/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 [all...] |
/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 [all...] |
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
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/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) [all...] |
/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 [all...] |
/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 [all...] |
/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]);
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/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);
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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) { [all...] |
/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);
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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) { [all...] |
/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) [all...] |
/external/skia/gm/ |
spritebitmap.cpp | 74 SkScalar sigma = 8; variable 75 sk_sp<SkImageFilter> filter(SkBlurImageFilter::Make(sigma, sigma, nullptr));
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/external/skqp/gm/ |
spritebitmap.cpp | 74 SkScalar sigma = 8; variable 75 sk_sp<SkImageFilter> filter(SkBlurImageFilter::Make(sigma, sigma, nullptr));
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/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);
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/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);
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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);
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/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);
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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);
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/external/tensorflow/tensorflow/core/grappler/costs/ |
op_performance_data.proto | 53 double sigma = 2; 58 double sigma = 2;
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/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,
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/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 [all...] |
/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);
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/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);
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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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