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obvaekernel

obvaekernel is a NumPy-first implementation of an overcomplete beta-VAE kernel feature map designed for PCA and kernel-method workflows.

Install

pip install .

Benchmark/data extras:

pip install .[benchmark]

Development/build extras:

pip install .[dev]

Quick Usage

import numpy as np
from obvaekernel import OBVAEKernel

X = np.random.randn(256, 12).astype("float32")
model = OBVAEKernel(latent_dim="auto", epochs=25, random_state=42)
Z = model.fit_transform(X)
K = model.kernel_matrix(X)

Benchmark Scripts

Run the root benchmark harness:

python main.py --mode quick --no-plots

Run the examples copy:

python examples/benchmark_main.py --mode full --include-weather

Build and Publish Checks

Build wheel + sdist:

python -m build

Validate distribution metadata:

python -m twine check dist/*

About

OBVAEK is a method for mapping data into a higher dimensional latent space to assess its comprising components

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