Example #1
0
    logfile = os.path.join(resultspath, 'training.log')
    log = open(logfile, 'a')
    log.write('---- Training with no razor variables ---- \n')

    for e in epochs:
        for s in sigma:
            for l in lrate:
                for i in range(trials):
                    log.write(
                        'Epochs: {0:2d}, Sigma: ({1:4f}, {2:4f}), Lrate: ({3:4f}, {4:4f}) \n'
                        .format(e, s[0], s[1], l[0], l[1]))
                    Map = SOM(datafile, razor=False, shape=(10, 10, 10))
                    Map.set_params(epochs=e,
                                   lrate=l,
                                   sigma=s,
                                   threshold=threshold)
                    Map.train_theano(quiet=True)
                    Map.test_theano(quiet=True)
                    # Map.show_map()
                    _, zeros = Map.negloglikelihood([3])
                    exc = Map.exclusion((0, 5), n=1000, show=False)
                    dis = Map.discovery((0, 5), n=1000, show=False)
                    log.write('contains {0:2d} zeros \n'.format(zeros))
                    log.write('exclusion: {0:5f} \n'.format(exc))
                    log.write('discovery: {0:5f} \n \n'.format(dis))
                    print 'contains {0:2d} zeros'.format(zeros)
                    print 'exclusion: {0:5f} '.format(exc)
                    print 'discovery: {0:5f} '.format(dis)
                    # Map.save_map(resultspath)
    log.close()
Example #2
0
	resultspath = os.path.join(datapath, 'SOM', dataset[0:-5])
	if not os.path.exists(resultspath):
		os.mkdir(resultspath)

	logfile = os.path.join(resultspath, 'training.log')
	log = open(logfile, 'a')
	log.write('---- Training with no razor variables ---- \n')

	for e in epochs:
		for s in sigma:
			for l in lrate:
				for i in range(trials):
					log.write('Epochs: {0:2d}, Sigma: ({1:4f}, {2:4f}), Lrate: ({3:4f}, {4:4f}) \n'.format(e, s[0], s[1], l[0], l[1]))
					Map = SOM(datafile, razor = False, shape = (10, 10, 10))
					Map.set_params(epochs = e, lrate = l, sigma = s, threshold = threshold)
					Map.train_theano(quiet = True)
					Map.test_theano(quiet = True)
					# Map.show_map()
					_, zeros = Map.negloglikelihood([3]) 
					exc = Map.exclusion((0, 5), n = 1000, show = False)
					dis = Map.discovery((0, 5), n = 1000, show = False)
					log.write('contains {0:2d} zeros \n'.format(zeros))						
					log.write('exclusion: {0:5f} \n'.format(exc))
					log.write('discovery: {0:5f} \n \n'.format(dis))
					print 'contains {0:2d} zeros'.format(zeros)
					print 'exclusion: {0:5f} '.format(exc)
					print 'discovery: {0:5f} '.format(dis)
					# Map.save_map(resultspath)
	log.close()