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* add Regression module, from eigen1, improved, with doc and unit-test.
* fix .normalized() so that Random().normalized() works; since the return type became complicated to write down i just let it return an actual vector, perhaps not optimal. * add Sparse/CMakeLists.txt. I suppose that it was intentional that it didn't have CMakeLists, but in <=2.0 releases I'll just manually remove Sparse.
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@@ -292,10 +292,13 @@ inline typename NumTraits<typename ei_traits<Derived>::Scalar>::Real MatrixBase<
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* \sa norm(), normalize()
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*/
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template<typename Derived>
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inline const typename MatrixBase<Derived>::ScalarQuotient1ReturnType
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inline const typename MatrixBase<Derived>::EvalType
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MatrixBase<Derived>::normalized() const
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{
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return *this / norm();
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typedef typename ei_nested<Derived>::type Nested;
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typedef typename ei_unref<Nested>::type _Nested;
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_Nested n(derived());
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return n / n.norm();
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}
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/** Normalizes the vector, i.e. divides it by its own norm.
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@@ -198,6 +198,15 @@ class Matrix : public MatrixBase<Matrix<_Scalar, _Rows, _Cols, _MaxRows, _MaxCol
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m_storage.resize(rows * cols, rows, cols);
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}
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inline void resize(int size)
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{
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EIGEN_STATIC_ASSERT_VECTOR_ONLY(Matrix)
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if(RowsAtCompileTime == 1)
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m_storage.resize(size, 1, size);
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else
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m_storage.resize(size, size, 1);
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}
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/** Copies the value of the expression \a other into *this.
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*
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* *this is resized (if possible) to match the dimensions of \a other.
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@@ -330,7 +330,7 @@ template<typename Derived> class MatrixBase
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Scalar dot(const MatrixBase<OtherDerived>& other) const;
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RealScalar norm2() const;
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RealScalar norm() const;
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const ScalarQuotient1ReturnType normalized() const;
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const EvalType normalized() const;
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void normalize();
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Transpose<Derived> transpose();
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@@ -37,13 +37,7 @@
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#define EIGEN_UNROLLING_LIMIT 100
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#endif
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#ifdef EIGEN_DEFAULT_TO_ROW_MAJOR
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#define EIGEN_DEFAULT_MATRIX_STORAGE_ORDER RowMajorBit
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#else
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#define EIGEN_DEFAULT_MATRIX_STORAGE_ORDER 0
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#endif
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#define EIGEN_DEFAULT_MATRIX_FLAGS EIGEN_DEFAULT_MATRIX_STORAGE_ORDER
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#define EIGEN_DEFAULT_MATRIX_FLAGS 0
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/** Define a hint size when dealing with large matrices and L2 cache friendlyness
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* More precisely, its square value represents the amount of bytes which can be assumed to stay in L2 cache.
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