Matplotlib- implement common probability distributions

The... Used in this experiment 4 Species of common distribution , Representative of continuous distribution ：beta Distribution 、 Normal distribution , Uniform distribution , Representative of discrete distribution ： The binomial distribution

1、 The import module

import numpy as np
from scipy.stats import beta, norm, uniform, binom
import matplotlib.pyplot as plt
from functools import wraps

2、 Define a drawing decorator with four parameters

The drawing decorator has four parameters to represent legend Of 2 Class description text ,y Axis label, The saved png File name .

#  Define a drawing decorator with four parameters 
def my_plot(label0=None,
            label1=None,
            ylabel='probability density function',
            fn=None):
    def decorate(f):
        @wraps(f)
        def myplot():
            fig = plt.figure(figsize=(16, 9))
            ax = fig.add_subplot(111)
            x, y, y1 = f()
            ax.plot(x, y, linewidth=2, c='r', label=label0)
            ax.plot(x, y1, linewidth=2, c='b', label=label1)
            ax.legend()
            plt.ylabel(ylabel)
            plt.show()
            plt.savefig('img/%s' % (fn, ))
            plt.close()

        return myplot

    return decorate

3、 Uniform distribution (uniform)

As you can see from the diagram , The red probability density function is only in 0~1 Will happen , Curve and x The shaft 0~1 The area enclosed by the section is Total probability 1.0.

@my_plot(label0='b-a=1.0', label1='b-a=2.0', fn='uniform.png')
def unif():
    x = np.arange(-0.01, 2.01, 0.01)
    y = uniform.pdf(x, loc=0.0, scale=1.0)
    y1 = uniform.pdf(x, loc=0.0, scale=2.0)
    return x, y, y1

 

4、 The binomial distribution

The red curve indicates that the probability of occurrence is 0.3, repeat 50 Density function of degree , The binomial distribution The expected value is 0.3*50 = 15 Time . See this 50 Experiments , The likely number of occurrences is 10~20. It can be compared with the blue curve .

@my_plot(
    label0='n=50,p=0.3',
    label1='n=50,p=0.7',
    fn='binom.png',
    ylabel='probability mass function')
def bino():
    x = np.arange(50)
    n, p, p1 = 50, 0.3, 0.7
    y = binom.pmf(x, n=n, p=p)
    y1 = binom.pmf(x, n=n, p=p1)
    return x, y, y1

 

5、 Gaussian distribution

The red curve indicates that the mean value is 0, The standard deviation is 1.0 The probability density function of , The standard deviation of the blue curve is larger , So it's shorter and fatter , It shows the diversity of values , And instability .

@my_plot(label0='u=0.,sigma=1.0', label1='u=0.,sigma=2.0', fn='guass.png')
def guass():
    x = np.arange(-5, 5, 0.1)
    y = norm.pdf(x, loc=0.0, scale=1.0)
    y1 = norm.pdf(x, loc=0., scale=2.0)
    return x, y, y1

 

6、beta Distribution

@my_plot(label0='a=10., b=30.', label1='a=4., b=4.', fn='beta.png')
def bet():
    x = np.arange(-0.1, 1, 0.001)
    y = beta.pdf(x, a=10., b=30.)
    y1 = beta.pdf(x, a=4., b=4.)
    return x, y, y1

 

7、 Plot all four distributions

distrs = [unif, bino, guass, bet]
for distri in distrs:
    distri()

Part of the code comes from official account ：Python And algorithmic community