comparison scripts/ABSapprox.py @ 27:1a88766113a9

A lot of things.
Fixed problem in Gap
Fixed multiprocessor versions of script (both PP and multiproc)

 """
 
 import numpy as np
 import scipy.io
 import math
-#import matplotlib.pyplot as plt
-#import matplotlib.cm as cm
+doplot = True
+try: 
+  import matplotlib.pyplot as plt
+except:
+  doplot = False
+if doplot:  
+  import matplotlib.cm as cm
 import pyCSalgos
 import pyCSalgos.GAP.GAP
 import pyCSalgos.SL0.SL0_approx
 
 # Define functions that prepare arguments for each algorithm call

   sigma_decrease_factor = 0.5
   mu_0 = 2
   L = 10
   return pyCSalgos.SL0.SL0_approx.SL0_approx(aggD,aggy,epsilon,sigmamin,sigma_decrease_factor,mu_0,L)
 
+def run_bp(y,M,Omega,D,U,S,Vt,epsilon,lbd):
+  
+  N,n = Omega.shape
+  #D = np.linalg.pinv(Omega)
+  #U,S,Vt = np.linalg.svd(D)
+  aggDupper = np.dot(M,D)
+  aggDlower = Vt[-(N-n):,:]
+  aggD = np.concatenate((aggDupper, lbd * aggDlower))
+  aggy = np.concatenate((y, np.zeros(N-n)))
+  
+  sigmamin = 0.001
+  sigma_decrease_factor = 0.5
+  mu_0 = 2
+  L = 10
+  return pyCSalgos.SL0.SL0_approx.SL0_approx(aggD,aggy,epsilon,sigmamin,sigma_decrease_factor,mu_0,L)
+
+
 # Define tuples (algorithm setup function, algorithm function, name)
 gap = (run_gap, 'GAP')
 sl0 = (run_sl0, 'SL0_approx')
 
 # Define which algorithms to run

   nalgosL = len(algosL)
   
   #Set up experiment parameters
   d = 50.0;
   sigma = 2.0
-  deltas = np.arange(0.05,0.95,0.05)
-  rhos = np.arange(0.05,0.95,0.05)
-  #deltas = np.array([0.15,0.95])
-  #rhos = np.array([0.15,0.95])
+  #deltas = np.arange(0.05,1.,0.05)
+  #rhos = np.arange(0.05,1.,0.05)
+  deltas = np.array([0.05, 0.45, 0.95])
+  rhos = np.array([0.05, 0.45, 0.95])
   #deltas = np.array([0.05])
   #rhos = np.array([0.05])
   #delta = 0.8;
   #rho   = 0.15;
-  numvects = 20; # Number of vectors to generate
+  numvects = 100; # Number of vectors to generate
   SNRdb = 20.;    # This is norm(signal)/norm(noise), so power, not energy
   # Values for lambda
   #lambdas = [0 10.^linspace(-5, 4, 10)];
   #lambdas = np.concatenate((np.array([0]), 10**np.linspace(-5, 4, 10)))
   lambdas = np.array([0., 0.0001, 0.01, 1, 100, 10000])

       scipy.io.savemat('ABSapprox.mat',tosave)
     except TypeError:
       print "Oops, Type Error"
       raise    
   # Show
-  #  for algotuple in algosN:
-  #    plt.figure()
-  #    plt.imshow(meanmatrix[algotuple[1]], cmap=cm.gray, interpolation='nearest')
-  #  for algotuple in algosL:
-  #    for ilbd in np.arange(lambdas.size):
-  #      plt.figure()
-  #      plt.imshow(meanmatrix[algotuple[1]][ilbd], cmap=cm.gray, interpolation='nearest')
-  #  plt.show()
+  if doplot:
+    for algotuple in algosN:
+      plt.figure()
+      plt.imshow(meanmatrix[algotuple[1]], cmap=cm.gray, interpolation='nearest',origin='lower')
+    for algotuple in algosL:
+      for ilbd in np.arange(lambdas.size):
+        plt.figure()
+        plt.imshow(meanmatrix[algotuple[1]][ilbd], cmap=cm.gray, interpolation='nearest',origin='lower')
+    plt.show()
   print "Finished."
   
 def genData(d,sigma,delta,rho,numvects,SNRdb):
 
   # Process parameters

   
   return mrelerrN,mrelerrL
   
 # Script main
 if __name__ == "__main__":
+
+  #import cProfile
+  #cProfile.run('mainrun()', 'profile')    
   mainrun()