def experiment2_():
L = 10
sigma = 1
code = sample_code(L, 1)
mu = -10
Ne = 2
sites = [random_site(L) for i in xrange(10000)]
apw_eps = [score(code, site) for site in sites]
site_sigma = sd(apw_eps)
pssm = sample_matrix(L, sqrt(site_sigma**2 / L))
#linear_eps = [score_seq(pssm, site) for site in sites]
def apw_phat(site):
ep = score(code, site)
return 1 / (1 + exp(ep - mu))**(Ne - 1)
def linear_phat(site):
ep = score_seq(pssm, site)
return 1 / (1 + exp(ep - mu))**(Ne - 1)
def sample_apw_site():
return mh(apw_phat, proposal=mutate_site, x0=random_site(L))
apw_chain = mh(apw_phat, proposal=mutate_site, x0=random_site(L))
linear_chain = mh(linear_phat, proposal=mutate_site, x0=random_site(L))
apw_fits = map(apw_phat, apw_chain)
linear_fits = map(linear_phat, linear_chain)
return apw_fits, linear_fits
def experiment3(trials=10):
mu = -10
Ne = 5
L = 10
sigma = 1
codes = [sample_code(L, sigma) for i in range(trials)]
pssms = [sample_matrix(L, sigma) for i in range(trials)]
sites = [random_site(L) for i in xrange(10000)]
apw_site_sigmas = [
sd([score(code, site) for site in sites]) for code in codes
]
linear_site_sigmas = [
sd([score_seq(pssm, site) for site in sites]) for pssm in pssms
]
def apw_phat(code, site):
ep = score(code, site)
return 1 / (1 + exp(ep - mu))**(Ne - 1)
def apw_occ(code, site):
ep = score(code, site)
return 1 / (1 + exp(ep - mu))
def linear_phat(pssm, site):
ep = score_seq(pssm, site)
return 1 / (1 + exp(ep - mu))**(Ne - 1)
def linear_occ(pssm, site):
ep = score_seq(pssm, site)
return 1 / (1 + exp(ep - mu))
apw_mean_fits = [
exp(
mean(
map(
log10,
mh(lambda s: apw_phat(code, s),
proposal=mutate_site,
x0=random_site(L),
capture_state=lambda s: apw_occ(code, s))[1:])))
for code in tqdm(codes)
]
linear_mean_fits = [
exp(
mean(
map(
log10,
mh(lambda s: linear_phat(pssm, s),
proposal=mutate_site,
x0=random_site(L),
capture_state=lambda s: linear_occ(pssm, s))[1:])))
for pssm in tqdm(pssms)
]
plt.scatter(apw_site_sigmas, apw_mean_fits, label='apw')
plt.scatter(linear_site_sigmas,
linear_mean_fits,
color='g',
label='linear')
plt.semilogy()
plt.legend(loc='lower right')
def experiment3(trials=10):
mu = -10
Ne = 5
L = 10
sigma = 1
codes = [sample_code(L, sigma) for i in range(trials)]
pssms = [sample_matrix(L, sigma) for i in range(trials)]
sites = [random_site(L) for i in xrange(10000)]
apw_site_sigmas = [sd([score(code,site) for site in sites]) for code in codes]
linear_site_sigmas = [sd([score_seq(pssm,site) for site in sites]) for pssm in pssms]
def apw_phat(code, site):
ep = score(code, site)
return 1/(1+exp(ep-mu))**(Ne-1)
def apw_occ(code, site):
ep = score(code, site)
return 1/(1+exp(ep-mu))
def linear_phat(pssm, site):
ep = score_seq(pssm, site)
return 1/(1+exp(ep-mu))**(Ne-1)
def linear_occ(pssm, site):
ep = score_seq(pssm, site)
return 1/(1+exp(ep-mu))
apw_mean_fits = [exp(mean(map(log10, mh(lambda s:apw_phat(code, s), proposal=mutate_site, x0=random_site(L),
capture_state = lambda s:apw_occ(code, s))[1:])))
for code in tqdm(codes)]
linear_mean_fits = [exp(mean(map(log10, mh(lambda s:linear_phat(pssm, s), proposal=mutate_site, x0=random_site(L),
capture_state = lambda s:linear_occ(pssm, s))[1:])))
for pssm in tqdm(pssms)]
plt.scatter(apw_site_sigmas, apw_mean_fits, label='apw')
plt.scatter(linear_site_sigmas, linear_mean_fits, color='g',label='linear')
plt.semilogy()
plt.legend(loc='lower right')
def experiment2_():
L = 10
sigma = 1
code = sample_code(L, 1)
mu = -10
Ne = 2
sites = [random_site(L) for i in xrange(10000)]
apw_eps = [score(code, site) for site in sites]
site_sigma = sd(apw_eps)
pssm = sample_matrix(L, sqrt(site_sigma**2/L))
#linear_eps = [score_seq(pssm, site) for site in sites]
def apw_phat(site):
ep = score(code, site)
return 1/(1+exp(ep-mu))**(Ne-1)
def linear_phat(site):
ep = score_seq(pssm, site)
return 1/(1+exp(ep-mu))**(Ne-1)
def sample_apw_site():
return mh(apw_phat, proposal=mutate_site, x0=random_site(L))
apw_chain = mh(apw_phat, proposal=mutate_site, x0=random_site(L))
linear_chain = mh(linear_phat, proposal=mutate_site, x0=random_site(L))
apw_fits = map(apw_phat, apw_chain)
linear_fits = map(linear_phat, linear_chain)
return apw_fits, linear_fits
def apw_phat(code, site):
ep = score(code, site)
return 1 / (1 + exp(ep - mu))**(Ne - 1)
def apw_occ(code, mu, site):
ep = score(code, site)
return 1 / (1 + exp(ep - mu))
def apw_occ(code, site):
ep = score(code, site)
return 1/(1+exp(ep-mu))
def apw_phat(code, site):
ep = score(code, site)
return 1/(1+exp(ep-mu))**(Ne-1)