[Scipy-svn] r5138 - trunk/doc/release

scipy-svn@scip... scipy-svn@scip...
Sun Nov 16 18:11:29 CST 2008


Author: damian.eads
Date: 2008-11-16 18:11:28 -0600 (Sun, 16 Nov 2008)
New Revision: 5138

Modified:
   trunk/doc/release/0.7.0-notes.rst
Log:
Polished release notes for spatial.distance and cluster.

Modified: trunk/doc/release/0.7.0-notes.rst
===================================================================
--- trunk/doc/release/0.7.0-notes.rst	2008-11-16 23:27:15 UTC (rev 5137)
+++ trunk/doc/release/0.7.0-notes.rst	2008-11-17 00:11:28 UTC (rev 5138)
@@ -68,20 +68,21 @@
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 This module adds new hierarchical clustering functionality to the
-cluster package. Its interface is similar to the hierarchical
-clustering functions provided in MATLAB(TM)'s Statistics Toolbox to
-facilitate easier migration to the NumPy/SciPy framework. Linkage
-methods implemented include single, complete, average, weighted,
-centroid, median, and ward. Several functions are provided for
-computing statistics on clusters including inconsistency statistics,
-cophenetic distance, and maximum distance of descendants. The fcluster
-and fclusterdata functions take hierarchical tree clusterings
-generated by these algorithms, cuts the tree, and labels the flat
-clusters. The leaders function finds the root of each flat cluster
-given a hierarchical clustering and labellings of its leaves. Finally, a
-matplotlib extension is provided for plotting dendrograms, which
-may be outputted to postscript or any other supported format.
+``scipy.cluster`` package. The function interfaces are similar to the
+functions provided MATLAB(TM)'s Statistics Toolbox to help facilitate
+easier migration to the NumPy/SciPy framework. Linkage methods
+implemented include single, complete, average, weighted, centroid,
+median, and ward.
 
+In addition, several functions are provided for computing
+inconsistency statistics, cophenetic distance, and maximum distance
+between descendants. The ``fcluster`` and ``fclusterdata`` functions
+transform a hierarchical clustering into a set of flat clusters. Since
+these flat clusters are generated by cutting the tree into a forest of
+trees, the ``leaders`` function takes a linkage and a flat clustering
+and finds the root of each tree in the forest. Finally, a matplotlib
+extension is provided for plotting dendrograms.
+
 New Spatial package
 ~~~~~~~~~~~~~~~~~~~
 
@@ -92,20 +93,22 @@
 of other algorithms. The API for both modules may change somewhat as user
 requirements become clearer.
 
-Also includes a submodule ``distance`` containing fast code for many definitions
-of *distance* between vectors.  These common distance functions are useful for
-for many agorthms including spatial statistics, clustering, and kd-trees.
-Distance and dissimilarity functions provided include Bray-Curtis, Canberra,
-Chebyshev, City Block, Cosine, Dice, Euclidean, Hamming, Jaccard, Kulsinski,
-Mahalanobis, Matching, Minkowski, Rogers-Tanimoto, Russell-Rao,
-Squared Euclidean, Standardized Euclidean, Sokal-Michener, Sokal-Sneath,
-and Yule. Two functions are provided for computing distances between
-collections of vectors: ``pdist`` and ``cdist``. ``pdist`` is similar to the
-MATLAB(TM) function and computes pairwise distances between a collection of
-vectors. ``cdist`` computes distances between vectors in two sets of vectors.
-``squareform`` converts between square distance matrices and condensed
-distance matrices.
+Also includes a ``distance`` module containing a collection of
+distance and dissimilarity functions for computing distances between
+vectors, which is useful for spatial statistics, clustering, and
+kd-trees.  Distance and dissimilarity functions provided include
+Bray-Curtis, Canberra, Chebyshev, City Block, Cosine, Dice, Euclidean,
+Hamming, Jaccard, Kulsinski, Mahalanobis, Matching, Minkowski,
+Rogers-Tanimoto, Russell-Rao, Squared Euclidean, Standardized
+Euclidean, Sokal-Michener, Sokal-Sneath, and Yule.
 
+The ``pdist`` function computes pairwise distance between all
+unordered pairs of vectors in a set of vectors. The ``cdist`` computes
+the distance on all pairs of vectors in the Cartesian product of two
+sets of vectors.  Pairwise distance matrices are stored in condensed
+form, only the upper triangular is stored. ``squareform`` converts
+between square distance matrices and condensed distance matrices.
+
 Reworked fftpack package
 ~~~~~~~~~~~~~~~~~~~~~~~~
 



More information about the Scipy-svn mailing list