GreyTheory

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Grey forecasting, feature comparison, and PCA for Python.

Documentation · Getting started · Workbench · Benchmarks

Forecast a short sequence, rank features against a target, or explore spectra and process measurements. Start with a few lines of Python, or use the bundled HTML workbench.

What you can do

• Combine Grey + PCA: GreyPCA brings six PCA backends together with GRA component selection. PCA summarizes sample differences; with a reference target, GRA scores and selects related components. For spectra, images, and paths, add Grey-window features before PCA to include local patterns.
• Forecast: GM11 and two-level Convolution; GM0N / GM1N for ordered multivariate data.
• Compare: GRA feature ranking and mixed larger / smaller / target quality preferences.
• Explore: six PCA backends, raw-count PoissonPCA, Grey windows, images, and multidimensional paths.

Quick start

Requires Python 3.11+. Download or clone the repository, then run:

python3 run.py

Use the library

Install into the Python environment used by your own program:

python -m pip install .

from greytheory import GM11

series = [223.3, 227.3, 230.5, 238.1, 242.9, 251.1]
model = GM11(background_mode="corrected").fit(series)
print(model.predict(2))

Choose a PCA method

Set method on GreyPCA. The same choices are available in the workbench.

from greytheory.analysis import GreyPCA

features = [[1.0, 2.0, 0.8], [1.5, 2.4, 1.2],
            [2.0, 3.1, 1.1], [2.5, 3.4, 1.8]]
model = GreyPCA(n_components=2, method="pca", standardize=True)
components = model.fit_transform(features)
print(components)

For nonnegative raw counts, use the PoissonPCA adapter. Component selection, parameters, and the distinction between GRA and PCA rankings are in the usage guide. → GreyPCA

Explore with the workbench

workbench opens the GreyTheory graphical analysis interface. Load spectra, DOE data, or handwriting images, choose methods and parameters, then inspect charts and feature rankings:

python3 run.py workbench

The first launch creates .venv and installs the analysis and desktop dependencies; later launches reuse it. Analysis runs locally without a server.

You can also open the bundled HTML directly for offline previews of precomputed results. New settings require the command above or request/result JSON exchange.

HTML · Read the illustrated walkthrough

PVD demo: 15 synthetic runs. The projection shows sample differences; the ranking compares process inputs with the selected target.

All demo inputs and results are in data/demo/. Open the demo data browser to choose files and find the matching workbench actions.

More demos

Try delivery routes, driver assignment, artwork spectral differences, order forecasting, 2D–4D trajectories, spectroscopy, and semiconductor tools. The image tab also compares AI-generated or uploaded handwriting images.

Read the illustrated walkthrough · PVD CSV · CVD CSV

Documentation

Development

python -m pip install -e . -r requirements-dev.txt
python -m pytest -q
python -m build

Benchmarks · Issues

License

MIT · Third-party notices

References