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/dports/math/apache-commons-math/commons-math3-3.6.1-src/src/main/java/org/apache/commons/math3/random/
H A DCorrelatedRandomVectorGenerator.java87 RealMatrix covariance, double small, in CorrelatedRandomVectorGenerator() argument
89 int order = covariance.getRowDimension(); in CorrelatedRandomVectorGenerator()
96 new RectangularCholeskyDecomposition(covariance, small); in CorrelatedRandomVectorGenerator()
116 public CorrelatedRandomVectorGenerator(RealMatrix covariance, double small, in CorrelatedRandomVectorGenerator() argument
118 int order = covariance.getRowDimension(); in CorrelatedRandomVectorGenerator()
125 new RectangularCholeskyDecomposition(covariance, small); in CorrelatedRandomVectorGenerator()
/dports/math/cgal/CGAL-5.3/include/CGAL/
H A Dlinear_least_squares_fitting_points_3.h53 typename DiagonalizeTraits::Covariance_matrix covariance = {{ 0., 0., 0., 0., 0., 0. }}; in linear_least_squares_fitting_3() local
54 …assemble_covariance_matrix_3(first,beyond,covariance,c,k,(Point*) nullptr,tag, diagonalize_traits); in linear_least_squares_fitting_3()
57 return fitting_plane_3(covariance,c,plane,k,diagonalize_traits); in linear_least_squares_fitting_3()
86 typename DiagonalizeTraits::Covariance_matrix covariance = {{ 0., 0., 0., 0., 0., 0. }}; in linear_least_squares_fitting_3() local
87 …assemble_covariance_matrix_3(first,beyond,covariance,c,k,(Point*) nullptr,tag, diagonalize_traits); in linear_least_squares_fitting_3()
90 return fitting_line_3(covariance,c,line,k,diagonalize_traits); in linear_least_squares_fitting_3()
H A Dlinear_least_squares_fitting_cuboids_3.h55 typename DiagonalizeTraits::Covariance_matrix covariance = {{ 0., 0., 0., 0., 0., 0. }}; in linear_least_squares_fitting_3() local
56 …assemble_covariance_matrix_3(first,beyond,covariance,c,k,(Iso_cuboid*) nullptr,tag, diagonalize_tr… in linear_least_squares_fitting_3()
59 return fitting_plane_3(covariance,c,plane,k,diagonalize_traits); in linear_least_squares_fitting_3()
87 typename DiagonalizeTraits::Covariance_matrix covariance = {{ 0., 0., 0., 0., 0., 0. }}; in linear_least_squares_fitting_3() local
88 …assemble_covariance_matrix_3(first,beyond,covariance,c,k,(Iso_cuboid*) nullptr,tag,diagonalize_tra… in linear_least_squares_fitting_3()
91 return fitting_plane_3(covariance,c,plane,k,diagonalize_traits); in linear_least_squares_fitting_3()
187 typename DiagonalizeTraits::Covariance_matrix covariance = {{ 0., 0., 0., 0., 0., 0. }}; in linear_least_squares_fitting_3() local
188 …assemble_covariance_matrix_3(first,beyond,covariance,c,k,(Iso_cuboid*) nullptr,tag,diagonalize_tra… in linear_least_squares_fitting_3()
191 return fitting_line_3(covariance,c,line,k,diagonalize_traits); in linear_least_squares_fitting_3()
218 typename DiagonalizeTraits::Covariance_matrix covariance = {{ 0., 0., 0., 0., 0., 0. }}; in linear_least_squares_fitting_3() local
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/dports/graphics/blender/blender-2.91.0/intern/libmv/libmv/tracking/
H A Dkalman_filter.h33 Eigen::Matrix<T, N, N> covariance; member
66 state->covariance = state_transition_matrix_ * in Step()
67 state->covariance * in Step()
82 state->covariance * in Update()
87 Eigen::Matrix<T, 6, 2> kalman_gain = state->covariance * in Update()
93 state->covariance = (Eigen::Matrix<T, N, N>::Identity() - in Update()
95 state->covariance; in Update()
/dports/math/mlpack/mlpack-3.4.2/src/mlpack/core/dists/
H A Dgaussian_distribution.hpp30 arma::mat covariance; member in mlpack::distribution::GaussianDistribution
53 covariance(arma::eye<arma::mat>(dimension, dimension)), in GaussianDistribution()
64 GaussianDistribution(const arma::vec& mean, const arma::mat& covariance);
162 const arma::mat& Covariance() const { return covariance; } in Covariance()
167 void Covariance(const arma::mat& covariance);
169 void Covariance(arma::mat&& covariance);
185 ar & BOOST_SERIALIZATION_NVP(covariance); in serialize()
/dports/math/openturns/openturns-1.18/python/src/
H A DUserDefinedStationaryCovarianceModel_doc.i.in2 "Stationary covariance model defined by the User.
