| /external/elfutils/src/backends/ |
| alpha_auxv.c | 33 #define BACKEND alpha_
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| alpha_corenote.c | 39 #define BACKEND alpha_
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| alpha_init.c | 34 #define BACKEND alpha_
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| alpha_regs.c | 36 #define BACKEND alpha_
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| alpha_retval.c | 36 #define BACKEND alpha_
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| alpha_symbol.c | 38 #define BACKEND alpha_
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| /external/ceres-solver/examples/ |
| fields_of_experts.h | 81 explicit FieldsOfExpertsLoss(double alpha) : alpha_(alpha) { }
85 const double alpha_; member in class:ceres::examples::FieldsOfExpertsLoss
137 std::vector<double> alpha_; member in class:ceres::examples::FieldsOfExperts
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| fields_of_experts.cc | 83 rho[0] = alpha_ * log(sum);
84 rho[1] = alpha_ * c * inv;
85 rho[2] = - alpha_ * c * c * inv * inv;
111 alpha_.resize(num_filters_);
113 foe_file >> alpha_[i]; local
147 return new FieldsOfExpertsLoss(alpha_[alpha_index]);
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| /external/eigen/Eigen/src/Core/products/ |
| GeneralMatrixMatrixTriangular_MKL.h | 89 MKLTYPE alpha_, beta_; \
91 /* Set alpha_ & beta_ */ \
92 assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(alpha_, alpha); \
94 MKLFUNC(&uplo, &trans, &n, &k, &alpha_, lhs, &lda, &beta_, res, &ldc); \
114 RTYPE alpha_, beta_; \
117 /* Set alpha_ & beta_ */ \
118 /* assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(alpha_, alpha); */\
120 alpha_ = alpha.real(); \
130 MKLFUNC(&uplo, &trans, &n, &k, &alpha_, (MKLTYPE*)a_ptr, &lda, &beta_, (MKLTYPE*)res, &ldc); \
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| SelfadjointMatrixMatrix_MKL.h | 60 MKLTYPE alpha_, beta_; \
69 /* Set alpha_ & beta_ */ \
70 assign_scalar_eig2mkl(alpha_, alpha); \
89 MKLPREFIX##symm(&side, &uplo, &m, &n, &alpha_, (const MKLTYPE*)a, &lda, (const MKLTYPE*)b, &ldb, &beta_, (MKLTYPE*)res, &ldc); \
111 MKLTYPE alpha_, beta_; \
121 /* Set alpha_ & beta_ */ \
122 assign_scalar_eig2mkl(alpha_, alpha); \
157 MKLPREFIX##hemm(&side, &uplo, &m, &n, &alpha_, (const MKLTYPE*)a, &lda, (const MKLTYPE*)b, &ldb, &beta_, (MKLTYPE*)res, &ldc); \
187 MKLTYPE alpha_, beta_; \
195 /* Set alpha_ & beta_ */
[all...] |
| GeneralMatrixMatrix_MKL.h | 69 MKLTYPE alpha_, beta_; \
82 /* Set alpha_ & beta_ */ \
83 assign_scalar_eig2mkl(alpha_, alpha); \
106 MKLPREFIX##gemm(&transa, &transb, &m, &n, &k, &alpha_, (const MKLTYPE*)a, &lda, (const MKLTYPE*)b, &ldb, &beta_, (MKLTYPE*)res, &ldc); \
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| SelfadjointMatrixVector_MKL.h | 89 MKLTYPE alpha_, beta_; \
92 assign_scalar_eig2mkl(alpha_, alpha); \
101 MKLFUNC(&uplo, &n, &alpha_, (const MKLTYPE*)lhs, &lda, (const MKLTYPE*)x_ptr, &incx, &beta_, (MKLTYPE*)res, &incy); \
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| TriangularMatrixVector_MKL.h | 110 MKLTYPE alpha_, beta_; \
111 assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(alpha_, alpha); \
129 MKLPREFIX##axpy(&n, &alpha_,(const MKLTYPE*)x, &incx, (MKLTYPE*)_res, &incy); \
148 MKLPREFIX##gemv(&trans, &m, &n, &alpha_, (const MKLTYPE*)a, &lda, (const MKLTYPE*)x, &incx, &beta_, (MKLTYPE*)y, &incy); \
195 MKLTYPE alpha_, beta_; \
196 assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(alpha_, alpha); \
214 MKLPREFIX##axpy(&n, &alpha_,(const MKLTYPE*)x, &incx, (MKLTYPE*)_res, &incy); \
233 MKLPREFIX##gemv(&trans, &n, &m, &alpha_, (const MKLTYPE*)a, &lda, (const MKLTYPE*)x, &incx, &beta_, (MKLTYPE*)y, &incy); \
