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comparison stdparams_exact.py @ 15:a27cfe83fe12

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Changing, changing, trying to get a common framework for batch jobs
author Nic Cleju <nikcleju@gmail.com>
date Tue, 20 Mar 2012 17:18:23 +0200
parents f2eb027ed101
children 7fdf964f4edd
comparison
equal deleted inserted replaced
14:f2eb027ed101 15:a27cfe83fe12
6 """ 6 """
7 7
8 import numpy 8 import numpy
9 from algos import * 9 from algos import *
10 10
11 #========================== 11 paramstest = dict()
12 # Standard parameters 12 paramstest['algos'] = exact_gap,exact_sl0,exact_bp,exact_ompeps,exact_tst # tuple of algorithms
13 #========================== 13 #paramstest['algos'] = exact_bp_cvxopt, # tuple of algorithms
14 # Standard parameters for quick testing 14 paramstest['d'] = 50.0
15 # Algorithms: GAP, SL0 and BP 15 paramstest['sigma'] = 1.2
16 # d=50, sigma = 2, delta and rho only 3 x 3, lambdas = 0, 1e-4, 1e-2, 1, 100, 10000 16 paramstest['deltas'] = numpy.array([0.05, 0.45, 0.95])
17 # Do save data, do save plots, don't show plots 17 paramstest['rhos'] = numpy.array([0.05, 0.45, 0.95])
18 # Useful for short testing 18 #deltas = numpy.array([0.6])
19 def stdtest(): 19 #deltas = numpy.arange(0.05,1.,0.05)
20 # Define which algorithms to run 20 #rhos = numpy.array([0.05])
21 #algos = exact_gap,exact_sl0,exact_bp,exact_ompeps,exact_tst # tuple of algorithms 21 paramstest['numvects'] = 10; # Number of vectors to generate
22 algos = exact_bp_cvxopt, # tuple of algorithms 22 paramstest['SNRdb'] = 100.; # This is norm(signal)/norm(noise), so power, not energy
23 23 paramstest['savedataname'] = 'exact_pt_stdtest.mat'
24 d = 50.0 24 paramstest['saveplotbase'] = 'exact_pt_stdtest_'
25 sigma = 1.2 25 paramstest['saveplotexts'] = ('png','pdf','eps')
26 deltas = numpy.array([0.05, 0.45, 0.95])
27 rhos = numpy.array([0.05, 0.45, 0.95])
28 #deltas = numpy.array([0.6])
29 #deltas = numpy.arange(0.05,1.,0.05)
30 #rhos = numpy.array([0.05])
31 numvects = 10; # Number of vectors to generate
32 SNRdb = 100.; # This is norm(signal)/norm(noise), so power, not energy
33
34 dosavedata = True
35 savedataname = 'exact_pt_stdtest.mat'
36 doshowplot = False
37 dosaveplot = True
38 saveplotbase = 'exact_pt_stdtest_'
39 saveplotexts = ('png','pdf','eps')
40
41 return algos,d,sigma,deltas,rhos,numvects,SNRdb,dosavedata,savedataname,\
42 doshowplot,dosaveplot,saveplotbase,saveplotexts
43
44 26
45 # Standard parameters 1 27 # Standard parameters 1
46 # All algorithms, 100 vectors 28 # All algorithms, 100 vectors
47 # d=50, sigma = 2, delta and rho full resolution (0.05 step), lambdas = 0, 1e-4, 1e-2, 1, 100, 10000 29 # d=50, sigma = 2, delta and rho full resolution (0.05 step), lambdas = 0, 1e-4, 1e-2, 1, 100, 10000
48 # Do save data, do save plots, don't show plots 30 # Do save data, do save plots, don't show plots
49 def std1(): 31 params1 = dict()
50 # Define which algorithms to run 32 params1['algos'] = exact_gap,exact_sl0,exact_bp_cvxopt,exact_ompeps,exact_tst # tuple of algorithms
51 algos = exact_gap,exact_sl0,exact_bp_cvxopt,exact_ompeps,exact_tst # tuple of algorithms 33 params1['d'] = 50.0;
52 34 params1['sigma'] = 1.2
53 d = 50.0; 35 params1['deltas'] = numpy.arange(0.05,1.,0.05)
54 sigma = 1.2 36 params1['rhos'] = numpy.arange(0.05,1.,0.05)
55 deltas = numpy.arange(0.05,1.,0.05) 37 params1['numvects'] = 100; # Number of vectors to generate
56 rhos = numpy.arange(0.05,1.,0.05) 38 params1['SNRdb'] = 100.; # This is norm(signal)/norm(noise), so power, not energy
57 numvects = 100; # Number of vectors to generate 39 params1['savedataname'] = 'exact_pt_std1.mat'
58 SNRdb = 100.; # This is norm(signal)/norm(noise), so power, not energy 40 params1['saveplotbase'] = 'exact_pt_std1_'
59 41 params1['saveplotexts'] = ('png','pdf','eps')
60 dosavedata = True
61 savedataname = 'exact_pt_std1.mat'
62 doshowplot = False
