| /external/libvpx/libvpx/vp8/decoder/ |
| decodemv.c | 100 const vp8_prob *up = vp8_mv_update_probs[i].prob;
180 const vp8_prob *prob; local
182 prob = vp8_sub_mv_ref_prob3[(aez << 2) | (lez << 1) | (lea)];
184 return prob;
215 const vp8_prob *prob; local
240 prob = get_sub_mv_ref_prob(leftmv.as_int, abovemv.as_int);
242 if (vp8_read(bc, prob[0])) {
243 if (vp8_read(bc, prob[1])) {
245 if (vp8_read(bc, prob[2])) {
|
| /external/libvpx/libvpx/vp8/encoder/ |
| treewriter.h | 36 /* Approximate length of an encoded bool in 256ths of a bit at given prob */
|
| /external/libvpx/libvpx/vpx_dsp/ |
| bitwriter.h | 16 #include "vpx_dsp/prob.h"
|
| prob.c | 11 #include "./prob.h"
|
| /external/llvm/test/Transforms/SampleProfile/ |
| fnptr.ll | 5 ; RUN: opt < %s -sample-profile -sample-profile-file=%S/Inputs/fnptr.prof | opt -analyze -branch-prob | FileCheck %s
6 ; RUN: opt < %s -sample-profile -sample-profile-file=%S/Inputs/fnptr.binprof | opt -analyze -branch-prob | FileCheck %s
8 ; RUN: opt < %s -passes=sample-profile -sample-profile-file=%S/Inputs/fnptr.prof | opt -analyze -branch-prob | FileCheck %s
9 ; RUN: opt < %s -passes=sample-profile -sample-profile-file=%S/Inputs/fnptr.binprof | opt -analyze -branch-prob | FileCheck %s
|
| discriminator.ll | 1 ; RUN: opt < %s -sample-profile -sample-profile-file=%S/Inputs/discriminator.prof | opt -analyze -branch-prob | FileCheck %s
2 ; RUN: opt < %s -passes=sample-profile -sample-profile-file=%S/Inputs/discriminator.prof | opt -analyze -branch-prob | FileCheck %s
|
| offset.ll | 1 ; RUN: opt < %s -sample-profile -sample-profile-file=%S/Inputs/offset.prof | opt -analyze -branch-prob | FileCheck %s
2 ; RUN: opt < %s -passes=sample-profile -sample-profile-file=%S/Inputs/offset.prof | opt -analyze -branch-prob | FileCheck %s
|
| /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/ |
| geometric_test.py | 71 pmf = geom.prob(x)
94 pmf = geom.prob(x)
110 pmf = geom.prob(x)
230 pmf = geom.prob(x)
|
| /external/tensorflow/tensorflow/python/ops/distributions/ |
| dirichlet.py | 104 dist.prob(x) # shape: []
109 dist.prob(x) # shape: [2]
113 dist.prob(x) # shape: [5, 7]
128 dist.prob(x) # shape: [2]
|
| dirichlet_multinomial.py | 98 distribution. When calling distribution functions (e.g., `dist.prob(counts)`),
139 dist.prob(counts) # Shape []
143 dist.prob(counts) # Shape [2]
147 dist.prob(counts) # Shape [5, 7]
159 dist.prob(counts) # Shape [2]
|
| multinomial.py | 59 length-`K` `prob` (probability) vectors (`K > 1`) such that
127 dist.prob(counts) # Shape []
131 dist.prob(counts) # Shape [2]
135 dist.prob(counts) # Shape [5, 7]
145 dist.prob(counts) # Shape [2]
|
| /external/webp/src/dec/ |
| vp8_dec.c | 33 const VP8BandProbas* const prob[],
437 const VP8BandProbas* const prob[],
439 const uint8_t* p = prob[n]->probas_[ctx];
445 p = prob[++n]->probas_[0];
449 const VP8ProbaArray* const p_ctx = &prob[n + 1]->probas_[0];
467 const VP8BandProbas* const prob[],
469 const uint8_t* p = prob[n]->probas_[ctx];
475 p = prob[++n]->probas_[0];
479 const VP8ProbaArray* const p_ctx = &prob[n + 1]->probas_[0];
|
| /packages/apps/Dialer/java/com/android/dialer/dialpadview/res/values-cs/ |
| strings.xml | 31 <string name="dialer_returnToInCallScreen" msgid="4111111633248125741">"Vrátit se k probíhajícímu hovoru"</string>
|
| /packages/providers/ContactsProvider/res/values-cs/ |
| strings.xml | 23 <string name="upgrade_out_of_memory_notification_title" msgid="8888171924684998531">"Probíhá upgrade úlo?i?t? kontakt?"</string>
|
