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david huber
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| /CComponents.java |
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src/main/java/ch/idsia/crema/model/transform/EmpiricalNetwork.java
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| package ch.idsia.crema.model.transform; | ||
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| import java.util.ArrayList; | ||
| import java.util.Arrays; | ||
| import java.util.LinkedList; | ||
| import java.util.List; | ||
| import java.util.function.BiFunction; | ||
| import java.util.stream.Stream; | ||
| import java.util.stream.StreamSupport; | ||
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| import org.apache.commons.collections4.IteratorUtils; | ||
| import org.jgrapht.Graph; | ||
| import org.jgrapht.graph.DefaultEdge; | ||
| import org.jgrapht.graph.DirectedAcyclicGraph; | ||
| import org.jgrapht.traverse.TopologicalOrderIterator; | ||
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| import com.google.common.collect.Lists; | ||
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| import ch.idsia.crema.core.Strides; | ||
| import ch.idsia.crema.data.DoubleTable; | ||
| import ch.idsia.crema.factor.bayesian.BayesianFactor; | ||
| import ch.idsia.crema.factor.bayesian.BayesianFactorFactory; | ||
| import ch.idsia.crema.inference.ve.order.TopologicalOrdering; | ||
| import ch.idsia.crema.model.causal.SCM; | ||
| import ch.idsia.crema.model.causal.SCM.VariableType; | ||
| import ch.idsia.crema.model.graphical.BayesianNetwork; | ||
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| import it.unimi.dsi.fastutil.ints.Int2ObjectMap; | ||
| import it.unimi.dsi.fastutil.ints.Int2ObjectOpenHashMap; | ||
| import it.unimi.dsi.fastutil.ints.IntArrayList; | ||
| import it.unimi.dsi.fastutil.ints.IntList; | ||
| import it.unimi.dsi.fastutil.ints.IntOpenHashSet; | ||
| import it.unimi.dsi.fastutil.ints.IntSet; | ||
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| public class EmpiricalNetwork | ||
| //implements BiFunction<SCM, DoubleTable, BayesianNetwork> | ||
| { | ||
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| // | ||
| // | ||
| // | ||
| // public BayesianNetwork apply(SCM model, DoubleTable data) { | ||
| // BayesianNetwork bn = new BayesianNetwork(); | ||
| // | ||
| // var comps = components(model); | ||
| // | ||
| // IntList topo_order = topological(model); | ||
| // | ||
| // for (int variable: topo_order) { | ||
| // bn.addVariable(variable, model.getSize(variable)); | ||
| // } | ||
| // | ||
| // for (int i = 0; i < topo_order.size(); ++i) { | ||
| // int variable = topo_order.getInt(i); | ||
| // | ||
| // // topologically before | ||
| // var topo_before = Arrays.stream(topo_order, 0, i); | ||
| // var friends = comps.get(variable); | ||
| // | ||
| // // get all nodes strictly topologically before variable and that are part of the component | ||
| // int[] parents = topo_before.filter(friends::contains).toArray(); | ||
| // bn.addParents(variable, parents); | ||
| // } | ||
| // | ||
| // quantify(bn, data); | ||
| // | ||
| // return bn; | ||
| // } | ||
| // | ||
| // | ||
| // | ||
| // public double loglikelihood(BayesianNetwork net, DoubleTable dataset) { | ||
| // | ||
| // int[] vars = net.getVariables(); | ||
| // | ||
| // double ll = 0; | ||
| // for (var row : dataset) { | ||
| // int[] states = row.getKey(); | ||
| // double rowll = 0; | ||
| // for(int variable : vars) { | ||
| // var factor = net.getFactor(variable); | ||
| // int offset = factor.getDomain().getPartialOffset(dataset.getColumns(), states); | ||
| // double p = factor.getValueAt(offset); | ||
| // rowll += Math.log(p); | ||
| // } | ||
| // ll += rowll * row.getValue(); | ||
