def prop(motif):
if random.random() < 0.5:
motif_p = mutate_motif_p(motif,
p) # probability of mutation per basepair
return motif_p
else:
motif_p = [site for site in motif]
i, j = random.choice((choose2(range(n))))
k = random.randrange(L)
site_ip = motif[i][:k / 2] + motif[j][k / 2:]
site_jp = motif[j][:k / 2] + motif[i][k / 2:]
motif_p[i] = site_ip
motif_p[j] = site_jp
return motif_p
def proposal(motif):
p = 4.0/(n*L)
return mutate_motif_p(motif,p)
def mutate(motif):
p = 0.5
return mutate_motif_p(motif,p/(n*L))
def proposal(motif):
p = 4.0 / (n * L)
return mutate_motif_p(motif, p)
def mutate(motif):
p = 0.5
return mutate_motif_p(motif, p / (n * L))
def Q(motif):
return mutate_motif_p(motif, p)
def prop(motif):
motif_p = mutate_motif_p(motif,
p) # probability of mutation per basepair
return motif_p
def prop(motif):
#return mutate_motif_p(motif,1) # on average, 1 mutation per motif, (binomially distributed)
return mutate_motif_p(
motif,
4) # on average, 4 mutation per motif, (binomially distributed)
def prop((motif, mu)):
motif_p = mutate_motif_p(motif,
p) # probability of mutation per basepair
mu_p = mu + random.gauss(0, 0.1)
return motif_p, mu_p