How to generate a random normal distribution of in

Question

How to generate a random integer as with np.random.randint(), but with a normal distribution around 0.

np.random.randint(-10, 10) returns integers with a discrete uniform distribution np.random.normal(0, 0.1, 1) returns floats with a normal distribution

What I want is a kind of combination between the two functions.

Answer 1

One other possible way to get a discrete distribution that looks like the normal distribution is to draw from a multinomial distribution where the probabilities are calculated from a normal distribution.

import scipy.stats as ss
import numpy as np
import matplotlib.pyplot as plt

x = np.arange(-10, 11)
xU, xL = x + 0.5, x - 0.5 
prob = ss.norm.cdf(xU, scale = 3) - ss.norm.cdf(xL, scale = 3)
prob = prob / prob.sum() #normalize the probabilities so their sum is 1
nums = np.random.choice(x, size = 10000, p = prob)
plt.hist(nums, bins = len(x))

Here, np.random.choice picks an integer from [-10, 10]. The probability for selecting an element, say 0, is calculated by p(-0.5 < x < 0.5) where x is a normal random variable with mean zero and standard deviation 3. I chooce std. dev. as 3 because this way p(-10 < x < 10) is almost 1.

The result looks like this:

Answer 2

It may be possible to generate a similar distribution from a Truncated Normal Distribution that is rounded up to integers. Here's an example with scipy's truncnorm().

import numpy as np
from scipy.stats import truncnorm
import matplotlib.pyplot as plt

scale = 3.
range = 10
size = 100000

X = truncnorm(a=-range/scale, b=+range/scale, scale=scale).rvs(size=size)
X = X.round().astype(int)

Let's see what it looks like

bins = 2 * range + 1
plt.hist(X, bins)

Answer 3

Here we start by getting values from the bell curve.

CODE:

#--------*---------*---------*---------*---------*---------*---------*---------*
# Desc: Discretize a normal distribution centered at 0
#--------*---------*---------*---------*---------*---------*---------*---------*

import sys
import random
from math import sqrt, pi
import numpy as np
import matplotlib.pyplot as plt

def gaussian(x, var):
    k1 = np.power(x, 2)
    k2 = -k1/(2*var)
    return (1./(sqrt(2. * pi * var))) * np.exp(k2)

#--------*---------*---------*---------*---------*---------*---------*---------#
while 1:#                          M A I N L I N E                             #
#--------*---------*---------*---------*---------*---------*---------*---------#
#                                  # probability density function
#                                  #   for discrete normal RV
    pdf_DGV = []
    pdf_DGW = []    
    var = 9
    tot = 0    
#                                  # create 'rough' gaussian
    for i in range(-var - 1, var + 2):
        if i ==  -var - 1:
            r_pdf = + gaussian(i, 9) + gaussian(i - 1, 9) + gaussian(i - 2, 9)
        elif i == var + 1:
            r_pdf = + gaussian(i, 9) + gaussian(i + 1, 9) + gaussian(i + 2, 9)
        else:
            r_pdf = gaussian(i, 9)
        tot = tot + r_pdf
        pdf_DGV.append(i)
        pdf_DGW.append(r_pdf)
        print(i, r_pdf)
#                                  # amusing how close tot is to 1!
    print('\nRough total = ', tot)
#                                  # no need to normalize with Python 3.6,
#                                  #   but can't help ourselves
    for i in range(0,len(pdf_DGW)):
        pdf_DGW[i] = pdf_DGW[i]/tot
#                                  # print out pdf weights
#                                  #   for out discrte gaussian
    print('\npdf:\n')
    print(pdf_DGW)

#                                  # plot random variable action
    rv_samples = random.choices(pdf_DGV, pdf_DGW, k=10000)
    plt.hist(rv_samples, bins = 100)
    plt.show()
    sys.exit()

OUTPUT:

-10 0.0007187932912256041
-9 0.001477282803979336
-8 0.003798662007932481
-7 0.008740629697903166
-6 0.017996988837729353
-5 0.03315904626424957
-4 0.05467002489199788
-3 0.0806569081730478
-2 0.10648266850745075
-1 0.12579440923099774
0 0.1329807601338109
1 0.12579440923099774
2 0.10648266850745075
3 0.0806569081730478
4 0.05467002489199788
5 0.03315904626424957
6 0.017996988837729353
7 0.008740629697903166
8 0.003798662007932481
9 0.001477282803979336
10 0.0007187932912256041

Rough total =  0.9999715875468381

pdf:

[0.000718813714486599, 0.0014773247784004072, 0.003798769940305483, 0.008740878047691289, 0.017997500190860556, 0.033159988420867426, 0.05467157824565407, 0.08065919989878699, 0.10648569402724471, 0.12579798346031068, 0.13298453855078374, 0.12579798346031068, 0.10648569402724471, 0.08065919989878699, 0.05467157824565407, 0.033159988420867426, 0.017997500190860556, 0.008740878047691289, 0.003798769940305483, 0.0014773247784004072, 0.000718813714486599]