def test_mixture_list_of_normals(self):
with Model() as model:
w = Dirichlet('w', floatX(np.ones_like(self.norm_w)))
mu = Normal('mu', 0., 10., shape=self.norm_w.size)
tau = Gamma('tau', 1., 1., shape=self.norm_w.size)
Mixture('x_obs',
w, [
Normal.dist(mu[0], tau=tau[0]),
Normal.dist(mu[1], tau=tau[1])
],
observed=self.norm_x)
step = Metropolis()
trace = sample(5000,
step,
random_seed=self.random_seed,
progressbar=False,
chains=1)
assert_allclose([
np.sort(trace['w'].mean(axis=0)),
np.sort(trace['mu'].mean(axis=0))
], [np.sort(self.norm_w), np.sort(self.norm_mu)],
rtol=0.1,
atol=0.1)
assert_allclose(np.sort(trace['mu'].mean(axis=0)),
np.sort(self.norm_mu),
rtol=0.1,
atol=0.1)
def test_mixture_list_of_normals(self):
with Model() as model:
w = Dirichlet("w",
floatX(np.ones_like(self.norm_w)),
shape=self.norm_w.size)
mu = Normal("mu", 0.0, 10.0, shape=self.norm_w.size)
tau = Gamma("tau", 1.0, 1.0, shape=self.norm_w.size)
Mixture(
"x_obs",
w,
[
Normal.dist(mu[0], tau=tau[0]),
Normal.dist(mu[1], tau=tau[1])
],
observed=self.norm_x,
)
step = Metropolis()
trace = sample(5000,
step,
random_seed=self.random_seed,
progressbar=False,
chains=1)
assert_allclose(np.sort(trace["w"].mean(axis=0)),
np.sort(self.norm_w),
rtol=0.1,
atol=0.1)
assert_allclose(np.sort(trace["mu"].mean(axis=0)),
np.sort(self.norm_mu),
rtol=0.1,
atol=0.1)
def test_dimensions(self):
a1 = Normal.dist(mu=0, sigma=1)
a2 = Normal.dist(mu=10, sigma=1)
mix = Mixture.dist(w=np.r_[0.5, 0.5], comp_dists=[a1, a2])
assert mix.mode.ndim == 0
assert mix.logp(0.0).ndim == 0
value = np.r_[0.0, 1.0, 2.0]
assert mix.logp(value).ndim == 1
def logp(self, observed):
"""Calculated the log likelihood of the observed streamflow given
simulated streamflow from GR4J"""
simulated = simulate_streamflow(self.precipitation,
self.evaporation,
self.S0,
self.Pr0,
self.R0,
self.x1,
self.x2,
self.x3,
self.x4,
self.x4_limit,
truncate_gradient=self.truncate,
tv_x1=self.tv_x1)
