From 19e437daf09e7c449bb644a072e6caad261f5d36 Mon Sep 17 00:00:00 2001 From: Lars Buitinck Date: Sun, 24 Jan 2016 15:50:36 +0100 Subject: Copyedit documentation: typos, spelling --- doc/TutorialReductionsVisitorsBroadcasting.dox | 14 ++++++-------- doc/UsingIntelMKL.dox | 2 +- 2 files changed, 7 insertions(+), 9 deletions(-) diff --git a/doc/TutorialReductionsVisitorsBroadcasting.dox b/doc/TutorialReductionsVisitorsBroadcasting.dox index 6d25ff0ea..f5322b4a6 100644 --- a/doc/TutorialReductionsVisitorsBroadcasting.dox +++ b/doc/TutorialReductionsVisitorsBroadcasting.dox @@ -101,17 +101,16 @@ row and column position are to be stored. These variables should be of type \verbinclude Tutorial_ReductionsVisitorsBroadcasting_visitors.out -Note that both functions also return the value of the minimum or maximum coefficient if needed, -as if it was a typical reduction operation. +Both functions also return the value of the minimum or maximum coefficient. \section TutorialReductionsVisitorsBroadcastingPartialReductions Partial reductions Partial reductions are reductions that can operate column- or row-wise on a Matrix or Array, applying the reduction operation on each column or row and -returning a column or row-vector with the corresponding values. Partial reductions are applied +returning a column or row vector with the corresponding values. Partial reductions are applied with \link DenseBase::colwise() colwise() \endlink or \link DenseBase::rowwise() rowwise() \endlink. A simple example is obtaining the maximum of the elements -in each column in a given matrix, storing the result in a row-vector: +in each column in a given matrix, storing the result in a row vector: @@ -133,8 +132,7 @@ The same operation can be performed row-wise: \verbinclude Tutorial_ReductionsVisitorsBroadcasting_rowwise.out
Example:Output:
-Note that column-wise operations return a 'row-vector' while row-wise operations -return a 'column-vector' +Note that column-wise operations return a row vector, while row-wise operations return a column vector. \subsection TutorialReductionsVisitorsBroadcastingPartialReductionsCombined Combining partial reductions with other operations It is also possible to use the result of a partial reduction to do further processing. @@ -176,7 +174,7 @@ The concept behind broadcasting is similar to partial reductions, with the diffe constructs an expression where a vector (column or row) is interpreted as a matrix by replicating it in one direction. -A simple example is to add a certain column-vector to each column in a matrix. +A simple example is to add a certain column vector to each column in a matrix. This can be accomplished with: @@ -253,7 +251,7 @@ is a new matrix whose size is the same as matrix m: \f[ \f] - (m.colwise() - v).colwise().squaredNorm() is a partial reduction, computing the squared norm column-wise. The result of -this operation is a row-vector where each coefficient is the squared Euclidean distance between each column in m and v: \f[ +this operation is a row vector where each coefficient is the squared Euclidean distance between each column in m and v: \f[ \mbox{(m.colwise() - v).colwise().squaredNorm()} = \begin{bmatrix} 1 & 505 & 32 & 50 diff --git a/doc/UsingIntelMKL.dox b/doc/UsingIntelMKL.dox index 84db992b6..02c62ad85 100644 --- a/doc/UsingIntelMKL.dox +++ b/doc/UsingIntelMKL.dox @@ -52,7 +52,7 @@ When doing so, a number of Eigen's algorithms are silently substituted with call These substitutions apply only for \b Dynamic \b or \b large enough objects with one of the following four standard scalar types: \c float, \c double, \c complex, and \c complex. Operations on other scalar types or mixing reals and complexes will continue to use the built-in algorithms. -In addition you can coarsely select choose which parts will be substituted by defining one or multiple of the following macros: +In addition you can choose which parts will be substituted by defining one or multiple of the following macros:
-- cgit v1.2.3
\c EIGEN_USE_BLAS Enables the use of external BLAS level 2 and 3 routines (currently works with Intel MKL only)