accuracyfile = open(options.outfile + "/accuracy", 'w') levenfile = open(options.outfile + "/levenacc", 'w') if True: def csn(): for c in charstream(refsource): if random.random() < LEVENMUTATE: yield random.choice(balph) else: yield c vcsn = vs(csn()) else: vcsn = resnet.extremeRI(len(balph)) if options.load: loadfile = open(options.load) (startgroup, group) = cPickle.load(loadfile) loadfile.close() if not options.store: options.store = options.load group._copyrepair() startgroup._copyrepair() else: group = resgroup.ResGroup(SIZE, NETSIZE, refdata, vcsn, INSIZE, len(balph), SPARSE, SCALE, PRETIMES) group._copyscrub() startgroup = copy.deepcopy(group)
yield np.mat([[1.0 if d == c else -1.0] for d in balph]) #balph = ['1', '0'] #def vcs(): # while True: # yield np.mat([[1.0],[-1.0]]) TIMES = 9000 RESSIZE = 600 # ressize, insize, sparseness, scale classifier = resnet.ResClass(RESSIZE, len(balph), 0.3, 0.5) classifier.pretrain(vcs(), resnet.extremeRI(len(balph)), TIMES, 80) teststring = [v for (v,_) in zip(vcs(), xrange(100))] testout = '' for out in classifier.run_signal(teststring): testout += '+' if out[0][0,0] > 0 else '-' print ''.join([helpers.pick_letter(balph, t) for t in teststring]) print testout teststring = [v for (v,_) in zip(resnet.extremeRI(len(balph)), xrange(100))] testout = '' for out in classifier.run_signal(teststring): testout += '+' if out[0][0,0] > 0 else '-'
