[Scipy-svn] r3115 - in trunk/Lib/sandbox/pyem: . data

[scipy-svn at scipy.org]

Author: cdavid
Date: 2007-06-22 03:55:20 -0500 (Fri, 22 Jun 2007)
New Revision: 3115

Modified:
   trunk/Lib/sandbox/pyem/TODO
   trunk/Lib/sandbox/pyem/data/setup.py
   trunk/Lib/sandbox/pyem/gmm_em.py
Log:
Add pendigits as a subpackage of data for distutils.

Modified: trunk/Lib/sandbox/pyem/TODO
===================================================================
--- trunk/Lib/sandbox/pyem/TODO	2007-06-22 08:39:13 UTC (rev 3114)
+++ trunk/Lib/sandbox/pyem/TODO	2007-06-22 08:55:20 UTC (rev 3115)
@@ -1,10 +1,9 @@
-# Last Change: Sat Jun 09 04:00 PM 2007 J
+# Last Change: Fri Jun 22 05:00 PM 2007 J
 
 Things which must be implemented for a 1.0 version (in importante order)
     - A classifier
     - handle rank 1 for 1d data
     - basic regularization
-    - docstrings
     - demo for pdf estimation, discriminant analysis and clustering
     - scaling of data: maybe something to handle scaling internally ?
 

Modified: trunk/Lib/sandbox/pyem/data/setup.py
===================================================================
--- trunk/Lib/sandbox/pyem/data/setup.py	2007-06-22 08:39:13 UTC (rev 3114)
+++ trunk/Lib/sandbox/pyem/data/setup.py	2007-06-22 08:55:20 UTC (rev 3115)
@@ -4,6 +4,7 @@
     from numpy.distutils.misc_util import Configuration
     config = Configuration('data',parent_package,top_path)
     config.add_subpackage('oldfaithful')
+    config.add_subpackage('pendigits')
     config.make_config_py() # installs __config__.py
     return config
 

Modified: trunk/Lib/sandbox/pyem/gmm_em.py
===================================================================
--- trunk/Lib/sandbox/pyem/gmm_em.py	2007-06-22 08:39:13 UTC (rev 3114)
+++ trunk/Lib/sandbox/pyem/gmm_em.py	2007-06-22 08:55:20 UTC (rev 3115)
@@ -1,5 +1,5 @@
 # /usr/bin/python
-# Last Change: Thu Jun 21 03:00 PM 2007 J
+# Last Change: Fri Jun 22 05:00 PM 2007 J
 
 """Module implementing GMM, a class to estimate Gaussian mixture models using
 EM, and EM, a class which use GMM instances to estimate models parameters using