polymahalanobis
A polynomial Mahalanobis distance classifier for multivariate / multispectral data.
Given a set of reference samples, PolyMahalanobis builds a nested sequence of
polynomial-expanded subspaces (via SVD) and evaluates how far a new sample is
from that reference distribution — useful for anomaly detection, similarity
scoring, and multispectral classification (e.g. vegetation health from
satellite/drone imagery bands).
Only dependency: numpy.
Install
pip install polymahalanobis
Usage
import numpy as np
from polymahalanobis import PolyMahalanobis
# samples.txt: one reference sample per line, band values space-separated
model = PolyMahalanobis("samples.txt", num_levels=3)
model.makeSpace()
new_pixels = np.array([[120.0, 45.0, 200.0]], dtype=np.float32)
distances = model.evaluate(new_pixels) # shape (N, num_levels)
final_distance = distances[:, -1] # accumulated distance, last level
evaluate(spectral_values): batched evaluation,(N, n_bands) -> (N, num_levels).evaluate_single(spectral_value): convenience for a single(n_bands,)sample.evaluate_image(image_multispectral): convenience for a full(H, W, n_bands)array, with de-duplication of repeated pixel values.
Full project
This library is the core algorithm extracted from the PolynomialMahalanobis project, which also includes a parallelized command-line pipeline for classifying multispectral GeoTIFF orthophotos. That pipeline is not part of this PyPI package — see the repository for it.
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