HypercubeCascade

HypercubeCascade is for high-dimensional data that has no natural clock — spectra, sensor frames, packed images, stills. Those are the same kinds of static fields people usually feed a spatial CNN, an MLP, or a similar feed-forward stack. HypercubeCascade puts two frozen hypercube preprocessors in series in front of the CNN: first an etalon transit (the HypercubeEtalon mechanism — a deterministic wave swept across every vertex/antipode cavity of the cube), then a short reservoir orbit (the HypercubeWTF mechanism — a frozen recurrent core driven by re-addressing the same field for T synthetic passes). A small HypercubeCNN head trains on the end state only. The CNN never sees the original field — it sees what the transit and the orbit leave behind.

That is the product idea: take a static field, pass it through two different frozen nonlinearities, and train a spatial readout on what remains. The aim is a preprocessor effective enough that the readout can be a single convolutional layer with a single channel and no pooling.

This package is the Python surface for that product (import hypercube_cascade). Full API reference: docs/Python_SDK.md. C++ integration guide: docs/CPP_SDK.md. Project home: github.com/dliptak001/HypercubeCascade.

HypercubeAI ecosystem

HypercubeESN · HypercubeCNN · HypercubeHopfield · HypercubeWTF · HypercubeEtalon · HypercubeCascade

HypercubeCascade is an experiment in the HypercubeAI project — our quest to systematically re-implement classical neural architectures on a Boolean hypercube topology instead of Euclidean grids or random graphs. The central thesis is “topology-native intelligence”: the hypercube’s algebraic structure (vertex-transitive symmetry, Hamming geometry, bitwise addressing) can serve as a first-class computational substrate.

A topology you don’t store — the graph is specified: connectivity is implicit in the vertex indices; with a seed and a few config scalars the whole preprocessor reconstructs mathematically.
Perfect homogeneity — every vertex has the same degree and the same local world, so local dynamics mean the same thing everywhere — no structural favorites baked in by a random graph.
Cheap navigation — each neighbor is a few bit operations on the vertex index, not a pointer chase through a stored edge list, so walks stay arithmetic and cache-friendly.
Topology-native pairing — the readout consumes the preprocessor output with zero geometric distortion, and the learned kernels exploit the same locality that generated the dynamics. The data never leaves the hypercube it was born on.

Each product in the family is a different architecture on that same foundation.

What is HypercubeCascade?

HypercubeEtalon preprocesses a static field with one etalon transit. HypercubeWTF preprocesses a static field with one reservoir orbit. HypercubeCascade is both of them, in series, on one cube: the transit output, times a gain, becomes the orbit drive, and the orbit's end state, times a second gain, is what the CNN head trains on.

In classical reservoir computing (and in both single-stage siblings):

Preprocessor weights are frozen
Only a readout is trained
Nonlinear dynamics expand and mix the drive into a rich state

Whether the two-stage pipeline has real product value is still an open question. Early studies suggest the second stage adds filtering on top of what the first stage already adds (see Early observations).

Pipeline

x  (your length-N field — already on the cube, no natural time)
    │
    ▼
 frozen etalon transit (one wave over every cavity)
    │
    ▼
 × interstage_scale → frozen reservoir orbit (T re-addressed passes)
    │
    ▼
 end-of-orbit state × readout_scale → HypercubeCNN → logits / values

Cube size from dim (N = 2^dim; dim 5…12). One dim serves all three stages.
Only the readout trains.
Everyday loop in this package: collect_batch → train → predict / predict_class, or one-shot fit (collect + train).

Unlike HypercubeESN’s Python API, there is no stream of small samples over real time and no next-step fit on a 1D signal. Each sample is one full field; the “time” is the short synthetic orbit; the CNN only ever sees the state at the end.

Full method list and knobs: docs/Python_SDK.md.

