diff stdparams_exact.py @ 15:a27cfe83fe12

Changing, changing, trying to get a common framework for batch jobs

--- a/stdparams_exact.py	Fri Mar 16 13:42:31 2012 +0200
+++ b/stdparams_exact.py	Tue Mar 20 17:18:23 2012 +0200
@@ -8,140 +8,51 @@
 import numpy
 from algos import *
 
-#==========================
-# Standard parameters
-#==========================
-# Standard parameters for quick testing
-# 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 stdtest():
-  # Define which algorithms to run
-  #algos = exact_gap,exact_sl0,exact_bp,exact_ompeps,exact_tst       # tuple of algorithms
-  algos = exact_bp_cvxopt,       # tuple of algorithms
-  
-  d = 50.0
-  sigma = 1.2
-  deltas = numpy.array([0.05, 0.45, 0.95])
-  rhos = numpy.array([0.05, 0.45, 0.95])
-  #deltas = numpy.array([0.6])
-  #deltas = numpy.arange(0.05,1.,0.05)
-  #rhos = numpy.array([0.05])
-  numvects = 10; # Number of vectors to generate
-  SNRdb = 100.;    # This is norm(signal)/norm(noise), so power, not energy
-  
-  dosavedata = True
-  savedataname = 'exact_pt_stdtest.mat'
-  doshowplot = False
-  dosaveplot = True
-  saveplotbase = 'exact_pt_stdtest_'
-  saveplotexts = ('png','pdf','eps')
-
-  return algos,d,sigma,deltas,rhos,numvects,SNRdb,dosavedata,savedataname,\
-          doshowplot,dosaveplot,saveplotbase,saveplotexts   
-
+paramstest = dict()
+paramstest['algos'] = exact_gap,exact_sl0,exact_bp,exact_ompeps,exact_tst       # tuple of algorithms
+#paramstest['algos'] = exact_bp_cvxopt,       # tuple of algorithms
+paramstest['d'] = 50.0
+paramstest['sigma'] = 1.2
+paramstest['deltas'] = numpy.array([0.05, 0.45, 0.95])
+paramstest['rhos'] = numpy.array([0.05, 0.45, 0.95])
+#deltas = numpy.array([0.6])
+#deltas = numpy.arange(0.05,1.,0.05)
+#rhos = numpy.array([0.05])
+paramstest['numvects'] = 10; # Number of vectors to generate
+paramstest['SNRdb'] = 100.;    # This is norm(signal)/norm(noise), so power, not energy
+paramstest['savedataname'] = 'exact_pt_stdtest.mat'
+paramstest['saveplotbase'] = 'exact_pt_stdtest_'
+paramstest['saveplotexts'] = ('png','pdf','eps')
 
 # Standard parameters 1
 # All algorithms, 100 vectors
 # 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
-  algos = exact_gap,exact_sl0,exact_bp_cvxopt,exact_ompeps,exact_tst       # tuple of algorithms
-  
-  d = 50.0;
-  sigma = 1.2
-  deltas = numpy.arange(0.05,1.,0.05)
-  rhos = numpy.arange(0.05,1.,0.05)
-  numvects = 100; # Number of vectors to generate
-  SNRdb = 100.;    # This is norm(signal)/norm(noise), so power, not energy
-  
-  dosavedata = True
-  savedataname = 'exact_pt_std1.mat'
-  doshowplot = False
-  dosaveplot = True
-  saveplotbase = 'exact_pt_std1_'
-  saveplotexts = ('png','pdf','eps')
+params1 = dict()
+params1['algos'] = exact_gap,exact_sl0,exact_bp_cvxopt,exact_ompeps,exact_tst       # tuple of algorithms
+params1['d'] = 50.0;
+params1['sigma'] = 1.2
+params1['deltas'] = numpy.arange(0.05,1.,0.05)
+params1['rhos'] = numpy.arange(0.05,1.,0.05)
+params1['numvects'] = 100; # Number of vectors to generate
+params1['SNRdb'] = 100.;    # This is norm(signal)/norm(noise), so power, not energy
+params1['savedataname'] = 'exact_pt_std1.mat'
+params1['saveplotbase'] = 'exact_pt_std1_'
+params1['saveplotexts'] = ('png','pdf','eps')
 
