def uniform_motif_with_ic_imh_ref(n,
L,
desired_ic,
epsilon=0.1,
iterations=None,
verbose=False,
num_chains=8):
correction_per_col = 3 / (2 * log(2) * n)
desired_ic_for_beta = desired_ic + L * correction_per_col
beta = find_beta_for_mean_motif_ic(n, L, desired_ic_for_beta)
ps = count_ps_from_beta(n, beta)
count_sampler = inverse_cdf_sampler(enumerate_counts(n), ps)
def Q(motif):
counts = [count_sampler() for i in range(L)]
cols = [sample_col_from_count(count) for count in counts]
motif_p = map(lambda site: "".join(site), transpose(cols))
return motif_p
def log_dQ(motif_p, motif):
return (beta * motif_ic(motif_p))
def log_f(motif):
in_range = abs(motif_ic(motif) - desired_ic) < epsilon
return 0 if in_range else -10.0**100
if iterations:
x0 = sample_until(lambda x: log_f(x) > -1, lambda: Q(None), 1)[0]
chain = mh(log_f,
proposal=Q,
dprop=log_dQ,
x0=x0,
iterations=iterations,
use_log=True,
verbose=False)
return chain
else: #use gelman rubin criterion
x0s = sample_until(lambda x: log_f(x) > -1, lambda: Q(None),
num_chains)
iterations = 100
converged = False
chains = [[] for _ in range(num_chains)]
while not converged:
for chain, x0 in zip(chains, x0s):
chain.extend(
mh(log_f,
proposal=Q,
dprop=log_dQ,
x0=x0,
iterations=iterations,
use_log=True,
verbose=False))
ic_chains = mmap(motif_ic, chains)
R_hat, neff = gelman_rubin(ic_chains)
if R_hat < 1.1:
return chains
else:
x0s = [chain[-1] for chain in chains]
iterations *= 2
def ror_experiment():
L = 10
n = 100
sigmas = np.linspace(0.1,10,10)
alphas = np.linspace(0,1,10)
for sigma in sigmas:
for alpha in alphas:
theta = - alpha * sigma * L
matrix = sample_matrix(L,sigma)
sampler = lambda : sample_motif_neglect_fg(matrix,1,Ne=2)[0]
motif = sample_until(lambda site:score_seq(matrix,site) < theta,sampler,n)
print sigma, alpha, total_motif_mi(motif)
def uniform_motif_with_ic_imh(n,
L,
desired_ic,
epsilon=0.1,
iterations=None,
verbose=False,
beta=None,
num_chains=8):
if beta is None:
correction_per_col = 3 / (2 * log(2) * n)
desired_ic_for_beta = desired_ic + L * correction_per_col
beta = find_beta_for_mean_motif_ic(n, L, desired_ic_for_beta)
ps = count_ps_from_beta(n, beta)
count_sampler = inverse_cdf_sampler(enumerate_counts(n), ps)
def Q(motif):
counts = [count_sampler() for i in range(L)]
cols = [sample_col_from_count(count) for count in counts]
motif_p = map(lambda site: "".join(site), transpose(cols))
return motif_p
def log_dQ(motif_p, motif):
return (beta * motif_ic(motif_p))
def log_f(motif):
in_range = abs(motif_ic(motif) - desired_ic) < epsilon
return 0 if in_range else -10.0**100
x0 = sample_until(lambda x: log_f(x) > -1, lambda: Q(None), 1)[0]
# first, determine probability of landing in range
ar = 0
iterations = 100
while ar == 0:
ar = mh(log_f,
proposal=Q,
dprop=log_dQ,
x0=x0,
iterations=iterations,
use_log=True,
verbose=False,
return_ar=True)
iterations *= 2
iterations = int(1.0 / ar * 10)
chain = mh(log_f,
proposal=Q,
dprop=log_dQ,
x0=x0,
iterations=iterations,
use_log=True,
verbose=False)
return chain
def uniform_motif_imh_tv(n, L, desired_ic, beta=None, epsilon=None, tv=0.01):
"""run uniform imh to within total variation bound tv"""
correction_per_col = 3 / (2 * log(2) * n)
desired_ic_for_beta = desired_ic + L * correction_per_col
if beta == None:
beta = find_beta_for_mean_motif_ic(n, L, desired_ic_for_beta)
if epsilon == None:
epsilon = 1.0 / (2 * beta)
print "maximally efficient epsilon:", epsilon
ps = count_ps_from_beta(n, beta)
count_sampler = inverse_cdf_sampler(enumerate_counts(n), ps)
def Qp(motif):
counts = [count_sampler() for i in range(L)]
cols = [sample_col_from_count(count) for count in counts]
motif_p = map(lambda site: "".join(site), transpose(cols))
return motif_p
def Q(motif):
return sample_until(lambda m: abs(motif_ic(m) - desired_ic) < epsilon,
lambda: Qp(None), 1)[0]
def log_dQ(motif_p, motif):
return (beta * motif_ic(motif_p))
def log_f(motif):
in_range = abs(motif_ic(motif) - desired_ic) < epsilon
return 0 if in_range else -10.0**100
alpha = exp(-2 * beta * epsilon)
iterations = int(ceil(log(tv) / log(1 - alpha)))
print "iterations:", iterations
x0 = sample_until(lambda x: log_f(x) > -1, lambda: Q(None), 1)[0]
# first, determine probability of landing in range
chain = mh(log_f,
proposal=Q,
dprop=log_dQ,
x0=x0,
iterations=iterations,
use_log=True,
verbose=False)
return chain
def rQ():
return sample_until(lambda M: inrange(M, desired_ic, epsilon),
rQ_raw,
1,
progress_bar=False)[0]
def Q(motif):
return sample_until(lambda m: abs(motif_ic(m) - desired_ic) < epsilon,
lambda: Qp(None), 1)[0]