13 The covariance model is built as follows. Let :math:`d` be the dimension of the square matrices.
19 The class builds a stationary covariance function :math:`C^{stat}` as a piecewise constant function…
38 Create a stationary covariance function:
43 Create a collection of :math:`N` covariance values contained in 1x1 symmetric matrices:
52 Create the user-defined stationary covariance model:
56 Compute the covariance function at a specific vertex tau:
69 The time grid associated to the collection of covariance matrices.
H A DDiracCovarianceModel_doc.i.in2 "Dirac covariance function.
31 The *Dirac* covariance function is a stationary covariance function with dimension :math:`d \geq 1`.
35 The *Dirac* covariance function is defined by:
41 …([-1,1])` is the spatial correlation matrix. We can define the spatial covariance matrix :math:`\m…
60 Create a standard Dirac covariance function:
73 Create a Dirac covariance function specifying the amplitude vector:
77 Create a Dirac covariance function specifying the amplitude vector and the correlation matrix:
H A DStationaryCovarianceModelFactory_doc.i.in2 "Estimation of the covariance model of a stationary process.
36 Using the relation between the covariance model end the spectral function, the covariance function …
141 "Estimate a stationary covariance model.
151 The collection of fields used to estimate the covariance model.
153 The field used to estimate the covariance model.
158 The estimated covariance model.
170 Create the stationary covariance model, a mesh and a process:
181 Estimate the covariance model supposing the stationarity:
188 "Estimate the covariance model as a User defined covariance model.
204 The field used to estimate the covariance model.
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H A DProductCovarianceModel_doc.i.in2 "Univariate covariance function defined as a product.
7 Collection of covariance models :math:`(C_k)_{1 \leq k \leq K}` of dimension :math:`d=1`.
11 The *product* covariance function is a covariance function with
13 This allows to create a higher input dimension covariance model
16 It defines a covariance model from the given collection as follows.
20 The product covariance function writes:
56 Create a product covariance function from two exponential functions, each one defined on :math:`\Rs…
H A DUserDefinedCovarianceModel_doc.i.in13 …covariance matrix, :math:`s_i` and :math:`s_j` be two vertices of the mesh, with :math:`i,j\in\{0,…
20 Create the covariance function at (s,t):
31 Create the large covariance matrix:
40 Create the covariance model:
52 The mesh associated to the collection of covariance matrices.
62 …The time grid associated to the collection of covariance matrices when the mesh can be interpreted…
/dports/math/R-cran-raster/raster/man/
H A DlayerStats.Rd6 \title{Correlation and (weighted) covariance}
10 Compute correlation and (weighted) covariance for multi-layer Raster objects. Like \code{\link{cell…
22 …{Character. The statistic to compute: either 'cov' (covariance), 'weighted.cov' (weighted covarian…
24 …e the same extent, resolution and number of layers as \code{x}) to compute the weighted covariance}
35 List with two items: the correlation or (weighted) covariance matrix, and the (weighted) means.
39 \author{Jonathan A. Greenberg & Robert Hijmans. Weighted covariance based on code by Mort Canty}
43 For the weighted covariance:
/dports/science/py-GPy/GPy-1.10.0/GPy/kern/src/
H A Dmultioutput_kern.py69 covariance = [[None for i in range(nl)] for j in range(nl)]
75 covariance[i][j] = kernels[i]
79 covariance[i][j] = cross_covariances.get((i,j))
81 covariance[i][j] = ZeroKern()
84 self.covariance = covariance
94 …[[[[ target.__setitem__((slices[i][k],slices2[j][l]), self.covariance[i][j].K(X[slices[i][k],:],X2…
123 …[[[[ self._update_gradients_full_wrapper(self.covariance[i][j], dL_dK[slices[i][k],slices2[j][l]],…
125 …[[[[ self._update_gradients_full_wrapper(self.covariance[i][j], dL_dK[slices[i][k],slices[j][l]], …
130 …[[ self._update_gradients_diag_wrapper(self.covariance[i][i], dL_dKdiag[slices[i][k]], X[slices[i]…
137 …[[[[ target.__setitem__((slices[i][k]), target[slices[i][k],:] + self.covariance[i][j].gradients_X…
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/dports/science/dakota/dakota-6.13.0-release-public.src-UI/docs/KeywordMetadata/
H A DDUPLICATE-derivatives1 Blurb:: Use derivatives to inform the MCMC proposal covariance.
