Source Code for Module pyvision.ml.pca

 1  ''' 
 2  Created on Jan 17, 2011 
 3   
 4  @author: bolme 
 5  ''' 
 6   
 7  import numpy as np 
 8  import scipy.linalg as la 
 9   
10 -def prcomp(data,center=True,scale=False): 
11      ''' 
12      Conduct a basic principal components analysis on the data. 
13       
14      This function has been compared to R to verify that it produces similar results. 
15       
16      @param data: a data matrix with vectors in rows 
17      @param center: subtract the mean vector from the data 
18      @param scale: scale the values to have unit variance 
19      @returns: stdevs,rotation,[center],[scale] 
20      ''' 
21      data = data.copy() 
22      r,c = data.shape 
23       
24      # center the data  
25      if center: 
26          ctr = data.mean(axis=0).reshape(1,c) 
27          data = data - ctr  
28       
29      # scale the data 
30      if scale: 
31          scl = data.std(axis=0,ddof=1).reshape(1,c) 
32          data = data/scl  
33           
34      # decompose the data using svd 
35      _,val,vt = la.svd(data,full_matrices=False) 
36   
37      # compute the standard deviations from the singular values 
38      standard_dev = val/np.sqrt(r-1) 
39       
40      # Vt.T are the basis vectors 
41      result = [standard_dev,vt.T] 
42       
43      # format result 
44      if center: 
45          result.append(ctr) 
46           
47      if scale: 
48          result.append(scl) 
49           
50      return result 
51   
52   
53  # Alias the name to pca 
54  pca = prcomp 
55