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dellaert merged 3 commits into from
Apr 20, 2025
Merged

Docs for symbolic folder #2105

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bbaacc2
Wrap BayesTree traversal
dellaert Apr 20, 2025
3c26d1f
Symbolic docs
dellaert Apr 20, 2025
8df8741
Add to toc
dellaert Apr 20, 2025
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17 changes: 17 additions & 0 deletions gtsam/linear/linear.i
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Original file line number Diff line number Diff line change
Expand Up @@ -657,6 +657,21 @@ virtual class GaussianBayesNet {
};

#include <gtsam/linear/GaussianBayesTree.h>
class GaussianBayesTreeClique {
GaussianBayesTreeClique();
GaussianBayesTreeClique(const gtsam::GaussianConditional* conditional);
bool equals(const gtsam::GaussianBayesTreeClique& other, double tol) const;
void print(string s = "", const gtsam::KeyFormatter& keyFormatter =
gtsam::DefaultKeyFormatter);
const gtsam::GaussianConditional* conditional() const;
bool isRoot() const;
gtsam::GaussianBayesTreeClique* parent() const;
size_t nrChildren() const;
gtsam::GaussianBayesTreeClique* operator[](size_t j) const;
size_t treeSize() const;
size_t numCachedSeparatorMarginals() const;
void deleteCachedShortcuts();
};
virtual class GaussianBayesTree {
// Standard Constructors and Named Constructors
GaussianBayesTree();
Expand All @@ -666,6 +681,8 @@ virtual class GaussianBayesTree {
gtsam::DefaultKeyFormatter);
size_t size() const;
bool empty() const;
const GaussianBayesTree::Roots& roots() const;
const gtsam::GaussianBayesTreeClique* operator[](size_t j) const;
size_t numCachedSeparatorMarginals() const;

string dot(const gtsam::KeyFormatter& keyFormatter =
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231 changes: 231 additions & 0 deletions gtsam/symbolic/doc/SymbolicBayesNet.ipynb
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@@ -0,0 +1,231 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SymbolicBayesNet"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A `SymbolicBayesNet` is a directed acyclic graph (DAG) composed of `SymbolicConditional` objects. It represents the structure of a factorized probability distribution P(X) = Π P(Xi | Parents(Xi)) purely in terms of variable connectivity.\n",
"\n",
"It is typically the result of running sequential variable elimination on a `SymbolicFactorGraph`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://colab.research.google.com/github/borglab/gtsam/blob/develop/gtsam/symbolic/doc/SymbolicBayesNet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"tags": [
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]
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install --quiet gtsam-develop"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"from gtsam import SymbolicConditional, SymbolicFactorGraph, Ordering\n",
"from gtsam.symbol_shorthand import X, L\n",
"import graphviz"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Creating a SymbolicBayesNet\n",
"\n",
"SymbolicBayesNets are usually created by eliminating a [SymbolicFactorGraph](SymbolicFactorGraph.ipynb). But you can also build them directly:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Directly Built Symbolic Bayes Net:\n",
" \n",
"size: 5\n",
"conditional 0: P( l1 | x0)\n",
"conditional 1: P( x0 | x1)\n",
"conditional 2: P( l2 | x1)\n",
"conditional 3: P( x1 | x2)\n",
"conditional 4: P( x2)\n"
]
}
],
"source": [
"from gtsam import SymbolicBayesNet\n",
"\n",
"# Create a new Bayes Net\n",
"symbolic_bayes_net = SymbolicBayesNet()\n",
"\n",
"# Add conditionals directly\n",
"symbolic_bayes_net.push_back(SymbolicConditional(L(1), X(0))) # P(l1 | x0)\n",
"symbolic_bayes_net.push_back(SymbolicConditional(X(0), X(1))) # P(x0 | x1)\n",
"symbolic_bayes_net.push_back(SymbolicConditional(L(2), X(1))) # P(l2 | x1)\n",
"symbolic_bayes_net.push_back(SymbolicConditional(X(1), X(2))) # P(x1 | x2)\n",
"symbolic_bayes_net.push_back(SymbolicConditional(X(2))) # P(x2)\n",
"\n",
"symbolic_bayes_net.print(\"Directly Built Symbolic Bayes Net:\\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Accessing Conditionals and Visualization"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Conditional at index 1: P( x0 | x1)\n"
]
},
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"source": [
"# Access a conditional by index\n",
"conditional_1 = bayes_net.at(1) # P(x0 | l1)\n",
"conditional_1.print(\"Conditional at index 1: \")\n",
"\n",
"# Visualize the Bayes Net structure\n",
"display(graphviz.Source(bayes_net.dot()))"
]
}
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