primate, short for Probabilistic Implicit Matrix Trace Estimator, is a Python package that provides estimators of quantities from matrices, linear operators, and matrix functions:
$$ f(A) \triangleq U f(\Lambda) U^{\intercal}, \quad \quad f : [a,b] \to \mathbb{R}$$
This definition is quite general in that different parameterizations of $f$ produce a variety of spectral quantities, including the matrix inverse $A^{-1}$, the matrix exponential $\mathrm{exp}(A)$, the matrix logarithm $\mathrm{log}(A)$, and so on.
Composing these with trace and diagonal estimators yields approximations for the numerical rank, the log-determinant, the Schatten norms, the eigencount, the Estrada index, the Heat Kernel Signature, and so on.
Notable features of primate include:
- Efficient methods for trace, diagonal, and matrix function approximation
- Support for arbitrary matrix types, e.g. NumPy arrays, sparse matrices, or LinearOperator's
- Support for arbitrary matrix functions, i.e.
Callable's (Python) andinvocable's1 (C++) - Matrix-free interface to the Lanczos and Golub-Welsch methods
- Various composable stopping criteria for easy and adaptive convergence checking
primate was partially inspired by the imate package---for a comparison of the two, see here.
Installation
primate is a standard PEP-517 package, and thus can be installed via pip:
python -m pip install scikit-primate
Assuming your platform is supported, no compilation is needed.
See the installation page for details.
Applications
Applications of matrix functions include characterizing folding in proteins, principal component regression, spectral clustering, Gaussian process likelihood estimation, counting triangles in distributed-memory networks, characterizing graph similarity, and deep neural loss landscape analysis.
If you have a particular application, feel free to make a computational notebook to illustrate it as a use-case!
-
This includes std::function's, C-style function pointers, functors, and lambda expressions. ↩
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Metadata
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Release files for scikit-primate 0.5.6
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Source distribution (sdist)
| File | Size | Uploaded | |
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| scikit_primate-0.5.6.tar.gz | 5.7 MB | Details |
Built distributions (wheels)
Total release size: 10.6 MB
Release files / scikit_primate-0.5.6.tar.gz
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| Size | 175.1 kB |
| Tags | CPython 3.9 macOS 11.0+ ARM64 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
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twine/5.1.1 CPython/3.12.7
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Transparency logRelease files / scikit_primate-0.5.6-cp39-cp39-macosx_10_9_x86_64.whl
| Download URL | scikit_primate-0.5.6-cp39-cp39-macosx_10_9_x86_64.whl |
|---|---|
| Size | 172.2 kB |
| Tags | CPython 3.9 macOS 10.9+ x86-64 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/5.1.1 CPython/3.12.7
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
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