def logp(self, value):
if self._is_logit:
lp = tt.switch(value, self._logit_p, -self._logit_p)
return -log1pexp(-lp)
else:
p = self.p
return bound(tt.switch(value, tt.log(p), tt.log(1 - p)),
value >= 0, value <= 1, p >= 0, p <= 1)
def logp(self, value):
if self._is_logit:
lp = tt.switch(value, self._logit_p, -self._logit_p)
return -log1pexp(-lp)
else:
p = self.p
return bound(
tt.switch(value, tt.log(p), tt.log(1 - p)),
value >= 0, value <= 1,
p >= 0, p <= 1)
def test_log1pexp():
vals = np.array([-1e20, -100, -10, -1e-4, 0, 1e-4, 10, 100, 1e20])
# import mpmath
# mpmath.mp.dps = 1000
# [float(mpmath.log(1 + mpmath.exp(x))) for x in vals]
expected = np.array([
0.0, 3.720075976020836e-44, 4.539889921686465e-05, 0.6930971818099453,
0.6931471805599453, 0.6931971818099453, 10.000045398899218, 100.0,
1e+20
])
actual = log1pexp(vals).eval()
npt.assert_allclose(actual, expected)
def test_log1pexp():
vals = np.array([-1e20, -100, -10, -1e-4, 0, 1e-4, 10, 100, 1e20])
# import mpmath
# mpmath.mp.dps = 1000
# [float(mpmath.log(1 + mpmath.exp(x))) for x in vals]
expected = np.array([
0.0,
3.720075976020836e-44,
4.539889921686465e-05,
0.6930971818099453,
0.6931471805599453,
0.6931971818099453,
10.000045398899218,
100.0,
1e+20])
actual = log1pexp(vals).eval()
npt.assert_allclose(actual, expected)
def logp(self, value):
r"""
Calculate log-probability of Bernoulli distribution at specified value.
Parameters
----------
value: numeric
Value(s) for which log-probability is calculated. If the log probabilities for multiple
values are desired the values must be provided in a numpy array or theano tensor
Returns
-------
TensorVariable
"""
if self._is_logit:
lp = tt.switch(value, self._logit_p, -self._logit_p)
return -log1pexp(-lp)
else:
p = self.p
return bound(tt.switch(value, tt.log(p), tt.log(1 - p)),
value >= 0, value <= 1, p >= 0, p <= 1)
