closest_pairs finds the closest pairs of points in a dataset
Project description
Closest Pairs :triangular_ruler:
Find the closest pairs in an array.
Getting Started
pip install closest_pairs
or install from source:
git clone https://github.com/justinshenk/closest-pairs
cd closest_pairs
pip install .
How to use
import closest_pairs
# X is an n x m numpy array
pairs, distances = closest_pairs.solve(X, n=1)
You can specify how many pairs you want to identify with n
.
Example
import closest_pairs
import numpy as np
import matplotlib.pyplot as plt
# Create dataset
X = np.random.random((100,2))
pairs, distance = closest_pairs.solve(X, n=1)
# Plot points
z, y = np.split(X, 2, axis=1)
fig, ax = plt.subplots()
ax.scatter(z, y)
for i, txt in enumerate(X):
if i in pairs:
ax.annotate(i, (z[i], y[i]), color='red')
else:
ax.annotate(i, (z[i], y[i]))
Check pairs:
In [10]: pairs
Out[10]:
array([[[ 7],
[16]],
[[96],
[50]]])
Output:
Caveats
closest_pairs
will reduce the dimensionality with PCA of your data to two-dimensions for faster processing.
It also removes the first point in a pair if n
>1. In rare cases this leads to false negatives if the data is highly overlapping.
Credit and Explanation
Python code modified from Andriy Lazorenko, packaged and made useful for >2 features by Justin Shenk.
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
closest_pairs-0.1.5.tar.gz
(4.0 kB
view hashes)
Built Distribution
Close
Hashes for closest_pairs-0.1.5-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | cc5c2900761931f962c459655e72e1415d3da76c44c8dc399625799aca33059c |
|
MD5 | a2ba88ee3dd3df98493f11be53efdbf5 |
|
BLAKE2b-256 | c2c29b75f8b78dae9ebd9e8e697c28b4de0097f70640c6ffb3ce03cace188c28 |