40 lines
1.3 KiB
Markdown
40 lines
1.3 KiB
Markdown
# BayesNet
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Bayesian Network Classifier with libtorch from scratch
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## Variable Elimination
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To decide the first variable to eliminate wel use the MinFill criterion, that is
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the variable that minimizes the number of edges that need to be added to the
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graph to make it triangulated.
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This is done by counting the number of edges that need to be added to the graph
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if the variable is eliminated. The variable with the minimum number of edges is
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chosen.
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In pgmpy this is done computing then the length of the combinations of the
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neighbors taken 2 by 2.
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Once the variable to eliminate is chosen, we need to compute the factors that
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need to be multiplied to get the new factor.
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This is done by multiplying all the factors that contain the variable to
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eliminate and then marginalizing the variable out.
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The new factor is then added to the list of factors and the variable to
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eliminate is removed from the list of variables.
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The process is repeated until there are no more variables to eliminate.
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## Code for combination
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// Combinations of length 2
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vector<string> combinations(vector<string> source)
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{
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vector<string> result;
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for (int i = 0; i < source.size(); ++i) {
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string temp = source[i];
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for (int j = i + 1; j < source.size(); ++j) {
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result.push_back(temp + source[j]);
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}
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}
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return result;
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}
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