| /external/opencv/cv/src/ |
| cvcondens.cpp | 237 float Prob = 1.f / conDens->SamplesNum;
272 conDens->flConfidence[j] = Prob;
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| /external/tensorflow/tensorflow/compiler/tf2xla/kernels/ |
| softmax_op.cc | 117 // backprop: prob - labels, where
118 // prob = exp(logits - max_logits) / sum(exp(logits - max_logits))
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| /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/ |
| mixture_test.py | 409 p_x = dist.prob(x)
413 dist_probs = [d.prob(x) for d in dist.components]
436 p_x = dist.prob(x)
441 dist_probs = [d.prob(x) for d in dist.components]
463 p_x = dist.prob(x)
467 dist_probs = [d.prob(x) for d in dist.components]
491 p_x = dist.prob(x)
495 dist_probs = [d.prob(x) for d in dist.components]
[all...] |
| mvn_full_covariance_test.py | 73 pdf = mvn.prob(x)
92 pdf = mvn.prob(x)
|
| half_normal_test.py | 99 pdf = halfnorm.prob(x)
118 pdf = halfnorm.prob(x)
189 dist.log_prob, dist.prob, dist.log_survival_function,
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| mvn_diag_test.py | 260 # numerically stable as `tf.log(mvn.prob(x_pl))`. However in this test
261 # we're testing a bug specific to `prob` and not `log_prob`;
265 neg_log_likelihood = -math_ops.reduce_sum(math_ops.log(mvn.prob(x_pl)))
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| mvn_tril_test.py | 56 pdf = mvn.prob(x)
76 pdf = mvn.prob(x)
97 pdf = mvn.prob(x)
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| /external/tensorflow/tensorflow/contrib/distributions/python/ops/ |
| cauchy.py | 78 dist.prob([0, 1.5])
90 dist.prob(3.)
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| mvn_full_covariance.py | 97 mvn.prob([-1., 0, 1]).eval() # shape: []
110 mvn.prob(x).eval() # shape: [2]
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| mvn_tril.py | 110 mvn.prob([-1., 0, 1]).eval() # shape: []
123 mvn.prob(x).eval() # shape: [2]
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| vector_exponential_diag.py | 101 vex.prob([3., 4.]).eval() # shape: []
114 vex.prob(x).eval() # shape: [2]
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| wishart.py | 479 dist.prob(x) # Shape is [], a scalar.
484 dist.prob(x) # Shape is [2].
493 dist.prob(x) # Shape is [2, 2].
595 dist.prob(x) # Shape is [], a scalar.
600 dist.prob(x) # Shape is [2].
609 dist.prob(x) # Shape is [2, 2].
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| /external/tensorflow/tensorflow/core/kernels/ |
| sparse_xent_op.h | 211 // backprop: prob - labels, where
212 // prob = exp(logits - max_logits) / sum(exp(logits - max_logits))
|
| /packages/providers/DownloadProvider/res/values-cs/ |
| strings.xml | 64 <string name="download_running" msgid="3925050393361158266">"Probíhá"</string>
66 <string name="download_running_percent" msgid="4305080769167320204">"Probíhá, <xliff:g id="PERCENTAGE">%s</xliff:g>"</string>
|
| /prebuilts/gcc/linux-x86/host/x86_64-w64-mingw32-4.8/lib/gcc/x86_64-w64-mingw32/4.8.3/plugin/include/ |
| basic-block.h | 942 check_probability (int prob)
944 gcc_checking_assert (prob >= 0 && prob <= REG_BR_PROB_BASE);
958 /* Apply probability PROB on frequency or count FREQ. */
[all...] |
| /external/tensorflow/tensorflow/contrib/bayesflow/python/ops/ |
| metropolis_hastings_impl.py | 104 # i.e., Prob(Current -> Proposed) = Prob(Proposed -> Current).
