def test_log_pdf(self):
mu = np.log(np.array([[0.5,3.0,5.0]]))
sigma = 0.5
x = np.log(np.array([[0.5,3.0,5.0]]))
p = model.pdf(x,mu,sigma)
p_test = model.log_pdf(x,mu,sigma)
assert_almost_equal(np.log(p),p_test)
def test_log_probability(self):
f = fileinput.input(os.path.join(data_path,"bambus2.scaffold.linear.fasta.one_contig"))
c = list(SeqIO.parse(f,"fasta"))
f.close()
dna_c = dna.DNA(id = c[0].id, seq = str(c[0].seq))
dna_c.calculate_signature()
k = 4**4
uniform_prob = np.ones((dna.DNA.kmer_hash_count))
for i,cnt in dna_c.signature.items():
uniform_prob[i] = 1./k
log_prob = ml.log_probability(dna_c,uniform_prob)
mu = np.log(np.array([0.5,3.0,5.0]))
sigma = 0.5
x = np.log(np.array([0.5,3.0,5.0]))
cov_matrix = np.array([x])
p_ig = ig.log_pdf(x,mu,sigma)
p_test = model.log_probability(dna_c,cov_matrix,uniform_prob,mu,sigma)
assert_equal(p_ig+log_prob,p_test)
def log_probability(seq,cov_matrix,prob_vector,mu,sigma,factor=1):
ig_p = isotropic_gaussian.log_pdf(cov_matrix,mu,sigma)
mu_p = multinomial.log_probability(seq,prob_vector)
return ig_p*factor+mu_p