HomeSort by relevance Sort by last modified time
    Searched refs:sigma (Results 1 - 25 of 56) sorted by null

1 2 3

  /external/iproute2/netem/
normal.c 18 normal(double x, double mu, double sigma)
20 return .5 + .5*erf((x-mu)/(sqrt(2.0)*sigma));
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));*/
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);
paretonormal.c 27 normal(double x, double mu, double sigma)
29 return .5 + .5*erf((x-mu)/(sqrt(2.0)*sigma));
  /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); \
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));
  /external/skia/legacy/include/effects/
SkBlurImageFilter.h 18 virtual bool asABlur(SkSize* sigma) const SK_OVERRIDE;
  /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());
  /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;
  /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
  /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;
  /frameworks/native/services/sensorservice/
Fusion.h 79 void update(const vec3_t& z, const vec3_t& Bi, float sigma);
  /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)
  /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}}
  /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);
  /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/srec/srec/include/
pre_desc.h 52 unsigned short sigma; member in struct:__anon15442
  /external/ceres-solver/examples/
bal_problem.cc 56 void PerturbPoint3(const double sigma, double* point) {
58 point[i] += RandNormal() * sigma;

Completed in 714 milliseconds

1 2 3