Index: /trunk/learn/scikits/learn/machine/em/Changelog
===================================================================
--- /trunk/learn/scikits/learn/machine/em/Changelog (revision 447)
+++ /trunk/learn/scikits/learn/machine/em/Changelog (revision 448)
@@ -1,2 +1,7 @@
+pyem (0.5.7dev) Sun, 22 Jul 2007 11:05:58 +0900
+
+	* pyem is now part of the scikits.learn package
+	* Renamed to em
+
 pyem (0.5.7dev) Mon, 28 May 2007 11:31:08 +0900
 
Index: /trunk/learn/scikits/learn/machine/em/README
===================================================================
--- /trunk/learn/scikits/learn/machine/em/README (revision 447)
+++ /trunk/learn/scikits/learn/machine/em/README (revision 448)
@@ -1,8 +1,7 @@
-Last Change: Sat Jun 09 12:00 PM 2007 J
+Last Change: Sun Jul 22 12:00 PM 2007 J
 
-pyem is a python module build upon numpy and scipy
-(see http://www.scipy.org/) for learning mixtures models
-using Expectation Maximization. For now, only Gaussian
-Mixture Models are implemented. Included features:
+em is a python module build upon numpy and scipy (see http://www.scipy.org/)
+for learning mixtures models using Expectation Maximization. For now, only
+Gaussian Mixture Models are implemented. Included features:
     
  * computation of Gaussian pdf for multi-variate Gaussian
@@ -11,3 +10,3 @@
  * Confidence ellipsoides with probability at arbitrary level
  * Classic EM for Gaussian Mixture Models
- * K-mean based and random initialization for EM available
+ * Experimental regularized (MAP) EM 
Index: /trunk/learn/scikits/learn/machine/em/tests/test_examples.py
===================================================================
--- /trunk/learn/scikits/learn/machine/em/tests/test_examples.py (revision 438)
+++ /trunk/learn/scikits/learn/machine/em/tests/test_examples.py (revision 448)
@@ -1,9 +1,9 @@
 #! /usr/bin/env python
-# Last Change: Sat Jul 21 03:00 PM 2007 J
+# Last Change: Sun Jul 22 12:00 PM 2007 J
 
 from numpy.testing import *
 
 set_package_path()
-from pyem.examples.examples import ex1, ex2, ex3, pdfestim, pdfestim1d
+from em.examples.examples import ex1, ex2, ex3, pdfestim, pdfestim1d
 restore_path()
 
Index: /trunk/learn/scikits/learn/machine/em/tests/test_gauss_mix.py
===================================================================
--- /trunk/learn/scikits/learn/machine/em/tests/test_gauss_mix.py (revision 358)
+++ /trunk/learn/scikits/learn/machine/em/tests/test_gauss_mix.py (revision 448)
@@ -1,4 +1,4 @@
 #! /usr/bin/env python
-# Last Change: Mon Jul 02 05:00 PM 2007 J
+# Last Change: Sun Jul 22 12:00 PM 2007 J
 
 # For now, just test that all mode/dim execute correctly
@@ -10,6 +10,6 @@
 
 set_package_path()
-from pyem import GM
-from pyem.densities import multiple_gauss_den
+from em import GM
+from em.densities import multiple_gauss_den
 restore_path()
 
Index: /trunk/learn/scikits/learn/machine/em/tests/test_gmm_em.py
===================================================================
--- /trunk/learn/scikits/learn/machine/em/tests/test_gmm_em.py (revision 350)
+++ /trunk/learn/scikits/learn/machine/em/tests/test_gmm_em.py (revision 448)
@@ -1,4 +1,4 @@
 #! /usr/bin/env python
-# Last Change: Sun Jul 01 06:00 PM 2007 J
+# Last Change: Sun Jul 22 12:00 PM 2007 J
 
 # For now, just test that all mode/dim execute correctly
@@ -11,5 +11,5 @@
 
 set_package_path()
-from pyem import GMM, GM, EM
+from em import GMM, GM, EM
 restore_path()
 