| // } | ||
| // return ll; | ||
| // } | ||
| // | ||
| // | ||
| // | ||
| // private void quantify(BayesianNetwork bn, DoubleTable data, int[] variables) { | ||
| // | ||
| // for (int variable : variables) { | ||
| // int[] parents = bn.getParents(variable); | ||
| // | ||
| // int[] target = new int[parents.length + 1]; | ||
| // System.arraycopy(parents, 0, target, 0, parents.length); | ||
| // target[parents.length] = variable; | ||
| // Arrays.sort(target); | ||
| // | ||
| // Strides domain = bn.getDomain(target); | ||
| // double[] values = data.getWeights(domain.getVariables(), domain.getSizes()); | ||
| // | ||
| // BayesianFactor factor = new BayesianFactor(domain, values, false); | ||
| // factor = factor.normalize(parents); | ||
| // | ||
| // bn.setFactor(variable, factor); | ||
| // } | ||
| // } | ||
| // | ||
| // | ||
| // private Int2ObjectMap<IntSet> components(SCM model) { | ||
| // | ||
| // List<IntSet> components = new LinkedList<IntSet>(); | ||
| // | ||
| // IntSet todo = new IntOpenHashSet(model.getVariables()); | ||
| // | ||
| // IntSet exogenous = new IntOpenHashSet(model.getExogenousSet()); | ||
| // IntSet exo_close = new IntOpenHashSet(); | ||
| // while (!exogenous.isEmpty()) { | ||
| // int exo = exogenous.iterator().next(); | ||
| // exogenous.remove(exo); | ||
| // | ||
| // // already processed | ||
| // if(exo_close.contains(exo)) continue; | ||
| // exo_close.add(exo); | ||
| // | ||
| // // visit connected components | ||
| // IntSet component = new IntOpenHashSet(model.getEndogenousChildren(exo)); | ||
| // components.add(component); | ||
| // | ||
| // IntSet open = new IntOpenHashSet(component); | ||
| // while(!open.isEmpty()) { | ||
| // int child = open.iterator().next(); | ||
| // open.remove(child); | ||
| // | ||
| // IntSet exo_parents = new IntOpenHashSet(model.getExogenousParents(child)); | ||
| // exo_parents.removeAll(exo_close); | ||
| // | ||
| // // we are processing the exogenous parents | ||
| // exogenous.removeAll(exo_parents); | ||
| // exo_close.addAll(exo_parents); | ||
| // | ||
| // // add to the open variables all the children of the exogenous | ||
| // for (int connected_exo_var : exo_parents) { | ||
| // | ||
| // // for each non closed exo var | ||
| // // add all children to open | ||
| // IntSet siblings = model.getChildrenSet(connected_exo_var); | ||
| // // remove already processed children | ||
| // siblings.removeAll(component); | ||
| // open.addAll(siblings); | ||
| // | ||
| // component.addAll(siblings); | ||
| // } | ||
| // } | ||
| // | ||
| // // add all parents of endogenous variables | ||
| // int[] compvars = component.toIntArray(); | ||
| // for (int variable : compvars) { | ||
| // var parents = model.getParentsSet(variable); | ||
| // parents.removeAll(model.getExogenousSet()); | ||
| // component.addAll(parents); | ||
| // } | ||
| // } | ||
| // | ||
| // | ||
| // // assign sets to all variables | ||
| // Int2ObjectMap<IntSet> sets = new Int2ObjectOpenHashMap<IntSet>(); | ||
| // | ||
| // for (IntSet component : components) { | ||
| // | ||
| // | ||
| // | ||
| // } | ||
| // return sets; | ||
| // } | ||
| // | ||
| // | ||
| // /** | ||
| // * get the topological ordering of endogenous vars | ||
| // * @param model | ||
| // * @return | ||
| // */ | ||
| // public IntList topological(SCM model) { | ||
| // IntSet exo = model.getExogenousSet(); | ||
| // | ||
| // TopologicalOrderIterator<Integer, DefaultEdge> iter = new TopologicalOrderIterator<Integer, DefaultEdge>(model.getNetwork()); | ||
| // IntList order = new IntArrayList(model.getVariables().length); | ||
| // while(iter.hasNext()) { | ||
| // order.add(iter.next()); | ||
| // } | ||
| // order.removeAll(exo); | ||
| // return order; | ||
| // } | ||
| } |