from __future__ import absolute_import, division, print_function, unicode_literals
import numpy
from keras import backend as K
from keras.engine.topology import InputLayer
if K.backend() == "theano":
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
_FLOATX = theano.config.floatX
else:
import tensorflow as tf
def random_laplace(shape, mu=0., b=1.):
'''
Draw random samples from a Laplace distriubtion.
See: https://en.wikipedia.org/wiki/Laplace_distribution#Generating_random_variables_according_to_the_Laplace_distribution
'''
U = K.random_uniform(shape, -0.5, 0.5)
return mu - b * K.sign(U) * K.log(1 - 2 * K.abs(U))
def random_normal(shape, mean=0.0, std=1.0):
return K.random_normal(shape, mean, std)
def random_multinomial(logits, seed=None):
'''
Theano function for sampling from a multinomal with probability given by `logits`
'''
if K.backend() == "theano":
if seed is None:
seed = numpy.random.randint(1, 10e6)
rng = RandomStreams(seed=seed)
return rng.multinomial(n=1, pvals=logits, ndim=None, dtype=_FLOATX)
elif K.backend() == "tensorflow":
return tf.one_hot(tf.squeeze(tf.multinomial(K.log(logits), num_samples=1)),
int(logits.shape[1]))
def random_gmm(pi, mu, sig):
'''
Sample from a gaussian mixture model. Returns one sample for each row in
the pi, mu and sig matrices... this is potentially wasteful (because you have to repeat
the matrices n times if you want to get n samples), but makes it easy to implment
code where the parameters vary as they are conditioned on different datapoints.
'''
normals = random_normal(K.shape(mu), mu, sig)
k = random_multinomial(pi)
return K.sum(normals * k, axis=1, keepdims=True)
