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/dports/science/InsightToolkit/ITK-5.0.1/Modules/Registration/Metricsv4/include/
H A DitkManifoldParzenWindowsPointSetFunction.hxx59 CovarianceMatrixType covariance( PointDimension, PointDimension ); in SetInputPointSet() local
61 covariance.SetIdentity(); in SetInputPointSet()
62 covariance *= this->m_KernelSigma; in SetInputPointSet()
67 inputGaussians[index]->SetCovariance( covariance ); in SetInputPointSet()
115 RealType covariance = kernelValue * ( neighbor[m] - point[m] ) * in SetInputPointSet() local
117 Cout(m, n) += covariance; in SetInputPointSet()
118 Cout(n, m) += covariance; in SetInputPointSet()
142 typename GaussianType::CovarianceMatrixType covariance in SetInputPointSet() local
144 covariance.SetIdentity(); in SetInputPointSet()
145 covariance *= Math::sqr( this->m_RegularizationSigma ); in SetInputPointSet()
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/dports/math/g2o/g2o-20201223_git/g2o/stuff/
H A Dunscented.h48 …igmaPoint <SampleType> > >& sigmaPoints, const SampleType& mean, const CovarianceType& covariance){ in sampleUnscented() argument
52 …assert (covariance.rows() == covariance.cols() && covariance.cols() == mean.size() && "Dimension M… in sampleUnscented()
63 cholDecomp.compute(covariance*(dim+lambda)); in sampleUnscented()
77 void reconstructGaussian(SampleType& mean, CovarianceType& covariance, in reconstructGaussian() argument
81 covariance.fill(0); in reconstructGaussian()
87 covariance += sigmaPoints[i]._wp * ( delta* delta.transpose() ) ; in reconstructGaussian()
/dports/finance/quantlib/QuantLib-1.20/ql/math/matrixutilities/
H A Dgetcovariance.hpp61 Matrix covariance(size,size); in getCovariance() local
70 covariance[i][i] = (*iIt) * (*iIt); in getCovariance()
71 covariance[i][j] = (*iIt) * (*jIt) * in getCovariance()
73 covariance[j][i] = covariance[i][j]; in getCovariance()
79 covariance[i][i] = (*iIt) * (*iIt); in getCovariance()
81 return covariance; in getCovariance()
/dports/math/mlpack/mlpack-3.4.2/src/mlpack/core/dists/
H A Ddiagonal_gaussian_distribution.hpp27 arma::vec covariance; member in mlpack::distribution::DiagonalGaussianDistribution
48 covariance(arma::ones<arma::vec>(dimension)), in DiagonalGaussianDistribution()
61 const arma::vec& covariance);
133 const arma::vec& Covariance() const { return covariance; } in Covariance()
136 void Covariance(const arma::vec& covariance);
139 void Covariance(arma::vec&& covariance);
147 ar & BOOST_SERIALIZATION_NVP(covariance); in serialize()
/dports/science/dakota/dakota-6.13.0-release-public.src-UI/packages/external/queso/test/test_gaussian_likelihoods/
H A Dtest_blockDiagonalCovariance.C44 const V & observations, const QUESO::GslBlockMatrix & covariance) in Likelihood() argument
46 observations, covariance) in Likelihood()
108 QUESO::GslBlockMatrix covariance(blockSizes, observations, 1.0); in main()
110 covariance.getBlock(0)(0, 0) = 1.0; in main()
111 covariance.getBlock(1)(0, 0) = 1.0; in main()
112 covariance.getBlock(1)(0, 1) = 2.0; in main()
113 covariance.getBlock(1)(1, 0) = 2.0; in main()
114 covariance.getBlock(1)(1, 1) = 8.0; in main()
118 observations, covariance); in main()
H A Dtest_fullCovariance.C43 const V & observations, const M & covariance) in Likelihood() argument
45 observations, covariance) in Likelihood()
102 QUESO::GslMatrix covariance(obsSpace.zeroVector()); in main()
103 covariance(0, 0) = 1.0; in main()
104 covariance(0, 1) = 2.0; in main()
105 covariance(1, 0) = 2.0; in main()
106 covariance(1, 1) = 8.0; in main()
110 observations, covariance); in main()
