swarmauri_distance_minkowski 0.10.0.dev5

Minkowski Distance for Swarmauri.

---

Swarmauri Distance Minkowski

A Python package implementing the Minkowski distance metric for vector comparison within the Swarmauri ecosystem. The metric generalizes common distances such as Euclidean (p = 2) and Manhattan (p = 1).

The distribution issues a DeprecationWarning announcing removal in v0.10.0. Consume the distance through Swarmauri's plugin interfaces or switch to an alternative implementation before that release.

Features

• Computes Minkowski distance between vectors using scipy.spatial.distance.
• Enforces matching vector dimensionality and raises ValueError when shapes differ.
• Offers a tunable p value along with batch helpers (distances, similarities).
• Derives a similarity score from distance as 1 / (1 + distance).

Installation

Install the package with your preferred Python packaging tool:

pip install swarmauri_distance_minkowski

poetry add swarmauri_distance_minkowski

uv pip install swarmauri_distance_minkowski

Usage

from swarmauri_distance_minkowski import MinkowskiDistance
from swarmauri_standard.vectors.Vector import Vector

# Create vectors for comparison
vector_a = Vector(value=[1, 2])
vector_b = Vector(value=[1, 2])

# Initialize Minkowski distance calculator (default p=2 for Euclidean distance)
distance_calculator = MinkowskiDistance()

# Calculate distance between vectors
distance = distance_calculator.distance(vector_a, vector_b)
print(f"Distance: {distance}")

# Calculate similarity between vectors
similarity = distance_calculator.similarity(vector_a, vector_b)
print(f"Similarity: {similarity}")

Running the example prints:

Distance: 0.0
Similarity: 1.0

Customize the p value to select different Minkowski norms, or supply a sequence of vectors to distances / similarities for batch comparisons.

Want to help?

If you want to contribute to swarmauri-sdk, read up on our guidelines for contributing that will help you get started.