/external/tensorflow/tensorflow/contrib/distributions/python/ops/ |
H A D | mvn_diag_plus_low_rank.py | 38 `scale` matrix; `covariance = scale @ scale.T` where `@` denotes 46 pdf(x; loc, scale) = exp(-0.5 ||y||**2) / Z, 47 y = inv(scale) @ (x - loc), 48 Z = (2 pi)**(0.5 k) |det(scale)|, 54 * `scale` is a linear operator in `R^{k x k}`, `cov = scale @ scale.T`, 58 A (non-batch) `scale` matri [all...] |
H A D | vector_exponential_diag.py | 37 `scale` matrix: `covariance = scale @ scale.T`, where `@` denotes 43 `scale` matrix + `loc`, applied to the positive half-space: 44 `Supp = {loc + scale @ x : x in R^k, x_1 > 0, ..., x_k > 0}`. On this set, 47 pdf(y; loc, scale) = exp(-||x||_1) / Z, for y in Supp 48 x = inv(scale) @ (y - loc), 49 Z = |det(scale)|, 55 * `scale` is a linear operator in `R^{k x k}`, `cov = scale [all...] |
H A D | vector_laplace_diag.py | 37 `scale` matrix: `covariance = 2 * scale @ scale.T`, where `@` denotes 45 pdf(x; loc, scale) = exp(-||y||_1) / Z, 46 y = inv(scale) @ (x - loc), 47 Z = 2**k |det(scale)|, 53 * `scale` is a linear operator in `R^{k x k}`, `cov = scale @ scale.T`, 57 A (non-batch) `scale` matri [all...] |
/external/webrtc/webrtc/modules/audio_coding/codecs/ilbc/ |
H A D | cb_mem_energy.c | 37 int scale, /* (i) The scaling of all energy values */ 52 energy = WebRtcSpl_DotProductWithScale( pp, pp, lTarget, scale); 62 WebRtcIlbcfix_CbMemEnergyCalc(energy, range, ppi, ppo, energyW16, energyShifts, scale, 0); 68 energy = WebRtcSpl_DotProductWithScale( pp, pp, lTarget, scale); 78 WebRtcIlbcfix_CbMemEnergyCalc(energy, range, ppi, ppo, energyW16, energyShifts, scale, base_size); 29 WebRtcIlbcfix_CbMemEnergy( size_t range, int16_t *CB, int16_t *filteredCB, size_t lMem, size_t lTarget, int16_t *energyW16, int16_t *energyShifts, int scale, size_t base_size ) argument
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H A D | cb_mem_energy_augmentation.c | 25 int scale, /* (i) The scaling of all energy values */ 42 nrjRecursive = WebRtcSpl_DotProductWithScale( CBmemPtr-19, CBmemPtr-19, 15, scale); 48 nrjRecursive += (*ppe * *ppe) >> scale; 53 energy += WebRtcSpl_DotProductWithScale(interpSamplesPtr, interpSamplesPtr, 4, scale); 58 energy += WebRtcSpl_DotProductWithScale(pp, pp, SUBL-lagcount, scale); 22 WebRtcIlbcfix_CbMemEnergyAugmentation( int16_t *interpSamples, int16_t *CBmem, int scale, size_t base_size, int16_t *energyW16, int16_t *energyShifts ) argument
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/external/skia/src/core/ |
H A D | SkPoint.cpp | 34 void SkPoint::scale(SkScalar scale, SkPoint* dst) const { argument 36 dst->set(fX * scale, fY * scale); 77 float mag, scale; local 80 scale = 1 / mag; 92 // have a non-zero value for scale (thanks to denormalized numbers). 93 scale = (float)(1 / magmag); 95 pt->set(x * scale, y * scale); 125 float scale; local 158 float scale; local [all...] |
/external/skqp/src/core/ |
H A D | SkPoint.cpp | 34 void SkPoint::scale(SkScalar scale, SkPoint* dst) const { argument 36 dst->set(fX * scale, fY * scale); 77 float mag, scale; local 80 scale = 1 / mag; 92 // have a non-zero value for scale (thanks to denormalized numbers). 93 scale = (float)(1 / magmag); 95 pt->set(x * scale, y * scale); 125 float scale; local 158 float scale; local [all...] |
