|
| 1 | +from random import randint |
| 2 | +from tempfile import TemporaryFile |
| 3 | +import numpy as np |
| 4 | +import math |
| 5 | + |
| 6 | + |
| 7 | + |
| 8 | +def _inPlaceQuickSort(A,start,end): |
| 9 | + count = 0 |
| 10 | + if start<end: |
| 11 | + pivot=randint(start,end) |
| 12 | + temp=A[end] |
| 13 | + A[end]=A[pivot] |
| 14 | + A[pivot]=temp |
| 15 | + |
| 16 | + p,count= _inPlacePartition(A,start,end) |
| 17 | + count += _inPlaceQuickSort(A,start,p-1) |
| 18 | + count += _inPlaceQuickSort(A,p+1,end) |
| 19 | + return count |
| 20 | + |
| 21 | +def _inPlacePartition(A,start,end): |
| 22 | + |
| 23 | + count = 0 |
| 24 | + pivot= randint(start,end) |
| 25 | + temp=A[end] |
| 26 | + A[end]=A[pivot] |
| 27 | + A[pivot]=temp |
| 28 | + newPivotIndex=start-1 |
| 29 | + for index in range(start,end): |
| 30 | + |
| 31 | + count += 1 |
| 32 | + if A[index]<A[end]:#check if current val is less than pivot value |
| 33 | + newPivotIndex=newPivotIndex+1 |
| 34 | + temp=A[newPivotIndex] |
| 35 | + A[newPivotIndex]=A[index] |
| 36 | + A[index]=temp |
| 37 | + |
| 38 | + temp=A[newPivotIndex+1] |
| 39 | + A[newPivotIndex+1]=A[end] |
| 40 | + A[end]=temp |
| 41 | + return newPivotIndex+1,count |
| 42 | + |
| 43 | +outfile = TemporaryFile() |
| 44 | +p = 100 # 1000 elements are to be sorted |
| 45 | + |
| 46 | + |
| 47 | + |
| 48 | + |
| 49 | +mu, sigma = 0, 1 # mean and standard deviation |
| 50 | +X = np.random.normal(mu, sigma, p) |
| 51 | +np.save(outfile, X) |
| 52 | +print('The array is') |
| 53 | +print(X) |
| 54 | + |
| 55 | + |
| 56 | + |
| 57 | + |
| 58 | + |
| 59 | + |
| 60 | +outfile.seek(0) # using the same array |
| 61 | +M = np.load(outfile) |
| 62 | +r = (len(M)-1) |
| 63 | +z = _inPlaceQuickSort(M,0,r) |
| 64 | + |
| 65 | +print("No of Comparisons for 100 elements selected from a standard normal distribution is :") |
| 66 | +print(z) |
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