Revision: 7926
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=7926&view=rev
Author: jdh2358
Date: 2009-11-03 20:27:23 +0000 (Tue, 03 Nov 2009)
Log Message:
-----------
added PCA helper class to mlab and deprecated prepca
Modified Paths:
--------------
trunk/matplotlib/lib/matplotlib/mlab.py
Modified: trunk/matplotlib/lib/matplotlib/mlab.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/mlab.py 2009-11-03 17:57:52 UTC (rev
7925)
+++ trunk/matplotlib/lib/matplotlib/mlab.py 2009-11-03 20:27:23 UTC (rev
7926)
@@ -759,6 +759,9 @@
def prepca(P, frac=0):
"""
+
+ 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.
@@ -778,6 +781,7 @@
R13 Neural Network Toolbox but is not found in later versions;
its successor seems to be called "processpcs".
"""
+ warnings.warn('This function is deprecated -- see class PCA instead')
U,s,v = np.linalg.svd(P)
varEach = s**2/P.shape[1]
totVar = varEach.sum()
@@ -789,6 +793,83 @@
Pcomponents = np.dot(Trans,P)
return Pcomponents, Trans, fracVar[ind]
+
+class PCA:
+ def __init__(self, a):
+ """
+ compute the SVD of a and store data for PCA. Use project to
+ project the data onto a reduced set of dimensions
+
+ Inputs:
+
+ *a*: a numobservations x numdims array
+
+ Attrs:
+
+ *a* a centered unit sigma version of input a
+
+ *numrows*, *numcols*: the dimensions of a
+
+ *mu* : a numdims array of means of a
+
+ *sigma* : a numdims array of atandard deviation of a
+
+ *fracs* : the proportion of variance of each of the principal
components
+
+ *Wt* : the weight vector for projecting a numdims point or array
into PCA space
+
+ *Y* : a projected into PCA space
+
+ """
+ n, m = a.shape
+ if n<m:
+ raise RuntimeError('we assume data in a is organized with
numrows>numcols')
+
+ self.numrows, self.numcols = n, m
+ self.mu = a.mean(axis=0)
+ self.sigma = a.std(axis=0)
+
+ a = self.center(a)
+
+ self.a = a
+
+ U, s, Vh = np.linalg.svd(a, full_matrices=False)
+
+
+ Y = np.dot(Vh, a.T).T
+
+ vars = s**2/float(len(s))
+ self.fracs = vars/vars.sum()
+
+
+ self.Wt = Vh
+ self.Y = Y
+
+
+ def project(self, x, minfrac=0.):
+ 'project x onto the principle axes, dropping any axes where fraction
of variance<minfrac'
+ x = np.asarray(x)
+
+ ndims = len(x.shape)
+
+ if (x.shape[-1]!=self.numcols):
+ raise ValueError('Expected an array with
dims[-1]==%d'%self.numcols)
+
+
+ Y = np.dot(self.Wt, self.center(x).T).T
+ mask = self.fracs>=minfrac
+ if ndims==2:
+ Yreduced = Y[:,mask]
+ else:
+ Yreduced = Y[mask]
+ return Yreduced
+
+
+
+ def center(self, x):
+ 'center the data using the mean and sigma from training set a'
+ return (x - self.mu)/self.sigma
+
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
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