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Added ExactInference and Factor classes

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This commit is contained in:
Ricardo Montañana Gómez
2023-07-02 20:39:13 +02:00
parent ad255625e8
commit 12f0e1e063
9 changed files with 172 additions and 44 deletions
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53
src/Network.cc
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@@ -1,4 +1,5 @@
#include "Network.h"
#include "ExactInference.h"
namespace bayesnet {
Network::Network() : laplaceSmoothing(1), root(nullptr), features(vector<string>()), className("") {}
Network::Network(int smoothing) : laplaceSmoothing(smoothing), root(nullptr), features(vector<string>()), className("") {}
@@ -26,6 +27,10 @@ namespace bayesnet {
root = nodes[name];
}
}
vector<string> Network::getFeatures()
{
return features;
}
void Network::setRoot(string name)
{
if (nodes.find(name) == nodes.end()) {
@@ -120,37 +125,7 @@ namespace bayesnet {
node->setCPT(cpt);
}
}
// pair<int, double> Network::predict_sample(const vector<int>& sample)
// {
// // For each possible class, calculate the posterior probability
// Node* classNode = nodes[className];
// int numClassStates = classNode->getNumStates();
// vector<double> classProbabilities(numClassStates, 0.0);
// for (int classState = 0; classState < numClassStates; ++classState) {
// // Start with the prior probability of the class
// classProbabilities[classState] = classNode->getCPT()[classState].item<double>();
// // Multiply by the likelihood of each feature given the class
// for (auto& pair : nodes) {
// if (pair.first != className) {
// Node* node = pair.second;
// int featureValue = featureValues[pair.first];
// // We use the class as the parent state to index into the CPT
// classProbabilities[classState] *= node->getCPT()[classState][featureValue].item<double>();
// }
// }
// }
// // Find the class with the maximum posterior probability
// auto maxElem = max_element(classProbabilities.begin(), classProbabilities.end());
// int predictedClass = distance(classProbabilities.begin(), maxElem);
// double maxProbability = *maxElem;
// return make_pair(predictedClass, maxProbability);
// }
vector<int> Network::predict(const vector<vector<int>>& samples)
{
vector<int> predictions;
@@ -195,15 +170,13 @@ namespace bayesnet {
throw invalid_argument("Sample size (" + to_string(sample.size()) +
") does not match the number of features (" + to_string(features.size()) + ")");
}
// Map the feature values to their corresponding nodes
map<string, int> featureValues;
for (int i = 0; i < features.size(); ++i) {
featureValues[features[i]] = sample[i];
auto inference = ExactInference(*this);
map<string, int> evidence;
for (int i = 0; i < sample.size(); ++i) {
evidence[features[i]] = sample[i];
}
// For each possible class, calculate the posterior probability
Network network = *this;
vector<double> classProbabilities = eliminateVariables(network, featureValues);
inference.setEvidence(evidence);
vector<double> classProbabilities = inference.variableElimination();
// Normalize the probabilities to sum to 1
double sum = accumulate(classProbabilities.begin(), classProbabilities.end(), 0.0);
@@ -217,8 +190,4 @@ namespace bayesnet {
return make_pair(predictedClass, maxProbability);
}
vector<double> eliminateVariables(network, featureValues)
{
}
}