comparison scripts/ABSapprox.py @ 33:116dcfacd1cc

Changed standard parameteres
matplotlib: not loading Cairo if on Windows

 """
 
 import numpy as np
 import scipy.io
 import math
+import os
 
 import pyCSalgos
 import pyCSalgos.GAP.GAP
 import pyCSalgos.SL0.SL0_approx
 import pyCSalgos.OMP.omp_QR

 sl0 = (run_sl0, 'SL0a')
 bp  = (run_bp, 'BP')
 ompeps = (run_ompeps, 'OMPeps')
 tst = (run_tst, 'TST')
 
-# Define which algorithms to run
-#  1. Algorithms not depending on lambda
-algosN = gap,   # tuple
-#  2. Algorithms depending on lambda (our ABS approach)
-#algosL = sl0,bp,ompeps,tst   # tuple
-algosL = sl0,tst
-  
 #==========================
 # Pool initializer function (multiprocessing)
 # Needed to pass the shared variable to the worker processes
 # The variables must be global in the module in order to be seen later in run_once_tuple()
 # see http://stackoverflow.com/questions/1675766/how-to-combine-pool-map-with-array-shared-memory-in-python-multiprocessing

     currmodule = sys.modules[__name__]
     currmodule.proccount = share
     currmodule.njobs = njobs
   
 #==========================
-# Interface functions
-#==========================
-def run_multiproc(ncpus=None):
-  d,sigma,deltas,rhos,lambdas,numvects,SNRdb,dosavedata,savedataname,doshowplot,dosaveplot,saveplotbase,saveplotexts = standard_params()
-  run_multi(algosN, algosL, d,sigma,deltas,rhos,lambdas,numvects,SNRdb,dosavedata=dosavedata,savedataname=savedataname,\
-            doparallel=True, ncpus=ncpus,\
-            doshowplot=doshowplot,dosaveplot=dosaveplot,saveplotbase=saveplotbase,saveplotexts=saveplotexts)
-
-def run():
-  d,sigma,deltas,rhos,lambdas,numvects,SNRdb,dosavedata,savedataname,doshowplot,dosaveplot,saveplotbase,saveplotexts = standard_params()
-  run_multi(algosN, algosL, d,sigma,deltas,rhos,lambdas,numvects,SNRdb,dosavedata=dosavedata,savedataname=savedataname,\
-            doparallel=False,\
-            doshowplot=doshowplot,dosaveplot=dosaveplot,saveplotbase=saveplotbase,saveplotexts=saveplotexts)
-  
-def standard_params():
-  #Set up standard experiment parameters
+# Standard parameters
+#==========================
+# Standard parameters 1
+# All algorithms
+# d=50, sigma = 2, delta and rho full resolution (0.05 step), lambdas = 0, 1e-4, 1e-2, 1, 100, 10000
+# Do save data, do save plots, don't show plots
+def std1():
+  # Define which algorithms to run
+  algosN = gap,                 # tuple of algorithms not depending on lambda
+  algosL = sl0,bp,ompeps,tst    # tuple of algorithms depending on lambda (our ABS approach)
+  
   d = 50.0;
   sigma = 2.0
-  #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;
+  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])
   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])
   
   dosavedata = True
   savedataname = 'approx_pt_std1.mat'
-  
   doshowplot = False
   dosaveplot = True
   saveplotbase = 'approx_pt_std1_'
   saveplotexts = ('png','pdf','eps')
-    
-  
-  return d,sigma,deltas,rhos,lambdas,numvects,SNRdb,dosavedata,savedataname,\
+
+  return algosN,algosL,d,sigma,deltas,rhos,lambdas,numvects,SNRdb,dosavedata,savedataname,\
           doshowplot,dosaveplot,saveplotbase,saveplotexts
-  
+          
+# Standard parameters 2
+# Algorithms: GAP, SL0 and BP
+# d=50, sigma = 2, delta and rho only 3 x 3, lambdas = 0, 1e-4, 1e-2, 1, 100, 10000
+# Do save data, do save plots, don't show plots
+# Useful for short testing 
+def std2():
+  # Define which algorithms to run
+  algosN = gap,      # tuple of algorithms not depending on lambda
+  algosL = sl0,bp    # tuple of algorithms depending on lambda (our ABS approach)
+  
+  d = 50.0
+  sigma = 2.0
+  deltas = np.array([0.05, 0.45, 0.95])
+  rhos = np.array([0.05, 0.45, 0.95])
+  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.array([0., 0.0001, 0.01, 1, 100, 10000])
+  
+  dosavedata = True
+  savedataname = 'approx_pt_std2.mat'
+  doshowplot = False
+  dosaveplot = True
+  saveplotbase = 'approx_pt_std2_'
+  saveplotexts = ('png','pdf','eps')
+
+  return algosN,algosL,d,sigma,deltas,rhos,lambdas,numvects,SNRdb,dosavedata,savedataname,\
+          doshowplot,dosaveplot,saveplotbase,saveplotexts          
+  
+#==========================
+# Interface run functions
+#==========================
+def run_mp(std=std2,ncpus=None):
+  
+  algosN,algosL,d,sigma,deltas,rhos,lambdas,numvects,SNRdb,dosavedata,savedataname,doshowplot,dosaveplot,saveplotbase,saveplotexts = std()
+  run_multi(algosN, algosL, d,sigma,deltas,rhos,lambdas,numvects,SNRdb,dosavedata=dosavedata,savedataname=savedataname,\
+            doparallel=True, ncpus=ncpus,\
+            doshowplot=doshowplot,dosaveplot=dosaveplot,saveplotbase=saveplotbase,saveplotexts=saveplotexts)
+
+def run(std=std2):
+  algosN,algosL,d,sigma,deltas,rhos,lambdas,numvects,SNRdb,dosavedata,savedataname,doshowplot,dosaveplot,saveplotbase,saveplotexts = std()
+  run_multi(algosN, algosL, d,sigma,deltas,rhos,lambdas,numvects,SNRdb,dosavedata=dosavedata,savedataname=savedataname,\
+            doparallel=False,\
+            doshowplot=doshowplot,dosaveplot=dosaveplot,saveplotbase=saveplotbase,saveplotexts=saveplotexts)  
 #==========================
 # Main functions  
 #==========================
 def run_multi(algosN, algosL, d, sigma, deltas, rhos, lambdas, numvects, SNRdb,
             doparallel=False, ncpus=None,\

     currmodule.proccount = multiprocessing.Value('I', 0) # 'I' = unsigned int, see docs (multiprocessing, array)
     
   if dosaveplot or doshowplot:
     try:
       import matplotlib
-      if doshowplot:
+      if doshowplot or os.name == 'nt':
         print "Importing matplotlib with default (GUI) backend... ",
       else:
         print "Importing matplotlib with \"Cairo\" backend... ",
         matplotlib.use('Cairo')
       import matplotlib.pyplot as plt