Index: /trunk/learn/scikits/learn/machine/em/tests/test_densities.py
===================================================================
--- /trunk/learn/scikits/learn/machine/em/tests/test_densities.py (revision 366)
+++ /trunk/learn/scikits/learn/machine/em/tests/test_densities.py (revision 448)
@@ -1,4 +1,4 @@
 #! /usr/bin/env python
-# Last Change: Thu Jul 12 05:00 PM 2007 J
+# Last Change: Sun Jul 22 12:00 PM 2007 J
 
 # TODO:
@@ -13,6 +13,6 @@
 
 set_package_path()
-from pyem.densities import gauss_den
-import pyem.densities
+from em.densities import gauss_den
+import em.densities
 restore_path()
 
@@ -167,5 +167,5 @@
             except FloatingPointError, e:
                 print "Catching underflow, as expected"
-            assert pyem.densities.logsumexp(a) == -1000.
+            assert em.densities.logsumexp(a) == -1000.
             try:
                 a = N.array([[-1000, -1000, -1000]])
@@ -175,5 +175,5 @@
             except FloatingPointError, e:
                 print "Catching underflow, as expected"
-            assert_array_almost_equal(pyem.densities.logsumexp(a), 
+            assert_array_almost_equal(em.densities.logsumexp(a), 
                                       -998.90138771)
         finally:
@@ -187,6 +187,6 @@
         mu = N.array([[-5], [-6]])
         va = N.array([[0.1], [0.1]])
-        y = pyem.densities.multiple_gauss_den(data, mu, va, log = True)
-        a1 = pyem.densities.logsumexp(y)
+        y = em.densities.multiple_gauss_den(data, mu, va, log = True)
+        a1 = em.densities.logsumexp(y)
         a2 = self.naive_logsumexp(y)
         assert_array_equal(a1, a2)
@@ -196,6 +196,6 @@
         mu = N.array([[-3, -1], [3, 3]])
         va = N.array([[1.1, 0.4], [0.6, 0.8], [0.4, 0.2], [0.3, 0.9]])
-        y = pyem.densities.multiple_gauss_den(data, mu, va, log = True)
-        a1 = pyem.densities.logsumexp(y)
+        y = em.densities.multiple_gauss_den(data, mu, va, log = True)
+        a1 = em.densities.logsumexp(y)
         a2 = self.naive_logsumexp(y)
         assert_array_almost_equal(a1, a2, DEF_DEC)
@@ -205,6 +205,6 @@
         mu = N.array([[-3, -1], [3, 3]])
         va = N.array([[1.1, 0.4], [0.6, 0.8]])
-        y = pyem.densities.multiple_gauss_den(data, mu, va, log = True)
-        a1 = pyem.densities.logsumexp(y)
+        y = em.densities.multiple_gauss_den(data, mu, va, log = True)
+        a1 = em.densities.logsumexp(y)
         a2 = self.naive_logsumexp(y)
         assert_array_almost_equal(a1, a2, DEF_DEC)
@@ -216,5 +216,5 @@
     def _test(self, level, decimal = DEF_DEC):
         try:
-            from pyem._c_densities import gauss_den as c_gauss_den
+            from em._c_densities import gauss_den as c_gauss_den
             Y   = c_gauss_den(self.X, self.mu, self.va)
             assert_array_almost_equal(Y, self.Yt, decimal)
@@ -237,7 +237,7 @@
 class test_gauss_ell(NumpyTestCase):
     def test_dim(self):
-        pyem.densities.gauss_ell([0, 1], [1, 2.], [0, 1])
-        try:
-            pyem.densities.gauss_ell([0, 1], [1, 2.], [0, 2])
+        em.densities.gauss_ell([0, 1], [1, 2.], [0, 1])
+        try:
+            em.densities.gauss_ell([0, 1], [1, 2.], [0, 2])
             raise AssertionError("this call should not succeed, bogus dim.")
         except ValueError, e:
Index: /trunk/learn/scikits/learn/machine/em/doc/index.txt
===================================================================
--- /trunk/learn/scikits/learn/machine/em/doc/index.txt (revision 363)
+++ /trunk/learn/scikits/learn/machine/em/doc/index.txt (revision 448)
@@ -1,10 +1,10 @@
 ..
     restindex
-        page-title: Pyem
-        crumb: Pyem
-        link-title: Pyem
+        page-title: Em
+        crumb: Em
+        link-title: Em
         encoding: utf-8
         output-encoding: None 
-        tags: python,pyem,Expectation Maximization,EM,online EM,recursive EM
+        tags: python,em,Expectation Maximization,EM,online EM,recursive EM
         file: basic_example1.py
         file: basic_example2.py
@@ -14,13 +14,13 @@
     /restindex
 
