Examples with DiscreteVariable edu.cmu.tetrad.data.DiscreteVariable used on opensource projects

Example 26 with DiscreteVariable

use of edu.cmu.tetrad.data.DiscreteVariable in project tetrad by cmu-phil.

the class BayesXmlParser method makeBayesPm.

private BayesPm makeBayesPm(List<Node> variables, Element element1) {
    if (!"parents".equals(element1.getQualifiedName())) {
        throw new IllegalArgumentException("Expecting 'parents' element.");
    }
    Dag graph = new Dag();
    for (Node variable : variables) {
        graph.addNode(variable);
    }
    Elements elements = element1.getChildElements();
    for (int i = 0; i < elements.size(); i++) {
        Element e1 = elements.get(i);
        if (!"parentsFor".equals(e1.getQualifiedName())) {
            throw new IllegalArgumentException("Expecting 'parentsFor' element.");
        }
        String varName = e1.getAttributeValue("name");
        Node var = namesToVars.get(varName);
        Elements elements1 = e1.getChildElements();
        for (int j = 0; j < elements1.size(); j++) {
            Element e2 = elements1.get(j);
            if (!"parent".equals(e2.getQualifiedName())) {
                throw new IllegalArgumentException("Expecting 'parent' element.");
            }
            String parentName = e2.getAttributeValue("name");
            Node parent = namesToVars.get(parentName);
            graph.addDirectedEdge(parent, var);
        }
    }
    BayesPm bayesPm = new BayesPm(graph);
    for (Node variable1 : variables) {
        DiscreteVariable graphVariable = (DiscreteVariable) variable1;
        List<String> categories = graphVariable.getCategories();
        bayesPm.setCategories(graphVariable, categories);
    }
    return bayesPm;
}

Example 27 with DiscreteVariable

use of edu.cmu.tetrad.data.DiscreteVariable in project tetrad by cmu-phil.

the class BayesXmlParser method getVariables.

private List<Node> getVariables(Element element0) {
    if (!"bnVariables".equals(element0.getQualifiedName())) {
        throw new IllegalArgumentException("Expecting 'bnVariables' element.");
    }
    List<Node> variables = new LinkedList<>();
    Elements elements = element0.getChildElements();
    for (int i = 0; i < elements.size(); i++) {
        Element e1 = elements.get(i);
        Elements e2Elements = e1.getChildElements();
        if (!"discreteVariable".equals(e1.getQualifiedName())) {
            throw new IllegalArgumentException("Expecting 'discreteVariable' " + "element.");
        }
        String name = e1.getAttributeValue("name");
        String isLatentVal = e1.getAttributeValue("latent");
        boolean isLatent = (isLatentVal != null) && ((isLatentVal.equals("yes")));
        Integer x = new Integer(e1.getAttributeValue("x"));
        Integer y = new Integer(e1.getAttributeValue("y"));
        int numCategories = e2Elements.size();
        List<String> categories = new LinkedList<>();
        for (int j = 0; j < numCategories; j++) {
            Element e2 = e2Elements.get(j);
            if (!"category".equals(e2.getQualifiedName())) {
                throw new IllegalArgumentException("Expecting 'category' " + "element.");
            }
            categories.add(e2.getAttributeValue("name"));
        }
        DiscreteVariable var = new DiscreteVariable(name, categories);
        if (isLatent) {
            var.setNodeType(NodeType.LATENT);
        }
        var.setCenterX(x);
        var.setCenterY(y);
        variables.add(var);
    }
    namesToVars = new HashMap<>();
    for (Node v : variables) {
        String name = v.getName();
        namesToVars.put(name, v);
    }
    return variables;
}

Example 28 with DiscreteVariable

use of edu.cmu.tetrad.data.DiscreteVariable in project tetrad by cmu-phil.

the class BdeMetric method computeObservedCounts.