# This restricts likelihood calculations to fewer than len(observed)
# points. This can potentially make for more rapid calculations.
if self.subsample_index is not None:
observed = observed[self.subsample_index]
simulated = simulated[self.subsample_index]
density = Normal.dist(mu=simulated, sd=self.sd)
return tt.sum(density.logp(observed))
def test_mixture_list_of_normals(self):
with Model() as model:
w = Dirichlet('w', np.ones_like(self.norm_w))
mu = Normal('mu', 0., 10., shape=self.norm_w.size)
tau = Gamma('tau', 1., 1., shape=self.norm_w.size)
Mixture('x_obs', w,
[Normal.dist(mu[0], tau=tau[0]), Normal.dist(mu[1], tau=tau[1])],
observed=self.norm_x)
step = Metropolis()
trace = sample(5000, step, random_seed=self.random_seed, progressbar=False)
assert_allclose(np.sort(trace['w'].mean(axis=0)),
np.sort(self.norm_w),
rtol=0.1, atol=0.1)
assert_allclose(np.sort(trace['mu'].mean(axis=0)),
np.sort(self.norm_mu),
rtol=0.1, atol=0.1)
def logp(self, x):
tau = self.tau
sd = self.sd
A = self.A
B = self.B
u = self.u
init = self.init
# x[0,:] = init
x_im1 = x[:-1]
x_i = x[1:]
u_im1 = u[:-1]
innov_like = Normal.dist(mu=T.dot(A, x_im1.T) + T.dot(B, u_im1.T),
tau=tau,
sd=sd).logp(x_i.T)
return T.sum(init.logp(x[0])) + T.sum(innov_like)
def test_normal_mixture_nd(self):
nd, ncomp = 3, 5
with Model() as model0:
mus = Normal('mus', shape=(nd, ncomp))
taus = Gamma('taus', alpha=1, beta=1, shape=(nd, ncomp))
ws = Dirichlet('ws', np.ones(ncomp))
mixture0 = NormalMixture('m', w=ws, mu=mus, tau=taus, shape=nd)
with Model() as model1:
mus = Normal('mus', shape=(nd, ncomp))
taus = Gamma('taus', alpha=1, beta=1, shape=(nd, ncomp))
ws = Dirichlet('ws', np.ones(ncomp))
comp_dist = [Normal.dist(mu=mus[:, i], tau=taus[:, i])
for i in range(ncomp)]
mixture1 = Mixture('m', w=ws, comp_dists=comp_dist, shape=nd)
testpoint = model0.test_point
testpoint['mus'] = np.random.randn(nd, ncomp)
assert_allclose(model0.logp(testpoint), model1.logp(testpoint))
assert_allclose(mixture0.logp(testpoint), mixture1.logp(testpoint))
def build_model():
y = shared(np.array([15, 10, 16, 11, 9, 11, 10, 18], dtype=np.float32))
with Model() as arma_model:
sigma = HalfCauchy('sigma', 5)
theta = Normal('theta', 0, sd=2)
phi = Normal('phi', 0, sd=2)
mu = Normal('mu', 0, sd=10)
err0 = y[0] - (mu + phi * mu)
def calc_next(last_y, this_y, err, mu, phi, theta):
nu_t = mu + phi * last_y + theta * err
return this_y - nu_t
err, _ = scan(fn=calc_next,
sequences=dict(input=y, taps=[-1, 0]),
outputs_info=[err0],
non_sequences=[mu, phi, theta])
Potential('like', Normal.dist(0, sd=sigma).logp(err))
variational.advi(n=2000)
return arma_model
def test_normal_mixture_nd(self):
nd, ncomp = 3, 5
with Model() as model0:
mus = Normal('mus', shape=(nd, ncomp))
taus = Gamma('taus', alpha=1, beta=1, shape=(nd, ncomp))
ws = Dirichlet('ws', np.ones(ncomp))
mixture0 = NormalMixture('m', w=ws, mu=mus, tau=taus, shape=nd)
with Model() as model1:
mus = Normal('mus', shape=(nd, ncomp))
taus = Gamma('taus', alpha=1, beta=1, shape=(nd, ncomp))
