/dports/devel/boost-python-libs/boost_1_72_0/libs/math/test/ |
H A D | bivariate_statistics_test.cpp | 36 using boost::math::statistics::covariance; 73 cov3 = covariance(u3, v3); in test_covariance() 76 cov3 = covariance(v3, u3); in test_covariance() 81 cov3 = covariance(u3, u3); in test_covariance() 109 Real cov_uu = covariance(u, u); in test_covariance() 111 Real cov_vv = covariance(v, v); in test_covariance()
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/dports/devel/boost-docs/boost_1_72_0/libs/math/test/ |
H A D | bivariate_statistics_test.cpp | 36 using boost::math::statistics::covariance; 73 cov3 = covariance(u3, v3); in test_covariance() 76 cov3 = covariance(v3, u3); in test_covariance() 81 cov3 = covariance(u3, u3); in test_covariance() 109 Real cov_uu = covariance(u, u); in test_covariance() 111 Real cov_vv = covariance(v, v); in test_covariance()
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/dports/math/R-cran-energy/energy/man/ |
H A D | dcovu.Rd | 1 \name{Unbiased distance covariance} 7 covariance and a bias-corrected estimator of 32 Unbiased distance covariance (SR2014) corresponds to the biased 35 For the original distance covariance test of independence (SRB2007, 36 SR2009), the distance covariance test statistic is the V-statistic 71 \concept{ distance covariance }
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/dports/science/gnudatalanguage/gdl-1.0.1/src/pro/ |
H A D | correlate.pro | 12 function CORRELATE, x, y, covariance=covariance, double=double 30 if KEYWORD_SET(covariance) then return, cov 51 ;; cov[i, j] = CORRELATE(x[i, *], x[j, *], double=double, covariance=covariance) 66 if KEYWORD_SET(covariance) then return, cov / (dims[1]-1)
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/dports/devel/boost-libs/boost_1_72_0/libs/math/test/ |
H A D | bivariate_statistics_test.cpp | 36 using boost::math::statistics::covariance; 73 cov3 = covariance(u3, v3); in test_covariance() 76 cov3 = covariance(v3, u3); in test_covariance() 81 cov3 = covariance(u3, u3); in test_covariance() 109 Real cov_uu = covariance(u, u); in test_covariance() 111 Real cov_vv = covariance(v, v); in test_covariance()
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/dports/devel/hyperscan/boost_1_75_0/libs/math/test/ |
H A D | bivariate_statistics_test.cpp | 36 using boost::math::statistics::covariance; 73 cov3 = covariance(u3, v3); in test_covariance() 76 cov3 = covariance(v3, u3); in test_covariance() 81 cov3 = covariance(u3, u3); in test_covariance() 109 Real cov_uu = covariance(u, u); in test_covariance() 111 Real cov_vv = covariance(v, v); in test_covariance()
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/dports/math/openturns/openturns-1.18/lib/src/Base/Algo/ |
H A D | KarhunenLoeveAlgorithmImplementation.cxx | 45 …veAlgorithmImplementation::KarhunenLoeveAlgorithmImplementation(const CovarianceModel & covariance, in KarhunenLoeveAlgorithmImplementation() argument 48 , covariance_(covariance) in KarhunenLoeveAlgorithmImplementation() 93 void KarhunenLoeveAlgorithmImplementation::setCovarianceModel(const CovarianceModel & covariance) in setCovarianceModel() argument 95 covariance_ = covariance; in setCovarianceModel()
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/dports/science/R-cran-etm/etm/man/ |
H A D | print.etm.Rd | 8 \S3method{print}{etm}(x, covariance = FALSE, whole = TRUE, ...) 12 \item{covariance}{Whether print the covariance matrix. Default is 14 \item{whole}{Whether to plot the entire covariance matrix. If set to
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/dports/math/openturns/openturns-1.18/python/test/ |
H A D | t_Dirac_std.expout | 7 covariance= 0.0 25 covariance= [[ 0 ]] 40 covariance= [[ 0 0 0 0 ] 58 covariance= [[ 0 0 0 0 ]
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/sandbox/nonparametric/ |
