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renaming: ei_matrix_storage -> DenseStorage
DenseStorageBase -> PlainObjectBase
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@@ -168,7 +168,7 @@ The right-hand side can also contain matrix expressions as discussed in \ref Tut
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\section TutorialMatrixSizesResizing Resizing
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The current size of a matrix can be retrieved by \link EigenBase::rows() rows()\endlink, \link EigenBase::cols() cols() \endlink and \link EigenBase::size() size()\endlink. These methods return the number of rows, the number of columns and the number of coefficients, respectively. Resizing a dynamic-size matrix is done by the \link DenseStorageBase::resize(Index,Index) resize() \endlink method.
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The current size of a matrix can be retrieved by \link EigenBase::rows() rows()\endlink, \link EigenBase::cols() cols() \endlink and \link EigenBase::size() size()\endlink. These methods return the number of rows, the number of columns and the number of coefficients, respectively. Resizing a dynamic-size matrix is done by the \link PlainObjectBase::resize(Index,Index) resize() \endlink method.
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<table class="example">
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<tr><th>Example:</th><th>Output:</th></tr>
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@@ -178,7 +178,7 @@ The current size of a matrix can be retrieved by \link EigenBase::rows() rows()\
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</tr></table>
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The resize() method is a no-operation if the actual matrix size doesn't change; otherwise it is destructive: the values of the coefficients may change.
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If you want a conservative variant of resize() which does not change the coefficients, use \link DenseStorageBase::conservativeResize() conservativeResize()\endlink, see \ref TopicResizing "this page" for more details.
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If you want a conservative variant of resize() which does not change the coefficients, use \link PlainObjectBase::conservativeResize() conservativeResize()\endlink, see \ref TopicResizing "this page" for more details.
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All these methods are still available on fixed-size matrices, for the sake of API uniformity. Of course, you can't actually
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resize a fixed-size matrix. Trying to change a fixed size to an actually different value will trigger an assertion failure;
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@@ -4,7 +4,7 @@ namespace Eigen {
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Hello! You are seeing this webpage because your program terminated on an assertion failure like this one:
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<pre>
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my_program: path/to/eigen/Eigen/src/Core/MatrixStorage.h:44:
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my_program: path/to/eigen/Eigen/src/Core/DenseStorage.h:44:
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Eigen::ei_matrix_array<T, Size, MatrixOptions, Align>::ei_matrix_array()
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[with T = double, int Size = 2, int MatrixOptions = 2, bool Align = true]:
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Assertion `(reinterpret_cast<size_t>(array) & 0xf) == 0 && "this assertion
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@@ -87,30 +87,30 @@ When we do
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\code
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Eigen::VectorXf u(size);
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\endcode
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the constructor that is called is Matrix::Matrix(int), in src/Core/Matrix.h. Besides some assertions, all it does is to construct the \a m_storage member, which is of type ei_matrix_storage\<float, Dynamic, Dynamic, 1\>.
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the constructor that is called is Matrix::Matrix(int), in src/Core/Matrix.h. Besides some assertions, all it does is to construct the \a m_storage member, which is of type DenseStorage\<float, Dynamic, Dynamic, 1\>.
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You may wonder, isn't it overengineering to have the storage in a separate class? The reason is that the Matrix class template covers all kinds of matrices and vector: both fixed-size and dynamic-size. The storage method is not the same in these two cases. For fixed-size, the matrix coefficients are stored as a plain member array. For dynamic-size, the coefficients will be stored as a pointer to a dynamically-allocated array. Because of this, we need to abstract storage away from the Matrix class. That's ei_matrix_storage.
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You may wonder, isn't it overengineering to have the storage in a separate class? The reason is that the Matrix class template covers all kinds of matrices and vector: both fixed-size and dynamic-size. The storage method is not the same in these two cases. For fixed-size, the matrix coefficients are stored as a plain member array. For dynamic-size, the coefficients will be stored as a pointer to a dynamically-allocated array. Because of this, we need to abstract storage away from the Matrix class. That's DenseStorage.
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Let's look at this constructor, in src/Core/MatrixStorage.h. You can see that there are many partial template specializations of ei_matrix_storages here, treating separately the cases where dimensions are Dynamic or fixed at compile-time. The partial specialization that we are looking at is:
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Let's look at this constructor, in src/Core/DenseStorage.h. You can see that there are many partial template specializations of DenseStorages here, treating separately the cases where dimensions are Dynamic or fixed at compile-time. The partial specialization that we are looking at is:
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\code
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template<typename T, int _Cols> class ei_matrix_storage<T, Dynamic, Dynamic, _Cols>
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template<typename T, int _Cols> class DenseStorage<T, Dynamic, Dynamic, _Cols>
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\endcode
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Here, the constructor called is ei_matrix_storage::ei_matrix_storage(int size, int rows, int columns)
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Here, the constructor called is DenseStorage::DenseStorage(int size, int rows, int columns)
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with size=50, rows=50, columns=1.
