A function used to find the number of partitions required to speed up the array searching process
Project description
Optimal Partition Search
Overview and Functionality
- Official Implementation of the paper - "Optimal Partition Search"
- 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
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:
python test.py
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
No source distribution files available for this release.See tutorial on generating distribution archives.
Built Distribution
Close
Hashes for Optimal_Partition_Search-0.0.1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 35bbe380ba8c1ee3efe53345858598a69d35d380879977e4501467a557e163f3 |
|
MD5 | 40b35a7cdcd2a7324917f389deda0259 |
|
BLAKE2b-256 | 815dc39b995a414f3610b515a5857df518f95f6a3400c318db15492ce8200767 |