/dports/science/dakota/dakota-6.13.0-release-public.src-UI/docs/KeywordMetadata/ |
H A D | method-bayes_calibration-calibrate_error_multipliers | 1 Blurb:: Calibrate hyper-parameter multipliers on the observation error covariance 4 observation error covariance (\ref 6 include \c one multiplier on the whole block-diagonal covariance 7 structure, one multiplier \c per_experiment covariance block, one 8 multiplier \c per_response covariance block, or separate multipliers
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/dports/math/R-cran-RHmm/RHmm/man/ |
H A D | setAsymptoticCovMat.rd | 3 \title{Set the asymptotic covariance matrix of a fitted HMM} 4 \description{This function sets the empirical asymptotic covariance matrix of the fitted HMM} 10 \item{asymptCovMat}{The covariance matrix of the fitted model}
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/dports/graphics/tesseract/tesseract-5.0.0/src/ccstruct/ |
H A D | quadlsq.cpp | 121 long double covariance = in fit() local 125 top96 = cubevar * covariance; in fit() 134 top96 = covariance - cubevar * a; in fit() 139 b = covariance / x_variance; in fit()
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/dports/science/py-scipy/scipy-1.7.1/scipy/stats/ |
H A D | kde.py | 241 output_dtype = np.common_type(self.covariance, points) 293 sum_cov = self.covariance + cov 336 stdev = ravel(sqrt(self.covariance))[0] 371 self.covariance, **extra_kwds) 411 sum_cov = small.covariance + large.covariance 459 zeros((self.d,), float), self.covariance, size=size 568 self.covariance = self._data_covariance * self.factor**2 570 L = linalg.cholesky(self.covariance*2*pi)
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/dports/math/openturns/openturns-1.18/python/test/ |
H A D | t_KrigingAlgorithm_std_hmat.py | 45 covariance = result.getConditionalCovariance(X) 46 covariancePoint = ot.Point(covariance.getImplementation()) 48 ott.assert_almost_equal(covariance, 105 covariance = result.getConditionalCovariance(inputSample) 107 covariance, ot.SquareMatrix(len(inputSample)), 0.0, 1e-3)
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/dports/math/openturns/openturns-1.18/python/src/ |
H A D | SquaredExponential_doc.i.in | 2 "Squared exponential covariance function. 27 The *squared exponential function* is a stationary covariance function with dimension :math:`d=1`. 50 Create a standard squared exponential covariance function: 62 Create a squared exponential covariance function specifying the scale vector (amplitude is fixed to… 67 Create a squared exponential covariance function specifying the scale vector and the amplitude :
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H A D | CovarianceModelFactoryImplementation_doc.i.in | 2 "Estimation of the covariance model of a process. 6 …an interface class for all the classes that build covariance models. OpenTURNS provides two covari…
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H A D | KrigingResult_doc.i.in | 103 "Accessor to the covariance coefficients. 125 "Accessor to the covariance model. 130 The covariance model of the Gaussian process *W* with its optimized parameters. 188 "Compute the conditional covariance of the Gaussian process on a point (or several points). 198 The point :math:`\vect{x}` where the conditional covariance of the output has to be evaluated. 205 …The conditional covariance :math:`\Cov{\vect{Y}(\omega, \vect{x})\, | \, \cC}` at point :math:`\v… 206 Or the conditional covariance matrix at the sample :math:`(\vect{\xi}_1, \dots, \vect{\xi}_M)`: 223 "Compute the conditional covariance of the Gaussian process on a point (or several points). 234 …The point :math:`\vect{x}` where the conditional marginal covariance of the output has to be evalu… 241 …The conditional covariance :math:`\Cov{\vect{Y}(\omega, \vect{x})\, | \, \cC}` at point :math:`\v… [all …]
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/dports/math/R-cran-lava/lava/tests/testthat/ |
H A D | test-constrain.R | 26 covariance(m,y1~y2) <- "C" 64 m <- covariance(lvm(),X1~X2) 93 covariance(l) <- bw.1 ~ bw.2 105 covariance(l) <- bw.1 ~ bw.2 107 covariance(l,~bw.1+bw.2) <- "s" 108 covariance(l,bw.1~bw.2) <- "r1" 110 covariance(l2,bw.1~bw.2) <- "r2"
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/dports/math/octave-forge-stk/stk/inst/examples/01_kriging_basics/ |
H A D | stk_example_kb01.m | 9 % A Matern covariance function is used for the Gaussian Process (GP) prior. 10 % The parameters of this covariance function are assumed to be known (i.e., 68 % We choose a Matern covariance with "fixed parameters" (in other words, the 69 % parameters of the covariance function are provided by the user rather than 74 % kriging) and a Matern covariance function. (Some default parameters are also 78 % NOTE: the suffix '_iso' indicates an ISOTROPIC covariance function, but the 81 % Parameters for the Matern covariance function
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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_diagonalCovarianceChain.C | 46 const V & observations, const V & covariance) in Likelihood() argument 48 observations, covariance) in Likelihood() 158 this->covariance = new QUESO::GslVector(this->obsSpace->zeroVector()); in BayesianInverseProblem() 159 (*(this->covariance))[0] = 2.0; in BayesianInverseProblem() 160 (*(this->covariance))[1] = 8.0; in BayesianInverseProblem() 167 *(this->paramDomain), *(this->observations), *(this->covariance)); in BayesianInverseProblem() 217 QUESO::GslVector * covariance; variable
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/dports/math/openturns/openturns-1.18/python/doc/pyplots/ |
H A D | KarhunenLoeveValidation.py | 8 covariance = ot.AbsoluteExponential([1.0]) variable 9 algo = ot.KarhunenLoeveP1Algorithm(mesh, covariance, threshold) 11 process = ot.GaussianProcess(covariance, mesh)
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/dports/math/cgal/CGAL-5.3/examples/Solver_interface/ |
H A D | diagonalize_matrix.cpp | 14 Eigen_matrix covariance = {{ 0., 0., 0., 0., 0., 0. }}; in main() local 18 covariance[i] = rand(); in main() 23 if(!(Diagonalize_traits::diagonalize_selfadjoint_covariance_matrix(covariance, in main()
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/dports/science/InsightToolkit/ITK-5.0.1/Modules/Core/ImageFunction/test/ |
H A D | itkCovarianceImageFunctionTest.cxx | 79 FunctionType::OutputType covariance; in itkCovarianceImageFunctionTest() local 81 covariance = function->EvaluateAtIndex( index ); in itkCovarianceImageFunctionTest() 82 std::cout << "function->EvaluateAtIndex( index ): " << covariance << std::endl; in itkCovarianceImageFunctionTest() 110 if( ! itk::Math::FloatAlmostEqual( itk::Math::abs( covariance[ix][iy] ), in itkCovarianceImageFunctionTest()
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/dports/math/stan/stan-2.28.2/src/stan/mcmc/ |
H A D | chains.hpp | 71 using boost::accumulators::tag::covariance; in covariance() 75 accumulator_set<double, stats<covariance<double, covariate1> > > acc; in covariance() 81 return boost::accumulators::covariance(acc) * M / (M - 1); in covariance() 90 using boost::accumulators::tag::covariance; in correlation() 104 double cov = boost::accumulators::covariance(acc_xy); in correlation() 450 return covariance(samples(chain, index1), samples(chain, index2)); in covariance() 453 double covariance(const int index1, const int index2) const { in covariance() function in stan::mcmc::chains 454 return covariance(samples(index1), samples(index2)); in covariance() 457 double covariance(const int chain, const std::string& name1, in covariance() function in stan::mcmc::chains 459 return covariance(chain, index(name1), index(name2)); in covariance() [all …]
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/dports/math/py-pystan/pystan-2.19.0.0/pystan/stan/src/stan/mcmc/ |
H A D | chains.hpp | 67 static double covariance(const Eigen::VectorXd& x, in covariance() function in stan::mcmc::chains 75 using boost::accumulators::tag::covariance; in covariance() 78 accumulator_set<double, stats<covariance<double, covariate1> > > acc; in covariance() 84 return boost::accumulators::covariance(acc) * M / (M-1); in covariance() 95 using boost::accumulators::tag::covariance; in correlation() 108 double cov = boost::accumulators::covariance(acc_xy); in correlation() 489 double covariance(const int index1, const int index2) const { in covariance() function in stan::mcmc::chains 490 return covariance(samples(index1), samples(index2)); in covariance() 493 double covariance(const int chain, const std::string& name1, in covariance() function in stan::mcmc::chains 495 return covariance(chain, index(name1), index(name2)); in covariance() [all …]
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/dports/biology/migrate/migrate-3.6.11/src/ |
H A D | correlation.c | 144 …->bayes->numparams,world->bayes->params, offset, locus,&world->bayes->histogram[locus].covariance); in covariance_bayes() 159 if(world->bayes->histogram[0].covariance==NULL) in covariance_summary() 161 if (target->covariance==NULL) in covariance_summary() 163 doublevec2d(&target->covariance,nn,nn); in covariance_summary() 169 cov = world->bayes->histogram[locus].covariance; in covariance_summary() 174 target->covariance[i][j] += cov[i][j]*invn; in covariance_summary()
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/dports/math/py-pandas/pandas-1.2.5/doc/source/user_guide/ |
H A D | computation.rst | 34 .. _computation.covariance: 39 :meth:`Series.cov` can be used to compute covariance between series 51 .. _computation.covariance.caveats: 56 for the covariance matrix which is unbiased. However, for many applications 57 this estimate may not be acceptable because the estimated covariance matrix 60 and/or a non-invertible covariance matrix. See `Estimation of covariance 112 Please see the :ref:`caveats <computation.covariance.caveats>` associated 114 :ref:`covariance section <computation.covariance>`.
