fmpdistribution 1.0.2

fmpdistribution - Package for computing probabilities for discrete and continuous random variables.

fmpdistribution package provides different functions to calculate probability and common statistics for Poisson, Binomial, Normal, Multinomial, and Exponential distributions. The package contains five classes, one for each probability distribution. Class Poisson

Method Summary

Class Binomial

Method Summary

Class Normal

Method Summary

Class Exponential

Method Summary

Class Multinomial

Method Summary

Dependencies:

• No external package is required

Installation:

In oder to compute probabilities, we must install fmpdistribution . Use the package installer (PIP) or package management system (conda) to install fmpdistribution.

 pip install fmpdistribution
 python -m pip install fmpdistribution
 conda install fmpdistribution 

How to use:

import the probability distribution calss from fmpdistribution
call the required function
provide input 
execute the code

####Example-1: A person receives on average 3 emails per hour. What the probability that he will receive (a) 4 emails in the next hour (b) Less than or equal to 4 (c) Greater than 4

Solution:

    from fmpdistribution.Poisson import Poisson
    pp = Poisson()
    mu = 3
    print(pp.pdf(4,mu))    # P(X=4)
    0.168031
     print(pp.cdf(4,mu))   # P(X<=4)
    0.815263
    print(pp.sf(4,mu))      # P(X>4)
    or print(1-pp.cdf(4,mu))
    0.184736
    #To get common statistics:
    print(pp.stats(mu))
    {'mean': 3, 'median': 3.326667, 'mode': 3, 'variance': 3, 'skewnes': 0.577350, 'kurtosis': 0.333333} 

####Example-2: In a computer science class 40% students belong to Asia, 50% to Europe and 10% to USA. If we select a random sample of 10 students, what is the probability that 3 candidates from Asia, 5 from Europe and 2 from USA?

Solution:

 from fmpdistribution.Multinomial import Multinomial
 import numpy as np`
 mn = Multinomial()
 n = 10
 x = [3,5,2]
 p = [0.40,0.50,0.10]
 print(mn.pdf(n,x,p)) # probability density function
 print(np.array(mn.cov(n,p)))  # covariance
	
0.050400
[[ 2.4 -2.  -0.4]
 [-2.   2.5 -0.5]
 [-0.4 -0.5  0.9]]

Version History

1.0.0 (Initial release)

License

This project is licensed under the MIT License - see the LICENSE.txt file for details