ws = Dirichlet('ws', np.ones(ncomp))
comp_dist = [
Normal.dist(mu=mus[:, i], tau=taus[:, i]) for i in range(ncomp)
]
mixture1 = Mixture('m', w=ws, comp_dists=comp_dist, shape=nd)
testpoint = model0.test_point
testpoint['mus'] = np.random.randn(nd, ncomp)
assert_allclose(model0.logp(testpoint), model1.logp(testpoint))
assert_allclose(mixture0.logp(testpoint), mixture1.logp(testpoint))
def build_model():
y = shared(np.array([15, 10, 16, 11, 9, 11, 10, 18], dtype=np.float32))
with Model() as arma_model:
sigma = HalfCauchy('sigma', 5)
theta = Normal('theta', 0, sd=2)
phi = Normal('phi', 0, sd=2)
mu = Normal('mu', 0, sd=10)
err0 = y[0] - (mu + phi * mu)
def calc_next(last_y, this_y, err, mu, phi, theta):
nu_t = mu + phi * last_y + theta * err
return this_y - nu_t
err, _ = scan(fn=calc_next,
sequences=dict(input=y, taps=[-1, 0]),
outputs_info=[err0],
non_sequences=[mu, phi, theta])
Potential('like', Normal.dist(0, sd=sigma).logp(err))
mu, sds, elbo = variational.advi(n=2000)
return arma_model
def test_mixture_of_mixture(self):
if theano.config.floatX == "float32":
rtol = 1e-4
else:
rtol = 1e-7
nbr = 4
with Model() as model:
# mixtures components
g_comp = Normal.dist(mu=Exponential("mu_g",
lam=1.0,
shape=nbr,
transform=None),
sigma=1,
shape=nbr)
l_comp = Lognormal.dist(mu=Exponential("mu_l",
lam=1.0,
shape=nbr,
transform=None),
sigma=1,
shape=nbr)
# weight vector for the mixtures
g_w = Dirichlet("g_w",
a=floatX(np.ones(nbr) * 0.0000001),
transform=None,
shape=(nbr, ))
l_w = Dirichlet("l_w",
a=floatX(np.ones(nbr) * 0.0000001),
transform=None,
shape=(nbr, ))
# mixture components
g_mix = Mixture.dist(w=g_w, comp_dists=g_comp)
l_mix = Mixture.dist(w=l_w, comp_dists=l_comp)
# mixture of mixtures
mix_w = Dirichlet("mix_w",
a=floatX(np.ones(2)),
transform=None,
shape=(2, ))
mix = Mixture("mix",
w=mix_w,
comp_dists=[g_mix, l_mix],
observed=np.exp(self.norm_x))
test_point = model.test_point
def mixmixlogp(value, point):
floatX = theano.config.floatX
priorlogp = (st.dirichlet.logpdf(
x=point["g_w"],
alpha=np.ones(nbr) * 0.0000001,
).astype(floatX) +
st.expon.logpdf(x=point["mu_g"]).sum(dtype=floatX) +
st.dirichlet.logpdf(
x=point["l_w"],
alpha=np.ones(nbr) * 0.0000001,
).astype(floatX) +
st.expon.logpdf(x=point["mu_l"]).sum(dtype=floatX) +
st.dirichlet.logpdf(
x=point["mix_w"],
alpha=np.ones(2),
).astype(floatX))
complogp1 = st.norm.logpdf(x=value,
loc=point["mu_g"]).astype(floatX)
mixlogp1 = logsumexp(np.log(point["g_w"]).astype(floatX) +
complogp1,
axis=-1,
keepdims=True)
complogp2 = st.lognorm.logpdf(value, 1.0, 0.0,
np.exp(point["mu_l"])).astype(floatX)
mixlogp2 = logsumexp(np.log(point["l_w"]).astype(floatX) +
complogp2,
axis=-1,
keepdims=True)
complogp_mix = np.concatenate((mixlogp1, mixlogp2), axis=1)
mixmixlogpg = logsumexp(np.log(point["mix_w"]).astype(floatX) +
complogp_mix,
axis=-1,
keepdims=False)
return priorlogp, mixmixlogpg
value = np.exp(self.norm_x)[:, None]
priorlogp, mixmixlogpg = mixmixlogp(value, test_point)
# check logp of mixture
assert_allclose(mixmixlogpg, mix.logp_elemwise(test_point), rtol=rtol)
