- 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
- 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
# 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:
python test.py