| /frameworks/ml/bordeaux/learning/stochastic_linear_ranker/native/ |
| stochastic_linear_ranker.h | 97 // RBF kernel, it implies the sigma parameter. In case of linear kernel,
140 // RBF kernel, it implies the sigma parameter. In case of linear kernel,
|
| /external/chromium_org/third_party/icu/source/data/unidata/ |
| SpecialCasing.txt | 202 # Special case for final form of sigma
204 03A3; 03C2; 03A3; 03A3; Final_Sigma; # GREEK CAPITAL LETTER SIGMA
208 # 03A3; 03C3; 03A3; 03A3; # GREEK CAPITAL LETTER SIGMA
209 # 03C3; 03C3; 03A3; 03A3; # GREEK SMALL LETTER SIGMA
210 # 03C2; 03C2; 03A3; 03A3; # GREEK SMALL LETTER FINAL SIGMA
214 # 03C3; 03C2; 03A3; 03A3; Final_Sigma; # GREEK SMALL LETTER SIGMA
215 # 03C2; 03C3; 03A3; 03A3; Not_Final_Sigma; # GREEK SMALL LETTER FINAL SIGMA
|
| /external/eigen/Eigen/src/Core/ |
| PermutationMatrix.h | 27 * The convention followed here is that if \f$ \sigma \f$ is a permutation, the corresponding permutation matrix
28 * \f$ P_\sigma \f$ is such that if \f$ (e_1,\ldots,e_p) \f$ is the canonical basis, we have:
29 * \f[ P_\sigma(e_i) = e_{\sigma(i)}. \f]
30 * This convention ensures that for any two permutations \f$ \sigma, \tau \f$, we have:
31 * \f[ P_{\sigma\circ\tau} = P_\sigma P_\tau. \f]
|
| /external/icu4c/data/unidata/ |
| SpecialCasing.txt | 204 # Special case for final form of sigma
206 03A3; 03C2; 03A3; 03A3; Final_Sigma; # GREEK CAPITAL LETTER SIGMA
210 # 03A3; 03C3; 03A3; 03A3; # GREEK CAPITAL LETTER SIGMA
211 # 03C3; 03C3; 03A3; 03A3; # GREEK SMALL LETTER SIGMA
212 # 03C2; 03C2; 03A3; 03A3; # GREEK SMALL LETTER FINAL SIGMA
216 # 03C3; 03C2; 03A3; 03A3; Final_Sigma; # GREEK SMALL LETTER SIGMA
217 # 03C2; 03C3; 03A3; 03A3; Not_Final_Sigma; # GREEK SMALL LETTER FINAL SIGMA
|
| /external/eigen/test/ |
| jacobisvd.cpp | 36 MatrixType sigma = MatrixType::Zero(rows,cols); local
37 sigma.diagonal() = svd.singularValues().template cast<Scalar>();
41 VERIFY_IS_APPROX(m, u * sigma * v.adjoint());
|
| /prebuilts/python/darwin-x86/2.7.5/lib/python2.7/ |
| random.py | 380 def normalvariate(self, mu, sigma):
383 mu is the mean, and sigma is the standard deviation.
386 # mu = mean, sigma = standard deviation
401 return mu + z*sigma
405 def lognormvariate(self, mu, sigma):
409 normal distribution with mean mu and standard deviation sigma.
410 mu can have any value, and sigma must be greater than zero.
413 return _exp(self.normalvariate(mu, sigma))
556 def gauss(self, mu, sigma):
559 mu is the mean, and sigma is the standard deviation. This i
[all...] |
| htmlentitydefs.py | 51 'Sigma': 0x03a3, # greek capital letter sigma, U+03A3 ISOgrk3
218 'sigma': 0x03c3, # greek small letter sigma, U+03C3 ISOgrk3
219 'sigmaf': 0x03c2, # greek small letter final sigma, U+03C2 ISOgrk3
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/ |
| random.py | 380 def normalvariate(self, mu, sigma):
383 mu is the mean, and sigma is the standard deviation.
386 # mu = mean, sigma = standard deviation
401 return mu + z*sigma
405 def lognormvariate(self, mu, sigma):
409 normal distribution with mean mu and standard deviation sigma.
