HypercubeCNN

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Python bindings for HypercubeCNN — a dependency-free convolutional neural network whose feature map is a Boolean hypercube. Choose a dimension DIM; each channel then lives on exactly N = 2^DIM vertices (for example DIM 6 → 64 sites, DIM 10 → 1024). A local filter at a vertex reaches only that site and its nearest neighbors, and every neighbor index is a single XOR on the binary address — no spatial grid, no adjacency list, no stencil table to store. The activations stay ordinary real-valued units (ReLU, tanh, …); only the topology is binary, so capacity is power-of-two by construction and packing non-cube data is host work.

You stack local layers, train for classification or regression, and save weights with a small architecture sidecar. Optional helpers map images onto length N; data that already lives at 2^D (reservoir or ESN state, fingerprints, product features) can drive the network with no packing step.

Compared with a standard vision CNN, the shared-weight stack and end-to-end training stay familiar — only the domain changes. A usual network slides a window on a rectangle and pads the borders; here every site has the same neighbors under the cube’s symmetry, with no image edge and no stencil table. Pixels are not native: pack them onto the N sites first, then train on those length-N inputs.

HypercubeAI ecosystem
One geometry. Three libraries. Topology-native intelligence.

HypercubeESN  ·  HypercubeCNN  ·  HypercubeHopfield

HypercubeCNN is a pillar of HypercubeAI — a family of tools that treat the Boolean hypercube as a first-class computational medium: dynamical reservoirs (ESN), convolutional learning on the same graph (CNN), and associative memory (Hopfield). Shared vertices, shared XOR neighborhoods, no bolted-on grid.

Installation

pip install hypercube-cnn

Pre-built wheels for Python 3.10–3.13 on Windows (x64), Linux (x86_64, aarch64), and macOS (x86_64, arm64). No compiler required.

From source

git clone https://github.com/dliptak001/HypercubeCNN.git
cd HypercubeCNN
pip install .

Requires Python 3.10+, a C++23 compiler, and CMake ≥ 3.21. On Windows with CLion’s bundled MinGW, put that toolchain’s bin (and Ninja) on PATH, set CMAKE_GENERATOR=Ninja, and point CC/CXX at the MinGW gcc/g++ — exact install paths change with the CLion version. Then:

pip install . --no-build-isolation --force-reinstall --no-deps

Quick start

import numpy as np
import hypercube_cnn as hc

net = hc.HCNNConfig(
    dim=6,
    num_outputs=3,
    layers=[
        hc.LayerSpec.conv(8, bn=True),
        hc.LayerSpec.pool("max"),
        hc.LayerSpec.conv(8),
    ],
    weight_seed=1,
).build()

x = np.random.randn(net.N).astype(np.float32)  # full capacity N = 2**dim
logits = net.predict(x)
cls = net.predict_class(x)
net.train_step(x, target=0, params=hc.TrainParams(learning_rate=1e-3))
net.save("model")  # model.hcnw + model.arch.json

Features

  • Core train/infer — classification (CE) and regression (MSE); NumPy float32
  • Architecture product — LayerSpec / HCNNConfig, export/import arch JSON
  • Model I/O — HCNW weights + arch sidecar (C++ interop); pickle as secondary
  • Spatial pack — SpatialEmbedder / SpatialAugmenter for H×W → length N
  • Train helpers — evaluate_classification / evaluate_regression, cosine_lr
  • Contracts — capacity input_channels * 2**dim; after packing, pass length-N inputs

Documentation

Full API reference: docs/Python_SDK.md

C++ contracts: docs/CPP_SDK.md

In-repo recipes: examples/python/

Project repository: github.com/dliptak001/HypercubeCNN

License

Apache-2.0

Project links

Data verified by PyPI on Aug 5, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Newer release available (1.0.5)

1 maintainer

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Avatar for dliptak001 from gravatar.com dliptak001

GitHub Statistics

Data verified by PyPI on Aug 5, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

Apache-2.0
View SPDX License List

Requires

Python >=3.10

Provides Extra

test

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1.0.5

21 files

1.0.4

21 files

This release

1.0.3 This release

21 files

1.0.2

21 files

1.0.1

21 files

1.0.0

21 files

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