def scimm_like(readsf, k, soft_assign):
new_k = determine_k(soft_assign, k)
priors = imm_cluster.update_priors([1.0 / new_k] * new_k, readsf, {}, {},
soft_assign)
(likelihood,
read_probs) = imm_cluster.get_read_probs(priors, {}, {}, soft_assign)
return likelihood
def get_entropy(readsf, k, soft_assign):
new_k = determine_k(soft_assign, k)
priors = imm_cluster.update_priors([1.0 / new_k] * new_k, readsf, {}, {}, soft_assign)
(like, read_probs) = imm_cluster.get_read_probs(priors, {}, {}, soft_assign)
entropy = 0.0
for r in read_probs:
for c in range(len(read_probs[r])):
if read_probs[r][c] > 0:
entropy += -read_probs[r][c] * math.log(read_probs[r][c])
return entropy
def get_entropy(readsf, k, soft_assign):
new_k = determine_k(soft_assign, k)
priors = imm_cluster.update_priors([1.0/new_k]*new_k, readsf, {}, {}, soft_assign)
(like, read_probs) = imm_cluster.get_read_probs(priors, {}, {}, soft_assign)
entropy = 0.0
for r in read_probs:
for c in range(len(read_probs[r])):
if read_probs[r][c] > 0:
entropy += -read_probs[r][c]*math.log(read_probs[r][c])
return entropy
def scimm_like(readsf, k, soft_assign):
new_k = determine_k(soft_assign, k)
priors = imm_cluster.update_priors([1.0 / new_k] * new_k, readsf, {}, {}, soft_assign)
(likelihood, read_probs) = imm_cluster.get_read_probs(priors, {}, {}, soft_assign)
return likelihood