H A D | kdecovclass.py | 18 def __init__(self, dataset, covariance): argument 19 self.covariance = covariance 23 self.inv_cov = np.linalg.inv(self.covariance) 24 self._norm_factor = np.sqrt(np.linalg.det(2*np.pi*self.covariance)) * self.n 34 self.inv_cov = np.linalg.inv(self.covariance) 35 self._norm_factor = np.sqrt(np.linalg.det(2*np.pi*self.covariance)) * self.n
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/covariance/ |
H A D | _shrunk_covariance.py | 178 covariance = empirical_covariance(X, assume_centered=self.assume_centered) 179 covariance = shrunk_covariance(covariance, self.shrinkage) 180 self._set_covariance(covariance) 478 covariance, shrinkage = ledoit_wolf( 482 self._set_covariance(covariance) 680 covariance, shrinkage = oas(X - self.location_, assume_centered=True) 682 self._set_covariance(covariance)
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/dports/science/py-nilearn/nilearn-0.8.1/nilearn/_utils/ |
H A D | glm.py | 243 def multiple_mahalanobis(effect, covariance): argument 263 if covariance.ndim == 2: 264 covariance = covariance[:, :, np.newaxis] 265 if effect.shape[0] != covariance.shape[0]: 267 if covariance.shape[0] != covariance.shape[1]: 271 Xt, Kt = np.ascontiguousarray(effect.T), np.ascontiguousarray(covariance.T)
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/dports/science/dakota/dakota-6.13.0-release-public.src-UI/docs/KeywordMetadata/ |
H A D | DUPLICATE-full_covariance | 2 Display the full covariance matrix 4 With a large number of responses, the covariance matrix can 6 force Dakota to output the full covariance matrix.
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H A D | DUPLICATE-diagonal_covariance | 2 Display only the diagonal terms of the covariance matrix 4 With a large number of responses, the covariance matrix can 6 suppress the off-diagonal covariance terms (to save compute and
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/dports/math/R-cran-robustbase/robustbase/tests/ |
H A D | MCD-specials.Rout.save | 90 alpha = 1: The minimum covariance determinant estimates based on 52 observations 134 alpha = 1: The minimum covariance determinant estimates based on 52 observations 226 alpha = 1: The minimum covariance determinant estimates based on 12 observations 230 The classical covariance matrix is singular. 273 alpha = 1: The minimum covariance determinant estimates based on 12 observations 277 The classical covariance matrix is singular. 312 The covariance matrix of the data is singular. 328 In covMcd(X) : The covariance matrix of the data is singular. 338 The covariance matrix of the data is singular. 365 The classical covariance matrix is singular. [all …]
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/dports/emulators/mess/mame-mame0226/3rdparty/bimg/3rdparty/nvtt/nvmath/ |
H A D | fitting.h | 19 Vector3 computeCovariance(int n, const Vector3 * points, float * covariance); 20 …(int n, const Vector3 * points, const float * weights, const Vector3 & metric, float * covariance); 22 Vector4 computeCovariance(int n, const Vector4 * points, float * covariance); 23 …(int n, const Vector4 * points, const float * weights, const Vector4 & metric, float * covariance);
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/dports/emulators/mame/mame-mame0226/3rdparty/bimg/3rdparty/nvtt/nvmath/ |
H A D | fitting.h | 19 Vector3 computeCovariance(int n, const Vector3 * points, float * covariance); 20 …(int n, const Vector3 * points, const float * weights, const Vector3 & metric, float * covariance); 22 Vector4 computeCovariance(int n, const Vector4 * points, float * covariance); 23 …(int n, const Vector4 * points, const float * weights, const Vector4 & metric, float * covariance);
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/dports/finance/R-cran-gmm/gmm/man/ |