-.. Last Change: Mon Jul 02 09:00 PM 2007 J
+.. Last Change: Sun Jul 22 11:00 AM 2007 J
 
 ===================================================
- PyEM, a python package for Gaussian mixture models
+ em, a python package for Gaussian mixture models
 ===================================================
 
 .. contents:: Tables of contents
 
-PyEM is a package which enables to create Gaussian Mixture Models
+EM is a package which enables to create Gaussian Mixture Models
 (diagonal and full covariance matrices supported), to sample them, 
 and to estimate them from data using Expectation Maximization algorithm.
@@ -30,5 +30,5 @@
 online EM (ie recursive EM) and variational Bayes implementation.
 
-PyEM is implemented in python, and uses the excellent numpy and scipy
+EM is implemented in python, and uses the excellent numpy and scipy
 packages. Numpy is a python packages which gives python a fast 
 multi-dimensional array capabilities (ala matlab and the likes); scipy
@@ -41,25 +41,28 @@
 
 .. _scipy: http://www.scipy.org
-
-Pyem depends on several packages to work:
+.. _scikits: https://projects.scipy.org/scipy/scikits/
+
+The toolbox depends on several packages to work:
 
  - numpy
- - matplotlib (if you wish to use the plotting facilities of pyem)
+ - scipy
+ - setuptools
+ - matplotlib (if you wish to use the plotting facilities: this is not mandatory)
 
 Those packages are likely to be already installed in a typical numpy/scipy environment.
 
-Since september 2006, pyem is included in the sandbox of `scipy`_.  The sandbox
-contains packages which are pending for approval in main scipy; that means it
-is not installed by default, and that you need to install scipy from sources.
-For the most up-to-date version of pyem, you need to download scipy from
-subversion, which contains the development branch of scipy.
-
-To install pyem, you just need to edit (or create if it does not exist)
-the file Lib/sandbox/enabled_packages.txt in scipy sources, and
-add one line with the name of the package (eg pyem).
-After, you just need to install scipy normally as explained 
-`here <http://www.scipy.org/Installing_SciPy>`_.
-
-You can (and should) also test pyem installation using the following:
+Since July 2007, the toolbox is included in the learn scikits (`scikits`_).
+There is no official release yet, but you can get the package through svn with
+the following command:
+
+svn co http://svn.scipy.org/svn/scikits/trunk scikits.dev
+
+Of course, you can also use a graphical tool such as TortoiseSVN on windows if
+you do not feel confortable with the command line. Then, you can install it
+with the following command in the scikits.dev directory:
+
+python setup.py install
+
+You can (and should) also test em installation using the following:
 
 .. raw:: html
@@ -67,6 +70,6 @@
     {+mycoloring}
 
-    from scipy.sandbox import pyem
-    pyem.test()
+    from scikits.learn.machine import em
+    em.test()
     {-mycoloring}
 
@@ -74,12 +77,11 @@
 ============
 
-Once you are inside a python interpreter, you can import pyem
-using the follwing command:
+Once you are inside a python interpreter, you can import the package using the
+follwing command:
 
 .. raw:: html
 
     {+mycoloring}
-
-    from scipy.sandbox import pyem
+    from scikits.learn.machine import em
     {-mycoloring}
 
