/external/eigen/unsupported/test/ |
H A D | cxx11_tensor_contraction.cpp | 22 Tensor<float, 2, DataLayout> mat1(2, 3); 26 mat1.setRandom(); 33 typedef TensorEvaluator<decltype(mat1.contract(mat2, dims3)), DefaultDevice> Evaluator; 34 Evaluator eval(mat1.contract(mat2, dims3), DefaultDevice()); 40 VERIFY_IS_APPROX(mat4(0,0), mat1(0,0)*mat2(0,0) + mat1(1,0)*mat2(1,0)); 41 VERIFY_IS_APPROX(mat4(0,1), mat1(0,0)*mat2(0,1) + mat1(1,0)*mat2(1,1)); 42 VERIFY_IS_APPROX(mat4(0,2), mat1(0,0)*mat2(0,2) + mat1( [all...] |
H A D | cxx11_tensor_comparisons.cpp | 19 Tensor<float, 3> mat1(2,3,7); 26 mat1.setRandom(); 29 lt = mat1 < mat2; 30 le = mat1 <= mat2; 31 gt = mat1 > mat2; 32 ge = mat1 >= mat2; 37 VERIFY_IS_EQUAL(lt(i,j,k), mat1(i,j,k) < mat2(i,j,k)); 38 VERIFY_IS_EQUAL(le(i,j,k), mat1(i,j,k) <= mat2(i,j,k)); 39 VERIFY_IS_EQUAL(gt(i,j,k), mat1(i,j,k) > mat2(i,j,k)); 40 VERIFY_IS_EQUAL(ge(i,j,k), mat1( [all...] |
H A D | cxx11_tensor_lvalue.cpp | 20 Tensor<float, 3> mat1(2,3,7); 24 mat1.setRandom(); 26 mat3 = mat1; 32 VERIFY_IS_APPROX(mat3(i,j,k), mat1(i,j,k) + mat2(i,j,k));
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H A D | cxx11_tensor_of_const_values.cpp | 20 TensorMap<Tensor<const float, 2>> mat1(data1, 2, 3); 30 rslt1 = mat1; 34 Tensor<float, 2> rslt3 = mat1; 37 Tensor<float, 2> rslt5(mat1); 56 TensorMap<Tensor<const float, 2>> mat1(data1, 2, 3); 66 sum1 = mat1 + mat2; 68 sum2 = mat2 + mat1; 82 TensorMap<Tensor<const float, 2>> mat1(data1, 2, 3); 90 mat2 += mat1;
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H A D | cxx11_tensor_expr.cpp | 72 TensorMap<Tensor<float, 2>> mat1(data1, 2, 3); 76 mat1(0,0) = 0.0; 77 mat1(0,1) = 1.0; 78 mat1(0,2) = 2.0; 79 mat1(1,0) = 3.0; 80 mat1(1,1) = 4.0; 81 mat1(1,2) = 5.0; 92 mat3 = mat1.abs(); 112 Tensor<float, 3> mat1(2,3,7); 119 mat1( [all...] |
H A D | cxx11_tensor_fixed_size.cpp | 133 TensorMap<TensorFixedSize<float, Sizes<2, 3> > > mat1(data1,2,3); 137 VERIFY_IS_EQUAL((mat1.size()), 2*3); 138 VERIFY_IS_EQUAL(mat1.rank(), 2); 139 // VERIFY_IS_EQUAL((mat1.dimension(0)), 2); 140 // VERIFY_IS_EQUAL((mat1.dimension(1)), 3); 142 mat1(0,0) = 0.0; 143 mat1(0,1) = 1.0; 144 mat1(0,2) = 2.0; 145 mat1(1,0) = 3.0; 146 mat1( 182 TensorFixedSize<float, Sizes<2, 3, 7> > mat1; local 228 TensorFixedSize<float, Sizes<2, 3, 7> > mat1; local [all...] |
H A D | cxx11_tensor_assign.cpp | 74 Tensor<int, 2> mat1(2,3); 77 mat1(0,0) = 0; 78 mat1(0,1) = 1; 79 mat1(0,2) = 2; 80 mat1(1,0) = 3; 81 mat1(1,1) = 4; 82 mat1(1,2) = 5; 98 mat3 = mat1; 115 mat1.setZero(); 117 mat1 [all...] |
H A D | cxx11_tensor_map.cpp | 71 Tensor<int, 2> mat1(2,3); 74 mat1(0,0) = 0; 75 mat1(0,1) = 1; 76 mat1(0,2) = 2; 77 mat1(1,0) = 3; 78 mat1(1,1) = 4; 79 mat1(1,2) = 5; 88 TensorMap<Tensor<const int, 2> > mat3(mat1.data(), 2, 3); 118 Tensor<int, 3> mat1(2,3,7); 125 mat1( [all...] |
