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Clean some previous changes and more cuda fixes
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@ -491,10 +491,10 @@ template<typename Derived> class DenseBase
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typedef VectorwiseOp<Derived, Vertical> ColwiseReturnType;
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typedef const VectorwiseOp<const Derived, Vertical> ConstColwiseReturnType;
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ConstRowwiseReturnType rowwise() const;
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RowwiseReturnType rowwise();
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ConstColwiseReturnType colwise() const;
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ColwiseReturnType colwise();
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EIGEN_DEVICE_FUNC ConstRowwiseReturnType rowwise() const;
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EIGEN_DEVICE_FUNC RowwiseReturnType rowwise();
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EIGEN_DEVICE_FUNC ConstColwiseReturnType colwise() const;
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EIGEN_DEVICE_FUNC ColwiseReturnType colwise();
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typedef CwiseNullaryOp<internal::scalar_random_op<Scalar>,PlainObject> RandomReturnType;
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static const RandomReturnType Random(Index rows, Index cols);
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@ -34,7 +34,6 @@
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} \
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};
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namespace Eigen
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{
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EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(real,scalar_real_op)
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@ -62,11 +61,9 @@ namespace Eigen
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EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(round,scalar_round_op)
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EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(floor,scalar_floor_op)
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EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(ceil,scalar_ceil_op)
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#ifndef __CUDACC__
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EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isnan,scalar_isnan_op)
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EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isinf,scalar_isinf_op)
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EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isfinite,scalar_isfinite_op)
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#endif
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template<typename Derived>
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inline const Eigen::CwiseUnaryOp<Eigen::internal::scalar_pow_op<typename Derived::Scalar>, const Derived>
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@ -794,7 +794,6 @@ bool (isfinite)(const T& x)
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#endif
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}
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#ifndef __CUDACC__
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template<typename T>
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bool (isfinite)(const std::complex<T>& x)
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{
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@ -812,7 +811,6 @@ bool (isinf)(const std::complex<T>& x)
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{
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return (numext::isinf(numext::real(x)) || numext::isinf(numext::imag(x))) && (!numext::isnan(x));
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}
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#endif
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template<typename T>
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EIGEN_DEVICE_FUNC
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@ -312,7 +312,7 @@ template<typename Derived> class MatrixBase
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template<bool Enable> inline const Derived& forceAlignedAccessIf() const { return derived(); }
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template<bool Enable> inline Derived& forceAlignedAccessIf() { return derived(); }
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Scalar trace() const;
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EIGEN_DEVICE_FUNC Scalar trace() const;
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template<int p> EIGEN_DEVICE_FUNC RealScalar lpNorm() const;
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@ -644,7 +644,6 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
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* \sa rowwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
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*/
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template<typename Derived>
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EIGEN_DEVICE_FUNC
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inline const typename DenseBase<Derived>::ConstColwiseReturnType
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DenseBase<Derived>::colwise() const
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{
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@ -656,7 +655,6 @@ DenseBase<Derived>::colwise() const
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* \sa rowwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
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*/
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template<typename Derived>
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EIGEN_DEVICE_FUNC
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inline typename DenseBase<Derived>::ColwiseReturnType
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DenseBase<Derived>::colwise()
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{
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@ -671,7 +669,6 @@ DenseBase<Derived>::colwise()
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* \sa colwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
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*/
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template<typename Derived>
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EIGEN_DEVICE_FUNC
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inline const typename DenseBase<Derived>::ConstRowwiseReturnType
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DenseBase<Derived>::rowwise() const
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{
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@ -683,7 +680,6 @@ DenseBase<Derived>::rowwise() const
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* \sa colwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
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*/
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template<typename Derived>
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EIGEN_DEVICE_FUNC
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inline typename DenseBase<Derived>::RowwiseReturnType
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DenseBase<Derived>::rowwise()
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{
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@ -22,6 +22,7 @@ struct visitor_impl
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row = (UnrollCount-1) % Derived::RowsAtCompileTime
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};
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EIGEN_DEVICE_FUNC
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static inline void run(const Derived &mat, Visitor& visitor)
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{
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visitor_impl<Visitor, Derived, UnrollCount-1>::run(mat, visitor);
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@ -32,6 +33,7 @@ struct visitor_impl
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template<typename Visitor, typename Derived>
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struct visitor_impl<Visitor, Derived, 1>
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{
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EIGEN_DEVICE_FUNC
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static inline void run(const Derived &mat, Visitor& visitor)
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{
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return visitor.init(mat.coeff(0, 0), 0, 0);
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@ -41,6 +43,7 @@ struct visitor_impl<Visitor, Derived, 1>
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template<typename Visitor, typename Derived>
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struct visitor_impl<Visitor, Derived, Dynamic>
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{
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EIGEN_DEVICE_FUNC
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static inline void run(const Derived& mat, Visitor& visitor)
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{
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visitor.init(mat.coeff(0,0), 0, 0);
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@ -57,6 +60,7 @@ template<typename XprType>
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class visitor_evaluator
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{
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public:
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EIGEN_DEVICE_FUNC
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explicit visitor_evaluator(const XprType &xpr) : m_evaluator(xpr), m_xpr(xpr) {}
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typedef typename XprType::Scalar Scalar;
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@ -67,11 +71,11 @@ public:
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CoeffReadCost = internal::evaluator<XprType>::CoeffReadCost
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};
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Index rows() const { return m_xpr.rows(); }
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Index cols() const { return m_xpr.cols(); }
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Index size() const { return m_xpr.size(); }
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EIGEN_DEVICE_FUNC Index rows() const { return m_xpr.rows(); }
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EIGEN_DEVICE_FUNC Index cols() const { return m_xpr.cols(); }
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EIGEN_DEVICE_FUNC Index size() const { return m_xpr.size(); }
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CoeffReturnType coeff(Index row, Index col) const
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EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index row, Index col) const
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{ return m_evaluator.coeff(row, col); }
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protected:
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@ -654,7 +654,7 @@ template<typename SolverType> struct direct_selfadjoint_eigenvalues<SolverType,3
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Index k(0), l(2);
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if(d0 > d1)
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{
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std::swap(k,l);
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numext::swap(k,l);
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d0 = d1;
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}
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@ -53,14 +53,17 @@ struct redux {
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void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const
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{
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using namespace Eigen;
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int N = 6;
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int N = 10;
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T x1(in+i);
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out[i*N+0] = x1.minCoeff();
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out[i*N+1] = x1.maxCoeff();
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out[i*N+2] = x1.sum();
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out[i*N+3] = x1.prod();
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// out[i*N+4] = x1.colwise().sum().maxCoeff();
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// out[i*N+5] = x1.rowwise().maxCoeff().sum();
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out[i*N+4] = x1.matrix().squaredNorm();
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out[i*N+5] = x1.matrix().norm();
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out[i*N+6] = x1.colwise().sum().maxCoeff();
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out[i*N+7] = x1.rowwise().maxCoeff().sum();
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out[i*N+8] = x1.matrix().colwise().squaredNorm().sum();
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}
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};
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