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Here is this constructor:
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\code
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inline ei_matrix_storage(int size, int rows, int) : m_data(ei_aligned_new<T>(size)), m_rows(rows) {}
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inline DenseStorage(int size, int rows, int) : m_data(ei_aligned_new<T>(size)), m_rows(rows) {}
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\endcode
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Here, the \a m_data member is the actual array of coefficients of the matrix. As you see, it is dynamically allocated. Rather than calling new[] or malloc(), as you can see, we have our own ei_aligned_new defined in src/Core/util/Memory.h. What it does is that if vectorization is enabled, then it uses a platform-specific call to allocate a 128-bit-aligned array, as that is very useful for vectorization with both SSE2 and AltiVec. If vectorization is disabled, it amounts to the standard new[].
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As you can see, the constructor also sets the \a m_rows member to \a size. Notice that there is no \a m_columns member: indeed, in this partial specialization of ei_matrix_storage, we know the number of columns at compile-time, since the _Cols template parameter is different from Dynamic. Namely, in our case, _Cols is 1, which is to say that our vector is just a matrix with 1 column. Hence, there is no need to store the number of columns as a runtime variable.
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As you can see, the constructor also sets the \a m_rows member to \a size. Notice that there is no \a m_columns member: indeed, in this partial specialization of DenseStorage, we know the number of columns at compile-time, since the _Cols template parameter is different from Dynamic. Namely, in our case, _Cols is 1, which is to say that our vector is just a matrix with 1 column. Hence, there is no need to store the number of columns as a runtime variable.
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When you call VectorXf::data() to get the pointer to the array of coefficients, it returns ei_matrix_storage::data() which returns the \a m_data member.
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When you call VectorXf::data() to get the pointer to the array of coefficients, it returns DenseStorage::data() which returns the \a m_data member.
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When you call VectorXf::size() to get the size of the vector, this is actually a method in the base class MatrixBase. It determines that the vector is a column-vector, since ColsAtCompileTime==1 (this comes from the template parameters in the typedef VectorXf). It deduces that the size is the number of rows, so it returns VectorXf::rows(), which returns ei_matrix_storage::rows(), which returns the \a m_rows member, which was set to \a size by the constructor.
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When you call VectorXf::size() to get the size of the vector, this is actually a method in the base class MatrixBase. It determines that the vector is a column-vector, since ColsAtCompileTime==1 (this comes from the template parameters in the typedef VectorXf). It deduces that the size is the number of rows, so it returns VectorXf::rows(), which returns DenseStorage::rows(), which returns the \a m_rows member, which was set to \a size by the constructor.
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\section ConstructionOfSumXpr Construction of the sum expression
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@@ -52,13 +52,13 @@ objects.
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These classes serve as base classes for the five core classes mentioned above. They are more internal and so
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less interesting for users of the Eigen library.
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- DenseStorageBase means dense (matrix or array) plain object, i.e. something that stores its own dense
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array of coefficients. This is where, for instance, the \link DenseStorageBase::resize() resize() \endlink
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methods go. \c %DenseStorageBase is inherited by \c %Matrix and by \c %Array. But above, we said that
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- PlainObjectBase means dense (matrix or array) plain object, i.e. something that stores its own dense
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array of coefficients. This is where, for instance, the \link PlainObjectBase::resize() resize() \endlink
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methods go. \c %PlainObjectBase is inherited by \c %Matrix and by \c %Array. But above, we said that
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\c %Matrix inherits \c %MatrixBase and \c %Array inherits \c %ArrayBase. So does that mean multiple
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inheritance? No, because \c %DenseStorageBase \e itself inherits \c %MatrixBase or \c %ArrayBase depending
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inheritance? No, because \c %PlainObjectBase \e itself inherits \c %MatrixBase or \c %ArrayBase depending
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on whether we are in the matrix or array case. When we said above that \c %Matrix inherited
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\c %MatrixBase, we omitted to say it does so indirectly via \c %DenseStorageBase. Same for \c %Array.
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\c %MatrixBase, we omitted to say it does so indirectly via \c %PlainObjectBase. Same for \c %Array.
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- DenseCoeffsBase means something that has dense coefficient accessors. It is a base class for
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\c %DenseBase. The reason for \c %DenseCoeffsBase to exist is that the set of available coefficient
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accessors is very different depending on whether a dense expression has direct memory access or not (the
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@@ -82,7 +82,7 @@ EigenBase<%Matrix>
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<-- DenseCoeffsBase<%Matrix> (direct access case)
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<-- DenseBase<%Matrix>
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<-- MatrixBase<%Matrix>
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<-- DenseStorageBase<%Matrix> (matrix case)
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<-- PlainObjectBase<%Matrix> (matrix case)
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<-- Matrix
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</pre>
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@@ -93,7 +93,7 @@ EigenBase<%Array>
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<-- DenseCoeffsBase<%Array> (direct access case)
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<-- DenseBase<%Array>
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<-- ArrayBase<%Array>
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<-- DenseStorageBase<%Array> (array case)
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<-- PlainObjectBase<%Array> (array case)
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<-- Array
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</pre>
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