comparison var/omp_app.py @ 55:020399d027b1

Changed directory structure - part 3

+""" 
+#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#
+# Bob L. Sturm <[e-mail]> 20111018
+# Department of Architecture, Design and Media Technology
+# Aalborg University Copenhagen
+# Lautrupvang 15, 2750 Ballerup, Denmark
+#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#
+"""
+
+import numpy as np
+from sklearn.utils import check_random_state
+import time
+
+from omp_sk_bugfix import orthogonal_mp
+from omp_QR import greed_omp_qr
+from omp_QR import omp_qr
+
+"""
+Run a problem suite involving sparse vectors in 
+ambientDimension dimensional space, with a resolution
+in the phase plane of numGradations x numGradations,
+and at each indeterminacy and sparsity pair run
+numTrials independent trials.
+
+Outputs a text file denoting successes at each phase point.
+For more on phase transitions, see:
+D. L. Donoho and J. Tanner, "Precise undersampling theorems," 
+Proc. IEEE, vol. 98, no. 6, pp. 913-924, June 2010.
+"""
+
+def runProblemSuite(ambientDimension,numGradations,numTrials):
+
+  idx = np.arange(ambientDimension)
+  phaseDelta = np.linspace(0.05,1,numGradations)
+  phaseRho = np.linspace(0.05,1,numGradations)
+  success = np.zeros((numGradations, numGradations))
+  
+  #Nic: init timers
+  t1all = 0
+  t2all = 0
+  t3all = 0
+  
+  deltaCounter = 0
+  # delta is number of measurements/
+  for delta in phaseDelta[:17]:
+    rhoCounter = 0
+    for rho in phaseRho:
+      print(deltaCounter,rhoCounter)
+      numMeasurements = int(delta*ambientDimension)
+      sparsity = int(rho*numMeasurements)
+      # how do I set the following to be random each time?
+      generator = check_random_state(100)
+      # create unit norm dictionary
+      D = generator.randn(numMeasurements, ambientDimension)
+      D /= np.sqrt(np.sum((D ** 2), axis=0))
+      # compute Gramian (for efficiency)
+      DTD = np.dot(D.T,D)
+  
+      successCounter = 0
+      trial = numTrials
+      while trial > 0:
+        # generate sparse signal with a minimum non-zero value
+        x = np.zeros((ambientDimension, 1))
+        idx2 = idx
+        generator.shuffle(idx2)
+        idx3 = idx2[:sparsity]
+        while np.min(np.abs(x[idx3,0])) < 1e-10 :
+           x[idx3,0] = generator.randn(sparsity)
+        # sense sparse signal
+        y = np.dot(D, x)
+        
+        # Nic: Use sparsify OMP function (translated from Matlab)
+        ompopts = dict({'stopCrit':'M', 'stopTol':2*sparsity})
+        starttime = time.time()                     # start timer
+        x_r2, errs, times = greed_omp_qr(y.squeeze().copy(), D.copy(), D.shape[1], ompopts)
+        t2all = t2all + time.time() - starttime     # stop timer
+        idx_r2 = np.nonzero(x_r2)[0]        
+        
+        # run to two times expected sparsity, or tolerance
+        # why? Often times, OMP can retrieve the correct solution
+        # when it is run for more than the expected sparsity
+        #x_r, idx_r = omp_qr(y,D,DTD,2*sparsity,1e-5)
+        # Nic: adjust tolerance to match with other function
+        starttime = time.time()                     # start timer
+        x_r, idx_r = omp_qr(y.copy(),D.copy(),DTD.copy(),2*sparsity,numMeasurements*1e-14/np.vdot(y,y))
+        t1all = t1all + time.time() - starttime     # stop timer        
+        
+        # Nic: test sklearn omp
+        starttime = time.time()                     # start timer
+        x_r3 = orthogonal_mp(D.copy(), y.copy(), n_nonzero_coefs=2*sparsity, tol=numMeasurements*1e-14, precompute_gram=False, copy_X=True)
+        idx_r3 = np.nonzero(x_r3)[0]
+        t3all = t3all + time.time() - starttime     # stop timer        
+        
+        # Nic: compare results
+        print 'diff1 = ',np.linalg.norm(x_r.squeeze() - x_r2.squeeze())
+        print 'diff2 = ',np.linalg.norm(x_r.squeeze() - x_r3.squeeze())
+        print 'diff3 = ',np.linalg.norm(x_r2.squeeze() - x_r3.squeeze())
+        print "Bob's total time = ", t1all
+        print "Nic's total time = ", t2all
+        print "Skl's total time = ", t3all
+        if np.linalg.norm(x_r.squeeze() - x_r2.squeeze()) > 1e-10 or \
+           np.linalg.norm(x_r.squeeze() - x_r3.squeeze()) > 1e-10 or \
+           np.linalg.norm(x_r2.squeeze() - x_r3.squeeze()) > 1e-10:
+            print "STOP: Different results"
+            print "Bob's residual: ||y - D x_r ||_2 = ",np.linalg.norm(y.squeeze() - np.dot(D,x_r).squeeze())
+            print "Nic's residual: ||y - D x_r ||_2 = ",np.linalg.norm(y.squeeze() - np.dot(D,x_r2).squeeze())
+            print "Skl's residual: ||y - D x_r ||_2 = ",np.linalg.norm(y.squeeze() - np.dot(D,x_r3).squeeze())
+            raise ValueError("Different results")
+  
+        # debais to remove small entries
+        for nn in idx_r:
+          if abs(x_r[nn]) < 1e-10:
+            x_r[nn] = 0
+  
+        # exact recovery condition using support
+        #if sorted(np.flatnonzero(x_r)) == sorted(np.flatnonzero(x)):
+        #  successCounter += 1
+        # exact recovery condition using error in solution
+        error = x - x_r
+        """ the following is the exact recovery condition in: A. Maleki 
+              and D. L. Donoho, "Optimally tuned iterative reconstruction 
+              algorithms for compressed sensing," IEEE J. Selected Topics 
+              in Signal Process., vol. 4, pp. 330-341, Apr. 2010. """
+        if np.vdot(error,error) < np.vdot(x,x)*1e-4:
+          successCounter += 1
+        trial -= 1
+  
+      success[rhoCounter,deltaCounter] = successCounter
+      if successCounter == 0:
+        break
+  
+      rhoCounter += 1
+      #np.savetxt('test.txt',success,fmt='#2.1d',delimiter=',')
+    deltaCounter += 1
+
+if __name__ == '__main__':
+  print ('Running problem suite')
+  ambientDimension = 400
+  numGradations = 30
+  numTrials = 1
+  
+  #import cProfile
+  #cProfile.run('runProblemSuite(ambientDimension,numGradations,numTrials)','profres')  
+  runProblemSuite(ambientDimension,numGradations,numTrials) 
+  print "Done"
+  
+  #import pstats
+  #p = pstats.Stats('D:\Nic\Dev2\profres')
+  #p.sort_stats('cumulative').print_stats(10)