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/external/tensorflow/tensorflow/python/kernel_tests/distributions/
Dgamma_test.py30 from tensorflow.python.ops.distributions import gamma as gamma_lib
55 gamma = gamma_lib.Gamma(concentration=alpha, rate=beta)
57 self.assertEqual(self.evaluate(gamma.batch_shape_tensor()), (5,))
58 self.assertEqual(gamma.batch_shape, tensor_shape.TensorShape([5]))
59 self.assertAllEqual(self.evaluate(gamma.event_shape_tensor()), [])
60 self.assertEqual(gamma.event_shape, tensor_shape.TensorShape([]))
69 gamma = gamma_lib.Gamma(concentration=alpha, rate=beta)
70 log_pdf = gamma.log_prob(x)
72 pdf = gamma.prob(x)
76 expected_log_pdf = stats.gamma.logpdf(x, alpha_v, scale=1 / beta_v)
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/external/python/cpython2/Lib/test/
Dmath_testcases.txt170 -- lgamma: log of absolute value of the gamma function --
250 -- inputs for which gamma(x) is tiny
275 -- gamma: Gamma function --
279 gam0000 gamma 0.0 -> inf divide-by-zero
280 gam0001 gamma -0.0 -> -inf divide-by-zero
281 gam0002 gamma inf -> inf
282 gam0003 gamma -inf -> nan invalid
283 gam0004 gamma nan -> nan
286 gam0010 gamma -1 -> nan invalid
287 gam0011 gamma -2 -> nan invalid
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/external/ImageMagick/MagickCore/
Dcomposite-private.h57 gamma, in CompositePixelOver() local
68 gamma=Sa+Da-Sa*Da; in CompositePixelOver()
69 gamma=PerceptibleReciprocal(gamma); in CompositePixelOver()
86 composite[i]=ClampToQuantum(gamma*MagickOver_((double) p->red,alpha, in CompositePixelOver()
92 composite[i]=ClampToQuantum(gamma*MagickOver_((double) p->green,alpha, in CompositePixelOver()
98 composite[i]=ClampToQuantum(gamma*MagickOver_((double) p->blue,alpha, in CompositePixelOver()
104 composite[i]=ClampToQuantum(gamma*MagickOver_((double) p->black,alpha, in CompositePixelOver()
127 gamma, in CompositePixelInfoOver() local
135 gamma=Sa+Da-Sa*Da; in CompositePixelInfoOver()
136 composite->alpha=(double) QuantumRange*RoundToUnity(gamma); in CompositePixelInfoOver()
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Dcomposite.c383 gamma; in CompositeOverImage() local
497 gamma=PerceptibleReciprocal(alpha); in CompositeOverImage()
498 pixel=QuantumRange*gamma*(Sca+Dca*(1.0-Sa)); in CompositeOverImage()
1291 gamma; local
1677 gamma=PerceptibleReciprocal(1.0-alpha);
1682 gamma=PerceptibleReciprocal(alpha);
1702 pixel=gamma*(source_dissolve*Sa*Sc+canvas_dissolve*Da*Dc);
1743 pixel=QuantumRange*gamma*(Sa*Da+Dca*(1.0-Sa));
1748 pixel=QuantumRange*gamma*(Dca*(1.0-Sa));
1751 pixel=QuantumRange*gamma*(Sa*Da-Sa*Da*MagickMin(1.0,(1.0-DcaDa)*
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Dpixel.c4429 gamma; in CatromWeights() local
4446 gamma=(*weights)[3]-(*weights)[0]; in CatromWeights()
4447 (*weights)[1]=alpha-(*weights)[0]+gamma; in CatromWeights()
4448 (*weights)[2]=x-(*weights)[3]-gamma; in CatromWeights()
4493 gamma, in InterpolatePixelChannel() local
4572 gamma=PerceptibleReciprocal(alpha[i])/count; in InterpolatePixelChannel()
4573 *pixel+=gamma*pixels[i]; in InterpolatePixelChannel()
4607 gamma=((epsilon.y*(epsilon.x*alpha[0]+delta.x*alpha[1])+delta.y* in InterpolatePixelChannel()