@@ -88,14 +90,14 @@
 -----------------------------------------
 
-Importing pyem gives access to 3 classes: GM (for Gausssian Mixture), GMM
-(Gaussian Mixture Model) and EM (for Expectation Maximization). The first
-class GM can be used to create an artificial Gaussian Model, samples it,
-or plot it. The following example show how to create 
-a 2 dimension Gaussian Model with 3 components, sample it and plot
-its confidence ellipsoids with matplotlib:
-
-.. raw:: html
-
-    {mycolorize;input/softwares/pyem/basic_example1.py}
+Importing the package gives access to 3 classes: GM (for Gausssian Mixture),
+GMM (Gaussian Mixture Model) and EM (for Expectation Maximization). The first
+class GM can be used to create an artificial Gaussian Model, samples it, or
+plot it. The following example show how to create a 2 dimension Gaussian Model
+with 3 components, sample it and plot its confidence ellipsoids with
+matplotlib:
+
+.. raw:: html
+
+    {mycolorize;input/softwares/em/basic_example1.py}
 
 .. raw:: latex
@@ -121,12 +123,12 @@
 
 If you want to learn a Gaussian mixture from data with EM, you need to use two
-classes from pyem: GMM and EM. You first create a GMM object from a GM
-instance; then you can give the GMM object as a parameter to the EM class to
-compute iterations of EM; once the EM has finished the computation, the GM
-instance of GMM contains the computed parameters.
-
-.. raw:: html
-
-    {mycolorize;input/softwares/pyem/basic_example2.py}
+classes from em: GMM and EM. You first create a GMM object from a GM instance;
+then you can give the GMM object as a parameter to the EM class to compute
+iterations of EM; once the EM has finished the computation, the GM instance of
+GMM contains the computed parameters.
+
+.. raw:: html
+
+    {mycolorize;input/softwares/em/basic_example2.py}
 
 .. raw:: latex
@@ -147,16 +149,15 @@
 -------------------------------------------------------
 
-The GMM class is also able to compute the bayesian information criterion
-(BIC), which can be used to assess the number of clusters into the data.
-It was first suggested by Schwarz (see bibliography), who gave a Bayesian
-argument for adopting the BIC. The BIC is derived from an approximation
-of the integrated likelihood of the model, based on regularity assumptions.
-The following code generates an artificial mixture of 4 clusters, runs
-EM with models of 1 to 6 clusters, and prints which number of clusters
-is the most likely from the BIC:
-
-.. raw:: html
-
-    {mycolorize;input/softwares/pyem/basic_example3.py}
+The GMM class is also able to compute the bayesian information criterion (BIC),
+which can be used to assess the number of clusters into the data.  It was first
+suggested by Schwarz (see bibliography), who gave a Bayesian argument for
+adopting the BIC. The BIC is derived from an approximation of the integrated
+likelihood of the model, based on regularity assumptions.  The following code
+generates an artificial mixture of 4 clusters, runs EM with models of 1 to 6
+clusters, and prints which number of clusters is the most likely from the BIC:
+
+.. raw:: html
+
+    {mycolorize;input/softwares/em/basic_example3.py}
 
 .. raw:: latex
@@ -170,7 +171,7 @@
     :height: 400
 
-The above example also shows that you can control the plotting
-parameters by using returned handles from plot methods. This can be
-useful for complex drawing.
+The above example also shows that you can control the plotting parameters by
+using returned handles from plot methods. This can be useful for complex
+drawing.
 
 Examples 
@@ -202,6 +203,6 @@
 TODO (this is fundamentally the same than pdf estimation, though)
 
-Using PyEM for supervised learning
-----------------------------------
+supervised learning (e.g discriminative analysis)
+-------------------------------------------------
 
 The following example shows how to do classification using discriminative
@@ -228,26 +229,27 @@
 ====================
 