H A D | vcov.Rd | 6 \title{Variance-covariance matrix of GMM or GEL} 20 \item{lambda}{If set to TRUE, the covariance matrix of the Lagrange multipliers is produced.} 21 \item{type}{Type of covariance matrix for the meat} 24 misspecification covariance matrix} 29 For tsls(), if vcov is set to a different value thand "Classical", a sandwich covariance matrix is …
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/dports/www/qt5-webengine/qtwebengine-everywhere-src-5.15.2/src/3rdparty/chromium/third_party/webrtc/modules/audio_processing/ns/ |
H A D | signal_model_estimator.cc | 41 float covariance = 0.f; in ComputeSpectralDiff() local 47 covariance += signal_diff * noise_diff; in ComputeSpectralDiff() 51 covariance *= kOneByFftSizeBy2Plus1; in ComputeSpectralDiff() 57 signal_variance - (covariance * covariance) / (noise_variance + 0.0001f); in ComputeSpectralDiff()
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/dports/net-im/tg_owt/tg_owt-d578c76/src/modules/audio_processing/ns/ |
H A D | signal_model_estimator.cc | 41 float covariance = 0.f; in ComputeSpectralDiff() local 47 covariance += signal_diff * noise_diff; in ComputeSpectralDiff() 51 covariance *= kOneByFftSizeBy2Plus1; in ComputeSpectralDiff() 57 signal_variance - (covariance * covariance) / (noise_variance + 0.0001f); in ComputeSpectralDiff()
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/dports/www/chromium-legacy/chromium-88.0.4324.182/third_party/webrtc/modules/audio_processing/ns/ |
H A D | signal_model_estimator.cc | 41 float covariance = 0.f; in ComputeSpectralDiff() local 47 covariance += signal_diff * noise_diff; in ComputeSpectralDiff() 51 covariance *= kOneByFftSizeBy2Plus1; in ComputeSpectralDiff() 57 signal_variance - (covariance * covariance) / (noise_variance + 0.0001f); in ComputeSpectralDiff()
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/dports/www/firefox/firefox-99.0/third_party/libwebrtc/modules/audio_processing/ns/ |
H A D | signal_model_estimator.cc | 41 float covariance = 0.f; in ComputeSpectralDiff() local 47 covariance += signal_diff * noise_diff; in ComputeSpectralDiff() 51 covariance *= kOneByFftSizeBy2Plus1; in ComputeSpectralDiff() 57 signal_variance - (covariance * covariance) / (noise_variance + 0.0001f); in ComputeSpectralDiff()
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/dports/audio/webrtc-audio-processing/webrtc-audio-processing-1.0/webrtc/modules/audio_processing/ns/ |
H A D | signal_model_estimator.cc | 41 float covariance = 0.f; in ComputeSpectralDiff() local 47 covariance += signal_diff * noise_diff; in ComputeSpectralDiff() 51 covariance *= kOneByFftSizeBy2Plus1; in ComputeSpectralDiff() 57 signal_variance - (covariance * covariance) / (noise_variance + 0.0001f); in ComputeSpectralDiff()
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/dports/math/cgal/CGAL-5.3/include/CGAL/Scale_space_reconstruction_3/ |
H A D | Weighted_PCA_smoother.h | 304 std::array<FT, 6> covariance = {{ 0., 0., 0., 0., 0., 0. }}; in operator() local 314 covariance[0] += w * v.x () * v.x (); in operator() 315 covariance[1] += w * v.x () * v.y (); in operator() 316 covariance[2] += w * v.x () * v.z (); in operator() 317 covariance[3] += w * v.y () * v.y (); in operator() 318 covariance[4] += w * v.y () * v.z (); in operator() 319 covariance[5] += w * v.z () * v.z (); in operator() 328 (covariance, eigenvalues, eigenvectors); in operator()
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/dports/math/openturns/openturns-1.18/python/src/ |
H A D | AbsoluteExponential_doc.i.in | 2 "Absolute exponential covariance function. 28 The *absolute exponential* function is a stationary covariance function with dimension :math:`d=1`. 51 Create a standard absolute exponential covariance function: 63 Create an absolute exponential covariance function specifying only the scale vector (amplitude is f… 68 Create an absolute exponential covariance function specifying the scale vector and the amplitude :
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