4609 gamma=PerceptibleReciprocal(gamma); in InterpolatePixelChannel()
4610 *pixel=gamma*(epsilon.y*(epsilon.x*pixels[0]+delta.x*pixels[1])+delta.y* in InterpolatePixelChannel()
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/external/python/cpython3/Lib/test/
Dmath_testcases.txt170 -- lgamma: log of absolute value of the gamma function --
250 -- inputs for which gamma(x) is tiny
275 -- gamma: Gamma function --
279 gam0000 gamma 0.0 -> inf divide-by-zero
280 gam0001 gamma -0.0 -> -inf divide-by-zero
281 gam0002 gamma inf -> inf
282 gam0003 gamma -inf -> nan invalid
283 gam0004 gamma nan -> nan
286 gam0010 gamma -1 -> nan invalid
287 gam0011 gamma -2 -> nan invalid
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/external/skia/src/core/
DSkMaskGamma.cpp16 SkScalar toLuma(SkScalar SkDEBUGCODE(gamma), SkScalar luminance) const override { in toLuma() argument
17 SkASSERT(SK_Scalar1 == gamma); in toLuma()
20 SkScalar fromLuma(SkScalar SkDEBUGCODE(gamma), SkScalar luma) const override { in fromLuma() argument
21 SkASSERT(SK_Scalar1 == gamma); in fromLuma()
27 SkScalar toLuma(SkScalar gamma, SkScalar luminance) const override { in toLuma() argument
28 return SkScalarPow(luminance, gamma); in toLuma()
30 SkScalar fromLuma(SkScalar gamma, SkScalar luma) const override { in fromLuma() argument
31 return SkScalarPow(luma, SkScalarInvert(gamma)); in fromLuma()
36 SkScalar toLuma(SkScalar SkDEBUGCODE(gamma), SkScalar luminance) const override { in toLuma() argument
37 SkASSERT(0 == gamma); in toLuma()
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DSkMaskGamma.h29 virtual SkScalar toLuma(SkScalar gamma, SkScalar luminance) const = 0;
31 virtual SkScalar fromLuma(SkScalar gamma, SkScalar luma) const = 0;
34 static U8CPU computeLuminance(SkScalar gamma, SkColor c) { in computeLuminance() argument
35 const SkColorSpaceLuminance& luminance = Fetch(gamma); in computeLuminance()
36 SkScalar r = luminance.toLuma(gamma, SkIntToScalar(SkColorGetR(c)) / 255); in computeLuminance()
37 SkScalar g = luminance.toLuma(gamma, SkIntToScalar(SkColorGetG(c)) / 255); in computeLuminance()
38 SkScalar b = luminance.toLuma(gamma, SkIntToScalar(SkColorGetB(c)) / 255); in computeLuminance()
43 return SkScalarRoundToInt(luminance.fromLuma(gamma, luma) * 255); in computeLuminance()
47 static const SkColorSpaceLuminance& Fetch(SkScalar gamma);
/external/skqp/src/core/
DSkMaskGamma.cpp16 SkScalar toLuma(SkScalar SkDEBUGCODE(gamma), SkScalar luminance) const override { in toLuma() argument
17 SkASSERT(SK_Scalar1 == gamma); in toLuma()
20 SkScalar fromLuma(SkScalar SkDEBUGCODE(gamma), SkScalar luma) const override { in fromLuma() argument
21 SkASSERT(SK_Scalar1 == gamma); in fromLuma()
27 SkScalar toLuma(SkScalar gamma, SkScalar luminance) const override { in toLuma() argument
28 return SkScalarPow(luminance, gamma); in toLuma()
30 SkScalar fromLuma(SkScalar gamma, SkScalar luma) const override { in fromLuma() argument
31 return SkScalarPow(luma, SkScalarInvert(gamma)); in fromLuma()
36 SkScalar toLuma(SkScalar SkDEBUGCODE(gamma), SkScalar luminance) const override { in toLuma() argument
37 SkASSERT(0 == gamma); in toLuma()
