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author | Dmitriy Morozov <dmitriy@mrzv.org> |

Tue, 01 Sep 2009 11:11:18 -0700 | |

changeset 8 | ef31983c4dd2 |

parent 7 | fc7a9f78a5db |

child 9 | 0c2b965c8824 |

Expanded README (better documentation for the methods of KDTree class)

--- a/README Sat Jun 20 13:00:20 2009 -0700 +++ b/README Tue Sep 01 11:11:18 2009 -0700 @@ -53,6 +53,15 @@ (assuming you are on Linux). +.. note:: + + One can get a distribution of ANN_ with the above patches applied as well as + other changes (e.g. reentrant `annkSearch()` method) from my repository: + + .. parsed-literal:: + + hg clone http://hg.mrzv.org/ANN + .. _gcc43.patch: http://aur.archlinux.org/packages/ann/ann/gcc43.patch .. _shared-libs.patch: http://aur.archlinux.org/packages/ann/ann/shared-libs.patch @@ -77,10 +86,31 @@ The main content of pyANN is the class `KDTree`. The class is initialized with a list of points, and provides three key methods: - :kSearch: - :kPriSearch: - :kFRSearch: + `__init__(lst)` + Initialize `KDTree` with a list of points (each point is a list of + coordinates). + + `kSearch(q,k,eps)` + Find `k` nearest neighbors of the query point `q` with an error of `eps`. + Returns a pair of lists: `(idxs, dists)`. The first is the list of + indices of the nearest neighbors; the second is the list of squared + distances to the corresponding points from the query point. + + `kPriSearch(q,k,eps)` + Same as above using priority search. -Additional auxiliary methods mimick ANN's functionality: + `kFRSearch(q, sqRad, k, eps)` + Fixed radius search. Find at most `k` nearest neighbors of the query point + `q` within radius `sqRad`, with an allowed error of `eps`. + + `__len__()` + Returns number of points in the `KDTree`. - + `dim()` + Dimensions of the point set. + +Additional auxiliary (global) function mimicks ANN's functionality: + + `max_pts_visit(maxPts)` + Maximum number of points to visit before terminating (will override + larger values of `k` in the above search functions).