comparison scripts/ABSapprox.py @ 25:dd0e78b5bb13

Added .squeeze() in GAP function to avoid strange error in numpy.delete(), which wasn't raised on my laptop but was raised on octave

 """
 
 import numpy as np
 import scipy.io
 import math
-import matplotlib.pyplot as plt
-import matplotlib.cm as cm
+#import matplotlib.pyplot as plt
+#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

   
   nalgosN = len(algosN)  
   nalgosL = len(algosL)
   
   #Set up experiment parameters
-  d = 50;
+  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([0.05,0.95])
-  #rhos = np.array([0.05,0.95])
+  #deltas = np.array([0.15,0.95])
+  #rhos = np.array([0.15,0.95])
   #deltas = np.array([0.05])
   #rhos = np.array([0.05])
   #delta = 0.8;
   #rho   = 0.15;
-  numvects = 100; # Number of vectors to generate
+  numvects = 20; # 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.concatenate((np.array([0]), 10**np.linspace(-5, 4, 10)))
+  lambdas = np.array([0., 0.0001, 0.01, 1, 100, 10000])
 
   meanmatrix = dict()
   for i,algo in zip(np.arange(nalgosN),algosN):
     meanmatrix[algo[1]]   = np.zeros((rhos.size, deltas.size))
   for i,algo in zip(np.arange(nalgosL),algosL):