| /external/opencv/ml/src/ |
| mlem.cpp | 369 CvMat prob = cvMat( 1, nclusters, CV_64F ); local
376 prob.data.ptr = probs->data.ptr + probs->step*i;
378 labels->data.i[i*lstep] = cvRound(predict(&sample, &prob));
414 CvMat prob; local
415 cvGetRow( params.probs, &prob, i );
416 cvMaxS( &prob, 0., &prob );
417 cvMinMaxLoc( &prob, 0, &maxval );
419 cvSet( &prob, cvScalar(1./nclusters) );
421 cvNormalize( &prob, &prob, 1., 0, CV_L1 )
[all...] |
| ml_inner_functions.cpp | 1854 double prob, maxprob, sum; local
1930 double prob; local
[all...] |
| /external/llvm/lib/Passes/ |
| PassRegistry.def | 92 FUNCTION_ANALYSIS("branch-prob", BranchProbabilityAnalysis())
156 FUNCTION_PASS("print<branch-prob>", BranchProbabilityPrinterPass(dbgs()))
|
| /external/llvm/test/Analysis/BranchProbabilityInfo/ |
| noreturn.ll | 2 ; RUN: opt < %s -analyze -branch-prob | FileCheck %s
3 ; RUN: opt < %s -passes='print<branch-prob>' -disable-output 2>&1 | FileCheck %s
|
| pr22718.ll | 1 ; RUN: opt < %s -analyze -branch-prob | FileCheck %s
2 ; RUN: opt < %s -passes='print<branch-prob>' -disable-output 2>&1 | FileCheck %s
|
| /external/tensorflow/tensorflow/contrib/gan/python/features/python/ |
| random_tensor_pool_impl.py | 122 prob = random_ops.random_uniform(
124 return control_flow_ops.cond(prob, lambda: dequeue_values,
|
| /external/tensorflow/tensorflow/core/kernels/ |
| xent_op.h | 107 // backprop: prob - labels, where
108 // prob = exp(logits - max_logits) / sum(exp(logits - max_logits))
|
| /external/llvm/lib/CodeGen/ |
| MachineBasicBlock.cpp | 516 for (auto Prob : Probs)
517 Sum += Prob.getNumerator();
528 BranchProbability Prob) {
532 Probs.push_back(Prob);
628 auto Prob = *FromMBB->Probs.begin();
629 addSuccessor(Succ, Prob);
645 auto Prob = *FromMBB->Probs.begin();
646 addSuccessor(Succ, Prob);
[all...] |
| /external/tensorflow/tensorflow/contrib/distributions/python/ops/ |
| poisson_lognormal.py | 164 approx= sum{ prob[d] Poisson(k | lambda(grid[d])) : d=0, ..., deg-1 }
168 parameterized by `loc`, `scale` and the `prob` vector is
199 = sum{ prob[d] Poisson(k | lambda=exp(grid[d]))
405 # E[Var[Z | V]] = sum{ prob[d] Var[d] : d=0, ..., deg-1 }
406 # Var[E[Z | V]] = sum{ prob[d] (Mean[d] - Mean)**2 : d=0, ..., deg-1 }
|
| /external/tensorflow/tensorflow/contrib/kfac/python/ops/ |
| loss_functions.py | 241 probability distribtion (whose log-prob defines the loss). Typically this
265 probability distribtion (whose log-prob defines the loss). Typically this
289 probability distribtion (whose log-prob defines the loss). Typically this
346 """Base class for neg log prob losses whose inputs are 'natural' parameters.
378 """Base class for neg log prob losses that use the TF Distribution classes."""
397 """Neg log prob loss for a normal distribution parameterized by a mean vector.
455 """Negative log prob loss for a normal distribution with mean and variance.
588 """Neg log prob loss for a categorical distribution parameterized by logits.
698 """Neg log prob loss for multiple Bernoulli distributions param'd by logits.
794 """Neg log prob loss for a categorical distribution with onehot targets
[all...] |
| /external/tensorflow/tensorflow/python/kernel_tests/distributions/ |
| student_t_test.py | 64 pdf = student.prob(t)
93 pdf = student.prob(t)
272 self.assertEqual(student.prob(2.).get_shape(), (3,))
283 self.assertEqual(student.prob(arg).get_shape(), shape)
439 pdfs = student.prob(samples)
441 mean_pdf = student.prob(student.mean())
464 pdfs = student.prob(samples)
|
