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Several changes in comments to keep Doxygen happy.

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Jitse Niesen
2010-07-25 20:29:07 +01:00
parent 3d9764ee24
commit 1420f8b3a1
10 changed files with 30 additions and 32 deletions
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doc/C07_TutorialReductionsVisitorsBroadcasting.dox
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@@ -37,7 +37,7 @@ The \em trace of a matrix, as returned by the function \c trace(), is the sum of
\subsection TutorialReductionsVisitorsBroadcastingReductionsNorm Norm reductions
Eigen also provides reductions to obtain the Euclidean norm or squared norm of a vector with \link MatrixBase::norm() norm() \endlink and \link Matrix::squaredNorm() squaredNorm() \endlink respectively.
Eigen also provides reductions to obtain the Euclidean norm or squared norm of a vector with \link MatrixBase::norm() norm() \endlink and \link MatrixBase::squaredNorm() squaredNorm() \endlink respectively.
These operations can also operate on matrices; in that case, they use the Frobenius norm. The following example shows these methods.
<table class="tutorial_code"><tr><td>
@@ -190,7 +190,7 @@ reductions and partial reductions.
Now that broadcasting, reductions and partial reductions have been introduced, we can dive into a more advanced example that finds
the nearest neighbour of a vector <tt>v</tt> within the columns of matrix <tt>m</tt>. The Euclidean distance will be used in this example,
computing the squared Euclidean distance with the partial reduction named \link DenseBase::squaredNorm() squaredNorm() \endlink:
computing the squared Euclidean distance with the partial reduction named \link MatrixBase::squaredNorm() squaredNorm() \endlink:
<table class="tutorial_code"><tr><td>
Example: \include Tutorial_ReductionsVisitorsBroadcasting_broadcast_1nn.cpp