/external/elfutils/backends/ |
alpha_auxv.c | 30 #define BACKEND alpha_
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alpha_corenote.c | 36 #define BACKEND alpha_
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alpha_init.c | 31 #define BACKEND alpha_
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alpha_regs.c | 33 #define BACKEND alpha_
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alpha_symbol.c | 34 #define BACKEND alpha_
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alpha_retval.c | 33 #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/webp/src/enc/ |
histogram.h | 36 int alpha_[256]; member in struct:__anon18225 95 p->alpha_[i] += a->alpha_[i];
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analysis.c | 71 // set segment susceptibility alpha_ / beta_ 95 enc->dqm_[n].alpha_ = clip(alpha, -127, 127); 208 const int alpha = mb->alpha_; 210 mb->alpha_ = centers[map[alpha]]; // for the record. 226 // Number of modes to inspect for alpha_ evaluation. For high-quality settings 352 it->mb_->alpha_ = best_alpha; // for later remapping. 364 mb->alpha_ = 0; 383 enc->dqm_[0].alpha_ = 0; 385 // Note: we can't compute this alpha_ / uv_alpha_. 395 enc->alpha_ = 0 [all...] |
histogram.c | 26 memset(p->alpha_, 0, sizeof(p->alpha_)); 83 ++histo->alpha_[PixOrCopyLiteral(v, 3)]; 156 + BitsEntropy(&p->alpha_[0], 256) 212 return HuffmanCost(&p->alpha_[0], 256) +
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vp8enci.h | 230 uint8_t alpha_; // quantization-susceptibility member in struct:__anon18235 243 int alpha_; // quant-susceptibility, range [-127,127]. Zero is neutral. member in struct:__anon18236 408 int alpha_; // global susceptibility (<=> complexity) member in struct:VP8Encoder
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backward_references.c | 377 double alpha_[VALUES_IN_BYTE]; member in struct:__anon18220 438 VALUES_IN_BYTE, histo.alpha_, m->alpha_); 449 return m->alpha_[v >> 24] +
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quant.c | 307 QualityToJPEGCompression(Q, enc->alpha_ / 255.) : 312 const double expn = 1. - amp * enc->dqm_[i].alpha_; [all...] |
frame.c | 675 case 7: *info = mb->alpha_; break;
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/external/ceres-solver/internal/ceres/ |
suitesparse.h | 104 double alpha_[2] = {alpha, 0}; local 106 cholmod_sdmult(A, 0, alpha_, beta_, x, y, &cc_);
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dogleg_strategy.h | 124 double alpha_; member in class:ceres::internal::DoglegStrategy
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dogleg_strategy.cc | 185 alpha_ = gradient_.squaredNorm() / Jg.squaredNorm(); 212 if (gradient_norm * alpha_ >= radius_) { 228 const double b_dot_a = -alpha_ * gradient_.dot(gauss_newton_step_); 229 const double a_squared_norm = pow(alpha_ * gradient_norm, 2.0); 243 dogleg_step = (-alpha_ * (1.0 - beta)) * gradient_
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