| /dports/databases/mysqlwsrep57-server/boost_1_59_0/boost/accumulators/statistics/ |
| H A D | covariance.hpp | 175 struct covariance struct
192 extractor<tag::abstract_covariance> const covariance = {}; variable
194 BOOST_ACCUMULATORS_IGNORE_GLOBAL(covariance)
197 using extract::covariance;
200 struct feature_of<tag::covariance<VariateType, VariateTag> >
208 struct as_weighted_feature<tag::covariance<VariateType, VariateTag> >
215 : feature_of<tag::covariance<VariateType, VariateTag> >
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| /dports/databases/mysql57-client/mysql-5.7.36/boost/boost_1_59_0/boost/accumulators/statistics/ |
| H A D | covariance.hpp | 175 struct covariance struct
192 extractor<tag::abstract_covariance> const covariance = {}; variable
194 BOOST_ACCUMULATORS_IGNORE_GLOBAL(covariance)
197 using extract::covariance;
200 struct feature_of<tag::covariance<VariateType, VariateTag> >
208 struct as_weighted_feature<tag::covariance<VariateType, VariateTag> >
215 : feature_of<tag::covariance<VariateType, VariateTag> >
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| /dports/science/InsightToolkit/ITK-5.0.1/Modules/Numerics/Statistics/test/ |
| H A D | itkGaussianMembershipFunctionTest.cxx | 78 MembershipFunctionType::CovarianceMatrixType covariance; in itkGaussianMembershipFunctionTest() local
79 covariance.SetSize(MeasurementVectorSize,MeasurementVectorSize); in itkGaussianMembershipFunctionTest()
80 covariance.SetIdentity(); in itkGaussianMembershipFunctionTest()
81 function->SetCovariance( covariance ); in itkGaussianMembershipFunctionTest()
83 if( function->GetCovariance() != covariance ) in itkGaussianMembershipFunctionTest()
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| /dports/science/dakota/dakota-6.13.0-release-public.src-UI/packages/external/queso/test/test_gaussian_likelihoods/ |
| H A D | test_diagonalCovariance.C | 43 const V & observations, const V & covariance) in Likelihood() argument
45 observations, covariance) in Likelihood()
103 QUESO::GslVector covariance(obsSpace.zeroVector()); in main()
104 covariance[0] = 1.0; in main()
105 covariance[1] = 2.0; in main()
109 observations, covariance); in main()
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| H A D | test_fullCovarianceChain.C | 46 const V & observations, const M & covariance) in Likelihood() argument
48 observations, covariance) in Likelihood()
158 this->covariance = new QUESO::GslMatrix(this->obsSpace->zeroVector()); in BayesianInverseProblem()
159 (*(this->covariance))(0, 0) = 1.0; in BayesianInverseProblem()
160 (*(this->covariance))(0, 1) = 2.0; in BayesianInverseProblem()
161 (*(this->covariance))(1, 0) = 2.0; in BayesianInverseProblem()
162 (*(this->covariance))(1, 1) = 8.0; in BayesianInverseProblem()
169 *(this->paramDomain), *(this->observations), *(this->covariance)); in BayesianInverseProblem()
219 QUESO::GslMatrix * covariance; variable
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| /dports/science/lammps/lammps-stable_29Sep2021/lib/kokkos/algorithms/unit_tests/ |
| H A D | TestRandom.hpp | 76 double covariance; member
85 covariance = 0.0; in RandomProperties()
95 covariance += add.covariance; in operator +=()
106 covariance += add.covariance; in operator +=()
166 prop.covariance += (tmp - mean) * (tmp2 - mean); in operator ()()
171 prop.covariance += (tmp2 - mean) * (tmp3 - mean); in operator ()()
240 prop.covariance += (count - mean) * (density_1d(i + 1) - mean); in operator ()()
279 prop.covariance += (count - mean) * (count_next - mean); in operator ()()
320 result.covariance / num_draws / 2 / variance_expect; in test_random_scalar()
359 << (result.covariance / HIST_DIM1D / HIST_DIM1D) << " || " in test_random_scalar()
