HypercubeWTF

HypercubeWTF 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. Classical reservoir computing wants a stream: a new low-dimensional sample each step, a state that evolves through real time. A still field offers no such sequence — the pattern is already complete — so HypercubeWTF repurposes the reservoir idea by inventing a short stretch of synthetic time. It places your length-N field on a frozen hypercube reservoir, drives a short orbit that encodes the field in the dynamics, and trains a small HypercubeCNN head on the end state only. The CNN head never sees the original field — it sees what the reservoir dynamics leave behind. Those dynamics are not a neutral pipe: early work suggests they can filter, reshape, and otherwise transform the field in ways a static pack-then-CNN path does not (see Early observations below).

That is the product idea: take a static field, encode it through a short stretch of reservoir dynamics, and train a spatial readout on what remains.

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

HypercubeAI ecosystem
_{One geometry. Topology-native intelligence.}

HypercubeESN · HypercubeCNN · HypercubeHopfield · HypercubeWTF

HypercubeWTF is an experiment in the HypercubeAI project — our quest to map AI and ML strategies onto the hypercube as a computational substrate.

Why the hypercube? A few properties keep showing up — and they explain why a frozen reservoir and a HypercubeCNN readout fit together so cleanly:

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 reservoir 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 reservoir’s 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:

Product	Natural data	Role of the hypercube
HypercubeESN	Low-dim streams over time	Frozen reservoir stepped each sample; multi-slice state → HypercubeCNN readout
HypercubeCNN	Static patterns on the cube	Trainable spatial conv/pool on the cube (no recurrent reservoir)
HypercubeHopfield	Patterns / attractors	Associative memory dynamics on the cube
HypercubeWTF	Static high-dim fields (no intrinsic time)	Same frozen hypercube reservoir discipline as ESN, driven for a short orbit per sample, then HypercubeCNN on the end state

What does WTF stand for?

The design goal was simple: take the HypercubeESN idea — frozen reservoir, trained head — and aim it at data that has no time. There was no lineage to steal a name from, so the usual naming exercise followed. Nothing stuck. After a few hours of “maybe this?” and “nah.”, the working monologue devolved to what the f*** do we call this project?

So we called it that.

HypercubeWTF

The monologue won — and the brand gained a little personality :-)

What is HypercubeWTF?

HypercubeESN processes temporal streams. HypercubeCNN processes spatial data with a trainable conv stack on the cube. HypercubeWTF also takes spatial data, but the conv stack is not first in line: each field first passes through a dynamical encoder (the reservoir). The CNN head never sees the original field; it sees an encoded end-of-orbit state produced by the reservoir dynamics.

In classical reservoir computing (and in HypercubeESN):

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

WTF uses that same idea on a still field. There is no natural next sample, so the library invents a short synthetic orbit: it re-addresses the same fixed field over the cube for a number of passes, then samples once at the end. Geometry and weights stay put; only the registration of the field moves.

Whether the dynamical encoding → CNN pipeline has real product value is still an open question. Early studies suggest interesting transformational behavior (see Early observations).

Pipeline

x  (your length-N field — already on the cube, no natural time)
    │
    ▼
 frozen hypercube reservoir runs a short orbit
    │
    ▼
 end-of-orbit features → HypercubeCNN → logits / values

Cube size from dim (N = 2^dim; dim 5…16).
Only the readout trains.
Everyday loop in this package: collect_episodes → 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)

The internal dynamics of this encoding appear to have some interesting properties we have only lightly explored — for example filtering white noise when present, acting closer to an identity map when noise is absent, and reducing sensitivity to training-data quality when noise is present. Treat that as early observation, not settled product behavior — the write-ups have the details and how we ran them:

Document	Question
WhiteNoiseFilter.md	Noisy test fields: does the reservoir orbit help vs pack-only → CNN?
TrainingDataQualitySensitivity.md	Degraded training data: how much does each path lose?

The studies use MNIST on small cubes because it is 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/mnist/.

Installation

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

pip install hypercube-wtf

import hypercube_wtf as hw
print(hw.__version__)

Package name on PyPI: hypercube-wtf. Import name: hypercube_wtf. Main type: hw.WTF.

