| /external/apache-commons-math/src/main/java/org/apache/commons/math/random/ |
| D | CorrelatedRandomVectorGenerator.java | 98 RealMatrix covariance, double small, in CorrelatedRandomVectorGenerator() argument
102 int order = covariance.getRowDimension(); in CorrelatedRandomVectorGenerator()
108 decompose(covariance, small); in CorrelatedRandomVectorGenerator()
126 public CorrelatedRandomVectorGenerator(RealMatrix covariance, double small, in CorrelatedRandomVectorGenerator() argument
130 int order = covariance.getRowDimension(); in CorrelatedRandomVectorGenerator()
136 decompose(covariance, small); in CorrelatedRandomVectorGenerator()
188 private void decompose(RealMatrix covariance, double small) in decompose() argument
191 int order = covariance.getRowDimension(); in decompose()
192 double[][] c = covariance.getData(); in decompose()
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/stat/regression/ |
| D | AbstractMultipleLinearRegression.java | 231 protected void validateCovarianceData(double[][] x, double[][] covariance) { in validateCovarianceData() argument
232 if (x.length != covariance.length) { in validateCovarianceData()
234 LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE, x.length, covariance.length); in validateCovarianceData()
236 if (covariance.length > 0 && covariance.length != covariance[0].length) { in validateCovarianceData()
239 covariance.length, covariance[0].length); in validateCovarianceData()
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| D | GLSMultipleLinearRegression.java | 56 public void newSampleData(double[] y, double[][] x, double[][] covariance) { in newSampleData() argument
60 validateCovarianceData(x, covariance); in newSampleData()
61 newCovarianceData(covariance); in newSampleData()
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| /external/ceres-solver/internal/ceres/ |
| D | covariance_test.cc | 315 Covariance covariance(options); in ComputeAndCompareCovarianceBlocks() local
316 EXPECT_TRUE(covariance.Compute(covariance_blocks, &problem_)); in ComputeAndCompareCovarianceBlocks()
322 GetCovarianceBlockAndCompare(block1, block2, covariance, expected_covariance); in ComputeAndCompareCovarianceBlocks()
324 GetCovarianceBlockAndCompare(block2, block1, covariance, expected_covariance); in ComputeAndCompareCovarianceBlocks()
331 const Covariance& covariance, in GetCovarianceBlockAndCompare() argument
339 EXPECT_TRUE(covariance.GetCovarianceBlock(block1, in GetCovarianceBlockAndCompare()
743 Covariance covariance(options); in ComputeAndCompare() local
744 EXPECT_TRUE(covariance.Compute(all_covariance_blocks_, &problem_)); in ComputeAndCompare()
755 covariance.GetCovarianceBlock(block_i, block_i, actual.data()); in ComputeAndCompare()
764 covariance.GetCovarianceBlock(block_i, block_j, actual.data()); in ComputeAndCompare()
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| D | CMakeLists.txt | 54 covariance.cc
243 CERES_TEST(covariance)
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/stat/correlation/ |
| D | Covariance.java | 166 double cov = covariance(matrix.getColumn(i), matrix.getColumn(j), biasCorrected); in computeCovarianceMatrix()
220 public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected) in covariance() method in Covariance
255 public double covariance(final double[] xArray, final double[] yArray) in covariance() method in Covariance
257 return covariance(xArray, yArray, true); in covariance()
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| D | PearsonsCorrelation.java | 95 public PearsonsCorrelation(Covariance covariance) { in PearsonsCorrelation() argument
96 RealMatrix covarianceMatrix = covariance.getCovarianceMatrix(); in PearsonsCorrelation()
100 nObs = covariance.getN(); in PearsonsCorrelation()
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| /external/autotest/client/site_tests/video_WebRtcCamera/ |
| D | ssim.js | 47 covariance: function(a, b, meanA, meanB) { method in Ssim
76 var sigmaXy = this.covariance(x, y, muX, muY);
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| /external/autotest/client/site_tests/video_WebRtcPeerConnectionWithCamera/ |
| D | ssim.js | 47 covariance: function(a, b, meanA, meanB) { method in Ssim
76 var sigmaXy = this.covariance(x, y, muX, muY);
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| /external/ImageMagick/coders/ |
| D | dds.c | 1368 static void ComputePrincipleComponent(const float *covariance, in ComputePrincipleComponent() argument
1380 row0.x = covariance[0]; in ComputePrincipleComponent()
1381 row0.y = covariance[1]; in ComputePrincipleComponent()
1382 row0.z = covariance[2]; in ComputePrincipleComponent()
1385 row1.x = covariance[1]; in ComputePrincipleComponent()
1386 row1.y = covariance[3]; in ComputePrincipleComponent()
1387 row1.z = covariance[4]; in ComputePrincipleComponent()
1390 row2.x = covariance[2]; in ComputePrincipleComponent()
1391 row2.y = covariance[4]; in ComputePrincipleComponent()
1392 row2.z = covariance[5]; in ComputePrincipleComponent()
[all …]
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| /external/clang/test/ARCMT/ |
| D | checking.m | 198 - (id) init03; // covariance
199 - (id) init04; // covariance
217 - (Test8_super*) init30; // id exception to covariance
221 - (Test8_super*) init34; // covariance
224 - (Test8*) init40; // id exception to covariance
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| /external/ceres-solver/docs/source/ |
| D | solving.rst | 2157 non-linear least squares solve is to analyze the covariance of the
2166 covariance. Then the maximum likelihood estimate of :math:`x` given
2172 And the covariance of :math:`x^*` is given by
2179 If :math:`J(x^*)` is rank deficient, then the covariance matrix :math:`C(x^*)`
2184 Note that in the above, we assumed that the covariance matrix for
2191 covariance of :math:`y`, then the maximum likelihood problem to be
2196 and the corresponding covariance estimate of :math:`x^*` is given by
2201 covariance matrix not equal to identity, then it is the user's
2205 where :math:`S^{-1/2}` is the inverse square root of the covariance
2222 :class:`Covariance` allows the user to evaluate the covariance for a
[all …]
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| D | features.rst | 71 the solution by evaluating all or part of the covariance
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| D | version_history.rst | 13 #. Added ``EIGEN_SPARSE_QR`` algorithm for covariance estimation using
22 #. The ``SPARSE_CHOLESKY`` algorithm for covariance estimation has
26 #. The ``SPARSE_QR`` algorithm for covariance estimation has been
228 #. Sparse and dense covariance estimation.
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| D | modeling.rst | 800 where, :math:`\mu` is a vector and :math:`S` is a covariance matrix,
802 root of the inverse of the covariance, also known as the stiffness
805 the covariance matrix :math:`S` is rank deficient.
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| /external/clang/test/SemaObjC/ |
| D | arc.m | 172 - (id) init03; // covariance
173 - (id) init04; // covariance
191 - (Test8_super*) init30; // id exception to covariance
196 - (Test8_super*) init34; // covariance
199 - (Test8*) init40; // id exception to covariance
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| /external/eigen/doc/ |
| D | FunctionsTakingEigenTypes.dox | 94 A Ref object can also be writable. Here is an example of a function computing the covariance matrix…
179 … done now, right? This is not completely true because in order for our covariance function to be g…
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| /external/ceres-solver/ |
| D | CMakeLists.txt | 115 This does not affect the covariance estimation algorithm, as it
256 MESSAGE(" This does not affect the covariance estimation algorithm ")
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