SF.net SVN: matplotlib:[7927] trunk/matplotlib/lib/matplotlib

[<jd...@us...>]

Revision: 7927
          http://matplotlib.svn.sourceforge.net/matplotlib/?rev=7927&view=rev
Author:   jdh2358
Date:     2009-11-03 20:51:55 +0000 (Tue, 03 Nov 2009)

Log Message:
-----------
added pca test

Modified Paths:
--------------
    trunk/matplotlib/lib/matplotlib/__init__.py
    trunk/matplotlib/lib/matplotlib/mlab.py

Added Paths:
-----------
    trunk/matplotlib/lib/matplotlib/tests/test_mlab.py

Modified: trunk/matplotlib/lib/matplotlib/__init__.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/__init__.py	2009-11-03 20:27:23 UTC (rev 7926)
+++ trunk/matplotlib/lib/matplotlib/__init__.py	2009-11-03 20:51:55 UTC (rev 7927)
@@ -905,6 +905,7 @@
     'matplotlib.tests.test_backend_svg',
     'matplotlib.tests.test_basic',
     'matplotlib.tests.test_cbook',
+    'matplotlib.tests.test_mlab',
     'matplotlib.tests.test_transforms',
     'matplotlib.tests.test_axes',
     'matplotlib.tests.test_dates',

Modified: trunk/matplotlib/lib/matplotlib/mlab.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/mlab.py	2009-11-03 20:27:23 UTC (rev 7926)
+++ trunk/matplotlib/lib/matplotlib/mlab.py	2009-11-03 20:51:55 UTC (rev 7927)
@@ -761,7 +761,7 @@
     """
 
     WARNING: this function is deprecated -- please see class PCA instead
-    
+
     Compute the principal components of *P*.  *P* is a (*numVars*,
     *numObs*) array.  *frac* is the minimum fraction of variance that a
     component must contain to be included.
@@ -870,6 +870,27 @@
         'center the data using the mean and sigma from training set a'
         return (x - self.mu)/self.sigma
 
+
+
+    @staticmethod
+    def _get_colinear():
+        c0 = np.array([
+            0.19294738,  0.6202667 ,  0.45962655,  0.07608613,  0.135818  ,
+            0.83580842,  0.07218851,  0.48318321,  0.84472463,  0.18348462,
+            0.81585306,  0.96923926,  0.12835919,  0.35075355,  0.15807861,
+            0.837437  ,  0.10824303,  0.1723387 ,  0.43926494,  0.83705486])
+
+        c1 = np.array([
+            -1.17705601, -0.513883  , -0.26614584,  0.88067144,  1.00474954,
+            -1.1616545 ,  0.0266109 ,  0.38227157,  1.80489433,  0.21472396,
+            -1.41920399, -2.08158544, -0.10559009,  1.68999268,  0.34847107,
+            -0.4685737 ,  1.23980423, -0.14638744, -0.35907697,  0.22442616])
+
+        c2 = c0 + 2*c1
+        c3 = -3*c0 + 4*c1
+        a = np.array([c3, c0, c1, c2]).T
+        return a
+
 def prctile(x, p = (0.0, 25.0, 50.0, 75.0, 100.0)):
     """
     Return the percentiles of *x*.  *p* can either be a sequence of

Added: trunk/matplotlib/lib/matplotlib/tests/test_mlab.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/tests/test_mlab.py	                        (rev 0)
+++ trunk/matplotlib/lib/matplotlib/tests/test_mlab.py	2009-11-03 20:51:55 UTC (rev 7927)
@@ -0,0 +1,11 @@
+import numpy as np
+import matplotlib.mlab as mlab
+
+@staticmethod
+def test_colinear_pca():
+    a = mlab.PCA._get_colinear()
+    pca = mlab.PCA(a)
+
+    assert(np.allclose(pca.fracs[2:], 0.))
+    assert(np.allclose(pca.Y[:,2:], 0.))
+


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