/external/tensorflow/tensorflow/python/ops/distributions/ |
H A D | laplace.py | 47 """The Laplace distribution with location `loc` and `scale` parameters. 58 where `loc = mu`, `scale = sigma`, and `Z` is the normalization constant. 63 The Lpalce distribution is a member of the [location-scale family]( 68 X ~ Laplace(loc=0, scale=1) 69 Y = loc + scale * X 76 scale, 80 """Construct Laplace distribution with parameters `loc` and `scale`. 82 The parameters `loc` and `scale` must be shaped in a way that supports 83 broadcasting (e.g., `loc / scale` is a valid operation). 88 scale 131 def scale(self): member in class:Laplace [all...] |
/external/dng_sdk/source/ |
H A D | dng_xy_coord.cpp | 60 real64 scale = 0.999999 / (temp.x + temp.y); local 61 temp.x *= scale; 62 temp.y *= scale;
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/external/fonttools/Lib/fontTools/misc/ |
H A D | fixedTools.py | 28 scale = 1 << precisionBits 29 value /= scale 30 eps = .5 / scale
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/external/fonttools/Tools/fontTools/misc/ |
H A D | fixedTools.py | 28 scale = 1 << precisionBits 29 value /= scale 30 eps = .5 / scale
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/external/libmojo/ui/gfx/geometry/ |
H A D | size.cc | 85 Size ScaleToCeiledSize(const Size& size, float scale) { argument 86 if (scale == 1.f) 88 return ToCeiledSize(ScaleSize(gfx::SizeF(size), scale, scale)); 97 Size ScaleToFlooredSize(const Size& size, float scale) { argument 98 if (scale == 1.f) 100 return ToFlooredSize(ScaleSize(gfx::SizeF(size), scale, scale)); 109 Size ScaleToRoundedSize(const Size& size, float scale) { argument 110 if (scale [all...] |
H A D | size_f.h | 53 void Scale(float scale) { argument 54 Scale(scale, scale); 86 inline SizeF ScaleSize(const SizeF& p, float scale) { argument 87 return ScaleSize(p, scale, scale);
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/external/mesa3d/src/gallium/auxiliary/util/ |
H A D | u_texture.c | 53 /* Compute sc = +/-scale and tc = +/-scale. 55 * though that can still sometimes happen with this scale factor... 57 * XXX: Yep, there is no safe scale factor that will prevent sampling 63 const float scale = allow_scale ? 0.9999f : 1.0f; local 64 const float sc = (2 * in_st[0] - 1) * scale; 65 const float tc = (2 * in_st[1] - 1) * scale;
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/external/mesa3d/src/util/ |
H A D | format_r11g11b10f.h | 112 const float scale = 1.0 / (1 << 20); local 113 f32.f = scale * mantissa; 118 float scale, decimal; local 121 scale = 1.0f / (1 << -exponent); 123 scale = (float) (1 << exponent); 126 f32.f = scale * decimal; 194 const float scale = 1.0 / (1 << 19); local 195 f32.f = scale * mantissa; 200 float scale, decimal; local 203 scale [all...] |
/external/pdfium/fxbarcode/pdf417/ |
H A D | BC_PDF417BarcodeRow.cpp | 47 std::vector<uint8_t>& CBC_BarcodeRow::getScaledRow(int32_t scale) { argument 48 m_output.resize(m_row.size() * scale); 50 m_output[i] = m_row[i / scale];
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/external/skia/gm/ |