# check model logp
assert_allclose(priorlogp + mixmixlogpg.sum(),
model.logp(test_point),
rtol=rtol)
# check input and check logp again
test_point["g_w"] = np.asarray([0.1, 0.1, 0.2, 0.6])
test_point["mu_g"] = np.exp(np.random.randn(nbr))
priorlogp, mixmixlogpg = mixmixlogp(value, test_point)
assert_allclose(mixmixlogpg, mix.logp_elemwise(test_point), rtol=rtol)
assert_allclose(priorlogp + mixmixlogpg.sum(),
model.logp(test_point),
rtol=rtol)
def test_normal_mixture_nd(self, nd, ncomp):
nd = to_tuple(nd)
ncomp = int(ncomp)
comp_shape = nd + (ncomp, )
test_mus = np.random.randn(*comp_shape)
test_taus = np.random.gamma(1, 1, size=comp_shape)
observed = generate_normal_mixture_data(w=np.ones(ncomp) / ncomp,
mu=test_mus,
sd=1 / np.sqrt(test_taus),
size=10)
with Model() as model0:
mus = Normal("mus", shape=comp_shape)
taus = Gamma("taus", alpha=1, beta=1, shape=comp_shape)
ws = Dirichlet("ws", np.ones(ncomp), shape=(ncomp, ))
mixture0 = NormalMixture("m",
w=ws,
mu=mus,
tau=taus,
shape=nd,
comp_shape=comp_shape)
obs0 = NormalMixture("obs",
w=ws,
mu=mus,
tau=taus,
shape=nd,
comp_shape=comp_shape,
observed=observed)
with Model() as model1:
mus = Normal("mus", shape=comp_shape)
taus = Gamma("taus", alpha=1, beta=1, shape=comp_shape)
ws = Dirichlet("ws", np.ones(ncomp), shape=(ncomp, ))
comp_dist = [
Normal.dist(mu=mus[..., i], tau=taus[..., i], shape=nd)
for i in range(ncomp)
]
mixture1 = Mixture("m", w=ws, comp_dists=comp_dist, shape=nd)
obs1 = Mixture("obs",
w=ws,
comp_dists=comp_dist,
shape=nd,
observed=observed)
with Model() as model2:
# Expected to fail if comp_shape is not provided,
# nd is multidim and it does not broadcast with ncomp. If by chance
# it does broadcast, an error is raised if the mixture is given
# observed data.
# Furthermore, the Mixture will also raise errors when the observed
# data is multidimensional but it does not broadcast well with
# comp_dists.
mus = Normal("mus", shape=comp_shape)
taus = Gamma("taus", alpha=1, beta=1, shape=comp_shape)
ws = Dirichlet("ws", np.ones(ncomp), shape=(ncomp, ))
if len(nd) > 1:
if nd[-1] != ncomp:
with pytest.raises(ValueError):
NormalMixture("m", w=ws, mu=mus, tau=taus, shape=nd)
mixture2 = None
else:
mixture2 = NormalMixture("m",
w=ws,
mu=mus,
tau=taus,
shape=nd)
else:
mixture2 = NormalMixture("m", w=ws, mu=mus, tau=taus, shape=nd)
observed_fails = False
if len(nd) >= 1 and nd != (1, ):
try:
np.broadcast(np.empty(comp_shape), observed)
except Exception:
observed_fails = True
if observed_fails:
with pytest.raises(ValueError):
NormalMixture("obs",
w=ws,
mu=mus,
tau=taus,
shape=nd,
observed=observed)
obs2 = None
else:
obs2 = NormalMixture("obs",
w=ws,
mu=mus,
tau=taus,
shape=nd,
observed=observed)
testpoint = model0.test_point
testpoint["mus"] = test_mus
testpoint["taus"] = test_taus
assert_allclose(model0.logp(testpoint), model1.logp(testpoint))
assert_allclose(mixture0.logp(testpoint), mixture1.logp(testpoint))
assert_allclose(obs0.logp(testpoint), obs1.logp(testpoint))
if mixture2 is not None and obs2 is not None:
