1. Python
  2. Contingency table in Python

Contingency table in Python

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Here we use the Bonnet recurrence relation of legendary polynomials, i.e. —

   

This can be implemented using Python, proceeding as follows:

We define Legendre polynomials as a function named P (n, x), where n is called the order of the polynomial and x is the evaluation point. Basic cases: if n is 0, then the value of the polynomial is always 1, and this is x when the order is 1. These are the initial values ​​needed for the repetition relation. 
For other values ​​of n, the function is defined recursively, directly from the Bonnet recursion. Thus, P (n, x) recursively returns the Legendre polynomial values ​​(a function effectively defined with other base cases of the same function.)

Below is a Python implementation —

# Legendre polynomial

def P ( n, x): 

if (n = = 0 ):

  return 1 # P0 = 1

elif (n = = 1 ):

return x # P1 = x

  else :

  return ((( 2 * n) - 1 ) * x * P (n - 1 , x ) - (n - 1 ) * P (n < / code> - 2 , x)) / float (n)

  
# Suppose we want to find a value
# 3rd order: legend polynomial for x = 5
# We can display the value as:

 
# Driver program

n = 3

X = 5

print ( " The value of the polynomial at given point is: " , P (n, X))

Output: 

 The value of the polynomial at given point is: 305.0

Now we can also plot Legendre polynomials (say 1st to 4th order) using matplotlib.

import matplotlib

 
# This is for use in a web browser, can be ignored.

matplotlib.use ( `Agg` ) 

  

import matplotlib.pyplot as plt

import numpy as np

 
# Create array of x values ​​

x = np.linspace ( - 1 , 1 , 200 ) 

 
# for which polynomials are evaluated and plotted

for i in range ( 1 , 5 ):

 

# Marking as ordered

plt.plot (x, P (i, x), label = “P” + str (i )) 

 

plt.legend (loc = "best" )

plt.xlabel ( "X" )

plt.ylabel ( "Pn" )

plt.show ()

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