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README.md

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Performs Principal Component Analysis (PCA) with optional graph distance for neighborhood composition.
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<!-- START doctoc generated TOC please keep comment here to allow auto update -->
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**Table of Contents**
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- [Geodesic Graph Distance](#geodesic-graph-distance)
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- [Why?](#why)
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- [What?](#what)
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- [Results](#results)
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- [Why not?](#why-not)
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- [Installation](#installation)
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- [Example](#example)
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- [Classic PCA](#classic-pca)
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- [Graph PCA](#graph-pca)
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<!-- END doctoc generated TOC please keep comment here to allow auto update -->
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# Geodesic Graph Distance
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Defaulting to user installation because normal site-packages is not writeable
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Processing /home/runner/work/graph-pca/graph-pca
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Processing ./.
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Installing build dependencies ... [?25l-
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[?25hBuilding wheels for collected packages: graph_pca
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[?25hBuilding wheels for collected packages: graph_pca
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Building wheel for graph_pca (pyproject.toml) ... [?25l-
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[?25h Created wheel for graph_pca: filename=graph_pca-0.2.0-cp310-cp310-linux_x86_64.whl size=261599 sha256=6a112c2f78ff62505fb3563d8fa18fbe86be43903dd7ae1f3887a96ee323fc0a
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Stored in directory: /home/runner/.cache/pip/wheels/7b/c4/c0/8f5feb247149d5b104495eb30718e47a9918e3e64bbd443a56
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[?25h Created wheel for graph_pca: filename=graph_pca-0.2.0-cp312-cp312-linux_x86_64.whl size=254570 sha256=2eee24bc137327bbfcd6e07c9b939904ea817189af3f516a0004a39b3814144e
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Stored in directory: /home/runner/.cache/pip/wheels/27/e7/b7/4ca70840b4dc1f90c760846748dadfba48b39c4259436dd2cd
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Successfully built graph_pca
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Installing collected packages: graph_pca
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Successfully installed graph_pca-0.2.0
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