Early observations (exploratory)

On the MNIST white-noise study (train clean, test with Gaussian field noise), the cascade behaves as a near-unity passthrough on clean fields and pulls ahead of both the etalon-only path and the pack-only bypass from σ = 0.3 upward. On a Raman baseline-extraction regression it matches the etalon-only sibling to within ~1% RMSE while training with a visibly more stable epoch profile. The write-ups have the details and how we ran them:

Document	Question
WhiteNoiseFilter.md	Noisy test fields: do two stages help vs one stage vs pack-only → CNN?
RamanBaselineExtraction/README.md	Baseline regression: cascade vs etalon-only, overlays and training profiles

The MNIST study uses small cubes because they are handy to pack and run, not because we are chasing digit accuracy. A more rigorous study is still needed before treating any of those results as settled. You can reproduce the same ideas from Python with this package (pack fields yourself, then collect, train, and predict). The original write-ups and C++ demos that produced the numbers live under examples/.

Installation

Preferred: install a pre-built wheel from PyPI (no compiler).

pip install hypercube-cascade

import hypercube_cascade as hc
print(hc.__version__)

Package name on PyPI: hypercube-cascade. Import name: hypercube_cascade. Main type: hc.Cascade.

Wheels target Python 3.10–3.14 on common Windows, Linux, and macOS machines. Runtime dependency: NumPy only.

From source (full repository)

To compile the extension yourself, clone this entire repository (not a minimal source-only download of the python/ folder alone — the C++ core and vendored HypercubeCNN live next to python/). You need Python 3.10+, a C++23 compiler, and CMake ≥ 3.20.

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

On Windows with CLion’s MinGW, put that compiler’s bin folder (and Ninja) on your PATH, then:

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

(Exact CLion paths change with the version.) Step-by-step toolchain notes: docs/Python_SDK.md.

Quick start

You bring each sample as a length-N float array (N = 2^dim). How you get there — pad an image, reshape a spectrum, invent a layout — is up to you. This package does not pack 784 pixels or 300 bins for you.

Shapes that matter:

Array	Shape	Notes
`fields`	`(count, N)`	one length-N field per row
`labels` (classification)	`(count,)`	integer class indices
`targets` (regression)	`(count, num_outputs)`	float targets

import numpy as np
import hypercube_cascade as hc

dim = 7
N = 2**dim
rng = np.random.default_rng(0)
fields = rng.standard_normal((200, N), dtype=np.float32)
labels = rng.integers(0, 4, size=200)

cas = hc.Cascade(
    dim=dim,
    exciter_subcube_dim=5,
    history_depth=4,
    T=50,
    ic_seed=2,
    readout_num_outputs=4,
    readout_task="classification",
    readout_epochs=80,
)
cas.fit(fields, labels)  # collect_batch + train

print(cas.N, cas.T, cas.num_collected)
print(f"train sanity check: {cas.accuracy_on_collected():.3f}")
print(cas.predict_class(fields[0]), cas.predict(fields[0]).shape)

cas.save("model.pkl")
loaded = hc.Cascade.load("model.pkl")

Step by step (same loop, more control)

cas = hc.Cascade(
    dim=7,
    exciter_subcube_dim=5,
    readout_num_outputs=4,
    readout_task="classification",
)
cas.collect_batch(fields_train, labels_train)
cas.train()
logits = cas.predict(fields_test[0])       # (num_outputs,) float32
cls = cas.predict_class(fields_test[0])    # int
test_acc = cas.accuracy(fields_test, labels_test)  # held-out, fresh maps

For regression, set readout_task="regression" and pass float targets instead of class labels. Then use r2_on_collected() / r2(fields, targets) the same way.

accuracy_on_collected and r2_on_collected only look at the samples you already trained on — they are a quick sanity check, not a test score. For real evaluation, hold fields out and call accuracy / r2 (or predict / predict_class yourself).

