comparison ABSlambda.py @ 21:d395461b92ae tip

Lots and lots of modifications. Approximate recovery script working.

   aggDupper = numpy.dot(M,D)
   aggDlower = Vt[-(N-n):,:]
   aggD = numpy.vstack((aggDupper, lbd * aggDlower))
   aggy = numpy.concatenate((y, numpy.zeros(N-n)))
   
-  return numpy.dot(D, pyCSalgos.SL0.SL0_approx.SL0_approx(aggD,aggy,epsilon,sigma_min,sigma_decrease_factor,mu_0,L,A_pinv,true_s))
+  #return numpy.dot(D, pyCSalgos.SL0.SL0_approx.SL0_approx(aggD,aggy,epsilon,sigma_min,sigma_decrease_factor,mu_0,L,A_pinv,true_s))
+  return numpy.dot(D, pyCSalgos.SL0.SL0_approx.SL0_approx_dai(aggD,aggy,epsilon,sigma_min,sigma_decrease_factor,mu_0,L,A_pinv,true_s))
   
 def bp(y,M,Omega,epsilon,lbd, x0, lbtol=1e-3, mu=10, cgtol=1e-8, cgmaxiter=200, verbose=False):
   """
   ABS-lambda: Basis Pursuit (based on l1magic toolbox)
   """    

   N,n = Omega.shape
   D = numpy.linalg.pinv(Omega)
   U,S,Vt = numpy.linalg.svd(D)
   aggDupper = numpy.dot(M,D)
   aggDlower = Vt[-(N-n):,:]
-  aggD = numpy.hstack((aggDupper, lbd * aggDlower))
+  aggD = numpy.vstack((aggDupper, lbd * aggDlower))
   aggy = numpy.concatenate((y, numpy.zeros(N-n)))
   
   opts = dict()
   opts['stopCrit'] = 'mse'
   opts['stopTol'] = epsilon**2 / aggy.size

   aggDlower = Vt[-(N-n):,:]
   aggD = numpy.vstack((aggDupper, lbd * aggDlower))
   aggy = numpy.concatenate((y, numpy.zeros(N-n)))
   
   tol = epsilon / numpy.linalg.norm(aggy)
-  return numpy.dot(D, pyCSalgos.RecomTST.RecommendedTST.RecommendedTST(aggD, aggy, nsweep, tol, xinitial, ro))
+  return numpy.dot(D, pyCSalgos.TST.RecommendedTST.RecommendedTST(aggD, aggy, nsweep, tol, xinitial, ro))