# Copyright (c) Alibaba, Inc. and its affiliates.
import numpy as np
import torch
[docs]class AliasMethod(object):
'''
https://hips.seas.harvard.edu/blog/2013/03/03/the-alias-method-efficient-sampling-with-many-discrete-outcomes/
'''
[docs] def __init__(self, probs):
if probs.sum() > 1:
probs.div_(probs.sum())
K = len(probs)
self.prob = torch.zeros(K)
self.alias = torch.LongTensor([0] * K)
# Sort the data into the outcomes with probabilities
# that are larger and smaller than 1/K.
smaller = []
larger = []
for kk, prob in enumerate(probs):
self.prob[kk] = K * prob
if self.prob[kk] < 1.0:
smaller.append(kk)
else:
larger.append(kk)
# Loop though and create little binary mixtures that
# appropriately allocate the larger outcomes over the
# overall uniform mixture.
while len(smaller) > 0 and len(larger) > 0:
small = smaller.pop()
large = larger.pop()
self.alias[small] = large
self.prob[large] = (self.prob[large] - 1.0) + self.prob[small]
if self.prob[large] < 1.0:
smaller.append(large)
else:
larger.append(large)
for last_one in smaller + larger:
self.prob[last_one] = 1
[docs] def cuda(self):
self.prob = self.prob.cuda()
self.alias = self.alias.cuda()
[docs] def draw(self, N):
'''
Draw N samples from multinomial
'''
K = self.alias.size(0)
kk = torch.zeros(
N, dtype=torch.long, device=self.prob.device).random_(0, K)
prob = self.prob.index_select(0, kk)
alias = self.alias.index_select(0, kk)
# b is whether a random number is greater than q
b = torch.bernoulli(prob)
oq = kk.mul(b.long())
oj = alias.mul((1 - b).long())
return oq + oj