63 dosaveplot = True
64 saveplotbase = 'exact_pt_std1_'
65 saveplotexts = ('png','pdf','eps')
66 42
67 return algos,d,sigma,deltas,rhos,numvects,SNRdb,dosavedata,savedataname,\ 43
68 doshowplot,dosaveplot,saveplotbase,saveplotexts
69
70
71 # Standard parameters 2 44 # Standard parameters 2
72 # All algorithms, 100 vectors 45 # All algorithms, 100 vectors
73 # d=20, sigma = 10, delta and rho full resolution (0.05 step), lambdas = 0, 1e-4, 1e-2, 1, 100, 10000 46 # d=20, sigma = 10, delta and rho full resolution (0.05 step), lambdas = 0, 1e-4, 1e-2, 1, 100, 10000
74 # Do save data, do save plots, don't show plots 47 # Do save data, do save plots, don't show plots
75 def std2(): 48 params2 = dict()
76 # Define which algorithms to run 49 params2['algos'] = exact_gap,exact_sl0,exact_bp_cvxopt,exact_ompeps,exact_tst # tuple of algorithms
77 algos = exact_gap,exact_sl0,exact_bp_cvxopt,exact_ompeps,exact_tst # tuple of algorithms 50 params2['d'] = 20.0
78 51 params2['sigma'] = 10.0
79 d = 20.0 52 params2['deltas'] = numpy.arange(0.05,1.,0.05)
80 sigma = 10.0 53 params2['rhos'] = numpy.arange(0.05,1.,0.05)
81 deltas = numpy.arange(0.05,1.,0.05) 54 params2['numvects'] = 100; # Number of vectors to generate
82 rhos = numpy.arange(0.05,1.,0.05) 55 params2['SNRdb'] = 100.; # This is norm(signal)/norm(noise), so power, not energy
83 numvects = 100; # Number of vectors to generate 56 params2['savedataname'] = 'exact_pt_std2.mat'
84 SNRdb = 100.; # This is norm(signal)/norm(noise), so power, not energy 57 params2['saveplotbase'] = 'exact_pt_std2_'
85 58 params2['saveplotexts'] = ('png','pdf','eps')
86 dosavedata = True
87 savedataname = 'exact_pt_std2.mat'
88 doshowplot = False
89 dosaveplot = True
90 saveplotbase = 'exact_pt_std2_'
91 saveplotexts = ('png','pdf','eps')
92
93 return algos,d,sigma,deltas,rhos,numvects,SNRdb,dosavedata,savedataname,\
94 doshowplot,dosaveplot,saveplotbase,saveplotexts
95
96
97 # # Standard parameters 3
98 ## All algorithms, 100 vectors
99 ## d=50, sigma = 2, delta and rho full resolution (0.05 step), lambdas = 0, 1e-4, 1e-2, 1, 100, 10000
100 ## Do save data, do save plots, don't show plots
101 ## IDENTICAL with 1 but with 10dB SNR noise
102 #def std3():
103 # # Define which algorithms to run
104 # algos = exact_gap,exact_sl0,exact_bp,exact_ompeps,exact_tst # tuple of algorithms
105 #
106 # d = 50.0;
107 # sigma = 2.0
108 # deltas = numpy.arange(0.05,1.,0.05)
109 # rhos = numpy.arange(0.05,1.,0.05)
110 # numvects = 100; # Number of vectors to generate
111 # SNRdb = 100.; # This is norm(signal)/norm(noise), so power, not energy
112 #
113 # dosavedata = True
114 # savedataname = 'exact_pt_std3.mat'
115 # doshowplot = False
116 # dosaveplot = True
117 # saveplotbase = 'exact_pt_std3_'
118 # saveplotexts = ('png','pdf','eps')
119 #
120 # return algos,d,sigma,deltas,rhos,numvects,SNRdb,dosavedata,savedataname,\
121 # doshowplot,dosaveplot,saveplotbase,saveplotexts
122
123 ## Standard parameters 4
124 ## All algorithms, 100 vectors
125 ## d=20, sigma = 10, delta and rho full resolution (0.05 step), lambdas = 0, 1e-4, 1e-2, 1, 100, 10000
126 ## Do save data, do save plots, don't show plots
127 ## Identical to 2 but with 10dB SNR noise
128 #def std4():
129 # # Define which algorithms to run
130 # algos = exact_gap,exact_sl0,exact_bp,exact_ompeps,exact_tst # tuple of algorithms
131 #
132 # d = 20.0
133 # sigma = 10.0
134 # deltas = numpy.arange(0.05,1.,0.05)
135 # rhos = numpy.arange(0.05,1.,0.05)
136 # numvects = 100; # Number of vectors to generate
137 # SNRdb = 10.; # This is norm(signal)/norm(noise), so power, not energy
138 #
139 # dosavedata = True
140 # savedataname = 'exact_pt_std4.mat'
141 # doshowplot = False
142 # dosaveplot = True
143 # saveplotbase = 'exact_pt_std4_'
144 # saveplotexts = ('png','pdf','eps')
145 #
146 # return algos,d,sigma,deltas,rhos,numvects,SNRdb,dosavedata,savedataname,\
147 # doshowplot,dosaveplot,saveplotbase,saveplotexts