Python Data Structure and Algorithm Tutorial – Shell Sort Algorithm

Hits: 84

Shell Sort Algorithm

 

In this tutorial, you will learn how shell sort works. Also, you will find working examples of shell sort in Python.

Shell sort is an algorithm that first sorts the elements far apart from each other and successively reduces the interval between the elements to be sorted. It is a generalized version of insertion sort.

In shell sort, elements at a specific interval are sorted. The interval between the elements is gradually decreased based on the sequence used. The performance of the shell sort depends on the type of sequence used for a given input array.

Some of the optimal sequences used are:

  • Shell’s original sequence: N/2 , N/4 , …, 1
  • Knuth’s increments: 1, 4, 13, …, (3k – 1) / 2
  • Sedgewick’s increments: 1, 8, 23, 77, 281, 1073, 4193, 16577...4j+1+ 3·2j+ 1
  • Hibbard’s increments: 1, 3, 7, 15, 31, 63, 127, 255, 511…
  • Papernov & Stasevich increment: 1, 3, 5, 9, 17, 33, 65,...
  • Pratt: 1, 2, 3, 4, 6, 9, 8, 12, 18, 27, 16, 24, 36, 54, 81....

 


How Shell Sort Works?

  1. Suppose, we need to sort the following array.
    Shell sort step
    Initial array
  2. We are using the shell’s original sequence (N/2, N/4, ...1) as intervals in our algorithm.In the first loop, if the array size is N = 8 then, the elements lying at the interval of N/2 = 4 are compared and swapped if they are not in order.
    1. The 0th element is compared with the 4th element.
    2. If the 0th element is greater than the 4th one then, the 4th element is first stored in temp variable and the 0th element (ie. greater element) is stored in the 4th position and the element stored in temp is stored in the 0th position.
      Shell Sort step
      Rearrange the elements at n/2 interval

      This process goes on for all the remaining elements.

      Shell Sort steps
      Rearrange all the elements at n/2 interval
  3. In the second loop, an interval of N/4 = 8/4 = 2 is taken and again the elements lying at these intervals are sorted.
    Shell Sort step
    Rearrange the elements at n/4 interval

    You might get confused at this point.

    Shell Sort step
    All the elements in the array lying at the current interval are compared.

    The elements at 4th and 2nd position are compared. The elements at 2nd and 0th position are also compared. All the elements in the array lying at the current interval are compared.

  4. The same process goes on for remaining elements.
    Shell Sort step
    Rearrange all the elements at n/4 interval
  5. Finally, when the interval is N/8 = 8/8 =1 then the array elements lying at the interval of 1 are sorted. The array is now completely sorted.
    Shell Sort step
    Rearrange the elements at n/8 interval

Shell Sort Algorithm

shellSort(array, size)
  for interval i <- size/2n down to 1
    for each interval "i" in array
        sort all the elements at interval "i"
end shellSort

Python Examples

/* Shell sort in python */

def shellSort(array, n):

    /* Rearrange elements at each n/2, n/4, n/8, ... intervals */
    interval = n // 2
    while interval > 0:
        for i in range(interval, n):
            temp = array[i]
            j = i
            while j >= interval and array[j - interval] > temp:
                array[j] = array[j - interval]
                j -= interval

            array[j] = temp
        interval //= 2


data = [9, 8, 3, 7, 5, 6, 4, 1]
size = len(data)
shellSort(data, size)
print('Sorted Array in Ascending Order:')
print(data)

Complexity

Shell sort is an unstable sorting algorithm because this algorithm does not examine the elements lying in between the intervals.

Time Complexity

  • Worst Case Complexity: less than or equal to O(n2)
    Worst case complexity for shell sort is always less than or equal to O(n2).According to Poonen Theorem, worst case complexity for shell sort is Θ(Nlog N)2/(log log N)2) or Θ(Nlog N)2/log log N) or Θ(N(log N)2) or something in between.
  • Best Case ComplexityO(n*log n)
    When the array is already sorted, the total number of comparisons for each interval (or increment) is equal to the size of the array.
  • Average Case ComplexityO(n*log n)
    It is around O(n1.25).

The complexity depends on the interval chosen. The above complexities differ for different increment sequences chosen. Best increment sequence is unknown.

Space Complexity:

The space complexity for shell sort is O(1).


Shell Sort Applications

Shell sort is used when:

  • calling a stack is overhead. uClibc library uses this sort.
  • recursion exceeds a limit. bzip2 compressor uses it.
  • Insertion sort does not perform well when the close elements are far apart. Shell sort helps in reducing the distance between the close elements. Thus, there will be less number of swappings to be performed.

 

 

Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

  • Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes

Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!

Latest end-to-end Learn by Coding Recipes in Project-Based Learning:

Applied Statistics with R for Beginners and Business Professionals

Data Science and Machine Learning Projects in Python: Tabular Data Analytics

Data Science and Machine Learning Projects in R: Tabular Data Analytics

Python Machine Learning & Data Science Recipes: Learn by Coding

R Machine Learning & Data Science Recipes: Learn by Coding

Comparing Different Machine Learning Algorithms in Python for Classification (FREE)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.  

Google –> SETScholars