---

name: numpy-ml description: "Routes numpy-ml users to the right classical ML, preprocessing, neural-component, probabilistic, and RL workflows." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

numpy-ml

numpy-ml is an educational machine-learning library implemented mostly in NumPy and SciPy. Use this root skill to choose the right workflow, install a compatible runtime, and confirm whether a task belongs to a focused sub-skill.

Verified compatibility

This legacy snapshot was verified on Python 3.8 with:

python -m pip install "numpy<1.24" "scipy<1.11" numpy-ml
python -c "import numpy_ml; print('ok')"

Why this matters:

• Python 3.10+ fails on this commit because the package imports collections.Hashable.
• NumPy 1.24+ can break older code paths that use removed aliases such as np.int and np.float.
• Base runtime use does not require Gym, PyTorch, TensorFlow, or plotting libraries.

Read references/repo-provenance.md when you need to check whether this skill matches the current checkout or before running refresh-repo-skill.

Quick checks

Run the bundled environment check when you are unsure whether the current Python can use the package:

python scripts/check_numpy_ml_environment.py

Run the cross-family smoke matrix when you want a fast confidence pass over the bundled helpers:

python scripts/api_smoke_matrix.py

Route map

User intent	Read next
linear models, trees, KNN/GP regression, or matrix factorization	sub-skills/supervised-and-tabular-models/SKILL.md
GMM, HMM, LDA, or n-gram language models	sub-skills/probabilistic-and-sequence-models/SKILL.md
activations, layers, losses, optimizers, schedulers, or toy NN models	sub-skills/neural-network-components/SKILL.md
standardization, encoding, tokenization, DSP, kernels, distances, graphs, queues, or samplers	sub-skills/preprocessing-and-utilities/SKILL.md
bandits, policy comparison, EnvModel, or optional Gym-backed RL agents	sub-skills/bandits-and-reinforcement-learning/SKILL.md

What to avoid

• Do not treat the repository's original comparison tests as runtime dependencies; they are optional diagnostics.
• Do not assume a fit method returns the model object. Many numpy-ml classes mutate in place and return None.
• Do not promise GPU support or modern autograd behavior; this library is a NumPy/SciPy reference implementation.

Helper references

• references/api-overview.md for the module map.
• references/compatibility.md for version and dependency constraints.
• references/troubleshooting.md for shared import and legacy-compatibility failures.

Best-fit rule

Choose the smallest sub-skill that owns the user-facing workflow. If the task starts with raw text, labels, signals, or feature dictionaries, route through preprocessing first. If the task needs a bandit or RL loop, use the RL sub-skill instead of the supervised one. If the task needs tensor-based deep learning or a modern framework, this package is probably the wrong tool.