nikcleju@14: # -*- coding: utf-8 -*-
nikcleju@14: """
nikcleju@17: Defines standard parameters for exact reconstruction simulation
nikcleju@17: Author: Nicolae Cleju
nikcleju@17: """
nikcleju@17: __author__ = "Nicolae Cleju"
nikcleju@17: __license__ = "GPL"
nikcleju@17: __email__ = "nikcleju@gmail.com"
nikcleju@14: 
nikcleju@14: 
nikcleju@14: import numpy
nikcleju@17: 
nikcleju@17: # Solver algorithms to run
nikcleju@14: from algos import *
nikcleju@14: 
nikcleju@17: 
nikcleju@17: # Test parameters
nikcleju@15: paramstest = dict()
nikcleju@15: paramstest['algos'] = exact_gap,exact_sl0,exact_bp,exact_ompeps,exact_tst       # tuple of algorithms
nikcleju@15: #paramstest['algos'] = exact_bp_cvxopt,       # tuple of algorithms
nikcleju@15: paramstest['d'] = 50.0
nikcleju@15: paramstest['sigma'] = 1.2
nikcleju@15: paramstest['deltas'] = numpy.array([0.05, 0.45, 0.95])
nikcleju@15: paramstest['rhos'] = numpy.array([0.05, 0.45, 0.95])
nikcleju@15: #deltas = numpy.array([0.6])
nikcleju@15: #deltas = numpy.arange(0.05,1.,0.05)
nikcleju@15: #rhos = numpy.array([0.05])
nikcleju@15: paramstest['numvects'] = 10; # Number of vectors to generate
nikcleju@15: paramstest['SNRdb'] = 100.;    # This is norm(signal)/norm(noise), so power, not energy
nikcleju@15: paramstest['savedataname'] = 'exact_pt_stdtest.mat'
nikcleju@15: paramstest['saveplotbase'] = 'exact_pt_stdtest_'
nikcleju@15: paramstest['saveplotexts'] = ('png','pdf','eps')
nikcleju@14: 
nikcleju@14: # Standard parameters 1
nikcleju@14: # All algorithms, 100 vectors
nikcleju@17: # d = 200, sigma = 1.2, delta and rho full resolution (0.05 step)
nikcleju@17: # Virtually no noise (100db)
nikcleju@15: params1 = dict()
nikcleju@15: params1['algos'] = exact_gap,exact_sl0,exact_bp_cvxopt,exact_ompeps,exact_tst       # tuple of algorithms
nikcleju@17: params1['d'] = 200.0;
nikcleju@15: params1['sigma'] = 1.2
nikcleju@15: params1['deltas'] = numpy.arange(0.05,1.,0.05)
nikcleju@15: params1['rhos'] = numpy.arange(0.05,1.,0.05)
nikcleju@15: params1['numvects'] = 100; # Number of vectors to generate
nikcleju@15: params1['SNRdb'] = 100.;    # This is norm(signal)/norm(noise), so power, not energy
nikcleju@15: params1['savedataname'] = 'exact_pt_std1.mat'
nikcleju@15: params1['saveplotbase'] = 'exact_pt_std1_'
nikcleju@15: params1['saveplotexts'] = ('png','pdf','eps')
nikcleju@14: 
nikcleju@15:         
nikcleju@14: # Standard parameters 2
nikcleju@14: # All algorithms, 100 vectors
nikcleju@17: # d = 20, sigma = 10, delta and rho full resolution (0.05 step)
nikcleju@17: # Virtually no noise (100db)
nikcleju@15: params2 = dict()
nikcleju@15: params2['algos'] = exact_gap,exact_sl0,exact_bp_cvxopt,exact_ompeps,exact_tst       # tuple of algorithms
nikcleju@15: params2['d'] = 20.0
nikcleju@15: params2['sigma'] = 10.0
nikcleju@15: params2['deltas'] = numpy.arange(0.05,1.,0.05)
nikcleju@15: params2['rhos'] = numpy.arange(0.05,1.,0.05)
nikcleju@15: params2['numvects'] = 100; # Number of vectors to generate
nikcleju@15: params2['SNRdb'] = 100.;    # This is norm(signal)/norm(noise), so power, not energy
nikcleju@15: params2['savedataname'] = 'exact_pt_std2.mat'
nikcleju@15: params2['saveplotbase'] = 'exact_pt_std2_'
nikcleju@15: params2['saveplotexts'] = ('png','pdf','eps')