-  return algos,d,sigma,deltas,rhos,numvects,SNRdb,dosavedata,savedataname,\
-          doshowplot,dosaveplot,saveplotbase,saveplotexts
-          
-         
+        
 # Standard parameters 2
 # All algorithms, 100 vectors
 # d=20, sigma = 10, 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 std2():
-  # Define which algorithms to run
-  algos = exact_gap,exact_sl0,exact_bp_cvxopt,exact_ompeps,exact_tst       # tuple of algorithms
-  
-  d = 20.0
-  sigma = 10.0
-  deltas = numpy.arange(0.05,1.,0.05)
-  rhos = numpy.arange(0.05,1.,0.05)
-  numvects = 100; # Number of vectors to generate
-  SNRdb = 100.;    # This is norm(signal)/norm(noise), so power, not energy
-  
-  dosavedata = True
-  savedataname = 'exact_pt_std2.mat'
-  doshowplot = False
-  dosaveplot = True
-  saveplotbase = 'exact_pt_std2_'
-  saveplotexts = ('png','pdf','eps')
-
-  return algos,d,sigma,deltas,rhos,numvects,SNRdb,dosavedata,savedataname,\
-          doshowplot,dosaveplot,saveplotbase,saveplotexts
-  
-  
-#  # Standard parameters 3
-## All algorithms, 100 vectors
-## 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
-## IDENTICAL with 1 but with 10dB SNR noise
-#def std3():
-#  # Define which algorithms to run
-#  algos = exact_gap,exact_sl0,exact_bp,exact_ompeps,exact_tst       # tuple of algorithms
-#  
-#  d = 50.0;
-#  sigma = 2.0
-#  deltas = numpy.arange(0.05,1.,0.05)
-#  rhos = numpy.arange(0.05,1.,0.05)
-#  numvects = 100; # Number of vectors to generate
-#  SNRdb = 100.;    # This is norm(signal)/norm(noise), so power, not energy
-#  
-#  dosavedata = True
-#  savedataname = 'exact_pt_std3.mat'
-#  doshowplot = False
-#  dosaveplot = True
-#  saveplotbase = 'exact_pt_std3_'
-#  saveplotexts = ('png','pdf','eps')
-#
-#  return algos,d,sigma,deltas,rhos,numvects,SNRdb,dosavedata,savedataname,\
-#          doshowplot,dosaveplot,saveplotbase,saveplotexts
-          
-## Standard parameters 4
-## All algorithms, 100 vectors
-## d=20, sigma = 10, 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
-## Identical to 2 but with 10dB SNR noise
-#def std4():
-#  # Define which algorithms to run
-#  algos = exact_gap,exact_sl0,exact_bp,exact_ompeps,exact_tst       # tuple of algorithms
-#  
-#  d = 20.0
-#  sigma = 10.0
-#  deltas = numpy.arange(0.05,1.,0.05)
-#  rhos = numpy.arange(0.05,1.,0.05)
-#  numvects = 100; # Number of vectors to generate
-#  SNRdb = 10.;    # This is norm(signal)/norm(noise), so power, not energy
-#  
-#  dosavedata = True
-#  savedataname = 'exact_pt_std4.mat'
-#  doshowplot = False
-#  dosaveplot = True
-#  saveplotbase = 'exact_pt_std4_'
-#  saveplotexts = ('png','pdf','eps')
-#
-#  return algos,d,sigma,deltas,rhos,numvects,SNRdb,dosavedata,savedataname,\
-#          doshowplot,dosaveplot,saveplotbase,saveplotexts
+params2 = dict()
+params2['algos'] = exact_gap,exact_sl0,exact_bp_cvxopt,exact_ompeps,exact_tst       # tuple of algorithms
+params2['d'] = 20.0
+params2['sigma'] = 10.0
+params2['deltas'] = numpy.arange(0.05,1.,0.05)
+params2['rhos'] = numpy.arange(0.05,1.,0.05)
+params2['numvects'] = 100; # Number of vectors to generate
+params2['SNRdb'] = 100.;    # This is norm(signal)/norm(noise), so power, not energy
+params2['savedataname'] = 'exact_pt_std2.mat'
+params2['saveplotbase'] = 'exact_pt_std2_'
+params2['saveplotexts'] = ('png','pdf','eps')
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