# [Scipy-svn] r5244 - trunk/scipy/cluster

scipy-svn@scip... scipy-svn@scip...
Thu Dec 11 21:04:47 CST 2008

Author: damian.eads
Date: 2008-12-11 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	2008-12-12 02:54:24 UTC (rev 5243)
+++ trunk/scipy/cluster/hierarchy.py	2008-12-12 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
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_s-c_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 easy-to-use 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
-----