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GaussianLikelihoodDiagonalCovariance.C
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1 //-----------------------------------------------------------------------bl-
2 //--------------------------------------------------------------------------
3 //
4 // QUESO - a library to support the Quantification of Uncertainty
5 // for Estimation, Simulation and Optimization
6 //
7 // Copyright (C) 2008-2017 The PECOS Development Team
8 //
9 // This library is free software; you can redistribute it and/or
10 // modify it under the terms of the Version 2.1 GNU Lesser General
11 // Public License as published by the Free Software Foundation.
12 //
13 // This library is distributed in the hope that it will be useful,
14 // but WITHOUT ANY WARRANTY; without even the implied warranty of
15 // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
16 // Lesser General Public License for more details.
17 //
18 // You should have received a copy of the GNU Lesser General Public
19 // License along with this library; if not, write to the Free Software
20 // Foundation, Inc. 51 Franklin Street, Fifth Floor,
21 // Boston, MA 02110-1301 USA
22 //
23 //-----------------------------------------------------------------------el-
24 
25 #include <cmath>
26 
27 #include <queso/GslVector.h>
28 #include <queso/GslMatrix.h>
29 #include <queso/VectorSet.h>
30 #include <queso/GaussianLikelihoodDiagonalCovariance.h>
31 
32 namespace QUESO {
33 
34 template<class V, class M>
36  const char * prefix, const VectorSet<V, M> & domainSet,
37  const V & observations, const V & covariance)
38  : LikelihoodBase<V, M>(prefix, domainSet, observations),
39  m_covariance(covariance)
40 {
41  if (covariance.sizeLocal() != observations.sizeLocal()) {
42  queso_error_msg("Covariance matrix not same size as observation vector");
43  }
44 }
45 
46 template<class V, class M>
48 {
49 }
50 
51 template<class V, class M>
52 double
54 {
55  V modelOutput(this->m_observations, 0, 0); // At least it's not a copy
56 
57  this->evaluateModel(domainVector, modelOutput);
58 
59  modelOutput -= this->m_observations; // Compute misfit
60  modelOutput *= modelOutput;
61  modelOutput /= this->m_covariance; // Multiply by inverse covriance matrix
62 
63  double norm2_squared = modelOutput.sumOfComponents(); // This is square of 2-norm
64 
65  return -0.5 * norm2_squared;
66 }
67 
68 } // End namespace QUESO
69 
A templated class for handling sets.
Definition: VectorSet.h:52
GaussianLikelihoodDiagonalCovariance(const char *prefix, const VectorSet< V, M > &domainSet, const V &observations, const V &covariance)
Default constructor.
A class that represents a Gaussian likelihood with diagonal covariance matrix.
virtual double lnValue(const V &domainVector) const
Logarithm of the value of the scalar function.
Base class for canned Gaussian likelihoods.

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