H A Dtest_fullCovarianceRandomCoefficient.C43 const V & observations, const M & covariance) in Likelihood() argument
45 observations, covariance) in Likelihood()
106 QUESO::GslMatrix covariance(obsSpace.zeroVector()); in main()
107 covariance(0, 0) = 1.0; in main()
108 covariance(0, 1) = 2.0; in main()
109 covariance(1, 0) = 2.0; in main()
110 covariance(1, 1) = 8.0; in main()
114 observations, covariance); in main()
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/covariance/
H A D_robust_covariance.py127 covariance = initial_estimates[1]
129 precision = linalg.pinvh(covariance)
140 det = fast_logdet(covariance)
144 precision = linalg.pinvh(covariance)
150 previous_covariance = covariance
154 precision = linalg.pinvh(covariance)
163 det = fast_logdet(covariance)
526 covariance = covariances_merged[0]
542 covariance = covariances_full[0]
569 covariance = covariances_full[0]
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/dports/math/py-chaospy/chaospy-4.3.3/chaospy/distributions/kernel/
H A Dbaseclass.py57 covariance = numpy.diag(scale*factor)**2
63 covariance = numpy.asfarray(h_mat)
64 if covariance.ndim in (0, 1):
65 covariance = covariance*numpy.eye(len(samples))
66 if covariance.ndim == 2:
67 covariance = covariance[numpy.newaxis]
69 covariance = numpy.rollaxis(covariance, 2, 0)
70 assert covariance.shape[1:] == (len(samples), len(samples))
82 self._covariance = covariance
85 self._permute, covariance), self._permute.T)
/dports/math/openturns/openturns-1.18/lib/test/
H A Dt_ExponentiallyDampedCosineModel_std.expout3 covariance matrix at t = 1 : class=SquareMatrix dimension=1 implementation=class=MatrixImplementati…
4 covariance matrix at t = -1 : class=SquareMatrix dimension=1 implementation=class=MatrixImplementat…
5 covariance matrix at t = 4 : class=SquareMatrix dimension=1 implementation=class=MatrixImplementati…
6 discretized covariance over the time grid=class=RegularGrid name=Unnamed start=0 step=0.333333 n=4 …
10 covariance matrix at t = 1 : class=SquareMatrix dimension=1 implementation=class=MatrixImplementati…
11 covariance matrix at t = -1 : class=SquareMatrix dimension=1 implementation=class=MatrixImplementat…
12 covariance matrix at t = 4 : class=SquareMatrix dimension=1 implementation=class=MatrixImplementati…
13 discretized covariance over the time grid=class=RegularGrid name=Unnamed start=0 step=0.333333 n=4 …
H A Dt_ExponentialModel_std.expout3 covariance matrix at t = 1 : class=SquareMatrix dimension=1 implementation=class=MatrixImplementati…
4 covariance matrix at t = -1 : class=SquareMatrix dimension=1 implementation=class=MatrixImplementat…
5 covariance matrix at t = 4 : class=SquareMatrix dimension=1 implementation=class=MatrixImplementati…
6 discretized covariance over the time grid=class=RegularGrid name=Unnamed start=0 step=0.333333 n=4 …
10 covariance matrix at t = 1 : class=SquareMatrix dimension=3 implementation=class=MatrixImplementati…
11 covariance matrix at t = -1 : class=SquareMatrix dimension=3 implementation=class=MatrixImplementat…
12 covariance matrix at t = 4 : class=SquareMatrix dimension=3 implementation=class=MatrixImplementati…
13 discretized covariance over the time grid=class=RegularGrid name=Unnamed start=0 step=0.333333 n=4 …
/dports/math/openturns/openturns-1.18/python/src/
H A DKroneckerCovarianceModel_doc.i.in2 "Multivariate stationary Kronecker covariance function.
32 …covariance function with output dimension :math:`d\geq1` from a correlation function :math:`\rho` …
34 This covariance function is defined by
40 where the output covariance matrix :math:`C^{stat}(\vect{s}, \vect{t})` is given by