Features

One class — hypercube_cascade.Cascade is the whole product surface
Map loop — collect / collect_batch → train → predict / predict_class
fit — clear, collect, and train when your arrays are ready
dim 5–12 — field length N = 2^dim; one dim for all three stages; orbit length T; etalon face exciter_subcube_dim
Two gains — interstage_scale (transit → orbit) and readout_scale (orbit → readout)
Classification or regression — readout_task fixed at construction
Held-out scoring — accuracy(fields, labels) / r2(fields, targets) map fresh in bulk
Bulk calls can parallelize — collect_threads (0 = auto)
Inspect a map — run(x) then last_features(), plus per-stage probes last_exciter() / last_interstage() / last_reservoir() for gain tuning
Save / load — save / load (pickle: config + readout weights; collected samples are not stored). Optional save_readout_hcnn_model / load_readout_hcnn_model for portable HCNW + arch JSON
NumPy float32 — arrays converted for you; prefer contiguous float32

Examples

For a first try, paste the Quick start after pip install hypercube-cascade. That is self-contained.

If you want a longer walk-through, the demo scripts on GitHub under python/examples/ are there to open or download — they are not added to your machine by pip.

Script	What it is for
synthetic_classification.py	Multi-class toy fields: `fit`, then train and test accuracy

# from a clone of HypercubeCascade, after: pip install hypercube-cascade
python python/examples/synthetic_classification.py

These use easy made-up fields so the API is obvious — not scores to publish. More notes: python/examples/README.md.

Documentation

Doc	Role
docs/Python_SDK.md	Canonical Python API — every method, layout, pickle, limits
python/examples/README.md	Demo scripts on GitHub
Project README	Product story and C++ demos from the repo root
docs/CPP_SDK.md	Native library guide (same product, C++)
docs/CascadeWhitePaper.md	The two-stage concept, mechanism by mechanism
WhiteNoiseFilter.md	Early white-noise study (MNIST as a test bed)

Ecosystem

HypercubeEtalon — the etalon transit alone; Cascade’s first stage.
HypercubeWTF — the reservoir orbit alone; Cascade’s second stage.
HypercubeCNN — cube-native conv stack; Cascade’s trainable head.
HypercubeESN — echo-state / reservoir computing on streams.
HypercubeHopfield — Hopfield-style dynamics on the cube.

License

Apache 2.0. See LICENSE.

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hypercube-cascade 1.0.0

HypercubeCascade

What is HypercubeCascade?

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Early observations (exploratory)

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Step by step (same loop, more control)

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Algorithm	Hash digest
SHA256	`12d9ab33b395f797beec2dd087b97b4f64c7104ee0ee23154c55b9cd9f7ea61e`
MD5	`d4b7cf53b6f3294eba3433c257b6350c`
BLAKE2b-256	`ae643b2c05355df4afa5f475e98beb5fe4711dd9f0e16875d925f6ea3254f5d1`

Algorithm	Hash digest
SHA256	`da8093daa207308c8944de449f2496677d7126987a1284e064638a1432343b06`
MD5	`841c9456196b290bf554841a0b4e183d`
BLAKE2b-256	`8fea6ea06f8995ff854a82a968bfcc7ae35a4612f6c48a49215c472f9787707a`

Algorithm	Hash digest
SHA256	`c18e896f8560779baf73fc2f19e060c444614f2e9d3ac36b28043999af64774c`
MD5	`924859e39afc97f5d4aea044e0c1d5e8`
BLAKE2b-256	`f862acda915ad008a952d91ef568c8f905c6aed27a3da938a171f0687bd719e2`

Algorithm	Hash digest
SHA256	`ec7d5dfd6b93736e66a9fd136ca9411d06775181f1bcd0f5fab3f2486e890ec1`
MD5	`358ba11602a21e812be13e9e814805de`
BLAKE2b-256	`a45a2c5f612e1b88e96ec394f71f94313d671c9ac60c0f442f6e9fe3a0559959`

Algorithm	Hash digest
SHA256	`40b78709d10e3b35fc1f500775a993f998be4e6748f323367154a1fe30084bc2`
MD5	`329f70184cb5185a28d45ce3ce8ed214`
BLAKE2b-256	`c6ee3eb57114a845da7e7ae17f3bca805e61cc8d47b184515284d7fbfee5d5fc`