assert_allclose(model0.logp(testpoint), model2.logp(testpoint))
if mixture2 is not None:
assert_allclose(mixture0.logp(testpoint), mixture2.logp(testpoint))
if obs2 is not None:
assert_allclose(obs0.logp(testpoint), obs2.logp(testpoint))
def test_normal_mixture_nd(self, nd, ncomp):
nd = to_tuple(nd)
ncomp = int(ncomp)
comp_shape = nd + (ncomp,)
test_mus = np.random.randn(*comp_shape)
test_taus = np.random.gamma(1, 1, size=comp_shape)
observed = generate_normal_mixture_data(w=np.ones(ncomp)/ncomp,
mu=test_mus,
sd=1/np.sqrt(test_taus),
size=10)
with Model() as model0:
mus = Normal('mus', shape=comp_shape)
taus = Gamma('taus', alpha=1, beta=1, shape=comp_shape)
ws = Dirichlet('ws', np.ones(ncomp))
mixture0 = NormalMixture('m', w=ws, mu=mus, tau=taus, shape=nd,
comp_shape=comp_shape)
obs0 = NormalMixture('obs', w=ws, mu=mus, tau=taus, shape=nd,
comp_shape=comp_shape,
observed=observed)
with Model() as model1:
mus = Normal('mus', shape=comp_shape)
taus = Gamma('taus', alpha=1, beta=1, shape=comp_shape)
ws = Dirichlet('ws', np.ones(ncomp))
comp_dist = [Normal.dist(mu=mus[..., i], tau=taus[..., i],
shape=nd)
for i in range(ncomp)]
mixture1 = Mixture('m', w=ws, comp_dists=comp_dist, shape=nd)
obs1 = Mixture('obs', w=ws, comp_dists=comp_dist, shape=nd,
observed=observed)
with Model() as model2:
# Expected to fail if comp_shape is not provided,
# nd is multidim and it does not broadcast with ncomp. If by chance
# it does broadcast, an error is raised if the mixture is given
# observed data.
# Furthermore, the Mixture will also raise errors when the observed
# data is multidimensional but it does not broadcast well with
# comp_dists.
mus = Normal('mus', shape=comp_shape)
taus = Gamma('taus', alpha=1, beta=1, shape=comp_shape)
ws = Dirichlet('ws', np.ones(ncomp))
if len(nd) > 1:
if nd[-1] != ncomp:
with pytest.raises(ValueError):
NormalMixture('m', w=ws, mu=mus, tau=taus,
shape=nd)
mixture2 = None
else:
mixture2 = NormalMixture('m', w=ws, mu=mus, tau=taus,
shape=nd)
else:
mixture2 = NormalMixture('m', w=ws, mu=mus, tau=taus,
shape=nd)
observed_fails = False
if len(nd) >= 1 and nd != (1,):
try:
np.broadcast(np.empty(comp_shape), observed)
except Exception:
observed_fails = True
if observed_fails:
with pytest.raises(ValueError):
NormalMixture('obs', w=ws, mu=mus, tau=taus,
shape=nd,
observed=observed)
obs2 = None
else:
obs2 = NormalMixture('obs', w=ws, mu=mus, tau=taus,
shape=nd,
observed=observed)
testpoint = model0.test_point
testpoint['mus'] = test_mus
testpoint['taus'] = test_taus
assert_allclose(model0.logp(testpoint), model1.logp(testpoint))
assert_allclose(mixture0.logp(testpoint), mixture1.logp(testpoint))
assert_allclose(obs0.logp(testpoint), obs1.logp(testpoint))
if mixture2 is not None and obs2 is not None:
assert_allclose(model0.logp(testpoint), model2.logp(testpoint))
if mixture2 is not None:
assert_allclose(mixture0.logp(testpoint), mixture2.logp(testpoint))
if obs2 is not None:
assert_allclose(obs0.logp(testpoint), obs2.logp(testpoint))
