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test_model_func.py
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test_model_func.py
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# Copyright 2020 The PyMC Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pymc3 as pm
import numpy as np
import scipy.stats as sp
from .checks import close_to
from .models import simple_model, mv_simple
tol = 2.0**-11
def test_logp():
start, model, (mu, sig) = simple_model()
lp = model.fastlogp
lp(start)
close_to(lp(start), sp.norm.logpdf(start['x'], mu, sig).sum(), tol)
def test_dlogp():
start, model, (mu, sig) = simple_model()
dlogp = model.fastdlogp()
close_to(dlogp(start), -(start['x'] - mu) / sig**2, 1. / sig**2 / 100.)
def test_dlogp2():
start, model, (_, sig) = mv_simple()
H = np.linalg.inv(sig)
d2logp = model.fastd2logp()
close_to(d2logp(start), H, np.abs(H / 100.))
def test_deterministic():
with pm.Model() as model:
x = pm.Normal('x', 0, 1)
y = pm.Deterministic('y', x**2)
assert model.y == y
assert model['y'] == y
def test_mapping():
with pm.Model() as model:
mu = pm.Normal('mu', 0, 1)
sd = pm.Gamma('sd', 1, 1)
y = pm.Normal('y', mu, sd, observed=np.array([.1, .5]))
lp = model.fastlogp
lparray = model.logp_array
point = model.test_point
parray = model.bijection.map(point)
assert lp(point) == lparray(parray)
randarray = np.random.randn(*parray.shape)
randpoint = model.bijection.rmap(randarray)
assert lp(randpoint) == lparray(randarray)