410 mu can have any value, and sigma must be greater than zero.
413 return _exp(self.normalvariate(mu, sigma))
556 def gauss(self, mu, sigma):
559 mu is the mean, and sigma is the standard deviation. This i
[all...] |
| htmlentitydefs.py | 51 'Sigma': 0x03a3, # greek capital letter sigma, U+03A3 ISOgrk3
218 'sigma': 0x03c3, # greek small letter sigma, U+03C3 ISOgrk3
219 'sigmaf': 0x03c2, # greek small letter final sigma, U+03C2 ISOgrk3
|
| /external/chromium_org/chrome/browser/media_galleries/fileapi/ |
| media_path_filter.cc | 42 FILE_PATH_LITERAL("x3f"), // (Sigma - image/x-x3f)
|
| /external/chromium_org/chrome/browser/thumbnails/ |
| content_analysis.h | 20 // Compute in-place gaussian gradient magnitude of |input_bitmap| with sigma
|
| /external/chromium_org/third_party/icu/source/i18n/ |
| casetrn.cpp | 94 // TODO test incremental mode with context-sensitive text (e.g. greek sigma)
|
| /external/chromium_org/v8/ |
| AUTHORS | 7 Sigma Designs Inc.
|
| /external/icu4c/i18n/ |
| casetrn.cpp | 96 // TODO test incremental mode with context-sensitive text (e.g. greek sigma)
|
| /external/opencv/cv/src/ |
| cvthresh.cpp | 258 double p_i, q2, mu2, val_i, sigma; local
275 sigma = q1*q2*(mu1 - mu2)*(mu1 - mu2);
276 if( sigma > max_sigma )
278 max_sigma = sigma;
|
| /external/tcpdump/ |
| ppp.h | 44 #define PPP_SNS 0x0233 /* Sigma Network Systems */
|
| /packages/inputmethods/LatinIME/java/res/xml/ |
| rowkeys_greek1.xml | 38 <!-- U+03C2: "?" GREEK SMALL LETTER FINAL SIGMA -->
|
| /external/libvpx/libvpx/vp9/common/ |
| vp9_postproc.c | 374 double vp9_gaussian(double sigma, double mu, double x) {
375 return 1 / (sigma * sqrt(2.0 * 3.14159265)) *
376 (exp(-(x - mu) * (x - mu) / (2 * sigma * sigma)));
382 double sigma; local
387 sigma = ai + .5 + .6 * (63 - qi) / 63.0;
390 * a gaussian distribution with sigma determined by q.
399 int a = (int)(.5 + 256 * vp9_gaussian(sigma, 0, i));
|
| /external/skia/src/effects/ |
| SkBlurMaskFilter.cpp | 490 SkScalar sigma = SkScalarMul(fRadius, kBLUR_SIGMA_SCALE); local
491 gpuPad = sigma * 3.0f;
556 // Outset srcRect and clipRect by 3 * sigma, to compute affected blur area.
577 float sigma = SkScalarToFloat(xformedRadius) * kBLUR_SIGMA_SCALE; local
583 clipRect, false, sigma, sigma);
|
| /external/openfst/src/include/fst/ |
| matcher.h | 36 // such as 'sigma' (all), 'rho' (rest), or 'phi' (fail).
557 // that match the label 'sigma_label' (sigma = "any"), and this in
558 // additions to transitions with the requested label. Each such sigma
631 FSTERROR() << "SigmaMatcher::Find: bad label (sigma)";
701 Label sigma_label_; // Label that represents the sigma transition
704 Label sigma_match_; // Current label that matches sigma transition
705 mutable Arc sigma_arc_; // Arc to return when sigma match
[all...] |
| /prebuilts/python/darwin-x86/2.7.5/lib/python2.7/encodings/ |
| cp737.py | 65 0x0091: 0x03a3, # GREEK CAPITAL LETTER SIGMA
89 0x00a9: 0x03c3, # GREEK SMALL LETTER SIGMA
90 0x00aa: 0x03c2, # GREEK SMALL LETTER FINAL SIGMA
326 u'\u03a3' # 0x0091 -> GREEK CAPITAL LETTER SIGMA
350 u'\u03c3' # 0x00a9 -> GREEK SMALL LETTER SIGMA
351 u'\u03c2' # 0x00aa -> GREEK SMALL LETTER FINAL SIGMA
600 0x03a3: 0x0091, # GREEK CAPITAL LETTER SIGMA
630 0x03c2: 0x00aa, # GREEK SMALL LETTER FINAL SIGMA
631 0x03c3: 0x00a9, # GREEK SMALL LETTER SIGMA
[all...] |
| cp869.py | 127 0x00cf: 0x03a3, # GREEK CAPITAL LETTER SIGMA
156 0x00ec: 0x03c3, # GREEK SMALL LETTER SIGMA
157 0x00ed: 0x03c2, # GREEK SMALL LETTER FINAL SIGMA
388 u'\u03a3' # 0x00cf -> GREEK CAPITAL LETTER SIGMA
417 u'\u03c3' # 0x00ec -> GREEK SMALL LETTER SIGMA
418 u'\u03c2' # 0x00ed -> GREEK SMALL LETTER FINAL SIGMA
613 0x03a3: 0x00cf, # GREEK CAPITAL LETTER SIGMA
644 0x03c2: 0x00ed, # GREEK SMALL LETTER FINAL SIGMA
645 0x03c3: 0x00ec, # GREEK SMALL LETTER SIGMA
[all...] |
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/encodings/ |
| cp737.py | 65 0x0091: 0x03a3, # GREEK CAPITAL LETTER SIGMA
89 0x00a9: 0x03c3, # GREEK SMALL LETTER SIGMA
90 0x00aa: 0x03c2, # GREEK SMALL LETTER FINAL SIGMA
326 u'\u03a3' # 0x0091 -> GREEK CAPITAL LETTER SIGMA
350 u'\u03c3' # 0x00a9 -> GREEK SMALL LETTER SIGMA
351 u'\u03c2' # 0x00aa -> GREEK SMALL LETTER FINAL SIGMA
600 0x03a3: 0x0091, # GREEK CAPITAL LETTER SIGMA
630 0x03c2: 0x00aa, # GREEK SMALL LETTER FINAL SIGMA
631 0x03c3: 0x00a9, # GREEK SMALL LETTER SIGMA
[all...] |
| cp869.py | 127 0x00cf: 0x03a3, # GREEK CAPITAL LETTER SIGMA
156 0x00ec: 0x03c3, # GREEK SMALL LETTER SIGMA
157 0x00ed: 0x03c2, # GREEK SMALL LETTER FINAL SIGMA
388 u'\u03a3' # 0x00cf -> GREEK CAPITAL LETTER SIGMA
417 u'\u03c3' # 0x00ec -> GREEK SMALL LETTER SIGMA
418 u'\u03c2' # 0x00ed -> GREEK SMALL LETTER FINAL SIGMA
613 0x03a3: 0x00cf, # GREEK CAPITAL LETTER SIGMA
644 0x03c2: 0x00ed, # GREEK SMALL LETTER FINAL SIGMA
645 0x03c3: 0x00ec, # GREEK SMALL LETTER SIGMA
[all...] |
| /prebuilts/ndk/9/sources/cxx-stl/gnu-libstdc++/4.8/include/ext/ |
| random | 608 * p(\overrightarrow{x}|\overrightarrow{\mu },\Sigma) =
609 * \frac{1}{\sqrt{(2\pi )^k\det(\Sigma))}}
611 * \Sigma ^{-1}(\overrightarrow{x}-\overrightarrow{\mu})}
615 * vectors of dimension @f$k@f$ and @f$\Sigma@f$ is the covariance
913 * p(x|\nu,\sigma) = \frac{x}{\sigma^2}
914 * \exp\left(-\frac{x^2+\nu^2}{2\sigma^2}\right)
915 * I_0\left(\frac{x \nu}{\sigma^2}\right)
918 * of order 0 and @f$\nu >= 0@f$ and @f$\sigma > 0@f$.
922 * <tr><td>Mean</td><td>@f$\sqrt{\pi/2}L_{1/2}(-\nu^2/2\sigma^2)@f$</td></tr
[all...] |
