/external/iproute2/netem/ |
normal.c | 18 normal(double x, double mu, double sigma) 20 return .5 + .5*erf((x-mu)/(sqrt(2.0)*sigma));
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stats.c | 24 double mu=0.0, sigma=0.0, sumsquare=0.0, sum=0.0, top=0.0, rho=0.0; local 44 sigma = sqrt((sumsquare - (double)n*mu*mu)/(double)(n-1)); 54 printf("sigma = %12.6f\n", sigma); 57 /*printf("correlation rho = %10.6f\n", top/((double)(n-1)*sigma*sigma));*/
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maketable.c | 51 arraystats(double *x, int limit, double *mu, double *sigma, double *rho) 63 *sigma = sqrt((sumsquare - (double)n*(*mu)*(*mu))/(double)(n-1)); 93 makedist(double *x, int limit, double mu, double sigma) 107 input = (x[i]-mu)/sigma; 200 double mu, sigma, rho; local 219 arraystats(x, limit, &mu, &sigma, &rho); 221 fprintf(stderr, "%d values, mu %10.4f, sigma %10.4f, rho %10.4f\n", 222 limit, mu, sigma, rho); 225 table = makedist(x, limit, mu, sigma);
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paretonormal.c | 27 normal(double x, double mu, double sigma) 29 return .5 + .5*erf((x-mu)/(sqrt(2.0)*sigma));
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/external/eigen/Eigen/src/Cholesky/ |
LLT_MKL.h | 75 static typename MatrixType::Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \ 76 { return Eigen::internal::llt_rank_update_lower(mat, vec, sigma); } \ 86 static typename MatrixType::Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \ 89 return llt_inplace<EIGTYPE, Lower>::rankUpdate(matt, vec.conjugate(), sigma); \
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LLT.h | 174 LLT rankUpdate(const VectorType& vec, const RealScalar& sigma = 1); 191 static typename MatrixType::Index llt_rank_update_lower(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) 207 if(sigma>0) 211 // i.e., for sigma > 0 212 temp = sqrt(sigma) * vec; 237 RealScalar swj2 = sigma*abs2(wj); 253 mat.col(j).tail(rs) = (nLjj/Ljj) * mat.col(j).tail(rs) + (nLjj * sigma*conj(wj)/gamma)*temp.tail(rs); 323 static typename MatrixType::Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma) 325 return Eigen::internal::llt_rank_update_lower(mat, vec, sigma); 346 static typename MatrixType::Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma) [all...] |
LDLT.h | 102 * \sa rankUpdate(w,sigma) 342 static bool updateInPlace(MatrixType& mat, MatrixBase<WDerived>& w, typename MatrixType::RealScalar sigma=1) 364 RealScalar swj2 = sigma*abs2(wj); 375 mat.col(j).tail(rs) += (sigma*conj(wj)/gamma)*w.tail(rs); 381 static bool update(MatrixType& mat, const TranspositionType& transpositions, Workspace& tmp, const WType& w, typename MatrixType::RealScalar sigma=1) 386 return ldlt_inplace<Lower>::updateInPlace(mat,tmp,sigma); 400 static EIGEN_STRONG_INLINE bool update(MatrixType& mat, TranspositionType& transpositions, Workspace& tmp, WType& w, typename MatrixType::RealScalar sigma=1) 403 return ldlt_inplace<Lower>::update(matt, transpositions, tmp, w.conjugate(), sigma); 445 /** Update the LDLT decomposition: given A = L D L^T, efficiently compute the decomposition of A + sigma w w^T. 447 * \param sigma a scalar, +1 for updates and -1 for "downdates," which correspond to removing previously-added col (…) [all...] |
/external/skia/gm/ |
spritebitmap.cpp | 81 SkScalar sigma = 8; local 82 SkAutoTUnref<SkImageFilter> filter(new SkBlurImageFilter(sigma, sigma));
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/external/skia/legacy/include/effects/ |
SkBlurImageFilter.h | 18 virtual bool asABlur(SkSize* sigma) const SK_OVERRIDE;
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/frameworks/rs/java/tests/ImageProcessing/src/com/android/rs/image/ |
threshold.fs | 39 // g(x) = ( 1 / sqrt( 2 * pi ) * sigma) * e ^ ( -x^2 / 2 * sigma^2 ) 41 // and sigma varies with radius. 42 // Based on some experimental radius values and sigma's 43 // we approximately fit sigma = f(radius) as 44 // sigma = radius * 0.4 + 0.6 46 // will resemble a box blur since with large sigma 48 float sigma = 0.4f * (float)radius + 0.6f; 54 float coeff1 = 1.0f / (sqrt( 2.0f * pi ) * sigma); 55 float coeff2 = - 1.0f / (2.0f * sigma * sigma) [all...] |
/frameworks/rs/java/tests/ImageProcessing2/src/com/android/rs/image/ |
threshold.fs | 39 // g(x) = ( 1 / sqrt( 2 * pi ) * sigma) * e ^ ( -x^2 / 2 * sigma^2 ) 41 // and sigma varies with radius. 42 // Based on some experimental radius values and sigma's 43 // we approximately fit sigma = f(radius) as 44 // sigma = radius * 0.4 + 0.6 46 // will resemble a box blur since with large sigma 48 float sigma = 0.4f * (float)radius + 0.6f; 54 float coeff1 = 1.0f / (sqrt( 2.0f * pi ) * sigma); 55 float coeff2 = - 1.0f / (2.0f * sigma * sigma) [all...] |
