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  /external/skqp/src/core/
SkBlurImageFilter.cpp 61 SkVector sigma, const sk_sp<SkSpecialImage> &input,
89 // the limit on sigma ensures consistent behaviour between the GPU and
94 SkVector sigma = SkVector::Make(localSigma.width(), localSigma.height()); local
95 ctm.mapVectors(&sigma, 1);
96 sigma.fX = SkMinScalar(SkScalarAbs(sigma.fX), MAX_SIGMA);
97 sigma.fY = SkMinScalar(SkScalarAbs(sigma.fY), MAX_SIGMA);
98 return sigma;
154 static int calculate_window(double sigma) {
593 const SkVector sigma = map_sigma(fSigma, ctx.ctm()); local
699 SkVector sigma = map_sigma(fSigma, ctm); local
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  /frameworks/base/libs/hwui/utils/
Blur.cpp 33 float Blur::convertSigmaToRadius(float sigma) {
34 return sigma > 0.5f ? (sigma - 0.5f) / BLUR_SIGMA_SCALE : 0.0f;
50 * for sigma and to preserve compatibility we have kept that logic.
52 * Based on some experimental radius and sigma values we approximate the
53 * equation sigma = f(radius) as sigma = radius * 0.3 + 0.6. The larger the
55 * large sigma the gaussian curve begins to lose its shape.
68 // g(x) = ( 1 / sqrt( 2 * pi ) * sigma) * e ^ ( -x^2 / 2 * sigma^2
71 float sigma = legacyConvertRadiusToSigma(radius); local
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Blur.h 28 // If radius > 0, return the corresponding sigma, else return 0
30 // If sigma > 0.5, return the corresponding radius, else return 0
31 ANDROID_API static float convertSigmaToRadius(float sigma);
32 // If the original radius was on an integer boundary then after the sigma to
  /external/tensorflow/tensorflow/python/kernel_tests/distributions/
normal_test.py 68 sigma = array_ops.ones(sigma_shape)
71 array_ops.shape(normal_lib.Normal(mu, sigma).sample()).eval())
102 sigma = constant_op.constant([math.sqrt(10.0)] * batch_size)
104 normal = normal_lib.Normal(loc=mu, scale=sigma)
122 expected_log_pdf = stats.norm(mu.eval(), sigma.eval()).logpdf(x)
130 sigma = constant_op.constant([[math.sqrt(10.0), math.sqrt(15.0)]] *
133 normal = normal_lib.Normal(loc=mu, scale=sigma)
155 expected_log_pdf = stats.norm(mu.eval(), sigma.eval()).logpdf(x)
163 sigma = self._rng.rand(batch_size) + 1.0
166 normal = normal_lib.Normal(loc=mu, scale=sigma)
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  /external/skia/tests/
BlurTest.cpp 110 SkScalar sigma = SkBlurMask::ConvertRadiusToSigma(SkIntToScalar(5)); local
116 paint.setMaskFilter(SkBlurMaskFilter::Make(blurStyle, sigma, flags));
160 SkScalar sigma,
171 if (!SkBlurMask::BlurGroundTruth(sigma, &dst, src, kNormal_SkBlurStyle)) {
200 // Implement a Gaussian function with 0 mean and std.dev. of 'sigma'.
201 static float gaussian(int x, SkScalar sigma) {
202 float k = SK_Scalar1/(sigma * sqrtf(2.0f*SK_ScalarPI));
203 float exponent = -(x * x) / (2 * sigma * sigma);
316 SkScalar sigma = 10.0f local
452 const SkScalar sigma = sigmas[j]; local
485 const SkScalar sigma = sigmas[j]; local
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  /external/skqp/tests/
BlurTest.cpp 109 SkScalar sigma = SkBlurMask::ConvertRadiusToSigma(SkIntToScalar(5)); local
115 paint.setMaskFilter(SkBlurMaskFilter::Make(blurStyle, sigma, flags));
159 SkScalar sigma,
170 if (!SkBlurMask::BlurGroundTruth(sigma, &dst, src, kNormal_SkBlurStyle)) {
199 // Implement a Gaussian function with 0 mean and std.dev. of 'sigma'.
