如何使用np.random.zipf为给定的值范围生成随机变量? [英] How can i generate random variables using np.random.zipf for a given range of values?

问题描述

我有给定的价格范围，我使用了随机制服从中获取随机生成的随机结果.我如何引入np.random.zipf做同样的事情?

I have a given price range and i had used random uniform to get random generated random results from it. How can i introduce np.random.zipf to do the same ?

我尝试了以下方法:

a = np.random.zipf((randint(1, 6000000)), size=None)
print(a)

但是它似乎没有提供任何返回值，并且它在不终止的情况下继续运行代码

But it seems to be providing no return values, and it keeps running the code without any termination

order_total_price_range1 = round(random.uniform(850, 560000), 5)
order_total_price_range2 = round(random.uniform(850, 560000), 5)

我希望从zipf分布中获取最大值和最小值，但目前未返回任何结果.

I expected to get max and min values from the zipf distribution, but currently not getting any results returned.

推荐答案

虽然@RobinNicole在Zipf分布中是正确的，但是您可以使用离散采样来模拟截断的Zipf.沿线

While @RobinNicole is right wrt Zipf distribution, you could simulate truncated Zipf using discrete sampling. Along the lines

import numpy as np
from matplotlib import pyplot as plt

def Zipf(a: np.float64, min: np.uint64, max: np.uint64, size=None):
    """
    Generate Zipf-like random variables,
    but in inclusive [min...max] interval
    """
    if min == 0:
        raise ZeroDivisionError("")

    v = np.arange(min, max+1) # values to sample
    p = 1.0 / np.power(v, a)  # probabilities
    p /= np.sum(p)            # normalized

    return np.random.choice(v, size=size, replace=True, p=p)

min = np.uint64(3)
max = np.uint64(8)

q = Zipf(1.2, min, max, 10000)
print(q)

h, bins = np.histogram(q, bins = int(max-min+1),range=(min-0.5,max+0.5))
print(h)
print(bins)

plt.hist(q, bins = bins)
plt.title("Zipf")
plt.show()

将制作这样的图形

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