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Copyedit documentation: typos, spelling

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Lars Buitinck 2016-01-24 15:50:36 +01:00
parent 34340458cb
commit 19e437daf0
2 changed files with 7 additions and 9 deletions
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14
doc/TutorialReductionsVisitorsBroadcasting.dox
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@ -101,17 +101,16 @@ row and column position are to be stored. These variables should be of type
\verbinclude Tutorial_ReductionsVisitorsBroadcasting_visitors.out
</td></tr></table>
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:
<table class="example">
<tr><th>Example:</th><th>Output:</th></tr>
@ -133,8 +132,7 @@ The same operation can be performed row-wise:
\verbinclude Tutorial_ReductionsVisitorsBroadcasting_rowwise.out
</td></tr></table>
<b>Note that column-wise operations return a 'row-vector' while row-wise operations
return a 'column-vector'</b>
<b>Note that column-wise operations return a row vector, while row-wise operations return a column vector.</b>
\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:
<table class="example">
@ -253,7 +251,7 @@ is a new matrix whose size is the same as matrix <tt>m</tt>: \f[
\f]
- <tt>(m.colwise() - v).colwise().squaredNorm()</tt> 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 <tt>m</tt> and <tt>v</tt>: \f[
this operation is a row vector where each coefficient is the squared Euclidean distance between each column in <tt>m</tt> and <tt>v</tt>: \f[
\mbox{(m.colwise() - v).colwise().squaredNorm()} =
\begin{bmatrix}
1 & 505 & 32 & 50

2
doc/UsingIntelMKL.dox
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@ -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<float>, and \c complex<double>.
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:
<table class="manual">
<tr><td>\c EIGEN_USE_BLAS </td><td>Enables the use of external BLAS level 2 and 3 routines (currently works with Intel MKL only)</td></tr>