aGrUM 3.0.0
a C++ library for (probabilistic) graphical models
structuralMetrics_tpl.h
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40
41#pragma once
42
43
44#include <agrum/BN/algorithms/structuralMetrics.h> // to ease IDE parser
45#ifndef DOXYGEN_SHOULD_SKIP_THIS
46
49
50namespace gum {
51
52 template < typename GS1, typename GS2 >
53 void StructuralMetrics::compare(const BayesNet< GS1 >& ref, const BayesNet< GS2 >& test) {
54 if (ref.size() != test.size()) { GUM_ERROR(OperationNotAllowed, "Graphs of different sizes") }
55 for (const NodeId node: ref.internalDag().asNodeSet()) {
56 if (!test.exists(ref.variable(node).name())) {
57 GUM_ERROR(InvalidNode, "Test doesn't contain node " << node << " from ref")
58 }
59 }
60 // Build a BN with ref's variables/NodeIds and test's arc structure (mapped
61 // by name): comparison and essential-graph extraction must operate on
62 // matching NodeIds, but the semantic identity is the variable name.
63 BayesNet< GS2 > aligned_test;
64 for (const NodeId id: ref.internalDag().asNodeSet()) {
65 aligned_test.add(ref.variable(id), id);
66 }
67 for (const Arc& arc: test.internalDag().arcs()) {
68 const NodeId tail = ref.idFromName(test.variable(arc.tail()).name());
69 const NodeId head = ref.idFromName(test.variable(arc.head()).name());
70 aligned_test.addArc(tail, head);
71 }
72
73 PDAG ref_eg = EssentialGraph(ref).pdag();
74 PDAG test_eg = EssentialGraph(aligned_test).pdag();
75 this->compare(ref_eg, test_eg);
76 }
77
78 template < GUM_Numeric GUM_SCALAR >
79 void StructuralMetrics::compare(const BayesNet< GUM_SCALAR >& ref, const PDAG& test) {
80 PDAG ref_eg = EssentialGraph(ref).pdag();
81 this->compare(ref_eg, test);
82 }
83
84 template < GUM_Numeric GUM_SCALAR >
85 void StructuralMetrics::compare(const PDAG& ref, const BayesNet< GUM_SCALAR >& test) {
86 PDAG test_eg = EssentialGraph(test).pdag();
87
88 this->compare(ref, test_eg);
89 }
90
91 template < typename GS1, typename GS2 >
92 double StructuralMetrics::sid(const BayesNet< GS1 >& ref, const BayesNet< GS2 >& test) const {
93 if (ref.size() != test.size()) { GUM_ERROR(OperationNotAllowed, "Graphs of different sizes") }
94 for (const NodeId node: ref.internalDag().asNodeSet()) {
95 if (!test.exists(ref.variable(node).name())) {
96 GUM_ERROR(InvalidNode, "Test doesn't contain node " << node << " from ref")
97 }
98 }
99 // Align test's DAG to ref's NodeIds by variable name (DAG-level only,
100 // no need to materialize an aligned BN since SID works on the DAG).
101 DAG aligned_test;
102 for (const NodeId id: ref.internalDag().asNodeSet()) {
103 aligned_test.addNodeWithId(id);
104 }
105 for (const Arc& arc: test.internalDag().arcs()) {
106 const NodeId tail = ref.idFromName(test.variable(arc.tail()).name());
107 const NodeId head = ref.idFromName(test.variable(arc.head()).name());
108 aligned_test.addArc(tail, head);
109 }
110 return this->sid(ref.internalDag(), aligned_test);
111 }
112} /* namespace gum */
113
114#endif /* DOXYGEN_SHOULD_SKIP_THIS */
Base classes for partially directed acyclic graphs.
Class representing a Bayesian network.
Definition BayesNet.h:93
Base class for partially directed acyclic graphs.
Definition PDAG.h:130
void compare(const DiGraph &ref, const DiGraph &test)
compare two DiGraphs (nodes matched by NodeId, no alignment)
double sid(const DAG &ref, const DAG &test) const
Confusion matrix.
Class building the essential Graph from a DAGmodel.
#define GUM_ERROR(type, msg)
Definition exceptions.h:76
Size NodeId
Type for node ids.
gum is the global namespace for all aGrUM entities
Definition agrum.h:46
A class for comparing graphs based on their structures.