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/dports/devel/godot2-tools/godot-2.1.6-stable/thirdparty/squish/
H A Dmaths.cpp52 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()
/dports/devel/godot2/godot-2.1.6-stable/thirdparty/squish/
H A Dmaths.cpp52 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()
/dports/graphics/openimageio/oiio-Release-2.2.16.0/src/dds.imageio/squish/
H A Dmaths.cpp52 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()
/dports/graphics/py-openimageio/oiio-Release-2.2.16.0/src/dds.imageio/squish/
H A Dmaths.cpp52 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()
/dports/math/py-pymc3/pymc-3.11.4/docs/source/
H A DGaussian_Processes.rst24 covariance function, :math:`k(x, x')`. Gaussian processes are a convenient
66 Mean and covariance functions
70 construction mean and covariance functions somewhat familiar. When first
76 other covariance functions.
88 :code:`active_dims`, which of those columns or dimensions the covariance
96 - The sum of two covariance functions is also a covariance function::
101 - The product of two covariance functions is also a covariance function::
106 - The product (or sum) of a covariance function with a scalar is a
107 covariance function::
114 After the covariance function is defined, it is now a function that is
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/dports/print/py-fonttools/fonttools-4.28.2/Lib/fontTools/pens/
H A DstatisticsPen.py36 self.covariance = 0
60 self.covariance = covariance = self.momentXY / area - meanX*meanY
64 correlation = covariance / (stddevX * stddevY)
67 slant = covariance / varianceY
/dports/math/openturns/openturns-1.18/python/src/
H A DGaussianNonLinearCalibration_doc.i.in16 The covariance matrix of the gaussian prior distribution of the parameter.
18 The covariance matrix of the gaussian distribution of the observations error.
24 covariance matrix.
27 The given observation error covariance can be either *local*,
46 with a zero mean and with a covariance matrix equal to the
110 "Accessor to the parameter prior covariance.
115 Parameter prior covariance."
120 "Accessor to the observation error covariance.
125 Observation error covariance."
130 "Accessor to the flag for a global observation error covariance.
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H A DExponentiallyDampedCosineModel_doc.i.in2 "Exponentially damped cosine covariance function.
26 The *exponentially damped cosine* function is a stationary covariance function with dimension :math…
30 The *exponentially damped cosine* covariance function is defined by:
51 Create a standard exponentially damped cosine covariance function:
63 Create a exponentially damped cosine covariance function specifying the amplitude and the scale:
67 Create a exponentially damped cosine covariance function specifying the amplitude and the scale:
/dports/graphics/dataplot/dataplot-2c1b27601a3b7523449de612613eadeead9a8f70/lib/frmenus/math/
H A Dwinscova.men1 This is file winscova.men--Compute Winsorized covariance of a variable
7 4. First variable for which to compute the covariance:
9 5. Second variable for which to compute the covariance:
15 8. Parameter to store the Winsorized covariance:
/dports/science/dakota/dakota-6.13.0-release-public.src-UI/docs/KeywordMetadata/
H A DDUPLICATE-multiplier4 The initial proposal covariance will be given by the prior variance
8 When using prior-based proposal covariance, the default is to use the
14 covariance. The multiplier can be used to scale all entries of the
15 prior variance in determining the initial proposal covariance.
H A Dmethod-nl2sol-covariance2 Determine how the final covariance matrix is computed
4 \c covariance
6 a final covariance matrix.
