From a3ab980816b4d5471c8c055f7ee99bfaf8e16d22 Mon Sep 17 00:00:00 2001
From: Prateek Chanda <prateekkol21@gmail.com>
Date: Fri, 3 Feb 2017 22:02:05 +0530
Subject: [PATCH] random_normaldistribution_quicksort

This is for creating an algorithm implementing QuickSort Algorithm where the pivot element is chosen randomly between first and last elements of the array and the array elements are taken from a Standard Normal Distribution.

This is different from the ordinary quicksort in the sense, that it applies more to real life problems , where elements usually follow a normal distribution. Also the pivot is randomized to make it a more generic one.
---
 sorts/random_normaldistribution_quicksort.py | 66 ++++++++++++++++++++
 1 file changed, 66 insertions(+)
 create mode 100644 sorts/random_normaldistribution_quicksort.py

diff --git a/sorts/random_normaldistribution_quicksort.py b/sorts/random_normaldistribution_quicksort.py
new file mode 100644
index 000000000..19b180578
--- /dev/null
+++ b/sorts/random_normaldistribution_quicksort.py
@@ -0,0 +1,66 @@
+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)