Source Code for Module pyvision.vector.linearreg

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33   
34  ''' 
35  Implementation of linear regression. 
36  ''' 
37  #import unittest 
38  #import random 
39  import numpy 
40  from numpy import dot,sqrt 
41  from numpy.linalg.linalg import pinv 
42   
43  #TODO: make this a subclass of VectorClassifier 
44 -class LinearReg: 
45 -    def __init__(self): 
46          pass 
47       
48 -    def train_least_squares(self, inputs, outputs): 
49          ''' 
50          inputs  - is a matrix where each row is an input. 
51          outputs - is a matrix where each row is a corresponding output. 
52           
53          based on: http://en.wikipedia.org/wiki/Linear_regression (2007/06/07) 
54          ''' 
55          self.mat = [] 
56          self.RMSE = [] 
57          # create the data matrix 
58          for output in range(outputs.shape[1]): 
59              print "Training output ", output 
60              y = outputs[:,output] 
61              #print "y:\n",y 
62              X = inputs 
63              tmp = numpy.ones(shape=(X.shape[0],1)) 
64              X = numpy.concatenate([tmp, X],axis=1) 
65              #print "X:\n",X 
66              B = dot(dot(pinv(dot(X.transpose(),X)),X.transpose()),y) 
67              #print "B:\n",B 
68               
69              E = y - dot(X,B) 
70              self.RMSE.append(sqrt((E*E).sum())) 
71              #print "E:", E, E < 0.0001 
72              self.mat.append( B ) 
73   
74          self.mat =  numpy.array(self.mat) 
75           
76          return self.RMSE 
77       
78 -    def map(self, data): 
79          X = numpy.concatenate([numpy.ones((1,)),data.flatten()]) 
80          #print X 
81          return dot(self.mat,X) 
82   
83 -    def __call__(self,data): 
84          return self.map(data) 
85