H A D | cxx11_tensor_simple.cpp | 115 Tensor<int, 2> mat1(2,3); 118 mat1(0,0) = 0; 119 mat1(0,1) = 1; 120 mat1(0,2) = 2; 121 mat1(1,0) = 3; 122 mat1(1,1) = 4; 123 mat1(1,2) = 5; 132 VERIFY_IS_EQUAL((mat1.rank()), 2); 133 VERIFY_IS_EQUAL((mat1.size()), 6); 134 VERIFY_IS_EQUAL((mat1 [all...] |
H A D | cxx11_tensor_forced_eval.cpp | 25 TensorMap<Tensor<float, 2> > mat1(m1.data(), 3,3); 29 mat3 = mat1;
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H A D | cxx11_tensor_of_strings.cpp | 20 TensorMap<Tensor<std::string, 2>> mat1(data1, 2, 3); 34 rslt1 = mat1; 38 Tensor<std::string, 2> rslt3 = mat1; 41 Tensor<std::string, 2> rslt5(mat1);
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/external/vixl/examples/aarch64/ |
H A D | neon-matrix-multiply.cc | 60 // mat1 -> x1 114 float mat1[kLength], mat2[kLength], output[kLength]; local 121 // float mat1[kLength] = { 1.0f, 52.03f, 4.43f, ... }; 124 mat1[0] = 1.0f; 125 mat1[4] = 2.0f; 126 mat1[8] = 3.0f; 127 mat1[12] = 4.0f; 128 mat1[1] = 52.03f; 129 mat1[5] = 12.24f; 130 mat1[ [all...] |
/external/eigen/doc/snippets/ |
H A D | Tutorial_AdvancedInitialization_ThreeWays.cpp | 2 MatrixXd mat1(size, size); 3 mat1.topLeftCorner(size/2, size/2) = MatrixXd::Zero(size/2, size/2); 4 mat1.topRightCorner(size/2, size/2) = MatrixXd::Identity(size/2, size/2); 5 mat1.bottomLeftCorner(size/2, size/2) = MatrixXd::Identity(size/2, size/2); 6 mat1.bottomRightCorner(size/2, size/2) = MatrixXd::Zero(size/2, size/2); 7 std::cout << mat1 << std::endl << std::endl;
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/external/eigen/test/ |
H A D | evaluators.cpp | 244 MatrixXd mat1(6,6), mat2(6,6); 245 VERIFY_IS_APPROX_EVALUATOR(mat1, MatrixXd::Identity(6,6)); 246 VERIFY_IS_APPROX_EVALUATOR(mat2, mat1); 247 copy_using_evaluator(mat2.transpose(), mat1); 248 VERIFY_IS_APPROX(mat2.transpose(), mat1); 256 VERIFY_IS_APPROX_EVALUATOR(mat2, mat1); 326 mat1.setRandom(); 329 copy_using_evaluator(matXcd.real(), mat1); 331 matXcd_ref.real() = mat1; 349 VERIFY_IS_APPROX_EVALUATOR(vec1, mat1 392 MatrixXd mat1, mat2, mat1ref, mat2ref; local [all...] |
H A D | product_large.cpp | 79 MatrixXf mat1(10,32); mat1.setRandom(); 81 MatrixXf r1 = mat1.row(2)*mat2.transpose(); 82 VERIFY_IS_APPROX(r1, (mat1.row(2)*mat2.transpose()).eval()); 84 MatrixXf r2 = mat1.row(2)*mat2; 85 VERIFY_IS_APPROX(r2, (mat1.row(2)*mat2).eval());
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H A D | ref.cpp | 100 MatrixType mat1 = MatrixType::Random(size,size), local 101 mat2 = mat1, 132 RefMatWithStride rm5 = mat1.row(i).transpose(); 133 VERIFY_IS_EQUAL(rm5, mat1.row(i).transpose()); 136 VERIFY_IS_EQUAL(mat1, mat2); 139 VERIFY_IS_APPROX(mat1, mat2);
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/external/vixl/test/aarch64/examples/ |
H A D | test-examples.cc | 252 float mat1[kLength], mat2[kLength], expected[kLength], output[kLength]; local 256 mat1[0] = 1.0f; 257 mat1[4] = 2.0f; 258 mat1[8] = 3.0f; 259 mat1[12] = 4.0f; 260 mat1[1] = 52.03f; 261 mat1[5] = 12.24f; 262 mat1[9] = 53.56f; 263 mat1[13] = 22.22f; 264 mat1[ [all...] |
