-
Notifications
You must be signed in to change notification settings - Fork 0
/
common.py
47 lines (38 loc) · 1.51 KB
/
common.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
35
36
37
38
39
40
41
42
43
44
45
46
# Copyright (c) 2016 Matthew Earl
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN
# NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE
# USE OR OTHER DEALINGS IN THE SOFTWARE.
"""
Definitions that don't fit elsewhere.
"""
__all__ = (
'DIGITS',
'LETTERS',
'CHARS',
'sigmoid',
'softmax',
)
import numpy
DIGITS = "0123456789"
LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
CHARS = LETTERS + DIGITS
def softmax(a):
exps = numpy.exp(a.astype(numpy.float64))
return exps / numpy.sum(exps, axis=-1)[:, numpy.newaxis]
def sigmoid(a):
return 1. / (1. + numpy.exp(-a))