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/dports/www/qt5-webengine/qtwebengine-everywhere-src-5.15.2/src/3rdparty/chromium/ui/gfx/
H A Dcolor_analysis.cc845 gfx::Matrix3F covariance = gfx::Matrix3F::Zeros(); in ComputeColorCovariance() local
847 return covariance; in ComputeColorCovariance()
887 covariance.set( in ComputeColorCovariance()
915 return covariance; in ComputeColorCovariance()
/dports/science/afni/afni-AFNI_21.3.16/src/pkundu/meica.libs/mdp/utils/
H A D__init__.py24 from covariance import (CovarianceMatrix, DelayCovarianceMatrix,
/dports/science/dakota/dakota-6.13.0-release-public.src-UI/packages/pecos/python/python_src/PyDakota/unit/
H A Dtest_compressed_sensing.py141 covariance = numpy.dot(self.diabetes_matrix.T, residual)
143 max_covariance = numpy.max(numpy.absolute(covariance))
147 num_non_zeros = len(covariance[max_covariance-eps<
148 numpy.absolute(covariance)])
171 covariance = numpy.dot(self.diabetes_matrix.T, residual)
173 max_covariance = numpy.max(numpy.absolute(covariance))
177 num_non_zeros = len(covariance[max_covariance-eps<
178 numpy.absolute(covariance)])
/dports/math/dieharder/dieharder-3.31.1/manual/
H A Ddieharder.tex1869 # weak inverse of the 120x120 covariance matrix yields a test
1872 # tribution with the specified 120x120 covariance matrix (with
2241 # the covariance matrix of the cell counts provides a chisquare
2297 # in the weak inverse of the covariance matrix of the cell
2537 # The S's are virtually normal with a certain covariance mat-
2591 # covariance matrices for the runs-up and runs-down are well
2593 # weak inverses of the covariance matrices. Runs are counted
3190 # weak inverse of the 120x120 covariance matrix yields a test
3193 # tribution with the specified 120x120 covariance matrix (with
3561 # the covariance matrix of the cell counts provides a chisquare
[all …]
/dports/math/dieharder/dieharder-3.31.1/
H A DChangeLog1542 statistic somehow. I have to covariance matrix precisely in hand --
1659 covariance matrix no matter what you do with it, which may be why
1710 the overlapping permutations covariance matrix itself. This test just
H A Ddieharder.abs214 covariance between the samples (or a gradually vanishing degree of
222 of the covariance matrices associated with overlapping samples seem to
224 (covariance) data or in dieharder-specific code it is difficult to say.
228 treatment of covariance. For that reason non-overlapping versions of
596 forming the weak inverse of covariance matrices in order to correct for
H A Ddieharder.html.in188 covariance between the samples (or a gradually vanishing degree of
196 of the covariance matrices associated with overlapping samples seem to
198 (covariance) data or in dieharder-specific code it is difficult to say.
202 treatment of covariance. For that reason non-overlapping versions of
570 forming the weak inverse of covariance matrices in order to correct for
/dports/math/dieharder/dieharder-3.31.1/include/dieharder/
H A Ddiehard_operm5.h.cruft23 # weak inverse of the 120x120 covariance matrix yields a test \n\
26 # tribution with the specified 120x120 covariance matrix (with \n\
/dports/math/dieharder/dieharder-3.31.1/libdieharder/
H A Ddiehard_operm5.cruft21 * weak inverse of the 120x120 covariance matrix yields a test ::
24 * tribution with the specified 120x120 covariance matrix (with ::
30 * I really think that the covariance matrix is going to have to
H A Ddiehard_operm5.c.save20 * weak inverse of the 120x120 covariance matrix yields a test ::
23 * tribution with the specified 120x120 covariance matrix (with ::
35 * (and hence avoids the covariance problem altogether) and can
37 * to compute the covariance matrix for the problem, but was unable
/dports/math/pspp/pspp-1.4.1/
H A DMakefile.in1144 src/math/covariance.lo src/math/correlation.lo \
2151 src/math/$(DEPDIR)/covariance.Plo \
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/regression/tests/
H A Dtest_processreg.py104 cv = f.covariance(mod.time[0:5], mod.exog_scale[0:5, :],
186 cv = f.covariance(mod.time[0:5], exog_scale, exog_smooth)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/regression/
H A Dprocess_regression.py690 def covariance(self, time, scale_params, smooth_params, scale_data, member in ProcessMLE
803 def covariance(self, time, scale, smooth): member in ProcessMLEResults
832 return self.model.covariance(time, self.scale_params,
853 return self.model.covariance(time,
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/sandbox/nonparametric/