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/HypercubeWTF.git
cd HypercubeWTF/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_wtf as hw

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)

wtf = hw.WTF(
    dim=dim,
    history_depth=4,
    T=100,
    ic_seed=2,
    readout_num_outputs=4,
    readout_task="classification",
    readout_epochs=80,
)
wtf.fit(fields, labels)  # collect_episodes + train

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

wtf.save("model.pkl")
loaded = hw.WTF.load("model.pkl")

Step by step (same loop, more control)

wtf = hw.WTF(
    dim=7,
    readout_num_outputs=4,
    readout_task="classification",
)
wtf.collect_episodes(fields_train, labels_train)
wtf.train()
logits = wtf.predict(fields_test[0])       # (num_outputs,) float32
cls = wtf.predict_class(fields_test[0])    # int

For regression, set readout_task="regression" and pass float targets instead of class labels. Then use r2_on_collected() the same way as the classification sanity check.

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 some fields out and call predict / predict_class yourself.

Features

One class — hypercube_wtf.WTF is the whole product surface
Episode loop — collect_episode / collect_episodes → train → predict / predict_class
fit — clear, collect, and train when your arrays are ready
dim 5–16 — field length N = 2^dim; orbit length T; end-of-orbit ages readout_slices (B)
Classification or regression — readout_task fixed at construction
Bulk collect can parallelize — collect_threads (0 = auto)
Train-only field noise — train_input_noise_sigma on collect, never on predict
Skip-the-orbit path — bypass_reservoir=True for pack-only comparisons (needs B = 1)
Inspect an episode — run_episode(x) then last_features()
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-wtf. 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 HypercubeWTF, after: pip install hypercube-wtf
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++)
WhiteNoiseFilter.md	Early white-noise study (MNIST as a test bed)
TrainingDataQualitySensitivity.md	Early training-quality study (MNIST as a test bed)

Ecosystem

HypercubeESN — echo-state / reservoir computing on streams; same cube + HCNN readout family.
HypercubeCNN — cube-native conv stack; WTF’s trainable head.
HypercubeHopfield — Hopfield-style dynamics on the cube.

License

Apache 2.0. See LICENSE.

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hypercube-wtf 1.0.0

HypercubeWTF

What does WTF stand for?

What is HypercubeWTF?

Pipeline

Early observations (exploratory)

Installation

From source (full repository)

Quick start

Step by step (same loop, more control)

Features

Examples

Documentation

Ecosystem

License

Project links

Key dates

1 maintainer

GitHub Statistics

License expression

Requires

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Algorithm	Hash digest
SHA256	`cd384ea39d0087bc252abf8a4ae38f15ada2db3ae848fe51e064039d0bb255cd`
MD5	`197ef0c1bb3417791cfd297e887d574b`
BLAKE2b-256	`0ac5c4d38c714fb0a257c8dbf59a9d729295632600a5771300dee2a65e2e2983`

Algorithm	Hash digest
SHA256	`b296798b38c82a93a1d46d61e476a382345a50bcab7ed36d265234a139f2ed27`
MD5	`96b00447d10a4d1850fa99db291fd697`
BLAKE2b-256	`cce6a2a7316c7d7dbbe90cbaa531125c0ee73d0e9e379e0f2093a5a382f9b471`

Algorithm	Hash digest
SHA256	`a2df916ab3008670d4ee73b54bd191b2a2ae9449771bac0b0a766e0b35324106`
MD5	`d023562e62fb7c3f49ddc3ece7e417b1`
BLAKE2b-256	`cfbe837590a7d68a09862197681bc8b11b56ccc9e2c17f8c6673302e5df87978`

Algorithm	Hash digest
SHA256	`34132ad31cc2472d80e10080a473609c6eb1344ec094196e5d1676230260556c`
MD5	`6bec68a06bb140fcd34f76d3a84eb3eb`
BLAKE2b-256	`7d5b4310695aac7e9657415f5368eeac7032f99b1fab5342985e997ee9df5819`

Algorithm	Hash digest
SHA256	`a034af43cd9bc3ae1751ec550466f4b27ffe7e9652f0f28f1b91a22dfb8d8826`
MD5	`0760cbc38afe87aa06fbda0e201fe269`
BLAKE2b-256	`3f4be0eb6ee0e2530288725fab24ff39333600c8e26bd434bbbd0276cd230582`