[all …]
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| /dports/devel/kokkos/kokkos-3.4.01/algorithms/unit_tests/ |
| H A D | TestRandom.hpp | 76 double covariance; member
85 covariance = 0.0; in RandomProperties()
95 covariance += add.covariance; in operator +=()
106 covariance += add.covariance; in operator +=()
166 prop.covariance += (tmp - mean) * (tmp2 - mean); in operator ()()
171 prop.covariance += (tmp2 - mean) * (tmp3 - mean); in operator ()()
240 prop.covariance += (count - mean) * (density_1d(i + 1) - mean); in operator ()()
279 prop.covariance += (count - mean) * (count_next - mean); in operator ()()
320 result.covariance / num_draws / 2 / variance_expect; in test_random_scalar()
359 << (result.covariance / HIST_DIM1D / HIST_DIM1D) << " || " in test_random_scalar()
[all …]
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| /dports/science/sparta/sparta-20Oct2021/lib/kokkos/algorithms/unit_tests/ |
| H A D | TestRandom.hpp | 76 double covariance; member
85 covariance = 0.0; in RandomProperties()
95 covariance += add.covariance; in operator +=()
106 covariance += add.covariance; in operator +=()
166 prop.covariance += (tmp - mean) * (tmp2 - mean); in operator ()()
171 prop.covariance += (tmp2 - mean) * (tmp3 - mean); in operator ()()
240 prop.covariance += (count - mean) * (density_1d(i + 1) - mean); in operator ()()
279 prop.covariance += (count - mean) * (count_next - mean); in operator ()()
320 result.covariance / num_draws / 2 / variance_expect; in test_random_scalar()
359 << (result.covariance / HIST_DIM1D / HIST_DIM1D) << " || " in test_random_scalar()
[all …]
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| /dports/graphics/dataplot/dataplot-2c1b27601a3b7523449de612613eadeead9a8f70/lib/frmenus/math/ |
| H A D | covarian.men | 1 This is file covarian.men--Compute covariance of a variable
7 4. First variable for which to compute the covariance:
9 5. Second variable for which to compute the covariance:
11 6. Parameter to store the covariance:
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| H A D | rankcova.men | 1 This is file rankcova.men--Compute rank covariance of a variable
7 4. First variable for which to compute the rank covariance:
9 5. Second variable for which to compute the rank covariance:
11 6. Parameter to store the rank covariance:
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| /dports/math/openturns/openturns-1.18/python/src/ |
| H A D | SphericalModel_doc.i.in | 2 "Spherical covariance function.
24 Radius of the sphere on which the covariance model is not zero.
30 The *spherical* function is a stationary covariance function with dimension :math:`d=1`.
57 Create a standard spherical covariance function:
69 Create a spherical covariance function specifying the scale, amplitude vectors:
73 Create a squared exponential covariance function specifying the scale vector, the amplitude and ra…
85 Radius of the sphere on which the covariance model is not zero."
95 Radius of the sphere on which the covariance model is not zero."
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| H A D | GaussianLinearCalibration_doc.i.in | 21 The covariance matrix of the gaussian prior distribution of the parameter.
23 The covariance matrix of the gaussian distribution of the observations error.
38 The given observation error covariance can be either *local*, ie the same matrix applies for each o…
48 with a zero mean and with a covariance matrix equal to the
113 "Accessor to the parameter prior covariance.
118 Parameter prior covariance."
123 "Accessor to the observation error covariance.
128 Observation error covariance."
133 "Accessor to the flag for a global observation error covariance.