/frameworks/rs/java/tests/ImageProcessing_jb/src/com/android/rs/image/ |
threshold.fs | 39 // g(x) = ( 1 / sqrt( 2 * pi ) * sigma) * e ^ ( -x^2 / 2 * sigma^2 ) 41 // and sigma varies with radius. 42 // Based on some experimental radius values and sigma's 43 // we approximately fit sigma = f(radius) as 44 // sigma = radius * 0.4 + 0.6 46 // will resemble a box blur since with large sigma 48 float sigma = 0.4f * (float)radius + 0.6f; 54 float coeff1 = 1.0f / (sqrt( 2.0f * pi ) * sigma); 55 float coeff2 = - 1.0f / (2.0f * sigma * sigma) [all...] |
/external/eigen/test/eigen2/ |
eigen2_svd.cpp | 34 MatrixType sigma = MatrixType::Zero(rows,cols); local 36 sigma.block(0,0,cols,cols) = svd.singularValues().asDiagonal(); 38 VERIFY_IS_APPROX(a, matU * sigma * svd.matrixV().transpose());
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/external/skia/legacy/include/core/ |
SkImageFilter.h | 79 * set the sigma to the values for horizontal and vertical. 81 virtual bool asABlur(SkSize* sigma) const;
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/frameworks/base/libs/hwui/utils/ |
Blur.cpp | 31 // g(x) = ( 1 / sqrt( 2 * pi ) * sigma) * e ^ ( -x^2 / 2 * sigma^2 ) 33 // and sigma varies with radius. 34 // Based on some experimental radius values and sigma's 35 // we approximately fit sigma = f(radius) as 36 // sigma = radius * 0.3 + 0.6 38 // will resemble a box blur since with large sigma 40 float sigma = 0.3f * (float) radius + 0.6f; local 46 float coeff1 = 1.0f / (sqrt(2.0f * pi) * sigma); 47 float coeff2 = - 1.0f / (2.0f * sigma * sigma) [all...] |
/external/v8/test/mjsunit/ |
cyrillic.js | 42 var SIGMA = "\u03a3"; 43 var sigma = "\u03c3"; variable 52 MIDDLE: SIGMA, // SIGMA 53 middle: sigma, // sigma 134 // Sigma is special because there are two lower case versions of the same upper 136 // convert everything to upper case, so the two sigma variants are equal to each 141 var regex = simple ? SIGMA : "[" + SIGMA + "]" [all...] |
regexp-UC16.js | 30 // "\u03a3\u03c2\u03c3\u039b\u03bb" - Sigma, final sigma, sigma, Lambda, lamda
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/external/eigen/Eigen/src/Geometry/ |
Umeyama.h | 130 const MatrixType sigma = one_over_n * dst_demean * src_demean.transpose(); local 132 JacobiSVD<MatrixType> svd(sigma, ComputeFullU | ComputeFullV); 139 if (sigma.determinant()<0) S(m-1) = -1;
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/frameworks/native/services/sensorservice/ |
Fusion.h | 79 void update(const vec3_t& z, const vec3_t& Bi, float sigma);
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/packages/inputmethods/LatinIME/native/jni/src/ |
proximity_info_utils.h | 119 // Normal distribution N(u, sigma^2). 122 NormalDistribution(const float u, const float sigma) 123 : mU(u), mSigma(sigma), 124 mPreComputedNonExpPart(1.0f / sqrtf(2.0f * M_PI_F * SQUARE_FLOAT(sigma))), 125 mPreComputedExponentPart(-1.0f / (2.0f * SQUARE_FLOAT(sigma))) {} 136 const float mPreComputedNonExpPart; // = 1 / sqrt(2 * PI * sigma^2) 137 const float mPreComputedExponentPart; // = -1 / (2 * sigma^2)
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/external/ceres-solver/docs/ |
curvefitting.tex | 5 \texttt{examples/data\_fitting.cc}. It contains data generated by sampling the curve $y = e^{0.3x + 0.1}$ and adding Gaussian noise with standard deviation $\sigma = 0.2$.}. Let us fit some data to the curve 73 \caption{Least squares data fitting to the curve $y = e^{0.3x + 0.1}$. Observations were generated by sampling this curve uniformly in the interval $x=(0,5)$ and adding Gaussian noise with $\sigma = 0.2$.\label{fig:exponential}}
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/external/eigen/test/ |
cholesky.cpp | 45 RealScalar sigma = internal::random<RealScalar>(); local 46 symmCpy += sigma * vec * vec.adjoint(); 53 chollo.rankUpdate(vec, sigma); 56 cholup.rankUpdate(vec, sigma);
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/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;
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/external/srec/srec/include/ |
pre_desc.h | 52 unsigned short sigma; member in struct:__anon15442
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/external/ceres-solver/examples/ |
bal_problem.cc | 56 void PerturbPoint3(const double sigma, double* point) { 58 point[i] += RandNormal() * sigma;
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