PyTorch阶乘函数

Question

似乎没有一个PyTorch函数来计算阶乘。在Pytork中有这样做的方法吗？我希望手动计算火炬中的泊松分布（我知道存在这种情况：https://pytorch.org/docs/stable/generated/torch.poisson.html)这个公式要求分母中有一个阶乘。

泊松分布：https://en.wikipedia.org/wiki/Poisson_distribution

Answer 1

内置的数学模块（docs）提供了一个函数，该函数以int的形式返回给定积分的阶乘。

import math

x = math.factorial(5)
print(x)
print(type(x))

输出

120
<class 'int'>

Answer 2

我想你可以在火炬上找到它。准时制_内置的。数学factorial但是pytorch以及numpy和scipy（numpy和scipy中的factorial）使用python的内置数学。阶乘：

import math

import numpy as np
import scipy as sp
import torch


print(torch.jit._builtins.math.factorial is math.factorial)
print(np.math.factorial is math.factorial)
print(sp.math.factorial is math.factorial)

True
True
True

但是，与此相反，除了“主流”数学之外，scipy。阶乘包含非常“特殊”的阶乘函数。特殊的阶乘。与math模块中的函数不同，它在阵列上运行：

from scipy import special

print(special.factorial is math.factorial)

False

# the all known factorial functions
factorials = (
    math.factorial,
    torch.jit._builtins.math.factorial,
    np.math.factorial,
    sp.math.factorial,
    special.factorial,
)

# Let's run some tests
tnsr = torch.tensor(3)

for fn in factorials:
    try:
        out = fn(tnsr)
    except Exception as err:
        print(fn.__name__, fn.__module__, ':', err)
    else:
        print(fn.__name__, fn.__module__, ':', out)

factorial math : 6
factorial math : 6
factorial math : 6
factorial math : 6
factorial scipy.special._basic : tensor(6., dtype=torch.float64)

tnsr = torch.tensor([1, 2, 3])

for fn in factorials:
    try:
        out = fn(tnsr)
    except Exception as err:
        print(fn.__name__, fn.__module__, ':', err)
    else:
        print(fn.__name__, fn.__module__, ':', out)

factorial math : only integer tensors of a single element can be converted to an index
factorial math : only integer tensors of a single element can be converted to an index
factorial math : only integer tensors of a single element can be converted to an index
factorial math : only integer tensors of a single element can be converted to an index
factorial scipy.special._basic : tensor([1., 2., 6.], dtype=torch.float64)