-
Notifications
You must be signed in to change notification settings - Fork 2.8k
/
Copy pathpoisson.py
34 lines (28 loc) · 1.15 KB
/
poisson.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
# Copyright 2018 Google LLC
#
# 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
#
# https://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 scipy.stats as osp_stats
from jax import lax
from jax._src.numpy.util import _wraps
from jax._src.numpy import lax_numpy as jnp
from jax.scipy.special import xlogy, gammaln
@_wraps(osp_stats.poisson.logpmf, update_doc=False)
def logpmf(k, mu, loc=0):
k, mu, loc = jnp._promote_args_inexact("poisson.logpmf", k, mu, loc)
zero = jnp._constant_like(k, 0)
x = lax.sub(k, loc)
log_probs = xlogy(x, mu) - gammaln(x + 1) - mu
return jnp.where(lax.lt(x, zero), -jnp.inf, log_probs)
@_wraps(osp_stats.poisson.pmf, update_doc=False)
def pmf(k, mu, loc=0):
return jnp.exp(logpmf(k, mu, loc))