138 Flag telling if the given observation error covariance is global or not."
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| /dports/math/openturns/openturns-1.18/python/test/ |
| H A D | t_KernelSmoothing_std.expout | 4 covariance= class=CovarianceMatrix dimension=2 implementation=class=MatrixImplementation name=Unnam…
13 covariance= class=CovarianceMatrix dimension=2 implementation=class=MatrixImplementation name=Unnam…
22 covariance= class=CovarianceMatrix dimension=2 implementation=class=MatrixImplementation name=Unnam…
31 covariance= class=CovarianceMatrix dimension=2 implementation=class=MatrixImplementation name=Unnam…
40 covariance= class=CovarianceMatrix dimension=2 implementation=class=MatrixImplementation name=Unnam…
49 covariance= class=CovarianceMatrix dimension=2 implementation=class=MatrixImplementation name=Unnam…
58 covariance= class=CovarianceMatrix dimension=2 implementation=class=MatrixImplementation name=Unnam…
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| /dports/math/R-cran-RHmm/RHmm/man/ |
| H A D | asymptoticIterSimCovMat.rd | 4 \title{Compute the asymptotic covariance matrix of a fitted HMM by simulation}
5 \description{This \sQuote{new} function computes the empirical asymptotic covariance matrix of the …
17 \item{cov: }{the current empirical covariance matrix of \eqn{\theta}}
27 and the empirical covariance matrix is computed.}
33 # First 10 computations of covariance matrix
35 # 10 more computations of covariance matrix
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| /dports/misc/visp/visp-3.4.0/modules/vision/test/pose/ |
| H A D | testPoseFeatures.cpp | 197 vpMatrix covariance = pose.getCovarianceMatrix(); in test_pose() local
198 …std::cout << covariance[0][0] << " " << covariance[1][1] << " " << covariance[2][2] << " " << cova… in test_pose()
199 << covariance[4][4] << " " << covariance[5][5] << " " << std::endl; in test_pose()
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| /dports/science/py-scikit-learn/scikit-learn-1.0.2/doc/modules/ |
| H A D | lda_qda.rst | 105 to share the same covariance matrix: :math:`\Sigma_k = \Sigma` for all
130 In the case of QDA, there are no assumptions on the covariance matrices
172 In this scenario, the empirical sample covariance is a poor
184 covariance matrix will be used) and a value of 1 corresponds to complete
197 the OAS estimator of covariance will yield a better classification
202 class. A covariance estimator should have a :term:`fit` method and a
203 ``covariance_`` attribute like all covariance estimators in the
204 :mod:`sklearn.covariance` module.
216 with Empirical, Ledoit Wolf and OAS covariance estimator.
223 covariance matrices.
[all …]
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| /dports/math/mlpack/mlpack-3.4.2/src/mlpack/methods/nca/ |
| H A D | nca_softmax_error_function.hpp | 74 double Evaluate(const arma::mat& covariance);
88 double Evaluate(const arma::mat& covariance,
100 void Gradient(const arma::mat& covariance, arma::mat& gradient);
118 void Gradient(const arma::mat& covariance,
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| /dports/games/jfsw/jfsw-c434002/jfbuild/libsquish/ |
| H A D | maths.cpp | 52 Sym3x3 covariance( 0.0f ); in ComputeWeightedCovariance() local
58 covariance[0] += a.X()*b.X(); in ComputeWeightedCovariance()
59 covariance[1] += a.X()*b.Y(); in ComputeWeightedCovariance()
60 covariance[2] += a.X()*b.Z(); in ComputeWeightedCovariance()
61 covariance[3] += a.Y()*b.Y(); in ComputeWeightedCovariance()
62 covariance[4] += a.Y()*b.Z(); in ComputeWeightedCovariance()
63 covariance[5] += a.Z()*b.Z(); in ComputeWeightedCovariance()
67 return covariance; in ComputeWeightedCovariance()
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| /dports/emulators/mess/mame-mame0226/3rdparty/bimg/3rdparty/libsquish/ |
| H A D | maths.cpp | 52 Sym3x3 covariance( 0.0f ); in ComputeWeightedCovariance() local
58 covariance[0] += a.X()*b.X(); in ComputeWeightedCovariance()
59 covariance[1] += a.X()*b.Y(); in ComputeWeightedCovariance()
60 covariance[2] += a.X()*b.Z(); in ComputeWeightedCovariance()
61 covariance[3] += a.Y()*b.Y(); in ComputeWeightedCovariance()