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/statespace/_smoothers/ |
H A D | _classical.pyx | 77 # Factorize the predicted state covariance matrix 110 # Scaled smoothed estimator covariance matrix 194 # Smoothed state covariance 229 # Factorize the predicted state covariance matrix 262 # Scaled smoothed estimator covariance matrix 346 # Smoothed state covariance 381 # Factorize the predicted state covariance matrix 414 # Scaled smoothed estimator covariance matrix 498 # Smoothed state covariance 566 # Scaled smoothed estimator covariance matrix [all …]
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/dports/sysutils/xvidcap/xvidcap-1.1.7/ffmpeg/libavutil/ |
H A D | lls.c | 48 m->covariance[i][j] *= decay; in av_update_lls() 49 m->covariance[i][j] += var[i]*var[j]; in av_update_lls() 56 double (*factor)[MAX_VARS+1]= (void*)&m->covariance[1][0]; in av_solve_lls() 57 double (*covar )[MAX_VARS+1]= (void*)&m->covariance[1][1]; in av_solve_lls() 58 double *covar_y = m->covariance[0]; in av_solve_lls()
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/dports/math/R-cran-car/car/man/ |
H A D | influence-mixed-models.Rd | 15 …link{dfbetas}}, \code{\link{cooks.distance}}, and influence on variance-covariance components based 40 …on the fixed effects; if \code{"var.cov"}, return influence on the variance-covariance components.} 45 \code{influence.lme} starts with the estimated variance-covariance components from \code{model} and… 50 influence on the variance-covariance components. 61 \item{\code{"var.cov.comps"}}{the estimated variance-covariance parameters for the model.} 62 …\item{\code{"var.cov.comps[-groups]"}}{a matrix with the estimated covariance parameters (in colum… 63 \item{\code{"vcov"}}{The estimated covariance matrix of the fixed-effects coefficients.} 64 …\item{\code{"vcov[-groups]"}}{a list each of whose elements is the estimated covariance matrix of …
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H A D | hccm.Rd | 17 Calculates heteroscedasticity-corrected covariance matrices 20 covariance matrices. 37 produces an error; otherwise, the aliased coefficients are ignored in the coefficient covariance 43 …The original White-corrected coefficient covariance matrix (\code{"hc0"}) for an unweighted model … 47 corrected covariance matrix. 54 The heteroscedasticity-corrected covariance matrix for the model. 77 A heteroskedastic consistent covariance matrix estimator and a direct test of heteroskedasticity.
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/dports/math/R-cran-fracdiff/fracdiff/tests/ |
H A D | ex.Rout.save | 41 [5] "ar" "ma" "covariance.dpq" "fnormMin" 54 + c("h", "covariance.dpq", "stderror.dpq", "correlation.dpq", "hessian.dpq") 56 > dns <- dimnames(fd1.$covariance.dpq) 61 + covariance.dpq = matrix(c(0.0005966, -0.0008052, -0.0001897, 79 + covariance.dpq = matrix(c(0.0005966, -0.0008052, -0.0001897, 105 + covariance.dpq = matrix(c(0.0004182859, -0.0007078449, -6.753008e-05, 134 + covariance.dpq = matrix(c( 5.4726e-05,-9.261e-05, -8.8353e-06, 156 + covariance.dpq = matrix(c(-0.0003545, 6e-04, 5.724e-05,
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/dports/math/cgal/CGAL-5.3/include/CGAL/ |
H A D | hierarchy_simplify_point_set.h | 244 std::array<FT, 6> covariance = {{ 0., 0., 0., 0., 0., 0. }}; in hierarchy_simplify_point_set() local 251 covariance[0] += d.x () * d.x (); in hierarchy_simplify_point_set() 252 covariance[1] += d.x () * d.y (); in hierarchy_simplify_point_set() 253 covariance[2] += d.x () * d.z (); in hierarchy_simplify_point_set() 254 covariance[3] += d.y () * d.y (); in hierarchy_simplify_point_set() 255 covariance[4] += d.y () * d.z (); in hierarchy_simplify_point_set() 256 covariance[5] += d.z () * d.z (); in hierarchy_simplify_point_set() 266 (covariance, eigenvalues, eigenvectors); in hierarchy_simplify_point_set()
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/dports/benchmarks/phoronix-test-suite/phoronix-test-suite-10.6.1/ob-cache/test-profiles/pts/polybench-c-1.2.0/ |
H A D | install.sh | 16 cc $CFLAGS -I utilities -I datamining/covariance utilities/polybench.c datamining/covariance/covari…
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