| /external/eigen/doc/snippets/ |
| PartialRedux_prod.cpp | 3 cout << "Here is the product of each row:" << endl << m.rowwise().prod() << endl;
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| PartialRedux_squaredNorm.cpp | 3 cout << "Here is the square norm of each row:" << endl << m.rowwise().squaredNorm() << endl;
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| PartialRedux_sum.cpp | 3 cout << "Here is the sum of each row:" << endl << m.rowwise().sum() << endl;
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| MatrixBase_rowwise.cpp | 3 cout << "Here is the sum of each row:" << endl << m.rowwise().sum() << endl;
5 << endl << m.cwiseAbs().rowwise().maxCoeff() << endl;
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| Vectorwise_reverse.cpp | 3 cout << "Here is the rowwise reverse of m:" << endl << m.rowwise().reverse() << endl;
7 << m.rowwise().reverse()(1,0) << endl;
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| DirectionWise_replicate_int.cpp | 3 cout << "v.rowwise().replicate(5) = ..." << endl;
4 cout << v.rowwise().replicate(5) << endl;
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| PartialRedux_count.cpp | 3 Matrix<ptrdiff_t, 3, 1> res = (m.array() >= 0.5).rowwise().count();
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| /external/eigen/doc/examples/ |
| Tutorial_ReductionsVisitorsBroadcasting_reductions_operatornorm.cpp | 16 cout << "infty-norm(m) = " << m.cwiseAbs().rowwise().sum().maxCoeff()
17 << " == " << m.rowwise().lpNorm<1>().maxCoeff() << endl;
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| Tutorial_ReductionsVisitorsBroadcasting_rowwise.cpp | 12 << mat.rowwise().maxCoeff() << std::endl;
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| Tutorial_ReductionsVisitorsBroadcasting_broadcast_simple_rowwise.cpp | 16 mat.rowwise() += v.transpose();
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| /external/eigen/test/ |
| vectorwiseop.cpp | 46 m2.rowwise() += rowvec;
47 VERIFY_IS_APPROX(m2, m1.rowwise() + rowvec);
50 VERIFY_RAISES_ASSERT(m2.rowwise() += rowvec.transpose());
51 VERIFY_RAISES_ASSERT(m1.rowwise() + rowvec.transpose());
64 m2.rowwise() -= rowvec;
65 VERIFY_IS_APPROX(m2, m1.rowwise() - rowvec);
68 VERIFY_RAISES_ASSERT(m2.rowwise() -= rowvec.transpose());
69 VERIFY_RAISES_ASSERT(m1.rowwise() - rowvec.transpose());
82 m2.rowwise() *= rowvec;
83 VERIFY_IS_APPROX(m2, m1.rowwise() * rowvec)
[all...] |
| geo_homogeneous.cpp | 64 VERIFY_IS_APPROX((v0.transpose().rowwise().homogeneous().eval()) * t2,
65 v0.transpose().rowwise().homogeneous() * t2);
66 VERIFY_IS_APPROX((m0.transpose().rowwise().homogeneous().eval()) * t2,
67 m0.transpose().rowwise().homogeneous() * t2);
70 VERIFY_IS_APPROX((v0.transpose().rowwise().homogeneous().eval()) * t3,
71 v0.transpose().rowwise().homogeneous() * t3);
72 VERIFY_IS_APPROX((m0.transpose().rowwise().homogeneous().eval()) * t3,
73 m0.transpose().rowwise().homogeneous() * t3);
113 VERIFY_IS_APPROX( (pts.transpose().rowwise().homogeneous() .lazyProduct( t2 )).rowwise().hnormalized(), (pts1.transpose()*t2).rowwise().hnormalized() )
[all...] |
| stable_norm.cpp | 105 VERIFY_IS_APPROX(vrand.rowwise().stableNorm(), vrand.rowwise().norm());
106 VERIFY_IS_APPROX(vrand.rowwise().blueNorm(), vrand.rowwise().norm());
