[Scipysvn] r5244  trunk/scipy/cluster
scipysvn@scip...
scipysvn@scip...
Thu Dec 11 21:04:47 CST 2008
Author: damian.eads
Date: 20081211 21:04:43 0600 (Thu, 11 Dec 2008)
New Revision: 5244
Modified:
trunk/scipy/cluster/hierarchy.py
Log:
Minor fixes in hierarchy documentation.
Modified: trunk/scipy/cluster/hierarchy.py
===================================================================
 trunk/scipy/cluster/hierarchy.py 20081212 02:54:24 UTC (rev 5243)
+++ trunk/scipy/cluster/hierarchy.py 20081212 03:04:43 UTC (rev 5244)
@@ 333,13 +333,13 @@
The following are common calling conventions:
 1. Z = centroid(y)
+ 1. ``Z = centroid(y)``
Performs centroid/UPGMC linkage on the condensed distance
matrix ``y``. See ``linkage`` for more information on the return
structure and algorithm.
 2. Z = centroid(X)
+ 2. ``Z = centroid(X)``
Performs centroid/UPGMC linkage on the observation matrix ``X``
using Euclidean distance as the distance metric. See ``linkage``
@@ 372,13 +372,13 @@
The following are common calling conventions:
 1. Z = median(y)
+ 1. ``Z = median(y)``
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)
+ 2. ``Z = median(X)``
Performs median/WPGMC linkage on the observation matrix ``X``
using Euclidean distance as the distance metric. See linkage
@@ 410,13 +410,13 @@
The following are common calling conventions:
 1. Z = ward(y)
 Performs Ward's linkage on the condensed distance matrix Z. See
+ 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.
 2. Z = ward(X)
 Performs Ward's linkage on the observation matrix X using
+ 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.
@@ 484,7 +484,7 @@
The following are methods for calculating the distance between the
newly formed cluster :math:`u` and each :math:`v`.
 * method=``single`` assigns
+ * method='single' assigns
.. math::
d(u,v) = \min(dist(u[i],v[j]))
@@ 493,7 +493,7 @@
:math:`j` in cluster :math:`v`. This is also known as the
Nearest Point Algorithm.
 * method=``complete`` assigns
+ * method='complete' assigns
.. math::
d(u, v) = \max(dist(u[i],v[j]))
@@ 502,7 +502,7 @@
cluster :math:`v`. This is also known by the Farthest Point
Algorithm or Voor Hees Algorithm.
 * method=``average`` assigns
+ * method='average' assigns
.. math::
d(u,v) = \sum_{ij} \frac{d(u[i], v[j])}
@@ 524,7 +524,7 @@
* method='centroid' assigns
.. math::
 dist(s,t) = euclid(c_s, c_t)
+ dist(s,t) = c_sc_t_2
where :math:`c_s` and :math:`c_t` are the centroids of
clusters :math:`s` and :math:`t`, respectively. When two
@@ 536,11 +536,11 @@
:math:`v` in the forest. This is also known as the UPGMC
algorithm.
 * method='median' assigns math:`$d(s,t)$` like the ``centroid``
 method. When two clusters s and t are combined into a new
 cluster :math:`u`, the average of centroids s and t give the
 new centroid :math:`u`. This is also known as the WPGMC
 algorithm.
+ * method='median' assigns math:`d(s,t)` like the ``centroid``
+ method. When two clusters :math:`s` and :math:`t` are combined
+ into a new cluster :math:`u`, the average of centroids s and t
+ give the new centroid :math:`u`. This is also known as the
+ WPGMC algorithm.
* method='ward' uses the Ward variance minimization algorithm.
The new entry :math:`d(u,v)` is computed as follows,
@@ 633,7 +633,7 @@
:SeeAlso:
  to_tree: for converting a linkage matrix Z into a tree object.
+  to_tree: for converting a linkage matrix ``Z`` into a tree object.
"""
def __init__(self, id, left=None, right=None, dist=0, count=1):
@@ 781,7 +781,7 @@
def to_tree(Z, rd=False):
"""
 Converts a hierarchical clustering encoded in the matrix Z (by
+ Converts a hierarchical clustering encoded in the matrix ``Z`` (by
linkage) into an easytouse tree object. The reference r to the
root ClusterNode object is returned.
@@ 1299,8 +1299,8 @@
def correspond(Z, Y):
"""
 Checks if a linkage matrix Z and condensed distance matrix
 Y could possibly correspond to one another.
+ Checks if a linkage matrix ``Z`` and condensed distance matrix
+ ``Y`` could possibly correspond to one another.
They must have the same number of original observations for
the check to succeed.
@@ 1464,7 +1464,6 @@
 t : double
The threshold to apply when forming flat clusters.

 criterion : string
Specifies the criterion for forming flat clusters. Valid
values are 'inconsistent', 'distance', or 'maxclust' cluster
@@ 1496,8 +1495,8 @@
:Returns:
 T : ndarray
 A vector of length ``n``. ``T[i]`` is the flat cluster number to
 which original observation ``i`` belongs.
+ A vector of length ``n``. ``T[i]`` is the flat cluster number to
+ which original observation ``i`` belongs.
Notes

More information about the Scipysvn
mailing list