aGrUM 3.1.1
a C++ library for (probabilistic) graphical models
influenceDiagramGenerator_tpl.h
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40
41#pragma once
42
43
52
54
55namespace gum {
56 // Default constructor.
57 // Use the SimpleCPTGenerator for generating the IDs CPT.
58 template < GUM_Numeric GUM_SCALAR >
64
65 // Use this constructor if you want to use a different policy for generating
66 // CPT than the default one.
67 // The cptGenerator will be erased when the destructor is called.
68 // @param cptGenerator The policy used to generate CPT.
69 template < GUM_Numeric GUM_SCALAR >
71 ICPTGenerator< GUM_SCALAR >* cptGenerator) {
72 GUM_CONSTRUCTOR(InfluenceDiagramGenerator)
73 _cptGenerator_ = cptGenerator;
75 }
76
77 // Use this constructor if you want to use a different policy for generating
78 // UT than the default one.
79 // The utGenerator will be erased when the destructor is called.
80 // @param utGenerator The policy used to generate UT.
81 template < GUM_Numeric GUM_SCALAR >
87
88 // Use this constructor if you want to use a different policy for generating
89 // both CPT & UT than the defaults ones.
90 // The cptGenerator and utGenerator will be erased when the destructor is
91 // called.
92 // @param cptGenerator The policy used to generate CPT.
93 // @param utGenerator The policy used to generate UT.
94 template < GUM_Numeric GUM_SCALAR >
96 ICPTGenerator< GUM_SCALAR >* cptGenerator,
97 UTGenerator* utGenerator) {
98 GUM_CONSTRUCTOR(InfluenceDiagramGenerator)
99 _cptGenerator_ = cptGenerator;
100 _utGenerator_ = utGenerator;
101 }
102
103 // Destructor.
104 template < GUM_Numeric GUM_SCALAR >
110
111 // Generates an influence diagram using floats.
112 // @param nbrNodes The number of nodes in the generated ID.
113 // @param arcdensity The probability of adding an arc between two nodes.
114 // @param chanceNodeDensity The proportion of chance node
115 // @param utilityNodeDensity The proportion of utility node
116 // @param max_modality Each DRV has from 2 to max_modality modalities
117 // @return A IDs randomly generated.
118 template < GUM_Numeric GUM_SCALAR >
121 GUM_SCALAR arcDensity,
122 GUM_SCALAR chanceNodeDensity,
123 GUM_SCALAR utilityNodeDensity,
124 Size max_modality) {
125 auto influenceDiagram = new InfluenceDiagram< GUM_SCALAR >();
126 // First we add nodes
128 Size nb_mod;
129
130 for (Idx i = 0; i < nbrNodes; ++i) {
131 const auto varName = std::format("{}", i);
132 nb_mod = (max_modality == 2) ? 2 : 2 + randomValue(max_modality - 1);
133
134 GUM_SCALAR cnd = chanceNodeDensity;
135 GUM_SCALAR und = utilityNodeDensity;
136
137 auto d = (GUM_SCALAR)randomProba();
138
139 if (d < cnd)
140 map.insert(i, influenceDiagram->addChanceNode(RangeVariable(varName, "", 0, nb_mod - 1)));
141 else if (d < (cnd + und))
142 map.insert(i, influenceDiagram->addUtilityNode(RangeVariable(varName, "", 0, 0)));
143 else
144 map.insert(i, influenceDiagram->addDecisionNode(RangeVariable(varName, "", 0, nb_mod - 1)));
145 }
146
147 // We add arcs
148 GUM_SCALAR p = arcDensity;
149
150 for (Size i = 0; i < nbrNodes; ++i)
151 if (!influenceDiagram->isUtilityNode(map[i]))
152 for (Size j = i + 1; j < nbrNodes; ++j)
153 if (((GUM_SCALAR)randomProba()) < p) { influenceDiagram->addArc(map[i], map[j]); }
154
155 // And fill the CPTs and UTs
156 for (Size i = 0; i < nbrNodes; ++i)
157 if (influenceDiagram->isChanceNode(map[i]))
158 _cptGenerator_->generateCPT(
159 influenceDiagram->cpt(map[i]).pos(influenceDiagram->variable(map[i])),
160 influenceDiagram->cpt(map[i]));
161 else if (influenceDiagram->isUtilityNode(map[i]))
162 _utGenerator_->generateUT(
163 influenceDiagram->utility(map[i]).pos(influenceDiagram->variable(map[i])),
164 influenceDiagram->utility(map[i]));
165
166 _checkTemporalOrder_(influenceDiagram);
167
168 return influenceDiagram;
169 }
170
171 template < GUM_Numeric GUM_SCALAR >
174 if (!infdiag->decisionOrderExists()) {
175 Sequence< NodeId > order = infdiag->topologicalOrder();
176
177 auto orderIter = order.begin();
178
179 while ((orderIter != order.end()) && (!infdiag->isDecisionNode(*orderIter)))
180 ++orderIter;
181
182 if (orderIter == order.end()) return;
183
184 NodeId parentDecision = (*orderIter);
185
186 ++orderIter;
187
188 for (; orderIter != order.end(); ++orderIter)
189 if (infdiag->isDecisionNode(*orderIter)) {
190 infdiag->addArc(parentDecision, (*orderIter));
191 parentDecision = (*orderIter);
192 }
193 }
194 }
195} /* namespace gum */
Sequence< NodeId > topologicalOrder() const
The topological order stays the same as long as no variable or arcs are added or erased src the topol...
The class for generic Hash Tables.
Definition hashTable.h:640
value_type & insert(const Key &key, const Val &val)
Adds a new element (actually a copy of this element) into the hash table.
InfluenceDiagram< GUM_SCALAR > * generateID(Size nbrNodes, GUM_SCALAR arcDensity, GUM_SCALAR chanceNodeDensity, GUM_SCALAR utilityNodeDensity, Size max_modality=2)
Generates an influence diagram using floats.
void _checkTemporalOrder_(InfluenceDiagram< GUM_SCALAR > *infdiag)
ICPTGenerator< GUM_SCALAR > * _cptGenerator_
Class representing an Influence Diagram.
void addArc(NodeId tail, NodeId head)
Add an arc in the ID, and update diagram's tensor nodes cpt if necessary.
bool decisionOrderExists() const
True if a directed path exist with all decision nodes.
bool isDecisionNode(NodeId varId) const
Returns true if node is a decision one.
Defines a discrete random variable over an integer interval.
<agrum/BN/generator/simpleCPTGenerator.h>
Class for generating Utility Tables.
Abstract class for generating Utility Tables.
Definition UTGenerator.h:63
std::size_t Size
In aGrUM, hashed values are unsigned long int.
Definition types.h:74
Size Idx
Type for indexes.
Definition types.h:79
Size NodeId
Type for node ids.
Idx randomValue(const Size max=2)
Returns a random Idx between 0 and max-1 included.
double randomProba()
Returns a random double between 0 and 1 included (i.e.
Class for generating Bayesian networks.
gum is the global namespace for all aGrUM entities
Definition agrum.h:46
Contains useful methods for random stuff.