我编写了一个简单的随机数生成器，如何绘制我编写的函数的分布图？

Question

This is my first time writing a random number generator and I was just messing around to see what I can do with just random formulas.

I am curious, however, with how bias my function is and with the distribution of the function (between 1 through 9). Here is my unnecessarily long code:

import time

class Random:
    """random generator"""

    def __init__(self):
        """ Random()-> create a random number generator with a random seed
        a seed is needed in order to generate random numbers"""
        self.seed = time.time()

    def random(self):
        """ Random.random() -> get a random number using a formula"""
        self.seed =(((int(self.seed)*129381249123+2019383)**0.74123)/517247) % 288371

    def get_ran_num(self):
        """ Random.get_ran_num() -> return a random integer from 1 thru 10"""
        self.random()
        return int(list(str(int(self.seed)))[3])


ranNum = Random()

It would be great if there exist some tools that can take a random function and then run it some thousand times and then graph the distribution of it.

Thank you in advance

p/s: How can I improve my RNG and make it even random-er?

Answer 1

I would try random.rand and matplotlib.

import numpy as np
import matplotlib.pyplot as plt


N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
area = np.pi * (15 * np.random.rand(N))**2  # 0 to 15 point radiuses

plt.scatter(x, y, s=area, c=colors, alpha=0.5)
plt.show()

Something like this?

Edit: Are you trying to generate a psuedo random number? You'd need a seed for the shift register anyway, so in that respect, I am not sure if it would be completely random.

Answer 2

If you just want a visual representation, you can pretty easily use

import matplotlib.pyplot as plt
# Random class here
ranNum = Random()
randNums = [ranNum.get_ran_num() for _ in range(100)]
nums = list(range(len(randNums)))
plt.plot(nums, randNums, 'ro')
plt.show()

Here it is for 100 random numbers:

However, I'm getting an IndexError when I go to higher ranges. You should probably fix the actual algorithm for whatever is causing that problem, but the way I put a band aid on it was:

def get_ran_num(self):
    """ Random.get_ran_num() -> return a random integer from 1 thru 10"""
    retval = None
    while True:
        try:
            self.random()
            retval = int(list(str(int(self.seed)))[3])
            break
        except IndexError as e:
            continue
    return retval

Here's a plot for 100,000 random numbers, which is pretty good. Contiguous lines where none is noticeably more dense than the others is what you're after, but you're going to need to do much better entropy analysis to find out anything more useful than a quick visual representation. In your case, it looks like 6 is a little more favored. Also, it looks like it repeats fairly often.

Answer 3

I would prefer histogram plot to check for uniformity of distribution of your random number generator.

import numpy as np
import matplotlib
import matplotlib.pyplot as plt 
myarr = np.random.randint(1000, size=100000)
plt.hist(myarr, bins=40)
plt.show()