Python StringWordFeatures.get_num_vectors Exemples

Exemple #1

Fichier : distribution_histogram_modular.py Projet : memimo/shogun-liblinear

def histogram ():
	print 'Histogram'

	from shogun.Features import StringWordFeatures, StringCharFeatures, DNA
	from shogun.Distribution import Histogram

	order=3
	gap=0
	reverse=False

	charfeat=StringCharFeatures(DNA)
	charfeat.set_features(fm_dna)
	feats=StringWordFeatures(charfeat.get_alphabet())
	feats.obtain_from_char(charfeat, order-1, order, gap, reverse)

	histo=Histogram(feats)
	histo.train()

	histo.get_histogram()

	num_examples=feats.get_num_vectors()
	num_param=histo.get_num_model_parameters()
	#for i in xrange(num_examples):
	#	for j in xrange(num_param):
	#		histo.get_log_derivative(j, i)

	histo.get_log_likelihood()
	histo.get_log_likelihood_sample()

Exemple #2

Fichier : distribution_hmm_modular.py Projet : AsherBond/shogun

def distribution_hmm_modular(fm_cube, N, M, pseudo, order, gap, reverse, num_examples):
	from shogun.Features import StringWordFeatures, StringCharFeatures, CUBE
	from shogun.Distribution import HMM, BW_NORMAL

	charfeat=StringCharFeatures(CUBE)
	charfeat.set_features(fm_cube)
	feats=StringWordFeatures(charfeat.get_alphabet())
	feats.obtain_from_char(charfeat, order-1, order, gap, reverse)

	hmm=HMM(feats, N, M, pseudo)
	hmm.train()
	hmm.baum_welch_viterbi_train(BW_NORMAL)

	num_examples=feats.get_num_vectors()
	num_param=hmm.get_num_model_parameters()
	for i in xrange(num_examples):
		for j in xrange(num_param):
			hmm.get_log_derivative(j, i)

	best_path=0
	best_path_state=0
	for i in xrange(num_examples):
		best_path+=hmm.best_path(i)
		for j in xrange(N):
			best_path_state+=hmm.get_best_path_state(i, j)

	lik_example = hmm.get_log_likelihood()
	lik_sample = hmm.get_log_likelihood_sample()

	return lik_example, lik_sample, hmm

Exemple #3

Fichier : distribution_hmm_modular.py Projet : behollis/muViewBranch

def distribution_hmm_modular(fm_cube, N, M, pseudo, order, gap, reverse, num_examples):
	from shogun.Features import StringWordFeatures, StringCharFeatures, CUBE
	from shogun.Distribution import HMM, BW_NORMAL

	charfeat=StringCharFeatures(CUBE)
	charfeat.set_features(fm_cube)
	feats=StringWordFeatures(charfeat.get_alphabet())
	feats.obtain_from_char(charfeat, order-1, order, gap, reverse)

	hmm=HMM(feats, N, M, pseudo)
	hmm.train()
	hmm.baum_welch_viterbi_train(BW_NORMAL)

	num_examples=feats.get_num_vectors()
	num_param=hmm.get_num_model_parameters()
	for i in range(num_examples):
		for j in range(num_param):
			hmm.get_log_derivative(j, i)

	best_path=0
	best_path_state=0
	for i in range(num_examples):
		best_path+=hmm.best_path(i)
		for j in range(N):
			best_path_state+=hmm.get_best_path_state(i, j)

	lik_example = hmm.get_log_likelihood()
	lik_sample = hmm.get_log_likelihood_sample()

	return lik_example, lik_sample, hmm

Exemple #4

Fichier : distribution_histogram_modular.py Projet : Anshul-Bansal/gsoc

def distribution_histogram_modular(fm_dna=traindna,
                                   order=3,
                                   gap=0,
                                   reverse=False):
    from shogun.Features import StringWordFeatures, StringCharFeatures, DNA
    from shogun.Distribution import Histogram

    charfeat = StringCharFeatures(DNA)
    charfeat.set_features(fm_dna)
    feats = StringWordFeatures(charfeat.get_alphabet())
    feats.obtain_from_char(charfeat, order - 1, order, gap, reverse)

    histo = Histogram(feats)
    histo.train()

    histo.get_histogram()

    num_examples = feats.get_num_vectors()
    num_param = histo.get_num_model_parameters()
    #for i in xrange(num_examples):
    #	for j in xrange(num_param):
    #		histo.get_log_derivative(j, i)

    out_likelihood = histo.get_log_likelihood()
    out_sample = histo.get_log_likelihood_sample()
    return histo, out_sample, out_likelihood

Exemple #5

Fichier : distribution_linearhmm_modular.py Projet : theoryno3/shogun

def distribution_linearhmm_modular(fm_dna=traindna,
                                   order=3,
                                   gap=0,
                                   reverse=False):

    from shogun.Features import StringWordFeatures, StringCharFeatures, DNA
    from shogun.Distribution import LinearHMM

    charfeat = StringCharFeatures(DNA)
    charfeat.set_features(fm_dna)
    feats = StringWordFeatures(charfeat.get_alphabet())
    feats.obtain_from_char(charfeat, order - 1, order, gap, reverse)

    hmm = LinearHMM(feats)
    hmm.train()

    hmm.get_transition_probs()

    num_examples = feats.get_num_vectors()
    num_param = hmm.get_num_model_parameters()
    for i in range(num_examples):
        for j in range(num_param):
            hmm.get_log_derivative(j, i)

    out_likelihood = hmm.get_log_likelihood()
    out_sample = hmm.get_log_likelihood_sample()

    return hmm, out_likelihood, out_sample

Exemple #6

Fichier : distribution_linearhmm_modular.py Projet : coodoing/shogun

def distribution_linearhmm_modular (fm_dna=traindna,order=3,gap=0,reverse=False):

	from shogun.Features import StringWordFeatures, StringCharFeatures, DNA
	from shogun.Distribution import LinearHMM

	charfeat=StringCharFeatures(DNA)
	charfeat.set_features(fm_dna)
	feats=StringWordFeatures(charfeat.get_alphabet())
	feats.obtain_from_char(charfeat, order-1, order, gap, reverse)

	hmm=LinearHMM(feats)
	hmm.train()

	hmm.get_transition_probs()

	num_examples=feats.get_num_vectors()
	num_param=hmm.get_num_model_parameters()
	for i in range(num_examples):
		for j in range(num_param):
			hmm.get_log_derivative(j, i)

	out_likelihood = hmm.get_log_likelihood()
	out_sample = hmm.get_log_likelihood_sample()

	return hmm,out_likelihood ,out_sample

Exemple #7

Fichier : distribution_linear_hmm_modular.py Projet : memimo/shogun-liblinear

def linear_hmm ():
	print 'LinearHMM'

	from shogun.Features import StringWordFeatures, StringCharFeatures, DNA
	from shogun.Distribution import LinearHMM

	order=3
	gap=0
	reverse=False

	charfeat=StringCharFeatures(DNA)
	charfeat.set_features(fm_dna)
	feats=StringWordFeatures(charfeat.get_alphabet())
	feats.obtain_from_char(charfeat, order-1, order, gap, reverse)

	hmm=LinearHMM(feats)
	hmm.train()

	hmm.get_transition_probs()

	num_examples=feats.get_num_vectors()
	num_param=hmm.get_num_model_parameters()
	for i in xrange(num_examples):
		for j in xrange(num_param):
			hmm.get_log_derivative(j, i)

	hmm.get_log_likelihood()
	hmm.get_log_likelihood_sample()