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  /external/eigen/doc/snippets/
PartialRedux_squaredNorm.cpp 3 cout << "Here is the square norm of each row:" << endl << m.rowwise().squaredNorm() << endl;
Tutorial_Map_using.cpp 15 cout << "Squared euclidean distance: " << (m1-m2).squaredNorm() << endl;
17 (m1-m2map).squaredNorm() << endl;
  /external/eigen/Eigen/src/SparseCore/
SparseFuzzy.h 24 return (actualA - actualB).squaredNorm() <= prec * prec * numext::mini(actualA.squaredNorm(), actualB.squaredNorm());
SparseDot.h 77 SparseMatrixBase<Derived>::squaredNorm() const
87 return sqrt(squaredNorm());
  /external/eigen/doc/examples/
Tutorial_ReductionsVisitorsBroadcasting_reductions_norm.cpp 18 cout << "v.squaredNorm() = " << v.squaredNorm() << endl;
24 cout << "m.squaredNorm() = " << m.squaredNorm() << endl;
Tutorial_ReductionsVisitorsBroadcasting_broadcast_1nn.cpp 20 (m.colwise() - v).colwise().squaredNorm().minCoeff(&index);
  /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());
geo_alignedbox.cpp 127 VERIFY_IS_APPROX( 53.0f, box.diagonal().squaredNorm() );
154 VERIFY_IS_APPROX( 62, box.diagonal().squaredNorm() );
sparse_vector.cpp 96 VERIFY_IS_APPROX(v1.squaredNorm(), refV1.squaredNorm());
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());
  /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();
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);
ConjugateGradient.h 45 RealScalar rhsNorm2 = rhs.squaredNorm();
54 RealScalar residualNorm2 = residual.squaredNorm();
76 residualNorm2 = residual.squaredNorm();
BasicPreconditioners.h 171 RealScalar sum = mat.innerVector(j).squaredNorm();
  /external/eigen/unsupported/Eigen/src/IterativeSolvers/
IterationController.h 134 { return converged(v.squaredNorm()); }
148 { return finished(double(v.squaredNorm())); }
MINRES.h 41 const RealScalar rhsNorm2(rhs.squaredNorm());
59 RealScalar residualNorm2(v_new.squaredNorm());
  /external/eigen/unsupported/doc/examples/
BVH_Example.cpp 20 double minimumOnObjectObject(const Vector2d &v1, const Vector2d &v2) { ++calls; return (v1 - v2).squaredNorm(); }
  /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);
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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)); }
mpreal_support.cpp 36 VERIFY(Eigen::internal::isApprox(A.array().abs2().sum(), A.squaredNorm()));
  /external/eigen/Eigen/src/Householder/
Householder.h 76 RealScalar tailSqNorm = size()==1 ? RealScalar(0) : tail.squaredNorm();
  /external/eigen/Eigen/src/Geometry/
Umeyama.h 126 const Scalar src_var = src_demean.rowwise().squaredNorm().sum() * one_over_n;

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