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Merged
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Feb 3, 2017
Merged

Create random_normaldistribution_quicksort.py #65

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66 changes: 66 additions & 0 deletions sorts/random_normaldistribution_quicksort.py
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from random import randint
from tempfile import TemporaryFile
import numpy as np
import math



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)