K-Nearest Neighbors Python Library
Project description
py4knn
K-Nearest Neighbors Python Library
Getting Started
This project is simply implementation of K-Nearest Neighbors algorithm in python programming language.
Prerequisites
This Project Has No Prerequisites
Installing
The easiest way to install py4knn is using pip
pip install py4knn
Usage
There is only 1 public method of knn class. It is predict method, it takes 5 argument namely x_train, t_train, x_test, k, and distance calculation method. We provide 6 distance namely euclidean, squared_euclidean, manhattan, canberra, chebyshev, and bray_curtis.
from py4knn.k_nearest_neighbors import knn
classifier = knn()
x_train = [[0.23,0.54],[0.45,1.23],[1.54,0.23],[0.93,0.535]]
t_train = [0,1,0,1]
x_test = [[0.34,0.65],[0.90,0.50]]
y = classifier.predict(x_train,t_train,x_test,1,'eclidean')
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
py4knn-0.0.4.tar.gz
(2.2 kB
view details)
Built Distribution
File details
Details for the file py4knn-0.0.4.tar.gz
.
File metadata
- Download URL: py4knn-0.0.4.tar.gz
- Upload date:
- Size: 2.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.1
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | a3a390e441cb1fe7fb0e2877f71c0427fc59dccfb6506f4f689a88eb7b9e5aa3 |
|
MD5 | 40e55b5fc484b50af107c9d44c85b0fe |
|
BLAKE2b-256 | b8bbbe5d09ddab116a51167767c28a439904a0b2344915eb006848aa53248c6b |
File details
Details for the file py4knn-0.0.4-py3-none-any.whl
.
File metadata
- Download URL: py4knn-0.0.4-py3-none-any.whl
- Upload date:
- Size: 2.8 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.1
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 758ee9d8e8e777e596a066f47622f50fa90983a309308d7ecf2f2b14d626af27 |
|
MD5 | 61e6c180fd129d4cefb2d18b6372ce13 |
|
BLAKE2b-256 | 981e9d826c24a9e1c81f52162623fb22836d61f5d303715cee4dcbf08176cf13 |