view tests/RecomTST_test.py @ 18:a8ff9a881d2f

GAP test almost working. For some data the results are not the same because of representation error, so the test doesn't fully work for now. But the results seem to be accurate.

# -*- coding: utf-8 -*-
"""
Created on Mon Oct 24 21:17:49 2011

@author: Nic

Test RecommendedTST algorithm
u"""

import numpy as np
import numpy.linalg
import scipy.io
import unittest
from pyCSalgos.RecomTST.RecommendedTST import RecommendedTST

class RecomTSTresults(unittest.TestCase):
  def testResults(self):
    mdict = scipy.io.loadmat('RecomTSTtestdata.mat')
    
    # A = system matrix
    # Y = matrix with measurements (on columns)
    # X0 = matrix with initial solutions (on columns)
    # Eps = vector with epsilon
    # Xr = matrix with correct solutions (on columns)
    for A,Y,X0,Tol,Xr in zip(mdict['cellA'].squeeze(),mdict['cellY'].squeeze(),mdict['cellX0'].squeeze(),mdict['cellTol'].squeeze(),mdict['cellXr'].squeeze()):
      for i in np.arange(Y.shape[1]):
        xr = RecommendedTST(A, Y[:,i], nsweep=300, tol=Tol.squeeze()[i], xinitial=X0[:,i])
        
        # check if found solution is the same as the correct cslution
        diff = numpy.linalg.norm(xr - Xr[:,i])
        err1 = numpy.linalg.norm(Y[:,i] - np.dot(A,xr))
        err2 = numpy.linalg.norm(Y[:,i] - np.dot(A,Xr[:,i]))
        norm1 = numpy.linalg.norm(xr,1)
        norm2 = numpy.linalg.norm(Xr[:,i],1)
                
        # Make a more robust condition:
        #  OK;    if   solutions are close enough (diff < 1e-6)
        #              or
        #              (
        #               Python solution fulfills the constraint better (or up to 1e-6 worse)
        #                 and
        #               Python solution has l1 norm no more than 1e-6 larger as the reference solution
        #                 (i.e. either norm1 < norm2   or   norm1>norm2 not by more than 1e-6)
        #              )
        #        
        #  ERROR: else        
        differr  = err1 - err2    # intentionately no abs(), since err1` < err2 is good
        diffnorm = norm1 - norm2  # intentionately no abs(), since norm1 < norm2 is good
        if diff < 1e-6 or (differr < 1e-6 and (diffnorm < 1e-6)):
          isok = True
        else:
          isok = False
        self.assertTrue(isok)
        
        #diff = numpy.linalg.norm(xr - Xr[:,i])
        #if diff > 1e-6:
        #    self.assertTrue(diff < 1e-6)

  
if __name__ == "__main__":
    #import cProfile
    #cProfile.run('unittest.main()', 'profres')
    unittest.main()    
    #suite = unittest.TestLoader().loadTestsFromTestCase(CompareResults)
    #unittest.TextTestRunner(verbosity=2).run(suite)