48 Create a Kronecker covariance model with identity output correlation matrix.
56 Create a Kronecker covariance model and specify the output correlation matrix.
65 Create a Kronecker covariance model and specify the output covariance matrix.
/dports/science/InsightToolkit/ITK-5.0.1/Modules/Segmentation/RegionGrowing/include/
H A DitkVectorConfidenceConnectedImageFilter.hxx161 CovarianceMatrixType covariance; in GenerateData() local
169 covariance = CovarianceMatrixType(dimension, dimension); in GenerateData()
212 covariance[ik][jk] /= seed_cnt; in GenerateData()
217 m_ThresholdFunction->SetCovariance(covariance); in GenerateData()
281 covariance = CovarianceMatrixType(dimension, dimension); in GenerateData()
298 covariance[i][i] += pixelValueI * pixelValueI; in GenerateData()
304 covariance[i][j] += product; in GenerateData()
305 covariance[j][i] += product; in GenerateData()
316 covariance[ii][jj] /= static_cast< double >( num ); in GenerateData()
324 covariance[ik][jk] -= mean[ik] * mean[jk]; in GenerateData()
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/dports/math/ceres-solver/ceres-solver-2.0.0/internal/ceres/
H A Dcovariance_test.cc509 Covariance covariance(options); in ComputeAndCompareCovarianceBlocksInTangentOrAmbientSpace() local
519 covariance, in ComputeAndCompareCovarianceBlocksInTangentOrAmbientSpace()
525 covariance, in ComputeAndCompareCovarianceBlocksInTangentOrAmbientSpace()
979 Covariance covariance(options); in TEST_F() local
1008 Covariance covariance(options); in TEST_F() local
1036 Covariance covariance(options); in TEST_F() local
1077 Covariance covariance(options); in TEST_F() local
1210 Covariance covariance(options); in TEST() local
1219 covariance.GetCovarianceBlock(&x, &x, &value); in TEST()
1245 Covariance covariance(options); in TEST() local
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/dports/math/pspp/pspp-1.4.1/src/math/
H A Dcovariance.c71 struct covariance struct
133 covariance_moments (const struct covariance *cov, int m) in covariance_moments() argument
142 struct covariance *
148 struct covariance *cov = xzalloc (sizeof *cov); in covariance_1pass_create()
183 struct covariance *
190 struct covariance *cov = xmalloc (sizeof *cov); in covariance_2pass_create()
226 cm_idx (const struct covariance *cov, int i, int j) in cm_idx()
518 cm_to_gsl (struct covariance *cov) in cm_to_gsl()
623 covariance_calculate (struct covariance *cov) in covariance_calculate()
726 covariance_destroy (struct covariance *cov) in covariance_destroy()
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/dports/science/dakota/dakota-6.13.0-release-public.src-UI/docs/KeywordMetadata/
H A DDUPLICATE-prior1 Blurb::Uses the covariance of the prior distributions to define the
2 MCMC proposal covariance.
5 proposal covariance from the covariance of the prior distributions.
6 This covariance is currently assumed to be diagonal without correlation.
14 Since this proposal covariance is defined globally, the chain does not
H A DDUPLICATE-proposal_covariance1 Blurb::Defines the technique used to generate the MCMC proposal covariance.
4 The proposal covariance is used to define a multivariate normal (MVN)
7 MVN probability density with prescribed covariance that is centered at
8 the current chain point. The accuracy of the proposal covariance has
13 The default proposal covariance is \c prior when no emulator is
18 The effect of the proposal covariance is reflected in the MCMC chain
26 emulator model), the derived-based proposal covariance forms a more
/dports/finance/quantlib/QuantLib-1.20/ql/legacy/libormarketmodels/
H A Dlfmprocess.cpp80 Matrix covariance(lfmParam_->covariance(t, x)); in drift() local
87 covariance.column_begin(k)+m,0.0) in drift()
88 - 0.5*covariance[k][k]; in drift()
99 Disposable<Matrix> LiborForwardModelProcess::covariance( in covariance() function in QuantLib::LiborForwardModelProcess
101 return lfmParam_->covariance(t, x)*dt; in covariance()
136 Matrix covariance = lfmParam_->covariance(t0, x0); in evolve() local
143 covariance.column_begin(k)+m,0.0) in evolve()
144 -0.5*covariance[k][k]) * dt; in evolve()
153 covariance.column_begin(k)+m,0.0) in evolve()
154 -0.5*covariance[k][k])*dt)+ r); in evolve()
/dports/science/dynare/dynare-4.6.4/matlab/
H A Dcovariance_mc_analysis.m3 % endogenous variables' covariance matrix.
70 if isfield(temporary_structure,'covariance')
71 temporary_structure = oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.Mean;
75 % Nothing to do (the covariance matrix is symmetric!).
82 % Nothing to do (the covariance matrix is symmetric!).