200 static float gaussian(int x, SkScalar sigma) {
201 float k = SK_Scalar1/(sigma * sqrtf(2.0f*SK_ScalarPI));
202 float exponent = -(x * x) / (2 * sigma * sigma);
315 SkScalar sigma = 10.0f local
451 const SkScalar sigma = sigmas[j]; local
484 const SkScalar sigma = sigmas[j]; local
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  /external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/direct/
NelderMead.java 45 private final double sigma; field in class:NelderMead
49 * for both gamma and sigma.</p>
55 this.sigma = 0.5;
62 * @param sigma shrinkage coefficient
65 final double gamma, final double sigma) {
69 this.sigma = sigma;
171 x[j] = xSmallest[j] + sigma * (x[j] - xSmallest[j]);
  /external/skia/src/effects/
SkEmbossMaskFilter.cpp 75 SkScalar sigma = matrix.mapRadius(fBlurSigma); local
77 if (!SkBlurMask::BoxBlur(dst, src, sigma, kInner_SkBlurStyle, kLow_SkBlurQuality)) {
83 margin->set(SkScalarCeilToInt(3*sigma), SkScalarCeilToInt(3*sigma));
127 const SkScalar sigma = buffer.readScalar(); local
128 return Make(sigma, light);
SkBlurMask.cpp 30 SkScalar SkBlurMask::ConvertSigmaToRadius(SkScalar sigma) {
31 return sigma > 0.5f ? (sigma - 0.5f) / kBLUR_SIGMA_SCALE : 0.0f;
99 SkScalar sigma, SkBlurStyle style, SkBlurQuality quality,
108 SkMaskBlurFilter blurFilter{sigma, sigma};
220 void SkBlurMask::ComputeBlurProfile(uint8_t* profile, int size, SkScalar sigma) {
221 SkASSERT(SkScalarCeilToInt(6*sigma) == size);
225 float invr = 1.f/(2*sigma);
255 unsigned int width, SkScalar sigma) {
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  /external/skqp/src/effects/
SkEmbossMaskFilter.cpp 62 SkScalar sigma = matrix.mapRadius(fBlurSigma); local
64 if (!SkBlurMask::BoxBlur(dst, src, sigma, kInner_SkBlurStyle, kLow_SkBlurQuality)) {
70 margin->set(SkScalarCeilToInt(3*sigma), SkScalarCeilToInt(3*sigma));
114 const SkScalar sigma = buffer.readScalar(); local
115 return Make(sigma, light);
SkBlurMask.cpp 29 SkScalar SkBlurMask::ConvertSigmaToRadius(SkScalar sigma) {
30 return sigma > 0.5f ? (sigma - 0.5f) / kBLUR_SIGMA_SCALE : 0.0f;
98 SkScalar sigma, SkBlurStyle style, SkBlurQuality quality,
107 SkMaskBlurFilter blurFilter{sigma, sigma};
222 uint8_t* SkBlurMask::ComputeBlurProfile(SkScalar sigma) {
223 int size = SkScalarCeilToInt(6*sigma);
228 float invr = 1.f/(2*sigma);
258 unsigned int width, SkScalar sigma) {
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  /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/
mvn_tril_test.py 44 sigma = math_ops.matmul(chol, chol, adjoint_b=True)
45 return chol.eval(), sigma.eval()
50 chol, sigma = self._random_chol(2, 2)
58 scipy_mvn = stats.multivariate_normal(mean=mu, cov=sigma)
70 chol, sigma = self._random_chol(2, 2)
78 scipy_mvn = stats.multivariate_normal(mean=mu, cov=sigma)
90 chol, sigma = self._random_chol(3, 2, 2)
103 scipy_mvn = stats.multivariate_normal(mean=mu[1, :], cov=sigma[1, :, :])
113 chol, sigma = self._random_chol(2, 2)
118 scipy_mvn = stats.multivariate_normal(mean=mu, cov=sigma)
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  /external/skia/gm/
spritebitmap.cpp 74 SkScalar sigma = 8; variable
75 sk_sp<SkImageFilter> filter(SkBlurImageFilter::Make(sigma, sigma, nullptr));
  /external/skqp/gm/
spritebitmap.cpp 74 SkScalar sigma = 8; variable
75 sk_sp<SkImageFilter> filter(SkBlurImageFilter::Make(sigma, sigma, nullptr));
  /external/apache-commons-math/src/main/java/org/apache/commons/math/random/
RandomData.java 177 * <li><code>sigma > 0</code> (otherwise an IllegalArgumentException
181 * @param sigma Standard deviation of the distribution
183 * standard deviation = sigma
185 double nextGaussian(double mu, double sigma);