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DSkMaskGamma.h29 virtual SkScalar toLuma(SkScalar gamma, SkScalar luminance) const = 0;
31 virtual SkScalar fromLuma(SkScalar gamma, SkScalar luma) const = 0;
34 static U8CPU computeLuminance(SkScalar gamma, SkColor c) { in computeLuminance() argument
35 const SkColorSpaceLuminance& luminance = Fetch(gamma); in computeLuminance()
36 SkScalar r = luminance.toLuma(gamma, SkIntToScalar(SkColorGetR(c)) / 255); in computeLuminance()
37 SkScalar g = luminance.toLuma(gamma, SkIntToScalar(SkColorGetG(c)) / 255); in computeLuminance()
38 SkScalar b = luminance.toLuma(gamma, SkIntToScalar(SkColorGetB(c)) / 255); in computeLuminance()
43 return SkScalarRoundToInt(luminance.fromLuma(gamma, luma) * 255); in computeLuminance()
47 static const SkColorSpaceLuminance& Fetch(SkScalar gamma);
/external/tensorflow/tensorflow/core/kernels/
Dsmooth-hinge-loss.h45 (label - wx - gamma * current_dual) / in ComputeUpdatedDual()
46 (num_partitions * example_weight * weighted_example_norm + gamma); in ComputeUpdatedDual()
64 return (-y_alpha + 0.5 * gamma * current_dual * current_dual) * in ComputeDualLoss()
72 if (y_wx <= 1 - gamma) return (1 - y_wx - gamma / 2) * example_weight; in ComputePrimalLoss()
73 return (1 - y_wx) * (1 - y_wx) * example_weight * 0.5 / gamma; in ComputePrimalLoss()
97 if (label * wx <= 1 - gamma) { in PrimalLossDerivative()
100 return (wx - label) / gamma; in PrimalLossDerivative()
103 double SmoothnessConstant() const final { return gamma; } in SmoothnessConstant()
108 const double gamma = 1;
Dbatch_norm_op.cc52 const Tensor& gamma = context->input(4); in Compute() local
66 OP_REQUIRES(context, gamma.dims() == 1, in Compute()
68 gamma.shape().DebugString())); in Compute()
76 var.vec<T>(), beta.vec<T>(), gamma.vec<T>(), variance_epsilon_, in Compute()
101 const Tensor& gamma = context->input(3); in Compute() local
113 OP_REQUIRES(context, gamma.dims() == 1, in Compute()
115 gamma.shape().DebugString())); in Compute()
137 OP_REQUIRES_OK(context, context->allocate_output(4, gamma.shape(), &dg)); in Compute()
156 var.vec<T>(), gamma.vec<T>(), out_backprop.tensor<T, 4>(), in Compute()
186 typename TTypes<T>::ConstVec beta, typename TTypes<T>::ConstVec gamma, \
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/external/tensorflow/tensorflow/contrib/solvers/python/ops/
Dlinear_equations.py91 alpha = state.gamma / util.dot(state.p, z)
95 gamma = util.dot(r, r)
96 beta = gamma / state.gamma
100 gamma = util.dot(r, q)
101 beta = gamma / state.gamma
103 return i + 1, cg_state(i + 1, x, r, p, gamma)
122 state = cg_state(i=i, x=x, r=r0, p=p0, gamma=gamma0)
130 gamma=state.gamma)
Dleast_squares.py81 return math_ops.logical_and(i < max_iter, state.gamma > tol)
86 alpha = state.gamma / util.l2norm_squared(q)
90 gamma = util.l2norm_squared(s)
91 beta = gamma / state.gamma
93 return i + 1, cgls_state(i + 1, x, r, p, gamma)
105 state = cgls_state(i=i, x=x, r=rhs, p=s0, gamma=gamma0)
113 gamma=state.gamma)
/external/apache-commons-math/src/main/java/org/apache/commons/math/distribution/
DChiSquaredDistributionImpl.java42 private GammaDistribution gamma; field in ChiSquaredDistributionImpl
81 gamma = new GammaDistributionImpl(df / 2.0, 2.0); in ChiSquaredDistributionImpl()
