[Scipy-svn] r4703 - trunk/scipy/cluster
scipy-svn@scip...
scipy-svn@scip...
Mon Sep 8 09:47:43 CDT 2008
Author: damian.eads
Date: 2008-09-08 09:47:40 -0500 (Mon, 08 Sep 2008)
New Revision: 4703
Modified:
trunk/scipy/cluster/hierarchy.py
Log:
RSTified more hierarchy docs.
Modified: trunk/scipy/cluster/hierarchy.py
===================================================================
--- trunk/scipy/cluster/hierarchy.py 2008-09-08 09:04:26 UTC (rev 4702)
+++ trunk/scipy/cluster/hierarchy.py 2008-09-08 14:47:40 UTC (rev 4703)
@@ -1093,23 +1093,23 @@
:Arguments:
- R : ndarray
- The inconsistency matrix to check for validity.
+ The inconsistency matrix to check for validity.
- warning : bool
- When ``True``, issues a Python warning if the inconsistency
- matrix passed is invalid.
+ When ``True``, issues a Python warning if the linkage
+ matrix passed is invalid.
- throw : bool
- When ``True``, throws a Python exception if the inconsistency
- matrix passed is invalid.
+ When ``True``, throws a Python exception if the linkage
+ matrix passed is invalid.
- name : string
- When passed this string is used to refer to the variable name
- of the invalid inconsistency matrix.
+ This string refers to the variable name of the invalid
+ linkage matrix.
:Returns:
- b : bool
- True iff the inconsistency matrix is valid.
+ - b : bool
+ True iff the inconsistency matrix is valid.
"""
R = np.asarray(R)
valid = True
@@ -1149,26 +1149,32 @@
def is_valid_linkage(Z, warning=False, throw=False, name=None):
"""
- is_valid_linkage(Z, t)
+ Checks the validity of a linkage matrix. A linkage matrix is valid
+ if it is a two dimensional nd-array (type double) with :math:`$n$`
+ rows and 4 columns. The first two columns must contain indices
+ between 0 and :math:`$2n-1$`. For a given row ``i``,
+ :math:`$0 \leq \mathtt{Z[i,0]} \leq i+n-1$` and
+ :math:`$0 \leq Z[i,1] \leq i+n-1$` (i.e. a cluster
+ cannot join another cluster unless the cluster being joined has
+ been generated.)
- Returns True if Z is a valid linkage matrix. The variable must
- be a 2-dimensional double numpy array with n rows and 4 columns.
- The first two columns must contain indices between 0 and 2n-1. For a
- given row i, 0 <= Z[i,0] <= i+n-1 and 0 <= Z[i,1] <= i+n-1 (i.e.
- a cluster cannot join another cluster unless the cluster being joined
- has been generated.)
+ :Arguments:
- is_valid_linkage(..., warning=True, name='V')
+ - warning : bool
+ When ``True``, issues a Python warning if the linkage
+ matrix passed is invalid.
- Invokes a warning if the variable passed is not a valid linkage. The message
- explains why the distance matrix is not valid. 'name' is used when referencing
- the offending variable.
+ - throw : bool
+ When ``True``, throws a Python exception if the linkage
+ matrix passed is invalid.
- is_valid_linkage(..., throw=True, name='V')
+ - name : string
+ This string refers to the variable name of the invalid
+ linkage matrix.
- Throws an exception if the variable passed is not a valid linkage. The message
- explains why variable is not valid. 'name' is used when referencing the offending
- variable.
+ :Returns:
+ - b : bool
+ True iff the inconsistency matrix is valid.
"""
Z = np.asarray(Z)
@@ -1212,8 +1218,16 @@
def numobs_linkage(Z):
"""
- Returns the number of original observations that correspond to a
- linkage matrix Z.
+ Returns the number of original observations of the linkage matrix
+ passed.
+
+ :Arguments:
+ - Z : ndarray
+ The linkage matrix on which to perform the operation.
+
+ :Returns:
+ - n : int
+ The number of original observations in the linkage.
"""
Z = np.asarray(Z)
is_valid_linkage(Z, throw=True, name='Z')
@@ -1221,13 +1235,27 @@
def Z_y_correspond(Z, Y):
"""
- yesno = Z_y_correspond(Z, Y)
+ Checks if a linkage matrix Z and condensed distance matrix
+ Y could possibly correspond to one another.
- Returns True 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. This function is useful as a sanity
- check in algorithms that make extensive use of linkage and distance
- matrices that must correspond to the same set of original observations.
+ They must have the same number of original observations for
+ the check to succeed.
+
+ This function is useful as a sanity check in algorithms that make
+ extensive use of linkage and distance matrices that must
+ correspond to the same set of original observations.
+
+ :Arguments:
+ - Z : ndarray
+ The linkage matrix to check for correspondance.
+
+ - Y : ndarray
+ The condensed distance matrix to check for correspondance.
+
+ :Returns:
+ - b : bool
+ A boolean indicating whether the linkage matrix and distance
+ matrix could possibly correspond to one another.
"""
Z = np.asarray(Z)
Y = np.asarray(Y)
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