/**
* Constructs a new on-the-fly BayesIM that will calculate conditional probabilities on the fly
* from the given discrete data set, for the given Bayes PM.
*
* @param bayesPm the given Bayes PM, which specifies a directed acyclic graph for a Bayes net and
* parametrization for the Bayes net, but not actual values for the parameters.
* @param dataSet the discrete data set from which conditional probabilities should be estimated
* on the fly.
*/
public OnTheFlyMarginalCalculator(BayesPm bayesPm, DataSet dataSet)
throws IllegalArgumentException {
if (bayesPm == null) {
throw new NullPointerException();
}
if (dataSet == null) {
throw new NullPointerException();
}
// Make sure all of the variables in the PM are in the data set;
// otherwise, estimation is impossible.
BayesUtils.ensureVarsInData(bayesPm.getVariables(), dataSet);
// DataUtils.ensureVariablesExist(bayesPm, dataSet);
this.bayesPm = new BayesPm(bayesPm);
// Get the nodes from the BayesPm. This fixes the order of the nodes
// in the BayesIm, independently of any change to the BayesPm.
// (This order must be maintained.)
Graph graph = bayesPm.getDag();
this.nodes = graph.getNodes().toArray(new Node[0]);
// Initialize.
initialize();
// Create a subset of the data set with the variables of the IM, in
// the order of the IM.
List<Node> variables = getVariables();
this.dataSet = dataSet.subsetColumns(variables);
// Create a tautologous proposition for evidence.
this.evidence = new Evidence(Proposition.tautology(this));
}
/**
* Estimates a Bayes IM using the variables, graph, and parameters in the given Bayes PM and the
* data columns in the given data set. Each variable in the given Bayes PM must be equal to a
* variable in the given data set. The Bayes IM so estimated is used as the initial Bayes net in
* the iterative procedure implemented in the maximize method.
*/
private void estimateIM(BayesPm bayesPm, DataSet dataSet) {
if (bayesPm == null) {
throw new NullPointerException();
}
if (dataSet == null) {
throw new NullPointerException();
}
// Make sure all of the variables in the PM are in the data set;
// otherwise, estimation is impossible.
// List pmvars = bayesPm.getVariables();
// List dsvars = dataSet.getVariables();
// List obsVars = observedIm.getBayesPm().getVariables();
// System.out.println("Bayes PM as received by estimateMixedIM: ");
// System.out.println(bayesPm);
// Graph g = bayesPm.getDag();
// System.out.println(g);
// DEBUG Prints:
// System.out.println("PM VARS " + pmvars);
// System.out.println("DS VARS " + dsvars);
// System.out.println("OBS IM Vars" + obsVars);
BayesUtils.ensureVarsInData(bayesPm.getVariables(), dataSet);
// DataUtils.ensureVariablesExist(bayesPm, dataSet);
// Create a new Bayes IM to store the estimated values.
this.estimatedIm = new MlBayesIm(bayesPm, MlBayesIm.RANDOM);
int numNodes = estimatedIm.getNumNodes();
for (int node = 0; node < numNodes; node++) {
int numRows = estimatedIm.getNumRows(node);
int numCols = estimatedIm.getNumColumns(node);
int[] parentVarIndices = estimatedIm.getParents(node);
if (nodes[node].getNodeType() == NodeType.LATENT) {
continue;
}
// int nodeObsIndex = estimatedIm.getCorrespondingNodeIndex(node, observedIm);
// System.out.println("nodes[node] name = " + nodes[node].getName());
Node nodeObs = observedIm.getNode(nodes[node].getName());
// System.out.println("nodeObs name = " + nodeObs.getName());
int nodeObsIndex = observedIm.getNodeIndex(nodeObs);
// int[] parentsObs = observedIm.getParents(nodeObsIndex);
// System.out.println("For node " + nodes[node] + " parents are: ");
boolean anyParentLatent = false;
for (int parentVarIndice : parentVarIndices) {
// System.out.println(nodes[parentVarIndices[p]]);
if (nodes[parentVarIndice].getNodeType() == NodeType.LATENT) {
anyParentLatent = true;
break;
}
}
if (anyParentLatent) {
continue;
}
// At this point node is measured in bayesPm and so are its parents.
for (int row = 0; row < numRows; row++) {
// int[] parentValues = estimatedIm.getParentValues(node, row);
// estimatedIm.randomizeRow(node, row);
// if the node and all its parents are measured get the probs
// from observedIm
// loop:
for (int col = 0; col < numCols; col++) {
double p = observedIm.getProbability(nodeObsIndex, row, col);
estimatedIm.setProbability(node, row, col, p);
}
}
}
}