8 The desired covariance approximation:
/dports/math/openturns/openturns-1.18/lib/test/
H A Dt_KernelSmoothing_std.expout4 covariance(smoothed)=class=CovarianceMatrix dimension=2 implementation=class=MatrixImplementation n…
11 covariance(smoothed)=class=CovarianceMatrix dimension=2 implementation=class=MatrixImplementation n…
18 covariance(smoothed)=class=CovarianceMatrix dimension=2 implementation=class=MatrixImplementation n…
25 covariance(smoothed)=class=CovarianceMatrix dimension=2 implementation=class=MatrixImplementation n…
32 covariance(smoothed)=class=CovarianceMatrix dimension=2 implementation=class=MatrixImplementation n…
39 covariance(smoothed)=class=CovarianceMatrix dimension=2 implementation=class=MatrixImplementation n…
46 covariance(smoothed)=class=CovarianceMatrix dimension=2 implementation=class=MatrixImplementation n…
/dports/misc/openmvg/openMVG-2.0/src/third_party/ceres-solver/internal/ceres/
H A Dcovariance_test.cc521 Covariance covariance(options); in ComputeAndCompareCovarianceBlocksInTangentOrAmbientSpace() local
531 covariance, in ComputeAndCompareCovarianceBlocksInTangentOrAmbientSpace()
537 covariance, in ComputeAndCompareCovarianceBlocksInTangentOrAmbientSpace()
982 Covariance covariance(options); in TEST_F() local
990 covariance.Compute(parameter_blocks, &problem_); in TEST_F()
1011 Covariance covariance(options); in TEST_F() local
1019 covariance.Compute(parameter_blocks, &problem_); in TEST_F()
1039 Covariance covariance(options); in TEST_F() local
1058 covariance.Compute(parameter_blocks, &problem_); in TEST_F()
1080 Covariance covariance(options); in TEST_F() local
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/dports/science/dakota/dakota-6.13.0-release-public.src-UI/src/
H A DC3Approximation.hpp122 Real covariance(Approximation& approx_2);
123 Real covariance(const RealVector& x, Approximation& approx_2);
207 Real covariance(C3FnTrainData& ftd1, C3FnTrainData& ftd2);
209 Real covariance(const RealVector &x, C3FnTrainData& ftd1,C3FnTrainData& ftd2);
373 inline Real C3Approximation::covariance(Approximation& approx_2) in covariance() function in Dakota::C3Approximation
377 return covariance(levApproxIter->second, c3_approx_rep_2->active_ftd()); in covariance()
385 return covariance(combinedC3FTData, c3_approx_rep_2->combined_ftd()); in combined_covariance()
390 covariance(const RealVector &x, Approximation& approx_2) in covariance() function in Dakota::C3Approximation
394 return covariance(x, levApproxIter->second, c3_approx_rep_2->active_ftd()); in covariance()
403 return covariance(x, combinedC3FTData, c3_approx_rep_2->combined_ftd()); in combined_covariance()
/dports/math/apache-commons-math/commons-math3-3.6.1-src/src/test/java/org/apache/commons/math3/stat/correlation/
H A DCovarianceTest.java161 … Assert.assertEquals(0d, new Covariance().covariance(noVariance, values, true), Double.MIN_VALUE); in testConstant()
162 …Assert.assertEquals(0d, new Covariance().covariance(noVariance, noVariance, true), Double.MIN_VALU… in testConstant()
184 new Covariance().covariance(one, two, false); in testInsufficientData()
214 … new Covariance().covariance(matrix.getColumn(2), matrix.getColumn(3), true), 10E-14); in testConsistency()
239 Assert.assertEquals(new Covariance().covariance(x, y), in testConsistency()
240 new Covariance().covariance(x, y, true), Double.MIN_VALUE); in testConsistency()
/dports/math/cgal/CGAL-5.3/include/CGAL/
H A Dlinear_least_squares_fitting_segments_3.h54 typename DiagonalizeTraits::Covariance_matrix covariance = {{ 0., 0., 0., 0., 0., 0. }}; in linear_least_squares_fitting_3() local
55 …assemble_covariance_matrix_3(first,beyond,covariance,c,k,(Segment*) nullptr,tag, diagonalize_trait… in linear_least_squares_fitting_3()
58 return fitting_plane_3(covariance,c,plane,k,diagonalize_traits); in linear_least_squares_fitting_3()
113 typename DiagonalizeTraits::Covariance_matrix covariance = {{ 0., 0., 0., 0., 0., 0. }}; in linear_least_squares_fitting_3() local
114 …assemble_covariance_matrix_3(first,beyond,covariance,c,k,(Segment*) nullptr,tag, diagonalize_trait… in linear_least_squares_fitting_3()
117 return fitting_line_3(covariance,c,line,k,diagonalize_traits); in linear_least_squares_fitting_3()
/dports/math/R-cran-lava/lava/inst/doc/
