Optimal Partition Search

Overview and Functionality

Official Implementation of the paper - "Optimal Partition Search" (IEEE Xplore) (ResearchGate)
Searches for the optimal number of partitions required to speed up the search process
Works for arrays having any data type (int, float, char, long, etc.)
Independent of the order of the elements in the array, i.e. can work for both sorted and unsorted array settings

Usage

Make sure you have Python version 3.9 or greater installed on your system
Run the following command on the terminal to install this package:

 pip install Optimal-Partition-Search

More details on how to install this package and other relevant information can be found here

Example

# test.py

from Optimal_Partition_Search import optimal_partition_search
import random
import numpy as np

# Example for array having integer values
array = random.sample(range(150), 100)
print(f'array: {array}')
element = int(input("Enter the item you want to search\n"))
optimal_partition = optimal_partition_search(array, element)
print("Optimal no. of partitions", optimal_partition)

# Example for array having float values
array = np.random.uniform(low=600.5, high=705.2, size=(10,))
print(f'array: {array}')
element = float(np.random.choice(array, 1))
optimal_partition = optimal_partition_search(array, element)
print("Optimal no. of partitions", optimal_partition)

# Example for array having character and string values
array = ['a', 'c', 'q', 'l', 'h', 's', 'tr', 'input']
print(f'array: {array}')
element = input("Enter the item you want to search\n")
optimal_partition = optimal_partition_search(array, element)
print("Optimal no. of partitions", optimal_partition)

Use the following command to run the examples given in the test.py file above: