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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.
67 lines
1.4 KiB
Python
67 lines
1.4 KiB
Python
from random import randint
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from tempfile import TemporaryFile
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import numpy as np
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import math
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def _inPlaceQuickSort(A,start,end):
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count = 0
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if start<end:
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pivot=randint(start,end)
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temp=A[end]
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A[end]=A[pivot]
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A[pivot]=temp
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p,count= _inPlacePartition(A,start,end)
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count += _inPlaceQuickSort(A,start,p-1)
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count += _inPlaceQuickSort(A,p+1,end)
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return count
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def _inPlacePartition(A,start,end):
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count = 0
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pivot= randint(start,end)
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temp=A[end]
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A[end]=A[pivot]
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A[pivot]=temp
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newPivotIndex=start-1
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for index in range(start,end):
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count += 1
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if A[index]<A[end]:#check if current val is less than pivot value
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newPivotIndex=newPivotIndex+1
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temp=A[newPivotIndex]
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A[newPivotIndex]=A[index]
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A[index]=temp
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temp=A[newPivotIndex+1]
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A[newPivotIndex+1]=A[end]
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A[end]=temp
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return newPivotIndex+1,count
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outfile = TemporaryFile()
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p = 100 # 1000 elements are to be sorted
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mu, sigma = 0, 1 # mean and standard deviation
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X = np.random.normal(mu, sigma, p)
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np.save(outfile, X)
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print('The array is')
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print(X)
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outfile.seek(0) # using the same array
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M = np.load(outfile)
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r = (len(M)-1)
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z = _inPlaceQuickSort(M,0,r)
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print("No of Comparisons for 100 elements selected from a standard normal distribution is :")
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print(z)
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