def calculate_k_mer_scores(bg_model, converted_seqs, K):
result = list()
for seq in converted_seqs:
LL, alpha, beta, c = bg_model.forward_backward(seq)
k_mer_LLs = k_mer_log_likelihoods(K=K, LL=LL, alpha=alpha, beta=beta, c=c)
result.append(k_mer_LLs)
return result
def calculate_k_mer_scores(bg_model, converted_seqs, K):
result = list()
for seq in converted_seqs:
LL, alpha, beta, c = bg_model.forward_backward(seq)
k_mer_LLs = k_mer_log_likelihoods(K=K, LL=LL, alpha=alpha, beta=beta, c=c)
result.append(k_mer_LLs)
return result
alpha_sum = alpha.sum(axis=1)
for i in xrange(len(c)-K+1):
if 0 == i:
result[i] = alpha_sum[K] / c[:K+1].prod()
else:
result[i] = alpha_sum[i+K-1] / alpha_sum[i-1] / c[i:i+K].prod()
return N.log(result)
def calculate_k_mer_scores(bg_model, converted_seqs, K):
result = list()
for seq in converted_seqs:
LL, alpha, beta, c = bg_model.forward_backward(seq)
k_mer_LLs = k_mer_log_likelihoods(K=K, LL=LL, alpha=alpha, beta=beta, c=c)
result.append(k_mer_LLs)
return result
for i, seq in enumerate(converted_seqs[:8]):
LL, alpha, c = bg_model.forward(seq)
bg_k_mer_LLs = k_mer_log_likelihoods_new(alpha, c)
P.plot(bg_k_mer_LLs, label='Seq %d' % i)
LL, alpha, beta, c = bg_model.forward_backward(converted_seqs[2])
bg_k_mer_LLs_new = k_mer_log_likelihoods_new(alpha, c)
bg_k_mer_LLs_old = k_mer_log_likelihoods(K=K, LL=LL, alpha=alpha, beta=beta, c=c)
P.figure()
P.plot(bg_k_mer_LLs_new, label='new')
P.plot(bg_k_mer_LLs_old, label='old')
P.legend()
P.show()
def calculate_k_mer_scores(bg_model, converted_seqs, K):
result = list()
for seq in converted_seqs:
LL, alpha, beta, c = bg_model.forward_backward(seq)
k_mer_LLs = k_mer_log_likelihoods(K=K,
LL=LL,
alpha=alpha,
beta=beta,
c=c)
result.append(k_mer_LLs)
return result
for i, seq in enumerate(converted_seqs[:8]):
LL, alpha, c = bg_model.forward(seq)
bg_k_mer_LLs = k_mer_log_likelihoods_new(alpha, c)
P.plot(bg_k_mer_LLs, label='Seq %d' % i)
LL, alpha, beta, c = bg_model.forward_backward(converted_seqs[2])
bg_k_mer_LLs_new = k_mer_log_likelihoods_new(alpha, c)
bg_k_mer_LLs_old = k_mer_log_likelihoods(K=K,
LL=LL,
alpha=alpha,
beta=beta,
c=c)
P.figure()
P.plot(bg_k_mer_LLs_new, label='new')
P.plot(bg_k_mer_LLs_old, label='old')
P.legend()
P.show()
