[Scipysvn] r4668  trunk/scipy/cluster
scipysvn@scip...
scipysvn@scip...
Sat Aug 23 13:10:49 CDT 2008
Author: damian.eads
Date: 20080823 13:10:44 0500 (Sat, 23 Aug 2008)
New Revision: 4668
Modified:
trunk/scipy/cluster/distance.py
trunk/scipy/cluster/hierarchy.py
Log:
Finished RSTifying linkage alias functions.
Modified: trunk/scipy/cluster/distance.py
===================================================================
 trunk/scipy/cluster/distance.py 20080823 17:56:19 UTC (rev 4667)
+++ trunk/scipy/cluster/distance.py 20080823 18:10:44 UTC (rev 4668)
@@ 79,7 +79,6 @@
 yule  the Yule dissimilarity (boolean). 
+++

References

Modified: trunk/scipy/cluster/hierarchy.py
===================================================================
 trunk/scipy/cluster/hierarchy.py 20080823 17:56:19 UTC (rev 4667)
+++ trunk/scipy/cluster/hierarchy.py 20080823 18:10:44 UTC (rev 4668)
@@ 243,13 +243,15 @@
:Parameters:
y : ndarray
 The upper triangular of the distance matrix y. The result of
+ The upper triangular of the distance matrix. The result of
``pdist`` is returned in this form.
:Returns:
Z : ndarray
The linkage matrix.
+ :SeeAlso:
+  linkage: for advanced creation of hierarchical clusterings.
"""
return linkage(y, method='single', metric='euclidean')
@@ 261,9 +263,14 @@
:Parameters:
y : ndarray
 The upper triangular of the distance matrix y. The result of
+ The upper triangular of the distance matrix. The result of
``pdist`` is returned in this form.
+ :Returns:
+ Z : ndarray
+ A linkage matrix containing the hierarchical clustering. See
+ the ``linkage`` function documentation for more information
+ on its structure.
"""
return linkage(y, method='complete', metric='euclidean')
@@ 275,8 +282,17 @@
:Parameters:
y : ndarray
 The upper triangular of the distance matrix y. The result of
+ The upper triangular of the distance matrix. The result of
``pdist`` is returned in this form.
+
+ :Returns:
+ Z : ndarray
+ A linkage matrix containing the hierarchical clustering. See
+ the ``linkage`` function documentation for more information
+ on its structure.
+
+ :SeeAlso:
+  linkage: for advanced creation of hierarchical clusterings.
"""
return linkage(y, method='average', metric='euclidean')
@@ 288,8 +304,17 @@
:Parameters:
y : ndarray
 The upper triangular of the distance matrix y. The result of
+ The upper triangular of the distance matrix. The result of
``pdist`` is returned in this form.
+
+ :Returns:
+ Z : ndarray
+ A linkage matrix containing the hierarchical clustering. See
+ the ``linkage`` function documentation for more information
+ on its structure.
+
+ :SeeAlso:
+  linkage: for advanced creation of hierarchical clusterings.
"""
return linkage(y, method='weighted', metric='euclidean')
@@ 298,56 +323,105 @@
Performs centroid/UPGMC linkage. See ``linkage`` for more
information on the return structure and algorithm.
+ :Parameters:
+ Q : ndarray
+ A condensed or redundant distance matrix. A condensed
+ distance matrix is a flat array containing the upper
+ triangular of the distance matrix. This is the form that
+ ``pdist`` returns. Alternatively, a collection of
+ m observation vectors in n dimensions may be passed as
+ a m by n array.
 Z = centroid(y)
+ :Returns:
+ Z : ndarray
+ A linkage matrix containing the hierarchical clustering. See
+ the ``linkage`` function documentation for more information
+ on its structure.
 Performs centroid/UPGMC linkage on the condensed distance matrix Z.
 See linkage for more information on the return structure and
 algorithm.
+ Calling Conventions
+ 
 (a condensed alias for linkage)
+ 1. Z = centroid(y)
 Z = centroid(X)
+ Performs centroid/UPGMC linkage on the condensed distance
+ matrix ``y``. See ``linkage`` for more information on the return
+ structure and algorithm.
 Performs centroid/UPGMC linkage on the observation matrix X using
 Euclidean distance as the distance metric. See linkage for more
 information on the return structure and algorithm.
+ 2. Z = centroid(X)
+ Performs centroid/UPGMC linkage on the observation matrix ``X``
+ using Euclidean distance as the distance metric. See ``linkage``
+ for more information on the return structure and algorithm.
+
+ :SeeAlso:
+  linkage: for advanced creation of hierarchical clusterings.
"""
return linkage(y, method='centroid', metric='euclidean')
def median(y):
"""
 Z = median(y)
+ Performs median/WPGMC linkage. See ``linkage`` for more
+ information on the return structure and algorithm.
 Performs median/WPGMC linkage on the condensed distance matrix Z.
 See linkage for more information on the return structure and
 algorithm.
+ :Parameters:
+ Q : ndarray
+ A condensed or redundant distance matrix. A condensed
+ distance matrix is a flat array containing the upper
+ triangular of the distance matrix. This is the form that
+ ``pdist`` returns. Alternatively, a collection of
+ m observation vectors in n dimensions may be passed as
+ a m by n array.
 Z = median(X)
+ Calling Conventions
+ 
 Performs median/WPGMC linkage on the observation matrix X using
 Euclidean distance as the distance metric. See linkage for more
 information on the return structure and algorithm.
+ 1. Z = median(y)
 (a condensed alias for linkage)
+ Performs median/WPGMC linkage on the condensed distance matrix
+ ``y``. See ``linkage`` for more information on the return
+ structure and algorithm.
+
+ 2. Z = median(X)
+
+ Performs median/WPGMC linkage on the observation matrix ``X``
+ using Euclidean distance as the distance metric. See linkage
+ for more information on the return structure and algorithm.
+
+ :SeeAlso:
+  linkage: for advanced creation of hierarchical clusterings.
"""
return linkage(y, method='median', metric='euclidean')
def ward(y):
"""
 Z = ward(y)
+ Performs Ward's linkage on a condensed or redundant distance
+ matrix. See linkage for more information on the return structure
+ and algorithm.
 Performs Ward's linkage on the condensed distance matrix Z. See
 linkage for more information on the return structure and algorithm.
+ :Parameters:
+ Q : ndarray
+ A condensed or redundant distance matrix. A condensed
+ distance matrix is a flat array containing the upper
+ triangular of the distance matrix. This is the form that
+ ``pdist`` returns. Alternatively, a collection of
+ m observation vectors in n dimensions may be passed as
+ a m by n array.
 Z = ward(X)
+ Calling Conventions
+ 
 Performs Ward's linkage on the observation matrix X using Euclidean
 distance as the distance metric. See linkage for more information
 on the return structure and algorithm.
+ 1. Z = ward(y)
+ Performs Ward's linkage on the condensed distance matrix Z. See
+ linkage for more information on the return structure and
+ algorithm.
 (a condensed alias for linkage)
+ 2. Z = ward(X)
+ Performs Ward's linkage on the observation matrix X using
+ Euclidean distance as the distance metric. See linkage for more
+ information on the return structure and algorithm.
+
+ :SeeAlso:
+  linkage: for advanced creation of hierarchical clusterings.
"""
return linkage(y, method='ward', metric='euclidean')
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