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| GeneralMatrixVector_MKL.h | 102 MKLTYPE alpha_, beta_; \
109 assign_scalar_eig2mkl(alpha_, alpha); \
118 MKLPREFIX##gemv(&trans, &m, &n, &alpha_, (const MKLTYPE*)lhs, &lda, (const MKLTYPE*)x_ptr, &incx, &beta_, (MKLTYPE*)res, &incy); \
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| TriangularMatrixMatrix_MKL.h | 137 MKLTYPE alpha_; \
139 /* Set alpha_*/ \
140 assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(alpha_, alpha); \
178 MKLPREFIX##trmm(&side, &uplo, &transa, &diag, &m, &n, &alpha_, (const MKLTYPE*)a, &lda, (MKLTYPE*)b, &ldb); \
251 MKLTYPE alpha_; \
253 /* Set alpha_*/ \
254 assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(alpha_, alpha); \
292 MKLPREFIX##trmm(&side, &uplo, &transa, &diag, &m, &n, &alpha_, (const MKLTYPE*)a, &lda, (MKLTYPE*)b, &ldb); \
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| /external/ceres-solver/internal/ceres/ |
| suitesparse.h | 129 double alpha_[2] = {alpha, 0}; local
131 cholmod_sdmult(A, 0, alpha_, beta_, x, y, &cc_);
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| dogleg_strategy.h | 126 double alpha_; member in class:ceres::internal::DoglegStrategy
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| dogleg_strategy.cc | 190 alpha_ = gradient_.squaredNorm() / Jg.squaredNorm();
217 if (gradient_norm * alpha_ >= radius_) {
233 const double b_dot_a = -alpha_ * gradient_.dot(gauss_newton_step_);
234 const double a_squared_norm = pow(alpha_ * gradient_norm, 2.0);
248 dogleg_step = (-alpha_ * (1.0 - beta)) * gradient_
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| /external/webp/src/enc/ |
| histogram.h | 38 uint32_t alpha_[NUM_LITERAL_CODES]; member in struct:__anon21231
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| analysis.c | 70 // set segment susceptibility alpha_ / beta_
94 enc->dqm_[n].alpha_ = clip(alpha, -127, 127);
215 const int alpha = mb->alpha_;
217 mb->alpha_ = centers[map[alpha]]; // for the record.
233 // Number of modes to inspect for alpha_ evaluation. We don't need to test all
353 it->mb_->alpha_ = best_alpha; // for later remapping.
365 mb->alpha_ = 0;
384 enc->dqm_[0].alpha_ = 0;
386 // Note: we can't compute this alpha_ / uv_alpha_ -> set to default value.
387 enc->alpha_ = 0
[all...] |
| vp8enci.h | 235 uint8_t alpha_; // quantization-susceptibility member in struct:__anon21242
248 int alpha_; // quant-susceptibility, range [-127,127]. Zero is neutral. member in struct:__anon21243
433 int alpha_; // global susceptibility (<=> complexity) member in struct:VP8Encoder
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| histogram.c | 132 ++histo->alpha_[PixOrCopyLiteral(v, 3)];
270 + PopulationCost(p->alpha_, NUM_LITERAL_CODES)
281 + BitsEntropy(p->alpha_, NUM_LITERAL_CODES)
309 *cost += GetCombinedEntropy(a->alpha_, b->alpha_, NUM_LITERAL_CODES);
389 const double alpha_cost = PopulationCost(h->alpha_, NUM_LITERAL_CODES);
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| backward_references.c | 456 double alpha_[VALUES_IN_BYTE]; member in struct:__anon21223
524 VALUES_IN_BYTE, histo->alpha_, m->alpha_);
535 return m->alpha_[v >> 24] +
[all...] |
| /external/webp/src/dsp/ |
| lossless_mips32.c | 379 ADD_VECTOR(a->alpha_, b->alpha_, out->alpha_, NUM_LITERAL_CODES, 0);
386 ADD_VECTOR_EQ(a->alpha_, out->alpha_, NUM_LITERAL_CODES, 0);
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| lossless_sse2.c | 487 AddVector(a->alpha_, b->alpha_, out->alpha_, NUM_LITERAL_CODES);
492 AddVectorEq(a->alpha_, out->alpha_, NUM_LITERAL_CODES);
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