# MIT License
#
# Copyright (c) 2017, Stefan Webb. All Rights Reserved.
#
# 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
import tensorflow as tf

def xavier_init(fan_in, fan_out, constant=1): 
	""" Xavier initialization of network weights"""
	# https://stackoverflow.com/questions/33640581/how-to-do-xavier-initialization-on-tensorflow
	low = -constant*np.sqrt(6.0/(fan_in + fan_out)) 
	high = constant*np.sqrt(6.0/(fan_in + fan_out))
	return tf.random_uniform((fan_in, fan_out), minval=low, maxval=high, dtype=tf.float32)

def 
(fan_in, fan_out, constant=1):
	return constant * math.sqrt(3.0 / (fan_in + fan_out))
xavier_std