def test_find_MAP_discrete():
tol = 2.0**-11
alpha = 4
beta = 4
n = 20
yes = 15
with Model() as model:
p = Beta('p', alpha, beta)
ss = Binomial('ss', n=n, p=p)
s = Binomial('s', n=n, p=p, observed=yes)
map_est1 = starting.find_MAP()
map_est2 = starting.find_MAP(vars=model.vars)
close_to(map_est1['p'], 0.6086956533498806, tol)
close_to(map_est2['p'], 0.695642178810167, tol)
assert map_est2['ss'] == 14
def test_find_MAP_discrete():
tol = 2.0**-11
alpha = 4
beta = 4
n = 20
yes = 15
with Model() as model:
p = Beta('p', alpha, beta)
ss = Binomial('ss', n=n, p=p)
s = Binomial('s', n=n, p=p, observed=yes)
map_est1 = starting.find_MAP()
map_est2 = starting.find_MAP(vars=model.vars)
close_to(map_est1['p'], 0.6086956533498806, tol)
close_to(map_est2['p'], 0.695642178810167, tol)
assert map_est2['ss'] == 14
def test_find_MAP_discrete():
tol = 2.0**-11
alpha = 4
beta = 4
n = 20
yes = 15
with Model() as model:
p = Beta("p", alpha, beta)
Binomial("ss", n=n, p=p)
Binomial("s", n=n, p=p, observed=yes)
map_est1 = starting.find_MAP()
map_est2 = starting.find_MAP(vars=model.value_vars)
close_to(map_est1["p"], 0.6086956533498806, tol)
close_to(map_est2["p"], 0.695642178810167, tol)
assert map_est2["ss"] == 14
def test_find_MAP():
tol = 2.0**-11 # 16 bit machine epsilon, a low bar
data = np.random.randn(100)
# data should be roughly mean 0, std 1, but let's
# normalize anyway to get it really close
data = (data - np.mean(data)) / np.std(data)
with Model() as model:
mu = Uniform('mu', -1, 1)
sigma = Uniform('sigma', .5, 1.5)
y = Normal('y', mu=mu, tau=sigma**-2, observed=data)
# Test gradient minimization
map_est1 = starting.find_MAP()
# Test non-gradient minimization
map_est2 = starting.find_MAP(fmin=starting.optimize.fmin_powell)
close_to(map_est1['mu'], 0, tol)
close_to(map_est1['sigma'], 1, tol)
close_to(map_est2['mu'], 0, tol)
close_to(map_est2['sigma'], 1, tol)
def test_find_MAP():
tol = 2.0**-11 # 16 bit machine epsilon, a low bar
data = np.random.randn(100)
# data should be roughly mean 0, std 1, but let's
# normalize anyway to get it really close
data = (data - np.mean(data)) / np.std(data)
with Model():
mu = Uniform("mu", -1, 1)
sigma = Uniform("sigma", 0.5, 1.5)
Normal("y", mu=mu, tau=sigma**-2, observed=data)
# Test gradient minimization
map_est1 = starting.find_MAP(progressbar=False)
# Test non-gradient minimization
map_est2 = starting.find_MAP(progressbar=False, method="Powell")
close_to(map_est1["mu"], 0, tol)
close_to(map_est1["sigma"], 1, tol)
close_to(map_est2["mu"], 0, tol)
close_to(map_est2["sigma"], 1, tol)
def test_find_MAP():
tol = 2.0**-11 # 16 bit machine epsilon, a low bar
data = np.random.randn(100)
# data should be roughly mean 0, std 1, but let's
# normalize anyway to get it really close
data = (data-np.mean(data))/np.std(data)
with Model() as model:
mu = Uniform('mu', -1, 1)
sigma = Uniform('sigma', .5, 1.5)
y = Normal('y', mu=mu, tau=sigma**-2, observed=data)
# Test gradient minimization
map_est1 = starting.find_MAP()
# Test non-gradient minimization
map_est2 = starting.find_MAP(fmin=starting.optimize.fmin_powell)
close_to(map_est1['mu'], 0, tol)
close_to(map_est1['sigma'], 1, tol)
close_to(map_est2['mu'], 0, tol)
close_to(map_est2['sigma'], 1, tol)