  /external/skia/src/gpu/effects/
GrRRectBlurEffect.fp 8 in float sigma;
101 static std::unique_ptr<GrFragmentProcessor> Make(GrContext* context, float sigma,
108 std::unique_ptr<GrFragmentProcessor> GrRRectBlurEffect::Make(GrContext* context, float sigma,
119 // Make sure we can successfully ninepatch this rrect -- the blur sigma has to be
130 sigma, xformedSigma,
156 SkScalar sigma = d->fRandom->nextRangeF(1.f,10.f);
159 return GrRRectBlurEffect::Make(d->context(), sigma, sigma, rrect, rrect);
189 float blurRadiusValue = 3.f * SkScalarCeilToScalar(sigma - 1 / 6.0f);
GrRectBlurEffect.cpp 28 auto sigma = _outer.sigma(); variable
29 (void)sigma; variable
115 auto sigma = _outer.sigma(); variable
116 (void)sigma; variable
127 pdman.set1f(profileSize, SkScalarCeilToScalar(6 * sigma));
161 float sigma = data->fRandom->nextRangeF(3, 8); local
164 return GrRectBlurEffect::Make(data->proxyProvider(), SkRect::MakeWH(width, height), sigma);
  /external/skqp/src/gpu/effects/
GrRRectBlurEffect.fp 8 in float sigma;
100 static std::unique_ptr<GrFragmentProcessor> Make(GrContext* context, float sigma,
107 std::unique_ptr<GrFragmentProcessor> GrRRectBlurEffect::Make(GrContext* context, float sigma,
118 // Make sure we can successfully ninepatch this rrect -- the blur sigma has to be
129 sigma, xformedSigma,
155 SkScalar sigma = d->fRandom->nextRangeF(1.f,10.f);
158 return GrRRectBlurEffect::Make(d->context(), sigma, sigma, rrect, rrect);
188 float blurRadiusValue = 3.f * SkScalarCeilToScalar(sigma - 1 / 6.0f);
GrRectBlurEffect.cpp 28 auto sigma = _outer.sigma(); variable
29 (void)sigma; variable
115 auto sigma = _outer.sigma(); variable
116 (void)sigma; variable
127 pdman.set1f(profileSize, SkScalarCeilToScalar(6 * sigma));
161 float sigma = data->fRandom->nextRangeF(3, 8); local
164 return GrRectBlurEffect::Make(data->proxyProvider(), SkRect::MakeWH(width, height), sigma);
  /external/tensorflow/tensorflow/core/grappler/costs/
op_performance_data.proto 53 double sigma = 2;
58 double sigma = 2;
  /frameworks/base/core/jni/android/graphics/
MaskFilter.cpp 25 SkScalar sigma = SkBlurMask::ConvertRadiusToSigma(radius); local
26 SkMaskFilter* filter = SkBlurMaskFilter::Make((SkBlurStyle)blurStyle, sigma).release();
40 SkScalar sigma = SkBlurMask::ConvertRadiusToSigma(radius); local
41 SkMaskFilter* filter = SkBlurMaskFilter::MakeEmboss(sigma,
  /external/tensorflow/tensorflow/python/ops/
math_grad_test.py 61 def _biasedRandN(self, shape, bias=0.1, sigma=1.0):
63 value = np.random.randn(*shape) * sigma
66 def _testGrad(self, shape, dtype=None, max_error=None, bias=None, sigma=None):
71 shape, bias=bias, sigma=sigma),
73 shape, bias=bias, sigma=sigma))
88 [3, 3], dtype=dtypes.float32, max_error=2e-5, bias=0.1, sigma=1.0)
90 [3, 3], dtype=dtypes.complex64, max_error=2e-5, bias=0.1, sigma=1.0)
94 [3, 3], dtype=dtypes.float32, max_error=100.0, bias=0.0, sigma=0.1
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  /cts/tests/tests/renderscript/src/android/renderscript/cts/
intrinsic_blur.rs 55 float sigma = 0.4f * (float)radius + 0.6f;
56 float coeff1 = 1.0f / (sqrt( 2.0f * pi ) * sigma);
57 float coeff2 = - 1.0f / (2.0f * sigma * sigma);
  /external/iproute2/netem/
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;
202 double mu, sigma, rho; local
221 arraystats(x, limit, &mu, &sigma, &rho);
223 fprintf(stderr, "%d values, mu %10.4f, sigma %10.4f, rho %10.4f\n",
224 limit, mu, sigma, rho);
227 table = makedist(x, limit, mu, sigma);
paretonormal.c 26 normal(double x, double mu, double sigma)
28 return .5 + .5*erf((x-mu)/(sqrt(2.0)*sigma));

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