nikcleju@4
|
1 # -*- coding: utf-8 -*-
|
nikcleju@4
|
2 """
|
nikcleju@4
|
3 Created on Mon Oct 24 21:17:49 2011
|
nikcleju@4
|
4
|
nikcleju@4
|
5 @author: Nic
|
nikcleju@4
|
6
|
nikcleju@4
|
7 Test RecommendedTST algorithm
|
nikcleju@4
|
8 """
|
nikcleju@4
|
9
|
nikcleju@4
|
10 import numpy as np
|
nikcleju@4
|
11 import numpy.linalg
|
nikcleju@4
|
12 import scipy.io
|
nikcleju@4
|
13 import unittest
|
nikcleju@4
|
14 from pyCSalgos.RecomTST.RecommendedTST import RecommendedTST
|
nikcleju@4
|
15
|
nikcleju@4
|
16 class RecomTSTresults(unittest.TestCase):
|
nikcleju@4
|
17 def testResults(self):
|
nikcleju@4
|
18 mdict = scipy.io.loadmat('RecomTSTtestdata.mat')
|
nikcleju@4
|
19
|
nikcleju@4
|
20 # A = system matrix
|
nikcleju@4
|
21 # Y = matrix with measurements (on columns)
|
nikcleju@4
|
22 # X0 = matrix with initial solutions (on columns)
|
nikcleju@4
|
23 # Eps = vector with epsilon
|
nikcleju@4
|
24 # Xr = matrix with correct solutions (on columns)
|
nikcleju@4
|
25 for A,Y,X0,Tol,Xr in zip(mdict['cellA'].squeeze(),mdict['cellY'].squeeze(),mdict['cellX0'].squeeze(),mdict['cellTol'].squeeze(),mdict['cellXr'].squeeze()):
|
nikcleju@4
|
26 for i in np.arange(Y.shape[1]):
|
nikcleju@4
|
27 xr = RecommendedTST(A, Y[:,i], nsweep=300, tol=Tol.squeeze()[i], xinitial=X0[:,i])
|
nikcleju@4
|
28
|
nikcleju@4
|
29 # check if found solution is the same as the correct cslution
|
nikcleju@4
|
30 diff = numpy.linalg.norm(xr - Xr[:,i])
|
nikcleju@4
|
31 self.assertTrue(diff < 1e-12)
|
nikcleju@4
|
32 #err1 = numpy.linalg.norm(Y[:,i] - np.dot(A,xr))
|
nikcleju@4
|
33 #err2 = numpy.linalg.norm(Y[:,i] - np.dot(A,Xr[:,i]))
|
nikcleju@4
|
34 #norm1 = numpy.linalg.norm(xr,1)
|
nikcleju@4
|
35 #norm2 = numpy.linalg.norm(Xr[:,i],1)
|
nikcleju@4
|
36 #print 'diff = ',diff
|
nikcleju@4
|
37 #print 'err1 = ',err1
|
nikcleju@4
|
38 #print 'err2 = ',err2
|
nikcleju@4
|
39 #print 'norm1 = ',norm1
|
nikcleju@4
|
40 #print 'norm2 = ',norm2
|
nikcleju@4
|
41 #
|
nikcleju@4
|
42 # It seems Matlab's linsolve and scipy solve are slightly different
|
nikcleju@4
|
43 # Therefore make a more robust condition:
|
nikcleju@4
|
44 # OK; if solutions are close enough (diff < 1e-6)
|
nikcleju@4
|
45 # or
|
nikcleju@4
|
46 # (
|
nikcleju@4
|
47 # they fulfill the constraint close enough (differr < 1e-6)
|
nikcleju@4
|
48 # and
|
nikcleju@4
|
49 # Python solution has l1 norm no more than 1e-6 larger as the reference solution
|
nikcleju@4
|
50 # (i.e. either norm1 < norm2 or norm1>norm2 not by more than 1e-6)
|
nikcleju@4
|
51 # )
|
nikcleju@4
|
52 #
|
nikcleju@4
|
53 # ERROR: else
|
nikcleju@4
|
54 #differr = abs((err1 - err2))
|
nikcleju@4
|
55 #diffnorm = norm1 - norm2 # intentionately no abs(), since norm1 < norm2 is good
|
nikcleju@4
|
56 #if diff < 1e-6 or (differr < 1e-6 and (diffnorm < 1e-6)):
|
nikcleju@4
|
57 # isok = True
|
nikcleju@4
|
58 #else:
|
nikcleju@4
|
59 # isok = False
|
nikcleju@4
|
60 #if not isok:
|
nikcleju@4
|
61 # print "should raise"
|
nikcleju@4
|
62 # #self.assertTrue(isok)
|
nikcleju@4
|
63 #self.assertTrue(isok)
|
nikcleju@4
|
64
|
nikcleju@4
|
65 if __name__ == "__main__":
|
nikcleju@4
|
66 unittest.main(verbosity=2)
|
nikcleju@4
|
67 #suite = unittest.TestLoader().loadTestsFromTestCase(CompareResults)
|
nikcleju@4
|
68 #unittest.TextTestRunner(verbosity=2).run(suite) |