def test_mixture_of_mixture(self):
if theano.config.floatX == 'float32':
rtol = 1e-4
else:
rtol = 1e-7
nbr = 4
with Model() as model:
# mixtures components
g_comp = Normal.dist(
mu=Exponential('mu_g', lam=1.0, shape=nbr, transform=None),
sigma=1,
shape=nbr)
l_comp = Lognormal.dist(
mu=Exponential('mu_l', lam=1.0, shape=nbr, transform=None),
sigma=1,
shape=nbr)
# weight vector for the mixtures
g_w = Dirichlet('g_w', a=floatX(np.ones(nbr)*0.0000001), transform=None)
l_w = Dirichlet('l_w', a=floatX(np.ones(nbr)*0.0000001), transform=None)
# mixture components
g_mix = Mixture.dist(w=g_w, comp_dists=g_comp)
l_mix = Mixture.dist(w=l_w, comp_dists=l_comp)
# mixture of mixtures
mix_w = Dirichlet('mix_w', a=floatX(np.ones(2)), transform=None)
mix = Mixture('mix', w=mix_w,
comp_dists=[g_mix, l_mix],
observed=np.exp(self.norm_x))
test_point = model.test_point
def mixmixlogp(value, point):
floatX = theano.config.floatX
priorlogp = st.dirichlet.logpdf(x=point['g_w'],
alpha=np.ones(nbr)*0.0000001,
).astype(floatX) + \
st.expon.logpdf(x=point['mu_g']).sum(dtype=floatX) + \
st.dirichlet.logpdf(x=point['l_w'],
alpha=np.ones(nbr)*0.0000001,
).astype(floatX) + \
st.expon.logpdf(x=point['mu_l']).sum(dtype=floatX) + \
st.dirichlet.logpdf(x=point['mix_w'],
alpha=np.ones(2),
).astype(floatX)
complogp1 = st.norm.logpdf(x=value,
loc=point['mu_g']).astype(floatX)
mixlogp1 = logsumexp(np.log(point['g_w']).astype(floatX) +
complogp1,
axis=-1, keepdims=True)
complogp2 = st.lognorm.logpdf(value,
1.,
0.,
np.exp(point['mu_l'])).astype(floatX)
mixlogp2 = logsumexp(np.log(point['l_w']).astype(floatX) +
complogp2,
axis=-1, keepdims=True)
complogp_mix = np.concatenate((mixlogp1, mixlogp2), axis=1)
mixmixlogpg = logsumexp(np.log(point['mix_w']).astype(floatX) +
complogp_mix,
axis=-1, keepdims=True)
return priorlogp, mixmixlogpg
value = np.exp(self.norm_x)[:, None]
priorlogp, mixmixlogpg = mixmixlogp(value, test_point)
# check logp of mixture
assert_allclose(mixmixlogpg, mix.logp_elemwise(test_point),
rtol=rtol)
# check model logp
assert_allclose(priorlogp + mixmixlogpg.sum(),
model.logp(test_point),
rtol=rtol)
# check input and check logp again
test_point['g_w'] = np.asarray([.1, .1, .2, .6])
test_point['mu_g'] = np.exp(np.random.randn(nbr))
priorlogp, mixmixlogpg = mixmixlogp(value, test_point)
assert_allclose(mixmixlogpg, mix.logp_elemwise(test_point),
rtol=rtol)
assert_allclose(priorlogp + mixmixlogpg.sum(),
model.logp(test_point),
rtol=rtol)
theta = Normal('theta', 0, sd=2)
phi = Normal('phi', 0, sd=2)
mu = Normal('mu', 0, sd=10)
err0 = y[0] - (mu + phi*mu)
def calc_next(last_y, this_y, err, mu, phi, theta):
nu_t = mu + phi*last_y + theta*err
return this_y - nu_t
err, _ = scan(fn=calc_next,
sequences=dict(input=y, taps=[-1,0]),
outputs_info=[err0],
non_sequences=[mu, phi, theta])
like = Potential('like', Normal.dist(0, sd=sigma).logp(err))
with arma_model:
mu, sds, elbo = variational.advi(n=2000)
def run(n=1000):
if n == "short":
n = 50
with arma_model:
trace = sample(1000)
burn = n/10
traceplot(trace[burn:])