/external/autotest/client/site_tests/graphics_SanAngeles/src/ |
H A D | matrixop.h | 14 // result = mat1 * mat2 16 Matrix4x4 mat1, Matrix4x4 mat2);
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H A D | matrixop.c | 20 void Matrix4x4_Multiply(Matrix4x4 result, Matrix4x4 mat1, Matrix4x4 mat2) argument 29 tmp[i*4 + j] += mat1[i*4 + k] * mat2[k*4 + j];
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/external/opencv/cxcore/src/ |
H A D | cxnorm.cpp | 979 CvMat stub1, *mat1 = (CvMat*)imgB; local 985 if( !mat1 ) 987 mat1 = mat2; 1009 if( CV_IS_MAT(mat1) && (!mat2 || CV_IS_MAT(mat2)) && !mask ) 1013 if( !CV_ARE_TYPES_EQ( mat1, mat2 )) 1016 if( !CV_ARE_SIZES_EQ( mat1, mat2 )) 1022 size = cvGetMatSize( mat1 ); 1023 type = CV_MAT_TYPE(mat1->type); 1027 if( CV_IS_MAT_CONT( mat1->type & mat2_flag )) 1035 const float* src1data = mat1 [all...] |
/external/autotest/client/cros/ |
H A D | ec.py | 416 mat1 = re.match(mat1_re, ln) 417 if mat1: 418 flash_dict['ptype'] = int(mat1.group(1)) 419 flash_dict['vid'] = mat1.group(2) 420 flash_dict['pid'] = mat1.group(3)
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/external/opencv/cv/src/ |
H A D | cvaccum.cpp | 594 CvMat stub1, *mat1 = (CvMat*)arrA; local 605 CV_CALL( mat1 = cvGetMat( mat1, &stub1, &coi1 )); 612 if( !CV_ARE_CNS_EQ( mat1, mat2 ) || !CV_ARE_CNS_EQ( mat1, sum )) 618 if( !CV_ARE_SIZES_EQ( mat1, sum ) || !CV_ARE_SIZES_EQ( mat2, sum )) 621 size = cvGetMatSize( mat1 ); 622 type = CV_MAT_TYPE( mat1->type ); 624 mat1_step = mat1->step; 637 if( CV_IS_MAT_CONT( mat1 [all...] |
/external/eigen/Eigen/src/IterativeLinearSolvers/ |
H A D | IncompleteLUT.h | 230 SparseMatrix<Scalar,ColMajor, StorageIndex> mat1 = amat; local 232 // FIXME for a matrix with nearly symmetric pattern, mat2+mat1 is the appropriate choice. 233 // on the other hand for a really non-symmetric pattern, mat2*mat1 should be prefered... 234 SparseMatrix<Scalar,ColMajor, StorageIndex> AtA = mat2 + mat1; 240 SparseMatrix<Scalar,ColMajor, StorageIndex> mat1 = amat; 242 ordering(mat1,m_Pinv);
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/external/opencv/cxcore/include/ |
H A D | cxtypes.h | 593 #define CV_ARE_TYPES_EQ(mat1, mat2) \ 594 ((((mat1)->type ^ (mat2)->type) & CV_MAT_TYPE_MASK) == 0) 596 #define CV_ARE_CNS_EQ(mat1, mat2) \ 597 ((((mat1)->type ^ (mat2)->type) & CV_MAT_CN_MASK) == 0) 599 #define CV_ARE_DEPTHS_EQ(mat1, mat2) \ 600 ((((mat1)->type ^ (mat2)->type) & CV_MAT_DEPTH_MASK) == 0) 602 #define CV_ARE_SIZES_EQ(mat1, mat2) \ 603 ((mat1)->rows == (mat2)->rows && (mat1)->cols == (mat2)->cols)
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/external/tensorflow/tensorflow/contrib/kfac/python/ops/ |
H A D | utils.py | 119 def kronecker_product(mat1, mat2): 121 m1, n1 = mat1.get_shape().as_list() 122 mat1_rsh = array_ops.reshape(mat1, [m1, 1, n1, 1])
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