws TooManyEvaluationsException, 086 DimensionMismatchException { 087 // Retrieve settings. 088 parseOptimizationData(optData); 089 // Check input consistency. 090 checkParameters(); 091 // Set up base class and perform computation. 092 return super.optimize(optData); 093 } 094 095 /** 096 * Gets the weight matrix of the observations. 097 * 098 * @return the weight matrix. 099 */ 100 public RealMatrix getWeight() { 101 return weightMatrix.copy(); 102 } 103 /** 104 * Gets the observed values to be matched by the objective vector 105 * function. 106 * 107 * @return the target values. 108 */ 109 public double[] getTarget() { 110 return target.clone(); 111 } 112 113 /** 114 * Gets the number of observed values. 115 * 116 * @return the length of the target vector. 117 */ 118 public int getTargetSize() { 119 return target.length; 120 } 121 122 /** 123 * Scans the list of (required and optional) optimization data that 124 * characterize the problem. 125 * 126 * @param optData Optimization data. The following data will be looked for: 127 * <ul> 128 * <li>{@link Target}</li> 129 * <li>{@link Weight}</li> 130 * <li>{@link ModelFunction}</li> 131 * </ul> 132 */ 133 private void parseOptimizationData(OptimizationData... optData) { 134 // The existing values (as set by the previous call) are reused if 135 // not provided in the argument list. 136 for (OptimizationData data : optData) { 137 if (data instanceof ModelFunction) { 138 model = ((ModelFunction) data).getModelFunction(); 139 continue; 140 } 141 if (data instanceof Target) { 142 target = ((Target) data).getTarget(); 143 continue; 144 } 145 if (data instanceof Weight) { 146 weightMatrix = ((Weight) data).getWeight(); 147 continue; 148 } 149 } 150 } 151 152 /** 153 * Check parameters consistency. 154 * 155 * @throws DimensionMismatchException if {@link #target} and 156 * {@link #weightMatrix} have inconsistent dimensions. 157 */ 158 private void checkParameters() { 159 if (target.length != weightMatrix.getColumnDimension()) { 160 throw new DimensionMismatchException(target.length, 161 weightMatrix.getColumnDimension()); 162 } 163 } 164 }