100 gamma.setAlpha(degreesOfFreedom / 2.0); in setDegreesOfFreedomInternal()
108 return gamma.getAlpha() * 2.0; in getDegreesOfFreedom()
132 return gamma.density(x); in density()
143 return gamma.cumulativeProbability(x); in cumulativeProbability()
182 return Double.MIN_VALUE * gamma.getBeta(); in getDomainLowerBound()
256 this.gamma = g; in setGammaInternal()
/external/libpng/tests/
Dpngstest16 gamma="$1"
27 test "$gamma" = "linear" && g="$f";;
30 test "$gamma" = "sRGB" && g="$f";;
33 test "$gamma" = "1.8" && g="$f";;
36 test "$gamma" = "none" && g="$f";;
54 exec ./pngstest --tmpfile "${gamma}-${alpha}-" --log ${1+"$@"} $args
/external/eigen/unsupported/test/
Dcxx11_tensor_sugar.cpp44 const float gamma = 0.14f; in test_scalar_sugar_add_mul() local
46 Tensor<float, 3> R = A.constant(gamma) + A * A.constant(alpha) + B * B.constant(beta); in test_scalar_sugar_add_mul()
47 Tensor<float, 3> S = A * alpha + B * beta + gamma; in test_scalar_sugar_add_mul()
48 Tensor<float, 3> T = gamma + alpha * A + beta * B; in test_scalar_sugar_add_mul()
64 const float gamma = 0.14f; in test_scalar_sugar_sub_div() local
67 Tensor<float, 3> R = A.constant(gamma) - A / A.constant(alpha) in test_scalar_sugar_sub_div()
69 Tensor<float, 3> S = gamma - A / alpha - beta / B - delta; in test_scalar_sugar_sub_div()
/external/ImageMagick/coders/
Dhdr.c144 gamma; in ReadHDRImage() local
306 image->gamma=StringToDouble(value,(char **) NULL); in ReadHDRImage()
488 gamma=pow(2.0,pixel[3]-(128.0+8.0)); in ReadHDRImage()
489 SetPixelRed(image,ClampToQuantum(QuantumRange*gamma*pixel[0]),q); in ReadHDRImage()
490 SetPixelGreen(image,ClampToQuantum(QuantumRange*gamma*pixel[1]),q); in ReadHDRImage()
491 SetPixelBlue(image,ClampToQuantum(QuantumRange*gamma*pixel[2]),q); in ReadHDRImage()
726 if (image->gamma != 0.0) in WriteHDRImage()
729 image->gamma); in WriteHDRImage()
771 gamma; in WriteHDRImage() local
777 gamma=QuantumScale*GetPixelRed(image,p); in WriteHDRImage()
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/external/tensorflow/tensorflow/contrib/layers/python/layers/
Dnormalization.py123 beta, gamma = None, None
145 gamma = variables.model_variable('gamma',
152 gamma = array_ops.reshape(gamma, params_shape_broadcast)
159 inputs, mean, variance, beta, gamma, epsilon, name='instancenorm')
327 beta, gamma = None, None
349 gamma = variables.model_variable('gamma',
355 gamma = array_ops.reshape(gamma, params_shape_broadcast)
372 if gamma is not None:
373 gain *= gamma
374 offset *= gamma
/external/libaom/libaom/av1/common/x86/
Dwarp_plane_sse4.c457 static INLINE void prepare_vertical_filter_coeffs(int gamma, int sy, in prepare_vertical_filter_coeffs() argument
460 (__m128i *)(warped_filter + ((sy + 0 * gamma) >> WARPEDDIFF_PREC_BITS))); in prepare_vertical_filter_coeffs()
462 (__m128i *)(warped_filter + ((sy + 2 * gamma) >> WARPEDDIFF_PREC_BITS))); in prepare_vertical_filter_coeffs()
464 (__m128i *)(warped_filter + ((sy + 4 * gamma) >> WARPEDDIFF_PREC_BITS))); in prepare_vertical_filter_coeffs()
466 (__m128i *)(warped_filter + ((sy + 6 * gamma) >> WARPEDDIFF_PREC_BITS))); in prepare_vertical_filter_coeffs()
480 (__m128i *)(warped_filter + ((sy + 1 * gamma) >> WARPEDDIFF_PREC_BITS))); in prepare_vertical_filter_coeffs()