6 covariance from the Hessian of the misfit function (negative log
14 The default is \c prior based proposal covariance. This is a more
19 When derivatives are specified for defining the proposal covariance, the
20 misfit Hessian and its inverse (the MVN proposal covariance) will be
29 cannot be inverted to form the proposal covariance, fallback to the
32 Since this proposal covariance is locally accurate, it should be
43 initialize the proposal covariance at the start of the chain.
H A DDUPLICATE-update_period1 Blurb:: Period at which to update derivative-based proposal covariance
4 For derivative-based proposal covariance, this specifies the period
5 (number of accepted MCMC samples) after which the proposal covariance
11 proposal covariance at the start of the chain, but not updated
29 update_period = 40 # update proposal covariance every 40 points
/dports/science/InsightToolkit/ITK-5.0.1/Modules/Core/ImageFunction/test/
H A DitkMahalanobisDistanceThresholdImageFunctionTest.cxx72 FunctionType::CovarianceMatrixType covariance( Dimension, Dimension ); in itkMahalanobisDistanceThresholdImageFunctionTest() local
79 covariance.fill( 0.0 ); in itkMahalanobisDistanceThresholdImageFunctionTest()
80 covariance[0][0] = 100.0; in itkMahalanobisDistanceThresholdImageFunctionTest()
81 covariance[1][1] = 200.0; in itkMahalanobisDistanceThresholdImageFunctionTest()
82 covariance[2][2] = 300.0; in itkMahalanobisDistanceThresholdImageFunctionTest()
84 function->SetCovariance( covariance ); in itkMahalanobisDistanceThresholdImageFunctionTest()
87 TEST_SET_GET_VALUE( covariance, function->GetCovariance() ); in itkMahalanobisDistanceThresholdImageFunctionTest()
/dports/math/openturns/openturns-1.18/python/doc/examples/data_analysis/distribution_fitting/
H A Dplot_estimate_multivariate_distribution.py50 def find_neighbours(head, covariance, to_visit, visited): argument
51 N = covariance.getDimension()
57 if covariance[head, i] > 0:
59 component = find_neighbours(i, covariance, to_visit, visited)
64 def connected_components(covariance): argument
65 N = covariance.getDimension()
71 component = find_neighbours(head, covariance, to_visit, visited)
/dports/math/openturns/openturns-1.18/python/test/
H A Dt_GaussianProcess_std.expout2 myProcess1 = GaussianProcess(trend=[x0]->[0.0], covariance=ExponentialModel(scale=[1], amplitude=[…
28 myProcess2 = GaussianProcess(trend=[t]->[4.0], covariance=ExponentialModel(scale=[1], amplitude=[1…
42 myProcess3 = GaussianProcess(trend=[t]->[sin(t)], covariance=ExponentialModel(scale=[1], amplitude…
68 model= GaussianProcess(trend=[x0]->[0.0,0.0,0.0], covariance=ExponentialModel(scale=[2], amplitude=…
69 marginal= GaussianProcess(trend=[x0]->[0.0,0.0], covariance=ExponentialModel(scale=[2], amplitude=[…
/dports/math/mlpack/mlpack-3.4.2/src/mlpack/methods/gmm/
H A Dpositive_definite_constraint.hpp36 static void ApplyConstraint(arma::mat& covariance) in ApplyConstraint() argument
44 covariance = arma::symmatu(covariance); in ApplyConstraint()
45 if (!arma::eig_sym(eigval, eigvec, covariance)) in ApplyConstraint()
66 covariance = eigvec * arma::diagmat(eigval) * eigvec.t(); in ApplyConstraint()
H A Deigenvalue_ratio_constraint.hpp62 void ApplyConstraint(arma::mat& covariance) const in ApplyConstraint()
67 covariance = arma::symmatu(covariance); in ApplyConstraint()
68 if (!arma::eig_sym(eigenvalues, eigenvectors, covariance)) in ApplyConstraint()
81 covariance = eigenvectors * arma::diagmat(eigenvalues) * eigenvectors.t(); in ApplyConstraint()
/dports/graphics/dataplot/dataplot-2c1b27601a3b7523449de612613eadeead9a8f70/lib/frmenus/math/
H A Dmatr_prc.men16 9. The original data matrix can be raw data, a covariance matrix,
18 11. calculated from either a covariance matrix or a correlation
