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Canonical Correlation Analysis Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic methods in a scikit-learn style framework
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取自儲存庫 README
正在載入完整 README。 以下為已儲存的摘錄。
CCA-Zoo is a Python library of reference implementations of Canonical Correlation Analysis
(CCA) algorithms from the literature, from classical CCA (Hotelling 1936) through sparse,
kernel, deep, and probabilistic variants — each documented with the paper it comes from. It's
also built to be used directly: every model follows the same
scikit-learn estimator API (fit, transform, fit_transform,
score), is fully typed (PEP 561), and is tested against known closed-form solutions where one
exists.
(substitute pip install for uv add if you're not using uv)檢視完整 README ↗目前無法載入完整 README。請透過原文連結閱讀。 以下為已儲存的摘錄。
CCA-Zoo is a Python library of reference implementations of Canonical Correlation Analysis
(CCA) algorithms from the literature, from classical CCA (Hotelling 1936) through sparse,
kernel, deep, and probabilistic variants — each documented with the paper it comes from. It's
also built to be used directly: every model follows the same
scikit-learn estimator API (fit, transform, fit_transform,
score), is fully typed (PEP 561), and is tested against known closed-form solutions where one
exists.
(substitute pip install for uv add if you're not using uv)檢視完整 README ↗