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00001 /********************************************************************* 00002 * Software License Agreement (BSD License) 00003 * 00004 * Copyright (c) 2008, Willow Garage, Inc. 00005 * All rights reserved. 00006 * 00007 * Redistribution and use in source and binary forms, with or without 00008 * modification, are permitted provided that the following conditions 00009 * are met: 00010 * 00011 * * Redistributions of source code must retain the above copyright 00012 * notice, this list of conditions and the following disclaimer. 00013 * * Redistributions in binary form must reproduce the above 00014 * copyright notice, this list of conditions and the following 00015 * disclaimer in the documentation and/or other materials provided 00016 * with the distribution. 00017 * * Neither the name of the Willow Garage nor the names of its 00018 * contributors may be used to endorse or promote products derived 00019 * from this software without specific prior written permission. 00020 * 00021 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 00022 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 00023 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS 00024 * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE 00025 * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, 00026 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, 00027 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; 00028 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 00029 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT 00030 * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN 00031 * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 00032 * POSSIBILITY OF SUCH DAMAGE. 00033 *********************************************************************/ 00034 00035 /* Author: Wim Meeussen */ 00036 /* modified for SRS by Alex Noyvirt */ 00037 00038 #include "srs_people_tracking_filter/gaussian_vector.h" 00039 #include <wrappers/rng/rng.h> 00040 #include <cmath> 00041 #include <cassert> 00042 00043 using namespace tf; 00044 00045 namespace BFL 00046 { 00047 GaussianVector::GaussianVector(const Vector3& mu, const Vector3& sigma) 00048 : Pdf<Vector3> ( 1 ), 00049 mu_(mu), 00050 sigma_(sigma), 00051 sigma_changed_(true) 00052 { 00053 for (unsigned int i=0; i<3; i++) 00054 assert(sigma[i] > 0); 00055 } 00056 00057 00058 GaussianVector::~GaussianVector(){} 00059 00060 00061 std::ostream& operator<< (std::ostream& os, const GaussianVector& g) 00062 { 00063 os << "Mu :\n" << g.ExpectedValueGet() << endl 00064 << "Sigma:\n" << g.CovarianceGet() << endl; 00065 return os; 00066 } 00067 00068 void GaussianVector::sigmaSet( const Vector3& sigma ) 00069 { 00070 sigma_ = sigma; 00071 sigma_changed_ = true; 00072 } 00073 00074 Probability GaussianVector::ProbabilityGet(const Vector3& input) const 00075 { 00076 if (sigma_changed_){ 00077 sigma_changed_ = false; 00078 // 2 * sigma^2 00079 for (unsigned int i=0; i<3; i++) 00080 sigma_sq_[i] = 2 * sigma_[i] * sigma_[i]; 00081 // sqrt 00082 sqrt_ = 1/ sqrt(M_PI*M_PI*M_PI* sigma_sq_[0] * sigma_sq_[1] * sigma_sq_[2]); 00083 } 00084 00085 Vector3 diff = input - mu_; 00086 return sqrt_ * exp( - (diff[0]*diff[0]/sigma_sq_[0]) 00087 - (diff[1]*diff[1]/sigma_sq_[1]) 00088 - (diff[2]*diff[2]/sigma_sq_[2]) ); 00089 } 00090 00091 00092 bool 00093 GaussianVector::SampleFrom (vector<Sample<Vector3> >& list_samples, const int num_samples, int method, void * args) const 00094 { 00095 list_samples.resize(num_samples); 00096 vector<Sample<Vector3> >::iterator sample_it = list_samples.begin(); 00097 for (sample_it=list_samples.begin(); sample_it!=list_samples.end(); sample_it++) 00098 SampleFrom( *sample_it, method, args); 00099 00100 return true; 00101 } 00102 00103 00104 bool 00105 GaussianVector::SampleFrom (Sample<Vector3>& one_sample, int method, void * args) const 00106 { 00107 one_sample.ValueSet( Vector3(rnorm(mu_[0], sigma_[0]), 00108 rnorm(mu_[1], sigma_[1]), 00109 rnorm(mu_[2], sigma_[2]))); 00110 return true; 00111 } 00112 00113 00114 Vector3 00115 GaussianVector::ExpectedValueGet ( ) const 00116 { 00117 return mu_; 00118 } 00119 00120 SymmetricMatrix 00121 GaussianVector::CovarianceGet () const 00122 { 00123 SymmetricMatrix sigma(3); sigma = 0; 00124 for (unsigned int i=0; i<3; i++) 00125 sigma(i+1,i+1) = pow(sigma_[i],2); 00126 return sigma; 00127 } 00128 00129 GaussianVector* 00130 GaussianVector::Clone() const 00131 { 00132 return new GaussianVector(mu_, sigma_); 00133 } 00134 00135 } // End namespace BFL