-Pyem is implemented in python (100% of the code has a python implementation),
-but thanks to the Moore Law, it is reasonably fast so that it can be used for
-other problems than toys problem. On my computer (linux on bi xeon 3.2 Ghz, 2Gb
-RAM), running 10 iterations of EM algorithm on 100 000 samples of dimension 15,
-for a diagonal model with 30 components, takes around 1 minute and 15 seconds:
-this makes the implementation usable for moderately complex problems such as
-speaker recognition using MFCC. If this is too slow, there is a C
-implementation for Gaussian densities which can be enabled by commenting out
-one line in pyem/gmm_em.py, which should gives a speed up of a factor 2 at
-least; this has not been tested much, though, so beware.
-
-Also, increasing the number of components and/or dimension is much more
-expensive than increasing the number of samples: a 5 dimension model of 100
-components will be much slower to estimate with 500 samples than a 5 dimension
-10 components with 5000 samples. This is because loops on dimension/components
-are in python, whereas loops on samples are in C (through numpy). I don't
+The package is implemented in python (100% of the code has a python
+implementation, so that it can be used for educational purpose too), but thanks
+to the Moore Law, it is reasonably fast so that it can be used for other
+problems than toys problem. On my computer (linux on bi xeon 3.2 Ghz, 2Gb RAM),
+running 10 iterations of EM algorithm on 100 000 samples of dimension 15, for a
+diagonal model with 30 components, takes around 1 minute and 15 seconds: this
+makes the implementation usable for moderately complex problems such as speaker
+recognition using MFCC. If this is too slow, there is a C implementation for
+Gaussian densities which can be enabled by commenting out one line in the file
+gmm_em.py, which should gives a speed up of a factor 2 at least; this has not
+been tested much, though, so beware.
+
+Also, increasing the number of components or dimension is much more expensive
+than increasing the number of samples: a 5 dimension model of 100 components
+will be much slower to estimate with 500 samples than a 5 dimension 10
+components with 5000 samples. This is because loops on dimension or components
+are in python, whereas loops on samples are in C (through numpy).  I don't
 think there is an easy fix to this problem. 
 
-Full covariances will be slow, because you cannot avoid nested loop
-in python this case without insane amount of memory. A C implementation
-may be implemented, but this is not my top priority; most of the time, you
-should avoid full covariance models anyway.
+Full covariances will be slow, because you cannot avoid nested loop in python
+this case without insane amount of memory. A C implementation may be
+implemented, but this is not my top priority; most of the time, you should
+avoid full covariance models if possible.
 
 TODO
@@ -267,5 +269,5 @@
 
  - add other models (mixtures of multinomial: easy, simple HMM: easy, other ?)
- - add bayes prior using MCMC (hard, use PyMCMC for sampling ?)
+ - add bayes prior using MCMC (hard, use PyMC for sampling ?)
 
 Bibliography
Index: /trunk/learn/scikits/learn/machine/em/doc/tutorial.pdf
===================================================================
--- /trunk/learn/scikits/learn/machine/em/doc/tutorial.pdf (revision 363)
+++ /trunk/learn/scikits/learn/machine/em/doc/tutorial.pdf (revision 448)
@@ -59,5 +59,5 @@
 endobj
 44 0 obj
-(Using PyEM for supervised learning)
+(supervised learning \(e.g discriminative analysis\))
 endobj
 45 0 obj
@@ -83,53 +83,59 @@
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Index: /trunk/learn/scikits/learn/machine/em/doc/Makefile
===================================================================
--- /trunk/learn/scikits/learn/machine/em/doc/Makefile (revision 363)
+++ /trunk/learn/scikits/learn/machine/em/doc/Makefile (revision 448)
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-# Last Change: Mon Jul 02 09:00 PM 2007 J
+# Last Change: Sun Jul 22 11:00 AM 2007 J
 
 # This makefile is used to build the pdf from the rest file and inlined code
 # from python examples
 
-py2tex	= PYTHONPATH=/home/david/local/lib/python2.4/site-packages pygmentize -l python -f tex
+py2tex	= pygmentize -l python -f tex
 rst2tex	= PYTHONPATH=/home/david/local/lib/python2.4/site-packages rst2newlatex.py \
 		  --stylesheet-path base.tex --user-stylesheet user.tex 
Index: /trunk/learn/scikits/learn/machine/em/examples/regularized_example.py
===================================================================
--- /trunk/learn/scikits/learn/machine/em/examples/regularized_example.py (revision 357)
+++ /trunk/learn/scikits/learn/machine/em/examples/regularized_example.py (revision 448)
@@ -1,4 +1,4 @@
 #! /usr/bin/env python
-# Last Change: Mon Jul 02 04:00 PM 2007 J
+# Last Change: Sun Jul 22 12:00 PM 2007 J
 