H A Dcorrelation.R21 covariance(y1 ~ y2, value='r') %>%
52 covariance(y1 ~ y2)
70 covariance(y1 ~ y2, value='c') %>%
85 covariance(y1 ~ y2, constrain=TRUE, rname='z')
107 covariance(y1 ~ s2, constrain=TRUE, rname='z')
120 covariance(s1 ~ s2, constrain=TRUE, rname='z')
/dports/math/openturns/openturns-1.18/validation/src/
H A DValidKronekerCovModel.py65 covariance = ot.CovarianceMatrix(correlation.getDimension()) variable
67 covariance[j, j] = sigma[j] * sigma[j]
69 covariance[i, j] = sigma[i] * sigma[j] * correlation[i, j]
70 covariance.checkSymmetry()
74 ott.assert_almost_equal(myModel(vertex), covariance * value, 1e-16, 1e-15)
/dports/print/py-fonttools3/fonttools-3.44.0/Lib/fontTools/pens/
H A DstatisticsPen.py38 self.covariance = 0
62 self.covariance = covariance = self.momentXY / area - meanX*meanY
66 correlation = covariance / (stddevX * stddevY)
69 slant = covariance / varianceY
/dports/math/R-cran-energy/energy/man/
H A DdcovU_stats.Rd3 \title{Unbiased distance covariance statistics}
6 covariance, distance variance, and a bias-corrected estimator of
29 Unbiased distance covariance (SR2014) corresponds to the biased
32 For the original distance covariance test of independence (SRB2007,
33 SR2009), the distance covariance test statistic is the V-statistic
69 \concept{ distance covariance }
/dports/math/g2o/g2o-20201223_git/g2o/examples/tutorial_slam2d/
H A Dsimulator.cpp85 Matrix3d covariance; in simulate() local
86 covariance.fill(0.); in simulate()
87 covariance(0, 0) = transNoise[0]*transNoise[0]; in simulate()
88 covariance(1, 1) = transNoise[1]*transNoise[1]; in simulate()
89 covariance(2, 2) = rotNoise*rotNoise; in simulate()
90 Matrix3d information = covariance.inverse(); in simulate()
233 Matrix2d covariance; covariance.fill(0.); in simulate() local
234 covariance(0, 0) = landmarkNoise[0]*landmarkNoise[0]; in simulate()
235 covariance(1, 1) = landmarkNoise[1]*landmarkNoise[1]; in simulate()
236 Matrix2d information = covariance.inverse(); in simulate()
/dports/math/R-cran-pbkrtest/pbkrtest/man/
H A Dkr-vcov.Rd15 \title{Ajusted covariance matrix for linear mixed models according
28 \item{phiA}{the estimated covariance matrix, this has attributed P, a
30 the variances of the covariance parameters of the random effetcs}
32 \item{SigmaG}{list: Sigma: the covariance matrix of Y; G: the G matrices that
38 sample approximation to the covariance matrix estimate of the
51 ## Here the adjusted and unadjusted covariance matrices are identical,
58 ## For comparison, an alternative estimate of the variance-covariance
/dports/science/py-scipy/scipy-1.7.1/scipy/stats/tests/
H A Dtest_kdeoth.py79 covariance = np.array([[1.0, 2.0], [2.0, 6.0]])
82 xn = np.random.multivariate_normal(mean, covariance, size=n_basesample).T
107 assert_almost_equal(gkde.integrate_gaussian(mean, covariance),
118 covariance = np.array([[1.0, 2.0], [2.0, 6.0]])
121 xn = np.random.multivariate_normal(mean, covariance, size=n_basesample).T
147 assert_almost_equal(gkde.integrate_gaussian(mean, covariance),
222 def __init__(self, dataset, covariance): argument
223 self.covariance = covariance
227 self.inv_cov = np.linalg.inv(self.covariance)
255 kde3 = _kde_subclass3(x1, kde.covariance)
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/dports/science/colt/colt/src/cern/colt/matrix/doublealgo/
H A DStatistic.java235 for (int i=covariance.columns(); --i >= 0; ) { in correlation()
239 double cov = covariance.getQuick(i,j); in correlation()
242 covariance.setQuick(i,j,corr); in correlation()
243 covariance.setQuick(j,i,corr); // symmetric in correlation()
246 for (int i=covariance.columns(); --i >= 0; ) covariance.setQuick(i,i,1); in correlation()
248 return covariance; in correlation()
278 covariance.setQuick(i,j,cov); in covariance()
279 covariance.setQuick(j,i,cov); // symmetric in covariance()
282 return covariance; in covariance()
410 System.out.println("\ncovar1="+covariance(A)); in demo1()
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/dports/math/mlpack/mlpack-3.4.2/src/mlpack/methods/gmm/
H A Ddiagonal_constraint.hpp27 static void ApplyConstraint(arma::mat& covariance) in ApplyConstraint() argument
30 covariance = arma::diagmat(arma::clamp(covariance.diag(), 1e-10, DBL_MAX)); in ApplyConstraint()

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