def test_mixture_of_mixture(self):
nbr = 4
with Model() as model:
# mixtures components
g_comp = Normal.dist(mu=Exponential('mu_g',
lam=1.0,
shape=nbr,
transform=None),
sigma=1,
shape=nbr)
l_comp = Lognormal.dist(mu=Exponential('mu_l',
lam=1.0,
shape=nbr,
transform=None),
sigma=1,
shape=nbr)
# weight vector for the mixtures
g_w = Dirichlet('g_w',
a=floatX(np.ones(nbr) * 0.0000001),
transform=None)
l_w = Dirichlet('l_w',
a=floatX(np.ones(nbr) * 0.0000001),
transform=None)
# mixture components
g_mix = Mixture.dist(w=g_w, comp_dists=g_comp)
l_mix = Mixture.dist(w=l_w, comp_dists=l_comp)
# mixture of mixtures
mix_w = Dirichlet('mix_w', a=floatX(np.ones(2)), transform=None)
mix = Mixture('mix',
w=mix_w,
comp_dists=[g_mix, l_mix],
observed=np.exp(self.norm_x))
test_point = model.test_point
def mixmixlogp(value, point):
priorlogp = st.dirichlet.logpdf(x=point['g_w'],
alpha=np.ones(nbr)*0.0000001,
) + \
st.expon.logpdf(x=point['mu_g']).sum() + \
st.dirichlet.logpdf(x=point['l_w'],
alpha=np.ones(nbr)*0.0000001,
) + \
st.expon.logpdf(x=point['mu_l']).sum() + \
st.dirichlet.logpdf(x=point['mix_w'],
alpha=np.ones(2),
)
complogp1 = st.norm.logpdf(x=value, loc=point['mu_g'])
mixlogp1 = logsumexp(np.log(point['g_w']) + complogp1,
axis=-1,
keepdims=True)
complogp2 = st.lognorm.logpdf(value, 1., 0., np.exp(point['mu_l']))
mixlogp2 = logsumexp(np.log(point['l_w']) + complogp2,
axis=-1,
keepdims=True)
complogp_mix = np.concatenate((mixlogp1, mixlogp2), axis=1)
mixmixlogpg = logsumexp(np.log(point['mix_w']) + complogp_mix,
axis=-1,
keepdims=True)
return priorlogp, mixmixlogpg
value = np.exp(self.norm_x)[:, None]
priorlogp, mixmixlogpg = mixmixlogp(value, test_point)
# check logp of mixture
assert_allclose(mixmixlogpg, mix.logp_elemwise(test_point))
# check model logp
assert_allclose(priorlogp + mixmixlogpg.sum(), model.logp(test_point))
# check input and check logp again
test_point['g_w'] = np.asarray([.1, .1, .2, .6])
test_point['mu_g'] = np.exp(np.random.randn(nbr))
priorlogp, mixmixlogpg = mixmixlogp(value, test_point)
assert_allclose(mixmixlogpg, mix.logp_elemwise(test_point))
assert_allclose(priorlogp + mixmixlogpg.sum(), model.logp(test_point))
with beta_carotene_model:
samples = fit(random_seed=None).sample(1000)
##plot posterior
from pymc3 import plot_posterior
plot_posterior(samples, varnames=['mean'], ref_val=240, color="LightSeaGreen")
##What is the probability a randomly choose person willhave higher beta carotene level more than 130
mus = samples["mean"]
sigmas = samples["sigma"]
random_samples = Normal.dist(mus, sigmas).random()
sns.distplot(random_samples, label='simulated')
sns.distplot(beat_carotene_levels_before_drug, label="observed")
###Doing the samething on the after taking drugs
with Model() as beta_carotene_model_after_drug:
mean = Uniform('mean', lower=0, upper=10000)
sigma = Uniform('sigma', 0, 100)
##Getting Likelihood and we assumed it cacanormal
with beta_carotene_model_after_drug:
print(beat_carotene_levels_after_drug)
y = Normal('beat_carotene_levels_after_drug',