112 # acceptance ratio is: [Prob(Proposed) / Prob(Current)] *
113 # [Prob(Proposed -> Current) / Prob(Current -> Proposed)]. The log of the
|
| /external/v4l2_codec2/vda/ |
| vp8_parser.cc | 27 #define BD_READ_BOOL_WITH_PROB_OR_RETURN(out, prob) \
29 if (!bd_.ReadBool(out, prob)) \
814 uint8_t prob; local
815 BD_READ_UNSIGNED_OR_RETURN(7, &prob);
816 ehdr->mv_probs[mv_ctx][p] = prob ? (prob << 1) : 1;
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| /device/linaro/bootloader/edk2/BaseTools/Source/C/LzmaCompress/Sdk/C/ |
| LzmaEnc.c | 602 static void RangeEnc_EncodeBit(CRangeEnc *p, CLzmaProb *prob, UInt32 symbol)
604 UInt32 ttt = *prob;
617 *prob = (CLzmaProb)ttt;
674 #define GET_PRICE(prob, symbol) \
675 p->ProbPrices[((prob) ^ (((-(int)(symbol))) & (kBitModelTotal - 1))) >> kNumMoveReducingBits];
677 #define GET_PRICEa(prob, symbol) \
678 ProbPrices[((prob) ^ ((-((int)(symbol))) & (kBitModelTotal - 1))) >> kNumMoveReducingBits];
680 #define GET_PRICE_0(prob) p->ProbPrices[(prob) >> kNumMoveReducingBits]
681 #define GET_PRICE_1(prob) p->ProbPrices[((prob) ^ (kBitModelTotal - 1)) >> kNumMoveReducingBits]
[all...] |
| /external/lzma/C/ |
| LzmaEnc.c | 602 static void RangeEnc_EncodeBit(CRangeEnc *p, CLzmaProb *prob, UInt32 symbol)
604 UInt32 ttt = *prob;
617 *prob = (CLzmaProb)ttt;
674 #define GET_PRICE(prob, symbol) \
675 p->ProbPrices[((prob) ^ (((-(int)(symbol))) & (kBitModelTotal - 1))) >> kNumMoveReducingBits];
677 #define GET_PRICEa(prob, symbol) \
678 ProbPrices[((prob) ^ ((-((int)(symbol))) & (kBitModelTotal - 1))) >> kNumMoveReducingBits];
680 #define GET_PRICE_0(prob) p->ProbPrices[(prob) >> kNumMoveReducingBits]
681 #define GET_PRICE_1(prob) p->ProbPrices[((prob) ^ (kBitModelTotal - 1)) >> kNumMoveReducingBits]
[all...] |
| /external/llvm/lib/CodeGen/ |
| MachineBlockPlacement.cpp | 532 * Prob(BB->Succ) > 2* Prob(BB->Pred)
534 * T = 2 * (1-Prob(BB->Pred). Since T + Prob(BB->Pred) == 1,
580 // prob(BB->Succ) > 2 * prob(BB->Pred)
596 // candidate edge BB->Succ. Edge S->BB is selected because prob(S->BB)
597 // is no less than prob(S->Pred). When real profile data is *available*, if
605 // strong biaaed branch at block S with Prob(S->BB) in order to select
607 // edge: Prob(Succ->BB) needs to >= HotProb in order to be selected (withou
[all...] |
| /external/llvm/unittests/Support/ |
| BranchProbabilityTest.cpp | 198 uint32_t Prob[2];
286 EXPECT_EQ(T.Result, BP(T.Prob[0], T.Prob[1]).scale(T.Num));
|
| /external/tensorflow/tensorflow/contrib/bayesflow/python/kernel_tests/ |
| monte_carlo_test.py | 88 prob = mc.expectation_importance_sampler(
94 self.assertEqual(p.batch_shape, prob.get_shape())
95 self.assertAllClose(0.5, prob.eval(), rtol=0.05)
|
| /external/tensorflow/tensorflow/python/ops/distributions/ |
| categorical.py | 140 dist.prob(0) # Shape []
144 dist.prob(counts) # Shape [2]
148 dist.prob(counts) # Shape [5, 7, 3]
|
| student_t.py | 92 single_dist.prob(1.)
103 multi_dist.prob([0, 1.5])
118 dist.prob(3.0)
|
| /external/xz-embedded/linux/lib/xz/ |
| xz_dec_lzma2.c | 497 static __always_inline int rc_bit(struct rc_dec *rc, uint16_t *prob)
503 bound = (rc->range >> RC_BIT_MODEL_TOTAL_BITS) * *prob;
506 *prob += (RC_BIT_MODEL_TOTAL - *prob) >> RC_MOVE_BITS;
511 *prob -= *prob >> RC_MOVE_BITS;
|