H A D | filltypespersp.cpp | 38 SkScalar scale, const SkPaint& paint) { 47 canvas->scale(scale, scale); 53 void showFour(SkCanvas* canvas, SkScalar scale, bool aa) { argument 67 scale, paint); 69 scale, paint); 71 scale, paint); 73 scale, paint); 110 const SkScalar scale variable 37 showPath(SkCanvas* canvas, int x, int y, SkPath::FillType ft, SkScalar scale, const SkPaint& paint) argument [all...] |
H A D | text_scale_skew.cpp | 17 for (float scale : { 0.5f, 0.71f, 1.0f, 1.41f, 2.0f }) { 18 p.setTextScaleX(scale);
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/external/skqp/gm/ |
H A D | filltypespersp.cpp | 38 SkScalar scale, const SkPaint& paint) { 47 canvas->scale(scale, scale); 53 void showFour(SkCanvas* canvas, SkScalar scale, bool aa) { argument 67 scale, paint); 69 scale, paint); 71 scale, paint); 73 scale, paint); 110 const SkScalar scale variable 37 showPath(SkCanvas* canvas, int x, int y, SkPath::FillType ft, SkScalar scale, const SkPaint& paint) argument [all...] |
/external/python/cpython2/Python/ |
H A D | strtod.c | 67 int sign, scale, dotseen; local 79 dotseen = 0, scale = 0; 95 if (!dotseen) scale++; 98 /* If a . has been seen, scale must be adjusted */ 99 if (dotseen) scale--; 104 /* If it precedes a ., scale must be adjusted */ 105 if (!dotseen) scale++; 139 expt += scale;
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/external/tensorflow/tensorflow/contrib/tpu/python/tpu/ |
H A D | tpu_test.py | 53 def make_regularizer(scale): 54 return lambda inputs: scale * math_ops.reduce_sum(math_ops.square(inputs)) 56 def training_step(inputs, scale): 62 kernel_regularizer=make_regularizer(scale)) 67 scale = array_ops.ones(shape=()) 70 tuple_shapes=[inputs.shape, scale.shape])
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/external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/ |
H A D | wishart_test.py | 46 """Compute Wishart variance for numpy scale matrix.""" 56 scale = make_pd(1., 2) 58 w = distributions.WishartCholesky(df, chol(scale)) 59 # sp.stats.wishart(df=4, scale=make_pd(1., 2)).entropy() 62 w = distributions.WishartCholesky(df=1, scale=[[1.]]) 63 # sp.stats.wishart(df=1,scale=1).entropy() 76 scale=chol(make_pd(1., 2))) 79 w = distributions.WishartCholesky(df=5, scale=[[1.]]) 84 scale = make_pd(1., 2) 86 w = distributions.WishartCholesky(df, chol(scale)) [all...] |
H A D | cauchy_test.py | 61 loc_shape, scale_shape = param_shapes["loc"], param_shapes["scale"] 65 scale = array_ops.ones(scale_shape) 68 cauchy_lib.Cauchy(loc, scale).sample()).eval()) 72 loc_shape, scale_shape = param_shapes["loc"], param_shapes["scale"] 91 scale = constant_op.constant([np.sqrt(10.0)] * batch_size) 93 cauchy = cauchy_lib.Cauchy(loc=loc, scale=scale) 110 expected_log_pdf = stats.cauchy(loc.eval(), scale.eval()).logpdf(x) 118 scale = constant_op.constant( 121 cauchy = cauchy_lib.Cauchy(loc=loc, scale [all...] |
/external/fec/ |
H A D | viterbi39_av.c | 223 vector unsigned short scale; local 227 scale = vp->new_metrics->v[0]; 229 scale = vec_min(scale,vp->new_metrics->v[i]); 231 scale = vec_min(scale,vec_sld(scale,scale,8)); 232 scale = vec_min(scale,vec_sl [all...] |
/external/freetype/src/tools/ |
H A D | cordic.py | 6 scale = units/math.pi variable 18 angle2 = round(angle*scale) # arctangent in FT_Angle units
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