20 13. @CE 5 1 14 50 input - raw data, use - covariance
22 15. @CE 5 3 14 50 input - covariance, use - covariance
23 16. @CE 5 4 14 50 input - covariance, use - correlation
/dports/graphics/pcl-pointclouds/pcl-pcl-1.12.0/geometry/include/pcl/geometry/impl/
H A Dpolygon_operations.hpp195 Eigen::Matrix2f covariance = Eigen::Matrix2f::Zero(); in approximatePolygon2D() local
201 covariance.coeffRef(0) += polygon[pIdx].x * polygon[pIdx].x; in approximatePolygon2D()
212 covariance.coeffRef(0) += polygon[pIdx].x * polygon[pIdx].x; in approximatePolygon2D()
213 covariance.coeffRef(1) += polygon[pIdx].x * polygon[pIdx].y; in approximatePolygon2D()
214 covariance.coeffRef(3) += polygon[pIdx].y * polygon[pIdx].y; in approximatePolygon2D()
219 covariance.coeffRef(2) = covariance.coeff(1); in approximatePolygon2D()
223 covariance *= norm; in approximatePolygon2D()
224 covariance.coeffRef(0) -= centroid[0] * centroid[0]; in approximatePolygon2D()
225 covariance.coeffRef(1) -= centroid[0] * centroid[1]; in approximatePolygon2D()
226 covariance.coeffRef(3) -= centroid[1] * centroid[1]; in approximatePolygon2D()
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/dports/emulators/mess/mame-mame0226/3rdparty/bimg/3rdparty/nvtt/nvmath/
H A Dfitting.cpp123 covariance[i] = 0.0f; in computeCovariance()
149 covariance[i] = 0.0f; in computeCovariance()
176 covariance[i] = 0.0f; in computeCovariance()
188 covariance[4] += v.y * v.y; in computeCovariance()
192 covariance[7] += v.z * v.z; in computeCovariance()
193 covariance[8] += v.z * v.w; in computeCovariance()
195 covariance[9] += v.w * v.w; in computeCovariance()
209 covariance[i] = 0.0f; in computeCovariance()
222 covariance[4] += a.y * b.y; in computeCovariance()
226 covariance[7] += a.z * b.z; in computeCovariance()
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/dports/emulators/mame/mame-mame0226/3rdparty/bimg/3rdparty/nvtt/nvmath/
H A Dfitting.cpp123 covariance[i] = 0.0f; in computeCovariance()
149 covariance[i] = 0.0f; in computeCovariance()
176 covariance[i] = 0.0f; in computeCovariance()
188 covariance[4] += v.y * v.y; in computeCovariance()
192 covariance[7] += v.z * v.z; in computeCovariance()
193 covariance[8] += v.z * v.w; in computeCovariance()
195 covariance[9] += v.w * v.w; in computeCovariance()
209 covariance[i] = 0.0f; in computeCovariance()
222 covariance[4] += a.y * b.y; in computeCovariance()
226 covariance[7] += a.z * b.z; in computeCovariance()
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/dports/math/octave-forge-stk/stk/inst/param/estim/
H A Dstk_param_getdefaultbounds.m1 % STK_PARAM_GETDEFAULTBOUNDS provides lower/upper bounds for covariance parameters
6 % parameters of a parameterized covariance function COVARIANCE_TYPE, given
9 % NOTE: user-defined covariance functions
11 % For user-defined covariance functions, lower/upper bounds can be provided
14 % a) if the covariance uses a dedicated class C for parameter values,
18 % b) otherwise, for a covariance function named mycov, simply provide a
69 % Special case of a covariance function with no parameters
131 % bounds during estimation for a user-defined covariance
132 % function called XXXX (in the case, where this covariance
139 warning(['Unable to initialize covariance parameters ' ...
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/dports/math/R-cran-gss/gss/man/
H A Dmkcov.Rd10 Generate entries of covariance functions for correlated data.
20 \item{n}{Dimension of covariance matrix.}
27 \code{mkcov.arma} generates covariance functions for ARMA(p,q)
30 \code{mkcov.long} generates covariance functions for longitudinal
33 \code{mkcov.known} allows one to use a known covariance matrix in
40 \item{env}{Constants in covariance function.}

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