 __doc__ = """Example of using RegularizedEM with pendigits data.
@@ -17,7 +17,7 @@
 import pylab as P
 
-#from scipy.sandbox import pyem
-from gauss_mix import GM
-from gmm_em import GMM, EM, RegularizedEM
+from scikits.learn.machine.em import EM, GM, GMM
+# Experimental RegularizedEM
+from scikits.learn.machine.em.gmm_em import RegularizedEM
 import utils
 
Index: /trunk/learn/scikits/learn/machine/em/examples/pdfestimation1d.py
===================================================================
--- /trunk/learn/scikits/learn/machine/em/examples/pdfestimation1d.py (revision 438)
+++ /trunk/learn/scikits/learn/machine/em/examples/pdfestimation1d.py (revision 448)
@@ -1,4 +1,4 @@
 #! /usr/bin/env python
-# Last Change: Sat Jul 21 03:00 PM 2007 J
+# Last Change: Sun Jul 22 12:00 PM 2007 J
 
 __doc__ = """This example shows how to do pdfestimation for one dimensional
@@ -16,7 +16,5 @@
 import matplotlib as MPL
 
-set_package_path()
-import pyem
-restore_path()
+from scikits.learn.machine.em import EM, GM, GMM
 import utils
 
@@ -34,7 +32,7 @@
 def cluster(data, k):
     d = data.shape[1]
-    gm = pyem.GM(d, k)
-    gmm = pyem.GMM(gm)
-    em = pyem.EM()
+    gm = GM(d, k)
+    gmm = GMM(gm)
+    em = EM()
     em.train(data, gmm, maxiter = 20)
     return gm, gmm.bic(data)
Index: /trunk/learn/scikits/learn/machine/em/examples/basic_example1.py
===================================================================
--- /trunk/learn/scikits/learn/machine/em/examples/basic_example1.py (revision 313)
+++ /trunk/learn/scikits/learn/machine/em/examples/basic_example1.py (revision 448)
@@ -1,5 +1,5 @@
 import numpy as N
 import pylab as P
-from scipy.sandbox.pyem import GM
+from scikits.learn.machine.em import GM
 
 #------------------------------
Index: /trunk/learn/scikits/learn/machine/em/examples/plotexamples.py
===================================================================
--- /trunk/learn/scikits/learn/machine/em/examples/plotexamples.py (revision 332)
+++ /trunk/learn/scikits/learn/machine/em/examples/plotexamples.py (revision 448)
@@ -1,4 +1,4 @@
 #! /usr/bin/env python
-# Last Change: Mon Jun 11 03:00 PM 2007 J
+# Last Change: Sun Jul 22 12:00 PM 2007 J
 
 # This is a simple test to check whether plotting ellipsoides of confidence and
@@ -9,7 +9,5 @@
 import pylab as P
 
-set_package_path()
-import pyem
-restore_path()
+from scikits.learn.machine.em import EM, GM, GMM
 
 # Generate a simple mixture model, plot its confidence ellipses + isodensity
@@ -19,9 +17,9 @@
 dim = [0, 2]
 # diag model
-w, mu, va = pyem.GM.gen_param(d, k)
-dgm = pyem.GM.fromvalues(w, mu, va)
+w, mu, va = GM.gen_param(d, k)
+dgm = GM.fromvalues(w, mu, va)
 # full model
-w, mu, va = pyem.GM.gen_param(d, k, 'full', spread = 1)
-fgm = pyem.GM.fromvalues(w, mu, va)
+w, mu, va = GM.gen_param(d, k, 'full', spread = 1)
+fgm = GM.fromvalues(w, mu, va)
 
 def plot_model(gm, dim):
Index: /trunk/learn/scikits/learn/machine/em/examples/basic_example2.py
===================================================================
--- /trunk/learn/scikits/learn/machine/em/examples/basic_example2.py (revision 313)
+++ /trunk/learn/scikits/learn/machine/em/examples/basic_example2.py (revision 448)
@@ -1,5 +1,5 @@
 from numpy.random import seed
 
