# 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))