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Fix typos in the user manual for the dD spatial searching pkg

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Nuno Miguel Nobre 2023-06-14 21:53:16 +01:00
parent 622f7ac884
commit 43d2188068
1 changed files with 4 additions and 4 deletions
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Spatial_searching/doc/Spatial_searching/Spatial_searching.txt
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@ -62,7 +62,7 @@ computation has to be re-invoked for a larger number of neighbors,
thereby performing redundant computations. Therefore, Hjaltason and thereby performing redundant computations. Therefore, Hjaltason and
Samet \cgalCite{hs-rsd-95} introduced <I>incremental nearest neighbor Samet \cgalCite{hs-rsd-95} introduced <I>incremental nearest neighbor
searching</I> in the sense that having obtained the `k` nearest searching</I> in the sense that having obtained the `k` nearest
neighbors, the `k + 1`st neighbor can be obtained without having neighbors, the `k + 1`th neighbor can be obtained without having
to calculate the `k + 1` nearest neighbor from scratch. to calculate the `k + 1` nearest neighbor from scratch.
Spatial searching typically consists of a preprocessing phase and a Spatial searching typically consists of a preprocessing phase and a
@ -400,7 +400,7 @@ splitting rule, needed to set the maximal allowed bucket size.
This example program has two 2-dimensional data sets: The first one containing This example program has two 2-dimensional data sets: The first one containing
collinear points with exponential increasing distances and the second collinear points with exponential increasing distances and the second
one with collinear points in the firstdimension and one point with a distance one with collinear points in the first dimension and one point with a distance
exceeding the spread of the other points in the second dimension. These are exceeding the spread of the other points in the second dimension. These are
the worst cases for the midpoint/median rules and can also occur in the worst cases for the midpoint/median rules and can also occur in
higher dimensions. higher dimensions.
@ -426,13 +426,13 @@ how to perform parallel queries:
\section Performance Performance \section Performance Performance
\subsection OrthogonalPerfomance Performance of the Orthogonal Search \subsection OrthogonalPerformance Performance of the Orthogonal Search
We took the gargoyle data set (Surface) from aim\@shape, and generated the same number of random points in the bbox of the gargoyle (Random). We took the gargoyle data set (Surface) from aim\@shape, and generated the same number of random points in the bbox of the gargoyle (Random).
We then consider three scenarios as data/queries. We then consider three scenarios as data/queries.
The data set contains 800K points. For each query point we compute the K=10,20,30 closest points, with the default splitter and for the bucket size 10 (default) and 20. The data set contains 800K points. For each query point we compute the K=10,20,30 closest points, with the default splitter and for the bucket size 10 (default) and 20.
The results were produced with the release 5.1 of \cgal, on an Intel i7 2.3 Ghz The results were produced with the release 5.1 of \cgal, on an Intel i7 2.3 GHz
laptop with 16 GB RAM, compiled with CLang++ 6 with the O3 option. laptop with 16 GB RAM, compiled with CLang++ 6 with the O3 option.
The values are the average of ten tests each. We show timings in seconds for both the building of the tree and the queries. The values are the average of ten tests each. We show timings in seconds for both the building of the tree and the queries.