-from scipy.sandbox.pyem import GM, GMM, EM
+from scikits.learn.machine.em import GM, GMM, EM
 import copy
 
Index: /trunk/learn/scikits/learn/machine/em/examples/basic_example3.py
===================================================================
--- /trunk/learn/scikits/learn/machine/em/examples/basic_example3.py (revision 313)
+++ /trunk/learn/scikits/learn/machine/em/examples/basic_example3.py (revision 448)
@@ -2,5 +2,5 @@
 from numpy.random import seed
 
-from scipy.sandbox.pyem import GM, GMM, EM
+from scikits.learn.machine.em import GM, GMM, EM
 import copy
 
Index: /trunk/learn/scikits/learn/machine/em/examples/pdfestimation.py
===================================================================
--- /trunk/learn/scikits/learn/machine/em/examples/pdfestimation.py (revision 356)
+++ /trunk/learn/scikits/learn/machine/em/examples/pdfestimation.py (revision 448)
@@ -1,4 +1,4 @@
 #! /usr/bin/env python
-# Last Change: Mon Jul 02 03:00 PM 2007 J
+# Last Change: Sun Jul 22 12:00 PM 2007 J
 
 # Example of doing pdf estimation with EM algorithm. Requires matplotlib.
@@ -6,5 +6,5 @@
 import pylab as P
 
-from scipy.sandbox import pyem
+from scikits.learn.machine.em import EM, GM, GMM
 import utils
 
@@ -24,7 +24,7 @@
 def cluster(data, k, mode = 'full'):
     d = data.shape[1]
-    gm = pyem.GM(d, k, mode)
-    gmm = pyem.GMM(gm)
-    em = pyem.EM()
+    gm = GM(d, k, mode)
+    gmm = GMM(gm)
+    em = EM()
     em.train(data, gmm, maxiter = 20)
     return gm, gmm.bic(data)
Index: /trunk/learn/scikits/learn/machine/em/examples/discriminant_analysis.py
===================================================================
--- /trunk/learn/scikits/learn/machine/em/examples/discriminant_analysis.py (revision 363)
+++ /trunk/learn/scikits/learn/machine/em/examples/discriminant_analysis.py (revision 448)
@@ -1,4 +1,4 @@
 #! /usr/bin/env python
-# Last Change: Mon Jul 02 09:00 PM 2007 J
+# Last Change: Sun Jul 22 12:00 PM 2007 J
 
 __doc__ = """Example of doing classification with mixture of Gaussian. Note
@@ -14,5 +14,5 @@
 import matplotlib as MPL
 
-from scipy.sandbox import pyem
+from scikits.learn.machine.em import EM, GMM, GM
 import utils
 
@@ -57,7 +57,7 @@
 def cluster(data, k, mode = 'full'):
     d = data.shape[1]
-    gm = pyem.GM(d, k, mode)
-    gmm = pyem.GMM(gm)
-    em = pyem.EM()
+    gm = GM(d, k, mode)
+    gmm = GMM(gm)
+    em = EM()
     em.train(data, gmm, maxiter = 20)
     return gm
Index: /trunk/learn/scikits/learn/machine/setup.py
===================================================================
--- /trunk/learn/scikits/learn/machine/setup.py (revision 444)
+++ /trunk/learn/scikits/learn/machine/setup.py (revision 448)
@@ -6,5 +6,7 @@
     print "============================================="
     config = Configuration('machine',parent_package,top_path)
+    # pyem should be suppresed as some points
     config.add_subpackage('pyem')
+    config.add_subpackage('em')
     config.add_subpackage('svm')
     #config.make_svn_version_py()  # installs __svn_version__.py
Index: /trunk/learn/scikits/learn/machine/pyem/__init__.py
===================================================================
--- /trunk/learn/scikits/learn/machine/pyem/__init__.py (revision 448)
+++ /trunk/learn/scikits/learn/machine/pyem/__init__.py (revision 448)
@@ -0,0 +1,4 @@
+#! /usr/bin/env python
+# Last Change: Sun Jul 22 10:00 AM 2007 J
+
+raise ImportError("pyem has been renamed to em")