Question

我正在编写癌细胞群体增长的模拟，我正在使用numpy.random函数来模拟获得或失去突变的细胞。我已经通过分析确定代码中的瓶颈（大约70％的运行时）是包含numpy.random函数的前几行。这里变量num_steps是一个大数字，大约一百万：

def simulate(mu, gamma, beta, num_steps, threshold):
    mutation_num = 0 # the index of the mutation (we assume each mutation only occurs once)
    population = {() : 1} # represents population: tuple of mutations and number of cells with those mutations
    for epoch in range(num_steps):
        next_population = {}
        for mutations, size in population.items():      
            born = np.random.binomial(size, birth_rate)
            if np.random.binomial(born, gamma):
                return True 
            mut_loss = 0 # initializing in case variable is not created
            if mutations:
                mut_gain, mut_loss, mut_same = np.random.multinomial(born, [mu, beta, 1-mu-beta])
            else:
                mut_gain, mut_same = np.random.multinomial(born, [mu, 1-mu])
.....

有没有办法让np.random.binomial和np.random.multinomial函数运行得更快？我尝试过使用Cython，但这并没有帮助。

Answer 1

说明我的评论：

In [81]: timeit np.random.binomial(1,1,1000)
46.4 µs ± 1.53 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
In [82]: %%timeit
    ...: for _ in range(1000):
    ...:     np.random.binomial(1,1)
    ...: 
4.77 ms ± 186 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

如果可能的话，一次调用而不是一次调用生成许多随机值。

提高numpy.random函数的速度

1 个答案: