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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Decomposition\n\nExample of creating a junction tree from a directed graph.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\nimport networkx as nx\n\nB = nx.DiGraph()\nB.add_nodes_from([\"A\", \"B\", \"C\", \"D\", \"E\", \"F\"])\nB.add_edges_from(\n [(\"A\", \"B\"), (\"A\", \"C\"), (\"B\", \"D\"), (\"B\", \"F\"), (\"C\", \"E\"), (\"E\", \"F\")]\n)\n\noptions = {\"with_labels\": True, \"node_color\": \"white\", \"edgecolors\": \"blue\"}\n\nfig = plt.figure(figsize=(6, 9))\naxgrid = fig.add_gridspec(3, 2)\n\nax1 = fig.add_subplot(axgrid[0, 0])\nax1.set_title(\"Bayesian Network\")\npos = nx.nx_agraph.graphviz_layout(B, prog=\"neato\")\nnx.draw_networkx(B, pos=pos, **options)\n\nmg = nx.moral_graph(B)\nax2 = fig.add_subplot(axgrid[0, 1], sharex=ax1, sharey=ax1)\nax2.set_title(\"Moralized Graph\")\nnx.draw_networkx(mg, pos=pos, **options)\n\njt = nx.junction_tree(B)\nax3 = fig.add_subplot(axgrid[1:, :])\nax3.set_title(\"Junction Tree\")\nax3.margins(0.15, 0.25)\nnsize = [2000 * len(n) for n in list(jt.nodes())]\npos = nx.nx_agraph.graphviz_layout(jt, prog=\"neato\")\nnx.draw_networkx(jt, pos=pos, node_size=nsize, **options)\n\nplt.tight_layout()\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.15"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