114 oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.density.(var1).(var2) = density;
119 oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.Mean.(var1).(var2) = p_mean;
120 oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.Median.(var1).(var2) = p_median;
121 oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.Variance.(var1).(var2) = p_var;
122 oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.HPDinf.(var1).(var2) = hpd_interval(1);
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/dports/math/cgal/CGAL-5.3/include/CGAL/
H A Dlinear_least_squares_fitting_segments_2.h67 typename DiagonalizeTraits::Covariance_matrix covariance = {{ 0., 0., 0. }}; in linear_least_squares_fitting_2() local
96 covariance[0] += transformation[0][0]; in linear_least_squares_fitting_2()
97 covariance[1] += transformation[0][1]; in linear_least_squares_fitting_2()
98 covariance[2] += transformation[1][1]; in linear_least_squares_fitting_2()
107 covariance[0] += -mass * ( c.x() * c.x()); in linear_least_squares_fitting_2()
108 covariance[1] += -mass * (c.x() * c.y()); in linear_least_squares_fitting_2()
109 covariance[2] += -mass * (c.y() * c.y()); in linear_least_squares_fitting_2()
117 (covariance, eigen_values, eigen_vectors); in linear_least_squares_fitting_2()
/dports/games/xray_re-tools/xray_re-tools-52721d2/sources/3rd-party/nvtt/nvtt/squish/
H A Dmaths.cpp45 Sym3x3 covariance( 0.0f ); in ComputeWeightedCovariance() local
51 covariance[0] += a.X()*b.X(); in ComputeWeightedCovariance()
52 covariance[1] += a.X()*b.Y(); in ComputeWeightedCovariance()
53 covariance[2] += a.X()*b.Z(); in ComputeWeightedCovariance()
54 covariance[3] += a.Y()*b.Y(); in ComputeWeightedCovariance()
55 covariance[4] += a.Y()*b.Z(); in ComputeWeightedCovariance()
56 covariance[5] += a.Z()*b.Z(); in ComputeWeightedCovariance()
60 return covariance; in ComputeWeightedCovariance()
/dports/games/0ad/0ad-0.0.23b-alpha/libraries/source/nvtt/src/src/nvtt/squish/
H A Dmaths.cpp45 Sym3x3 covariance( 0.0f ); in ComputeWeightedCovariance() local
51 covariance[0] += a.X()*b.X(); in ComputeWeightedCovariance()
52 covariance[1] += a.X()*b.Y(); in ComputeWeightedCovariance()
53 covariance[2] += a.X()*b.Z(); in ComputeWeightedCovariance()
54 covariance[3] += a.Y()*b.Y(); in ComputeWeightedCovariance()
55 covariance[4] += a.Y()*b.Z(); in ComputeWeightedCovariance()
56 covariance[5] += a.Z()*b.Z(); in ComputeWeightedCovariance()
60 return covariance; in ComputeWeightedCovariance()
/dports/graphics/nvidia-texture-tools/nvidia-texture-tools-2.0.8/src/nvtt/squish/
H A Dmaths.cpp45 Sym3x3 covariance( 0.0f ); in ComputeWeightedCovariance() local
51 covariance[0] += a.X()*b.X(); in ComputeWeightedCovariance()
52 covariance[1] += a.X()*b.Y(); in ComputeWeightedCovariance()
53 covariance[2] += a.X()*b.Z(); in ComputeWeightedCovariance()
54 covariance[3] += a.Y()*b.Y(); in ComputeWeightedCovariance()
55 covariance[4] += a.Y()*b.Z(); in ComputeWeightedCovariance()
56 covariance[5] += a.Z()*b.Z(); in ComputeWeightedCovariance()
60 return covariance; in ComputeWeightedCovariance()
/dports/science/py-nilearn/nilearn-0.8.1/doc/connectivity/
H A Dconnectome_extraction.rst4 Connectome extraction: inverse covariance for direct connections
28 Sparse inverse covariance for functional connectomes
31 Functional connectivity can be obtained by estimating a covariance
45 interesting to use the inverse covariance matrix, ie the *precision
56 >>> from sklearn.covariance import GraphicalLassoCV
64 The covariance matrix and inverse-covariance matrix (precision matrix)
87 .. centered:: |covariance| |precision|
98 :class:`sklearn.covariance.GraphicalLasso`.
105 .. topic:: **Exercise: computing sparse inverse covariance**
120 Sparse inverse covariance on multiple subjects
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/dports/math/mlpack/mlpack-3.4.2/src/mlpack/core/metrics/
H A Dmahalanobis_distance_impl.hpp29 arma::mat out = trans(m) * covariance * m; // 1x1 in Evaluate()
42 if (covariance.n_rows == 0) in Evaluate()
43 covariance = arma::eye<arma::mat>(a.n_elem, a.n_elem); in Evaluate()
46 arma::mat out = trans(m) * covariance * m; // 1x1; in Evaluate()
56 ar & BOOST_SERIALIZATION_NVP(covariance); in serialize()

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