From ea36ef407bc5ed3f8e931b9e2e2f552ec81e736c Mon Sep 17 00:00:00 2001
From: Anup Kumar Panwar <1anuppanwar@gmail.com>
Date: Sat, 4 Feb 2017 18:15:41 +0530
Subject: [PATCH] Revert "Update Normal Distribution QuickSort Readme"

---
 README.md                                     |  6 --
 sorts/normal_distribution_QuickSort_README.md | 80 -------------------
 2 files changed, 86 deletions(-)
 delete mode 100644 sorts/normal_distribution_QuickSort_README.md

diff --git a/README.md b/README.md
index fd3de1465..1d8d3386e 100644
--- a/README.md
+++ b/README.md
@@ -59,12 +59,6 @@ __Properties__
 
 ###### View the algorithm in [action][quick-toptal]
 
-
-
-
-![Normal Distribution QuickSort](https://github.com/prateekiiest/Python/blob/master/sorts/normal_distribution_QuickSort_README.md)
-
-
 ### Selection
 ![alt text][selection-image]
 
diff --git a/sorts/normal_distribution_QuickSort_README.md b/sorts/normal_distribution_QuickSort_README.md
deleted file mode 100644
index fc6d55ed5..000000000
--- a/sorts/normal_distribution_QuickSort_README.md
+++ /dev/null
@@ -1,80 +0,0 @@
-#Normal Distribution QuickSort
-
-
-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.
-
-
-##Array Elements
-
-The array elements are taken from a Standard Normal Distribution , having mean = 0 and standard deviation 1.
-
-####The code
-
-```python
-
->>> import numpy as np 
->>> from tempfile import TemporaryFile
->>> outfile = TemporaryFile()    
->>> p = 100 # 100 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)
-
-```
-
-------
-
-#### The Distribution of the Array elements.
-
-```python
->>> mu, sigma = 0, 1 # mean and standard deviation
->>> s = np.random.normal(mu, sigma, p)
->>> count, bins, ignored = plt.hist(s, 30, normed=True)
->>> plt.plot(bins , 1/(sigma * np.sqrt(2 * np.pi)) *np.exp( - (bins - mu)**2 / (2 * sigma**2) ),linewidth=2, color='r')
->>> plt.show()   
-
-```
-
-
-![Array_Element_Distribution](https://github.com/prateekiiest/Algorithms/blob/master/normaldistributionforarrayelements.png)
-
-
-
-
----
-
----------------------
-
---
-
-##Plotting the function for Checking 'The Number of Comparisons' taking place between Normal Distribution QuickSort and Ordinary QuickSort 
-
-```python
->>>import matplotlib.pyplot as plt
-
-    
-    # Normal Disrtibution QuickSort is red
->>> plt.plot([1,2,4,16,32,64,128,256,512,1024,2048],[1,1,6,15,43,136,340,800,2156,6821,16325],linewidth=2, color='r')
-    
-    #Ordinary QuickSort is green
->>> plt.plot([1,2,4,16,32,64,128,256,512,1024,2048],[1,1,4,16,67,122,362,949,2131,5086,12866],linewidth=2, color='g')
-
->>> plt.show()
-
-```
-
-
-----
-
-###The Plot
-
-* X axis denotes the number of elements to be sorted. 
-* Y axis denotes the number of comparisons taking place
-
-![Plot](https://github.com/prateekiiest/Algorithms/blob/master/normaldist.png)
-
-
-------------------