| /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/fonttools/Lib/fontTools/pens/ |
| D | statisticsPen.py | 38 self.covariance = 0
62 self.covariance = covariance = self.momentXY / area - meanX*meanY
66 correlation = covariance / (stddevX * stddevY)
69 slant = covariance / varianceY
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| /external/tensorflow/tensorflow/python/kernel_tests/distributions/ |
| D | dirichlet_multinomial_test.py | 262 dist.covariance(),
298 covariance = dist.covariance()
301 self.assertEqual([2, 2], covariance.get_shape())
302 self.assertAllClose(expected_covariance, self.evaluate(covariance))
335 covariance = dist.covariance()
339 self.assertEqual([4, 3, 3], covariance.get_shape())
340 self.assertAllClose(expected_covariance, self.evaluate(covariance))
353 covariance = dist.covariance()
354 covariance2 = dist2.covariance()
355 self.assertEqual([3, 5, 4, 4], covariance.get_shape())
[all …]
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| D | multinomial_test.py | 227 self.assertEqual((3, 3), dist.covariance().get_shape())
228 self.assertAllClose(expected_covariances, dist.covariance().eval())
242 self.assertEqual((4, 2, 2, 2), dist.covariance().get_shape())
243 self.assertAllClose(expected_covariances, dist.covariance().eval())
258 covariance = dist.covariance()
259 covariance2 = dist2.covariance()
260 self.assertEqual((3, 5, 4, 4), covariance.get_shape())
297 dist.covariance(),
328 dist.covariance(),
357 dist.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/tensorflow/tensorflow/contrib/factorization/g3doc/ |
| D | gmm.md | 9 parameters, which include the mean, covariance and mixture ratios of the
14 covariance can be either full or diagonal.
|
| /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/ |
| D | mvn_full_covariance_test.py | 48 mvn.covariance().eval()
63 mvn.covariance().eval()
107 covariance = self._random_pd_matrix(3, 5, 2, 2)
110 mu, covariance, validate_args=True)
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| D | vector_laplace_diag_test.py | 134 vla.covariance().eval())
148 vla.covariance().eval())
162 vla.covariance().eval())
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| D | vector_exponential_diag_test.py | 125 vex.covariance().eval())
139 vex.covariance().eval())
153 vex.covariance().eval())
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| D | mvn_diag_test.py | 150 mvn.covariance().eval())
164 mvn.covariance().eval())
178 mvn.covariance().eval())
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| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/ |
| D | math_utils.py | 478 def log_noninformative_covariance_prior(covariance): argument
495 covariance += array_ops.diag(1e-8 * array_ops.ones(
496 shape=[array_ops.shape(covariance)[0]], dtype=covariance.dtype))
497 power = -(math_ops.cast(array_ops.shape(covariance)[0] + 1,
498 covariance.dtype) / 2.)
499 return power * math_ops.log(linalg_ops.matrix_determinant(covariance))
502 def entropy_matched_cauchy_scale(covariance): argument
528 array_ops.matrix_diag_part(covariance))
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| D | ar_model.py | 462 covariance = prediction_ops["covariance"]
463 sigma = math_ops.sqrt(gen_math_ops.maximum(covariance, 1e-5))
698 covariance = prediction_ops["covariance"]
715 covariance = self._scale_back_variance(covariance)
722 predictions={"mean": prediction, "covariance": covariance,
978 covariance = prediction_ops["covariance"]
980 sigma = math_ops.sqrt(gen_math_ops.maximum(covariance, 1e-5))
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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);
|
| /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/ImageMagick/coders/ |
| D | dds.c | 1378 static void ComputePrincipleComponent(const float *covariance, in ComputePrincipleComponent() argument
1390 row0.x = covariance[0]; in ComputePrincipleComponent()
1391 row0.y = covariance[1]; in ComputePrincipleComponent()
1392 row0.z = covariance[2]; in ComputePrincipleComponent()
1395 row1.x = covariance[1]; in ComputePrincipleComponent()
1396 row1.y = covariance[3]; in ComputePrincipleComponent()
1397 row1.z = covariance[4]; in ComputePrincipleComponent()
1400 row2.x = covariance[2]; in ComputePrincipleComponent()
1401 row2.y = covariance[4]; in ComputePrincipleComponent()
1402 row2.z = covariance[5]; in ComputePrincipleComponent()
[all …]
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| /external/tensorflow/tensorflow/tools/api/golden/v1/ |
| D | tensorflow.distributions.-distribution.pbtxt | 55 name: "covariance"
56 argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], "
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| D | tensorflow.distributions.-normal.pbtxt | 64 name: "covariance"
65 argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], "
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| D | tensorflow.distributions.-categorical.pbtxt | 68 name: "covariance"
69 argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], "
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| D | tensorflow.distributions.-uniform.pbtxt | 64 name: "covariance"
65 argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], "
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| D | tensorflow.distributions.-exponential.pbtxt | 65 name: "covariance"
66 argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], "
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| D | tensorflow.distributions.-dirichlet.pbtxt | 64 name: "covariance"
65 argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], "
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| /external/eigen/doc/ |
| D | DenseDecompositionBenchmark.dox | 12 …rices, the reported timmings include the cost to compute the symmetric covariance matrix \f$ A^T A…
30 …oblems, and the reported timing include the cost to form the symmetric covariance matrix \f$ A^T A…
34 …t of Cholesky/LU decompositions is dominated by the computation of the symmetric covariance matrix.
|