107 VERIFY_IS_APPROX(vrand.rowwise().hypotNorm(), vrand.rowwise().norm());
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| array_reverse.cpp | 84 MatrixType m1_rr = m1.rowwise().reverse();
85 // Verify that PartialRedux::reverse() works (for rowwise())
113 m2.rowwise().reverseInPlace();
114 VERIFY_IS_APPROX(m2,m1.rowwise().reverse().eval());
123 m1.rowwise().reverse()(r, c) = x;
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| array_for_matrix.cpp | 45 VERIFY_IS_MUCH_SMALLER_THAN(m1.rowwise().sum().sum() - m1.sum(), m1.squaredNorm());
47 VERIFY_IS_MUCH_SMALLER_THAN(m1.rowwise().sum() - m2.rowwise().sum() - (m1-m2).rowwise().sum(), (m1-m2).squaredNorm());
56 VERIFY_IS_APPROX(m3.rowwise() += rv1, m1.rowwise() + rv1);
58 VERIFY_IS_APPROX(m3.rowwise() -= rv1, m1.rowwise() - rv1);
62 VERIFY_IS_APPROX(m1.block(0,0,rows,0).rowwise().prod(), ColVectorType::Ones(rows));
145 // TODO allows colwise/rowwise for arra
[all...] |
| array_replicate.cpp | 64 VERIFY_IS_APPROX(x2, v1.rowwise().replicate(f1));
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| array.cpp | 70 VERIFY_IS_APPROX(m1.abs().rowwise().sum().sum(), m1.abs().sum());
73 VERIFY_IS_MUCH_SMALLER_THAN(abs(m1.rowwise().sum().sum() - m1.sum()), m1.abs().sum());
75 VERIFY_IS_NOT_APPROX(((m1+m2).rowwise().sum()).sum(), m1.sum());
84 VERIFY_IS_APPROX(m3.rowwise() += rv1, m1.rowwise() + rv1);
86 VERIFY_IS_APPROX(m3.rowwise() -= rv1, m1.rowwise() - rv1);
202 // TODO allows colwise/rowwise for array
204 VERIFY_IS_APPROX(((m1.abs()+1)>RealScalar(0.1)).rowwise().count(), ArrayOfIndices::Constant(rows, cols));
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| eigensolver_complex.cpp | 66 Matrix<RealScalar,Dynamic,Dynamic> diffs = (vec1.rowwise().replicate(n) - vec2.rowwise().replicate(n).transpose()).cwiseAbs2();
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| geo_orthomethods.cpp | 47 // colwise/rowwise cross product
61 mcross = mat3.rowwise().cross(vec3);
103 // colwise/rowwise cross product
114 mcrossN3 = matN3.rowwise().cross(vec3);
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| householder.cpp | 69 m3.rowwise() = v1.transpose();
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| /external/eigen/Eigen/src/Geometry/ |
| Umeyama.h | 118 const VectorType src_mean = src.rowwise().sum() * one_over_n;
119 const VectorType dst_mean = dst.rowwise().sum() * one_over_n;
126 const Scalar src_var = src_demean.rowwise().squaredNorm().sum() * one_over_n;
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| /external/eigen/doc/ |
| tutorial.cpp | 46 std::cout << "m4.rowwise().sum():\n" << m4.rowwise().sum() << std::endl;
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| /external/eigen/unsupported/test/ |
| cxx11_tensor_forced_eval.cpp | 54 output.rowwise() -= input.colwise().maxCoeff();
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| /external/eigen/Eigen/src/Core/ |
| Reverse.h | 186 xpr.leftCols(half).swap(xpr.rightCols(half).rowwise().reverse());
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| /external/eigen/unsupported/Eigen/src/AutoDiff/ |
| AutoDiffVector.h | 64 Scalar sum() const { /*std::cerr << "sum \n\n";*/ /*std::cerr << m_jacobian.rowwise().sum() << "\n\n";*/ return Scalar(m_values.sum(), m_jacobian.rowwise().sum()); }
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