482 (__m128i *)(warped_filter + ((sy + 3 * gamma) >> WARPEDDIFF_PREC_BITS))); in prepare_vertical_filter_coeffs()
484 (__m128i *)(warped_filter + ((sy + 5 * gamma) >> WARPEDDIFF_PREC_BITS))); in prepare_vertical_filter_coeffs()
486 (__m128i *)(warped_filter + ((sy + 7 * gamma) >> WARPEDDIFF_PREC_BITS))); in prepare_vertical_filter_coeffs()
657 uint8_t *pred, __m128i *tmp, ConvolveParams *conv_params, int16_t gamma, in warp_vertical_filter() argument
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/external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/direct/
DMultiDirectional.java40 private final double gamma; field in MultiDirectional
47 this.gamma = 0.5; in MultiDirectional()
54 public MultiDirectional(final double khi, final double gamma) { in MultiDirectional() argument
56 this.gamma = gamma; in MultiDirectional()
90 final RealPointValuePair contracted = evaluateNewSimplex(original, gamma, comparator); in iterateSimplex()
DNelderMead.java42 private final double gamma; field in NelderMead
54 this.gamma = 0.5; in NelderMead()
65 final double gamma, final double sigma) { in NelderMead() argument
68 this.gamma = gamma; in NelderMead()
139 xC[j] = centroid[j] + gamma * (xR[j] - centroid[j]); in iterateSimplex()
154 xC[j] = centroid[j] - gamma * (centroid[j] - xWorst[j]); in iterateSimplex()
/external/libaom/libaom/test/
Dwarp_filter_test_util.cc30 int16_t *alpha, int16_t *beta, int16_t *gamma, in generate_warped_model() argument
68 *gamma = clamp(((int64_t)mat[4] * (1 << WARPEDMODEL_PREC_BITS)) / mat[2], in generate_warped_model()
76 (4 * abs(*gamma) + 4 * abs(*delta) >= (1 << WARPEDMODEL_PREC_BITS))) in generate_warped_model()
83 *gamma = ROUND_POWER_OF_TWO_SIGNED(*gamma, WARP_PARAM_REDUCE_BITS) * in generate_warped_model()
133 int16_t alpha, beta, gamma, delta; in RunSpeedTest() local
136 generate_warped_model(&rnd_, mat, &alpha, &beta, &gamma, &delta, in RunSpeedTest()
159 sub_x, sub_y, &conv_params, alpha, beta, gamma, delta); in RunSpeedTest()
194 int16_t alpha, beta, gamma, delta; in RunCheckOutput() local
211 generate_warped_model(&rnd_, mat, &alpha, &beta, &gamma, &delta, in RunCheckOutput()
233 beta, gamma, delta); in RunCheckOutput()
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/external/tensorflow/tensorflow/python/ops/
Dnn_batchnorm_test.py40 def _npBatchNorm(self, x, m, v, beta, gamma, epsilon, argument
43 y = y * gamma if scale_after_normalization else y
46 def _opsBatchNorm(self, x, m, v, beta, gamma, epsilon, argument
50 y = gamma * y
53 def _tfBatchNormV1(self, x, m, v, beta, gamma, epsilon, argument
58 x, m, v, beta, gamma, epsilon, scale_after_normalization)
60 def _tfBatchNormV1BW(self, x, m, v, beta, gamma, epsilon, argument
64 x, m, v, beta, gamma, epsilon, scale_after_normalization)
66 def _tfBatchNormV2(self, x, m, v, beta, gamma, epsilon, argument
71 gamma if scale_after_normalization else
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/external/libpng/contrib/libtests/
Dgentests.sh68 for gamma in "" --sRGB --linear --1.8
70 case "$gamma" in
80 gname="-$gamma";;
82 "$mp" $gamma "$1" "$2" "test-$1-$2$gname.png"

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