/external/eigen/doc/snippets/ |
PartialRedux_squaredNorm.cpp | 3 cout << "Here is the square norm of each row:" << endl << m.rowwise().squaredNorm() << endl;
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Tutorial_Map_using.cpp | 15 cout << "Squared euclidean distance: " << (m1-m2).squaredNorm() << endl; 17 (m1-m2map).squaredNorm() << endl;
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/external/eigen/Eigen/src/SparseCore/ |
SparseFuzzy.h | 24 return (actualA - actualB).squaredNorm() <= prec * prec * numext::mini(actualA.squaredNorm(), actualB.squaredNorm());
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SparseDot.h | 77 SparseMatrixBase<Derived>::squaredNorm() const 87 return sqrt(squaredNorm());
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/external/eigen/doc/examples/ |
Tutorial_ReductionsVisitorsBroadcasting_reductions_norm.cpp | 18 cout << "v.squaredNorm() = " << v.squaredNorm() << endl; 24 cout << "m.squaredNorm() = " << m.squaredNorm() << endl;
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Tutorial_ReductionsVisitorsBroadcasting_broadcast_1nn.cpp | 20 (m.colwise() - v).colwise().squaredNorm().minCoeff(&index);
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/external/eigen/Eigen/src/Core/ |
Dot.h | 63 * \sa squaredNorm(), norm() 93 EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::squaredNorm() const 102 * \sa lpNorm(), dot(), squaredNorm() 107 return numext::sqrt(squaredNorm()); 125 RealScalar z = n.squaredNorm(); 144 RealScalar z = squaredNorm(); 169 RealScalar z = (n/w).squaredNorm(); 191 RealScalar z = (derived()/w).squaredNorm(); 284 return numext::abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm(); [all...] |
/external/eigen/test/ |
stable_norm.cpp | 89 VERIFY_IS_NOT_APPROX(sqrt(copy(vbig.squaredNorm())), abs(sqrt(size)*big)); // here the default norm must fail 96 VERIFY_IS_NOT_APPROX(sqrt(copy(vsmall.squaredNorm())), abs(sqrt(size)*small)); // here the default norm must fail 118 VERIFY(!(numext::isfinite)(v.squaredNorm())); VERIFY((numext::isnan)(v.squaredNorm())); 129 VERIFY(!(numext::isfinite)(v.squaredNorm())); VERIFY(isPlusInf(v.squaredNorm())); 143 VERIFY(!(numext::isfinite)(v.squaredNorm())); VERIFY(isPlusInf(v.squaredNorm())); 160 VERIFY(!(numext::isfinite)(v.squaredNorm())); VERIFY((numext::isnan)(v.squaredNorm())); [all...] |
eigensolver_complex.cpp | 65 RealScalar tol = test_precision<RealScalar>()*test_precision<RealScalar>()*numext::maxi(vec1.squaredNorm(),vec2.squaredNorm());
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geo_alignedbox.cpp | 127 VERIFY_IS_APPROX( 53.0f, box.diagonal().squaredNorm() ); 154 VERIFY_IS_APPROX( 62, box.diagonal().squaredNorm() );
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sparse_vector.cpp | 96 VERIFY_IS_APPROX(v1.squaredNorm(), refV1.squaredNorm());
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array_for_matrix.cpp | 44 VERIFY_IS_MUCH_SMALLER_THAN(m1.colwise().sum().sum() - m1.sum(), m1.squaredNorm()); 45 VERIFY_IS_MUCH_SMALLER_THAN(m1.rowwise().sum().sum() - m1.sum(), m1.squaredNorm()); 46 VERIFY_IS_MUCH_SMALLER_THAN(m1.colwise().sum() + m2.colwise().sum() - (m1+m2).colwise().sum(), (m1+m2).squaredNorm()); 47 VERIFY_IS_MUCH_SMALLER_THAN(m1.rowwise().sum() - m2.rowwise().sum() - (m1-m2).rowwise().sum(), (m1-m2).squaredNorm());
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/external/eigen/Eigen/src/IterativeLinearSolvers/ |
LeastSquareConjugateGradient.h | 46 RealScalar rhsNorm2 = (mat.adjoint()*rhs).squaredNorm(); 55 RealScalar residualNorm2 = normal_residual.squaredNorm(); 73 Scalar alpha = absNew / tmp.squaredNorm(); // the amount we travel on dir 78 residualNorm2 = normal_residual.squaredNorm();
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BiCGSTAB.h | 45 RealScalar r0_sqnorm = r0.squaredNorm(); 46 RealScalar rhs_sqnorm = rhs.squaredNorm(); 67 while ( r.squaredNorm() > tol2 && i<maxIters ) 78 rho = r0_sqnorm = r.squaredNorm(); 95 RealScalar tmp = t.squaredNorm(); 104 tol_error = sqrt(r.squaredNorm()/rhs_sqnorm);
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ConjugateGradient.h | 45 RealScalar rhsNorm2 = rhs.squaredNorm(); 54 RealScalar residualNorm2 = residual.squaredNorm(); 76 residualNorm2 = residual.squaredNorm();
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BasicPreconditioners.h | 171 RealScalar sum = mat.innerVector(j).squaredNorm();
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/external/eigen/unsupported/Eigen/src/IterativeSolvers/ |
IterationController.h | 134 { return converged(v.squaredNorm()); } 148 { return finished(double(v.squaredNorm())); }
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MINRES.h | 41 const RealScalar rhsNorm2(rhs.squaredNorm()); 59 RealScalar residualNorm2(v_new.squaredNorm());
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/external/eigen/unsupported/doc/examples/ |
BVH_Example.cpp | 20 double minimumOnObjectObject(const Vector2d &v1, const Vector2d &v2) { ++calls; return (v1 - v2).squaredNorm(); }
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/external/eigen/unsupported/test/ |
NonLinearOptimization.cpp | 692 VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.1304802941E+02); 713 VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.1304802941E+02); 772 VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.2455138894E-01); 789 VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.2455138894E-01); 862 VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.5324382854E+00); 884 VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.5324382854E+00); 948 VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6419295283E-02); 965 VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6419295283E-02); [all...] |
levenberg_marquardt.cpp | 302 VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.1304802941E+02); 323 VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.1304802941E+02); 382 VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.2455138894E-01); 399 VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.2455138894E-01); 473 VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.5324382854E+00); 495 VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.5324382854E+00); 559 VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.6419295283E-02); 576 VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.6419295283E-02); 637 VERIFY(lm.fvec().squaredNorm() <= 1.4307867721E-25); 658 VERIFY(lm.fvec().squaredNorm() <= 1.4307867721E-25) [all...] |
BVH.cpp | 55 if((b.center - p).squaredNorm() < SQR(b.radius)) 72 if((b.center - v).squaredNorm() < SQR(b.radius)) 78 double minimumOnObject(const BallType &b) { ++calls; return (std::max)(0., (b.center - p).squaredNorm() - SQR(b.radius)); }
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mpreal_support.cpp | 36 VERIFY(Eigen::internal::isApprox(A.array().abs2().sum(), A.squaredNorm()));
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/external/eigen/Eigen/src/Householder/ |
Householder.h | 76 RealScalar tailSqNorm = size()==1 ? RealScalar(0) : tail.squaredNorm();
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/external/eigen/Eigen/src/Geometry/ |
Umeyama.h | 126 const Scalar src_var = src_demean.rowwise().squaredNorm().sum() * one_over_n;
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