62 covariance[4] += a.Y()*b.Z(); in ComputeWeightedCovariance()
63 covariance[5] += a.Z()*b.Z(); in ComputeWeightedCovariance()
67 return covariance; in ComputeWeightedCovariance()
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| /dports/games/supertuxkart/SuperTuxKart-1.2-src/lib/libsquish/ |
| H A D | maths.cpp | 52 Sym3x3 covariance( 0.0f ); in ComputeWeightedCovariance() local
58 covariance[0] += a.X()*b.X(); in ComputeWeightedCovariance()
59 covariance[1] += a.X()*b.Y(); in ComputeWeightedCovariance()
60 covariance[2] += a.X()*b.Z(); in ComputeWeightedCovariance()
61 covariance[3] += a.Y()*b.Y(); in ComputeWeightedCovariance()
62 covariance[4] += a.Y()*b.Z(); in ComputeWeightedCovariance()
63 covariance[5] += a.Z()*b.Z(); in ComputeWeightedCovariance()
67 return covariance; in ComputeWeightedCovariance()
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| /dports/devel/godot/godot-3.2.3-stable/thirdparty/squish/ |
| H A D | maths.cpp | 52 Sym3x3 covariance( 0.0f ); in ComputeWeightedCovariance() local
58 covariance[0] += a.X()*b.X(); in ComputeWeightedCovariance()
59 covariance[1] += a.X()*b.Y(); in ComputeWeightedCovariance()
60 covariance[2] += a.X()*b.Z(); in ComputeWeightedCovariance()
61 covariance[3] += a.Y()*b.Y(); in ComputeWeightedCovariance()
62 covariance[4] += a.Y()*b.Z(); in ComputeWeightedCovariance()
63 covariance[5] += a.Z()*b.Z(); in ComputeWeightedCovariance()
67 return covariance; in ComputeWeightedCovariance()
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| /dports/math/openturns/openturns-1.18/lib/src/Base/Stat/ |
| H A D | UserDefinedCovarianceModel.cxx | 45 const CovarianceMatrix & covariance) in UserDefinedCovarianceModel() argument
52 const UnsignedInteger fullDimension = covariance.getDimension(); in UserDefinedCovarianceModel()
55 covariance_ = covariance; in UserDefinedCovarianceModel()
141 CovarianceMatrix covariance(size * outputDimension_); in discretize() local
164 …covariance(rowBase + rowIndexLocal, columnBase + columnIndexLocal) = localCovarianceMatrix(rowInde… in discretize()
169 return covariance; in discretize()
191 CovarianceMatrix covariance = discretize(vertices); in discretizeAndFactorize() local
192 const TriangularMatrix choleskyFactor = covariance.computeCholesky(); in discretizeAndFactorize()
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| /dports/finance/quantlib/QuantLib-1.20/ql/models/marketmodels/models/ |
| H A D | flatvol.cpp | 88 Matrix covariance(numberOfRates_, numberOfRates_); in FlatVol() local
91 std::fill(covariance.begin(), covariance.end(), 0.0); in FlatVol()
104 covariance[i][j] += cov * corrMatrix[i][j]; in FlatVol()
117 covariance[i][j] += cov * corrMatrix[i][j]; in FlatVol()
127 covariance[j][i] = covariance[i][j]; in FlatVol()
131 pseudoRoots_[k] = rankReducedSqrt(covariance, in FlatVol()
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| /dports/emulators/mame/mame-mame0226/3rdparty/bimg/3rdparty/libsquish/ |
| H A D | maths.cpp | 52 Sym3x3 covariance( 0.0f ); in ComputeWeightedCovariance() local
58 covariance[0] += a.X()*b.X(); in ComputeWeightedCovariance()
59 covariance[1] += a.X()*b.Y(); in ComputeWeightedCovariance()
60 covariance[2] += a.X()*b.Z(); in ComputeWeightedCovariance()
61 covariance[3] += a.Y()*b.Y(); in ComputeWeightedCovariance()
62 covariance[4] += a.Y()*b.Z(); in ComputeWeightedCovariance()
63 covariance[5] += a.Z()*b.Z(); in ComputeWeightedCovariance()
67 return covariance; in ComputeWeightedCovariance()
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| /dports/devel/godot-tools/godot-3.2.3-stable/thirdparty/squish/ |
| H A D | maths.cpp | 52 Sym3x3 covariance( 0.0f ); in ComputeWeightedCovariance() local
58 covariance[0] += a.X()*b.X(); in ComputeWeightedCovariance()
59 covariance[1] += a.X()*b.Y(); in ComputeWeightedCovariance()
60 covariance[2] += a.X()*b.Z(); in ComputeWeightedCovariance()
61 covariance[3] += a.Y()*b.Y(); in ComputeWeightedCovariance()
62 covariance[4] += a.Y()*b.Z(); in ComputeWeightedCovariance()
63 covariance[5] += a.Z()*b.Z(); in ComputeWeightedCovariance()
67 return covariance; in ComputeWeightedCovariance()
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