H A Dkdecovclass.py18 def __init__(self, dataset, covariance): argument
19 self.covariance = covariance
23 self.inv_cov = np.linalg.inv(self.covariance)
24 self._norm_factor = np.sqrt(np.linalg.det(2*np.pi*self.covariance)) * self.n
34 self.inv_cov = np.linalg.inv(self.covariance)
35 self._norm_factor = np.sqrt(np.linalg.det(2*np.pi*self.covariance)) * self.n
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/sandbox/examples/thirdparty/
H A Dex_ratereturn.py93 from sklearn.covariance import LedoitWolf, OAS, MCD
/dports/math/gsl/gsl-2.7/statistics/
H A DMakefile.in108 median.lo covariance.lo quantiles.lo select.lo Sn.lo Qn.lo \
139 ./$(DEPDIR)/absdev.Plo ./$(DEPDIR)/covariance.Plo \
556 … skew.c kurtosis.c lag1.c p_variance.c minmax.c ttest.c mad.c median.c covariance.c quantiles.c se…
631 @AMDEP_TRUE@@am__include@ @am__quote@./$(DEPDIR)/covariance.Plo@am__quote@ # am--include-marker
1008 -rm -f ./$(DEPDIR)/covariance.Plo
1079 -rm -f ./$(DEPDIR)/covariance.Plo
/dports/math/p5-Math-Random/Math-Random-0.72/
H A Dexample.pl266 my @covariance = (0) x $p;
267 foreach $val (@covariance) {
276 my @ans = random_multivariate_normal($n,@mean,@covariance);
/dports/math/gnumeric/gnumeric-1.12.50/doc/
H A DMakefile.in566 figures/analysistools-covariance-ex1.png \
567 figures/analysistools-covariance-ex2.png \
568 figures/analysistools-covariance.png \
/dports/math/gnumeric/gnumeric-1.12.50/src/
H A DMakefile.in1158 dialogs/covariance.ui \
/dports/lang/python310/Python-3.10.1/Lib/test/
H A Dtest_statistics.py2421 statistics.covariance(x, y)
2437 statistics.covariance(x, y)
2455 self.assertAlmostEqual(statistics.covariance(x, y), result)
2461 self.assertAlmostEqual(statistics.covariance(x, y), 5)
2465 self.assertAlmostEqual(statistics.covariance(x, y), 0.1)
/dports/lang/python310/Python-3.10.1/Lib/
H A Dstatistics.py861 def covariance(x, y, /): function
/dports/lang/python311/Python-3.11.0a3/Lib/test/
H A Dtest_statistics.py2559 statistics.covariance(x, y)
2575 statistics.covariance(x, y)
2593 self.assertAlmostEqual(statistics.covariance(x, y), result)
2599 self.assertAlmostEqual(statistics.covariance(x, y), 5)
2603 self.assertAlmostEqual(statistics.covariance(x, y), 0.1)
/dports/lang/python311/Python-3.11.0a3/Lib/
H A Dstatistics.py956 def covariance(x, y, /): function
/dports/graphics/pcl-pointclouds/pcl-pcl-1.12.0/
H A DCHANGES.md111 * Fix the covariance calculation as suggested by @zxd123 [[#4466](https://github.com/PointCloudLibr…
1188 * Fix covariance calculation in PCA [[#2130]](https://github.com/PointCloudLibrary/pcl/pull/2130)
1777 * Double pass mean and covariance estimation are now employed in
1954 approximate covariance matrices
2150 * Added support for externally computed covariance matrices in
3027 * added explicit checks + errors for NaN covariance matrices
3315 * fixed unnormalized covariance matrix in `computeCovarianceMatrixNormalized`
4131 * normalized covariance matrix estimation _pcl::computeCovarianceMatrixNormalized_ (r32872)
4364 …* Added vectorAverage to features, which calculates mean and covariance matrix incrementally witho…
4464 * fixed a very ugly bug regarding covariance matrices having 0 elements (r28789)
[all …]
/dports/graphics/pcl-pointclouds/pcl-pcl-1.12.0/segmentation/src/
H A Dgrabcut_segmentation.cpp598 …g.determinant = g.covariance (0,0)*(g.covariance (1,1)*g.covariance (2,2) - g.covariance (1,2)*g.c… in fit()
599 …- g.covariance (0,1)*(g.covariance (1,0)*g.covariance (2,2) - g.covariance (1,2)*g.covariance (2,0… in fit()
600 …+ g.covariance (0,2)*(g.covariance (1,0)*g.covariance (2,1) - g.covariance (1,1)*g.covariance (2,0… in fit()
603 …g.inverse (0,0) = (g.covariance (1,1)*g.covariance (2,2) - g.covariance (1,2)*g.covariance (2,1))… in fit()
604 …g.inverse (1,0) = -(g.covariance (1,0)*g.covariance (2,2) - g.covariance (1,2)*g.covariance (2,0))… in fit()
605 …g.inverse (2,0) = (g.covariance (1,0)*g.covariance (2,1) - g.covariance (1,1)*g.covariance (2,0))… in fit()
606 …g.inverse (0,1) = -(g.covariance (0,1)*g.covariance (2,2) - g.covariance (0,2)*g.covariance (2,1))… in fit()
607 …g.inverse (1,1) = (g.covariance (0,0)*g.covariance (2,2) - g.covariance (0,2)*g.covariance (2,0))… in fit()
608 …g.inverse (2,1) = -(g.covariance (0,0)*g.covariance (2,1) - g.covariance (0,1)*g.covariance (2,0))… in fit()
609 …g.inverse (0,2) = (g.covariance (0,1)*g.covariance (1,2) - g.covariance (0,2)*g.covariance (1,1))… in fit()
[all …]

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