random_normal_distribution_quicksort.py

from __future__ import print_function
from random import randint
from tempfile import TemporaryFile
import numpy as np



def _inPlaceQuickSort(A,start,end):  
    count = 0
    if start<end:
        pivot=randint(start,end)
        temp=A[end]
        A[end]=A[pivot]
        A[pivot]=temp
        
        p,count= _inPlacePartition(A,start,end)
        count += _inPlaceQuickSort(A,start,p-1)
        count += _inPlaceQuickSort(A,p+1,end)
    return count

def _inPlacePartition(A,start,end):
    
    count = 0
    pivot= randint(start,end)
    temp=A[end]
    A[end]=A[pivot]
    A[pivot]=temp
    newPivotIndex=start-1
    for index in range(start,end):
               
        count += 1
        if A[index]<A[end]:#check if current val is less than pivot value
            newPivotIndex=newPivotIndex+1
            temp=A[newPivotIndex]
            A[newPivotIndex]=A[index]
            A[index]=temp
    
    temp=A[newPivotIndex+1]
    A[newPivotIndex+1]=A[end]
    A[end]=temp
    return newPivotIndex+1,count
    
outfile = TemporaryFile()    
p = 100 # 1000 elements are to be sorted




mu, sigma = 0, 1 # mean and standard deviation
X = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)

  
    



outfile.seek(0)  # using the same array
M = np.load(outfile)
r = (len(M)-1)
z = _inPlaceQuickSort(M,0,r) 

print("No of Comparisons for 100 elements selected from a standard normal distribution is :")
print(z)