// public double scoreLnGam() {
// 
// double[][][] priorProbs;
// double[][] priorProbsRowSum;
// 
// Graph graph = bayesPm.getDag();
// 
// int n = graph.getNumNodes();
// 
// observedCounts = new int[n][][];
// priorProbs = new double[n][][];
// 
// int[][] observedCountsRowSum = new int[n][];
// priorProbsRowSum = new double[n][];
// 
// bayesIm = new MlBayesIm(bayesPm);
// 
// for (int i = 0; i < n; i++) {
// //int numRows = bayesImMixed.getNumRows(i);
// int numRows = bayesIm.getNumRows(i);
// observedCounts[i] = new int[numRows][];
// priorProbs[i] = new double[numRows][];
// 
// observedCountsRowSum[i] = new int[numRows];
// priorProbsRowSum[i] = new double[numRows];
// 
// //for(int j = 0; j < bayesImMixed.getNumRows(i); j++) {
// for (int j = 0; j < numRows; j++) {
// 
// observedCountsRowSum[i][j] = 0;
// priorProbsRowSum[i][j] = 0;
// 
// //int numCols = bayesImMixed.getNumColumns(i);
// int numCols = bayesIm.getNumColumns(i);
// observedCounts[i][j] = new int[numCols];
// priorProbs[i][j] = new double[numCols];
// }
// }
// 
// //At this point set values in both observedCounts and priorProbs
// computeObservedCounts();
// //Set all priorProbs (i.e. estimated counts) to 1.0.  Eventually they may be
// //supplied as a parameter of the constructor of this class.
// for (int i = 0; i < n; i++) {
// for (int j = 0; j < bayesIm.getNumRows(i); j++) {
// for (int k = 0; k < bayesIm.getNumColumns(i); k++) {
// priorProbs[i][j][k] = 1.0;
// }
// }
// }
// 
// 
// for (int i = 0; i < n; i++) {
// for (int j = 0; j < bayesIm.getNumRows(i); j++) {
// for (int k = 0; k < bayesIm.getNumColumns(i); k++) {
// observedCountsRowSum[i][j] += observedCounts[i][j][k];
// priorProbsRowSum[i][j] += priorProbs[i][j][k];
// }
// }
// }
// 
// //double outerProduct = 1.0;
// double sum = 0.0;
// 
// //Debug print
// //System.out.println("counts and priors");
// //for(int i = 0; i < n; i++)
// //    for(int j = 0; j < bayesIm.getNumRows(i); j++) {
// //        System.out.println(observedCountsRowSum[i][j] + " " + priorProbsRowSum[i][j]);
// //    }
// 
// for (int i = 0; i < n; i++) {
// 
// int qi = bayesIm.getNumRows(i);
// //double prodj = 1.0;
// double sumj = 0.0;
// for (int j = 0; j < qi; j++) {
// 
// try {
// double numerator =
// ProbUtils.lngamma(priorProbsRowSum[i][j]);
// double denom = ProbUtils.lngamma(priorProbsRowSum[i][j] +
// observedCountsRowSum[i][j]);
// //System.out.println("num = " + numerator + " denom = " + denom);
// sumj += (numerator - denom);
// } catch (Exception e) {
// e.printStackTrace();
// }
// 
// int ri = bayesIm.getNumColumns(i);
// 
// //double prodk = 1.0;
// double sumk = 0.0;
// for (int k = 0; k < ri; k++) {
// try {
// sumk += ProbUtils.lngamma(
// priorProbs[i][j][k] + observedCounts[i][j][k]) -
// ProbUtils.lngamma(priorProbs[i][j][k]);
// } catch (Exception e) {
// e.printStackTrace();
// }
// }
// 
// sumj += sumk;
// }
// sum += sumj;
// }
// 
// return sum;
// }
private void computeObservedCounts() {
    for (int j = 0; j < dataSet.getNumColumns(); j++) {
        DiscreteVariable var = (DiscreteVariable) dataSet.getVariables().get(j);
        String varName = var.getName();
        Node varNode = bayesPm.getDag().getNode(varName);
        int varIndex = bayesIm.getNodeIndex(varNode);
        int[] parentVarIndices = bayesIm.getParents(varIndex);
        // This segment is for variables with no parents:
        if (parentVarIndices.length == 0) {
            // System.out.println("No parents");
            for (int col = 0; col < var.getNumCategories(); col++) {
                observedCounts[j][0][col] = 0;
            }
            for (int i = 0; i < dataSet.getNumRows(); i++) {
                // System.out.println("Case " + i);
                // If this case has a value for ar
                observedCounts[j][0][dataSet.getInt(i, j)] += 1.0;
            // System.out.println("Adding 1.0 to " + varName +
            // " row 0 category " + mixedData[j][i]);
            }
        // Print estimated counts:
        // System.out.println("Estimated counts:  ");
        // Print counts for each value of this variable with no parents.
        // for(int m = 0; m < ar.getNumSplits(); m++)
        // System.out.print("    " + m + " " + observedCounts[j][0][m]);
        // System.out.println();
        } else {
            // For variables with parents:
            int numRows = bayesIm.getNumRows(varIndex);
            for (int row = 0; row < numRows; row++) {
                int[] parValues = bayesIm.getParentValues(varIndex, row);
                for (int col = 0; col < var.getNumCategories(); col++) {
                    observedCounts[varIndex][row][col] = 0;
                }
                for (int i = 0; i < dataSet.getNumRows(); i++) {
                    // for a case where the parent values = parValues increment the estCount
                    boolean parentMatch = true;
                    for (int p = 0; p < parentVarIndices.length; p++) {
                        if (parValues[p] != dataSet.getInt(i, parentVarIndices[p])) {
                            parentMatch = false;
                            break;
                        }
                    }
                    if (!parentMatch) {
                        // Not a matching case; go to next.
                        continue;
                    }
                    observedCounts[j][row][dataSet.getInt(i, j)] += 1;
                }
            // }
            // Print estimated counts:
            // System.out.println("Estimated counts:  ");
            // System.out.println("    Parent values:  ");
            // for (int i = 0; i < parentVarIndices.length; i++) {
            // Variable par = (Variable) dataSet.getVariableNames().get(parentVarIndices[i]);
            // System.out.print("    " + par.getNode() + " " + parValues[i] + "    ");
            // }
            // System.out.println();
            // for(int m = 0; m < ar.getNumSplits(); m++)
            // System.out.print("    " + m + " " + observedCounts[j][row][m]);
            // System.out.println();
            }
        }
    }
// else
}

Example 29 with DiscreteVariable

use of edu.cmu.tetrad.data.DiscreteVariable in project tetrad by cmu-phil.

the class EmBayesProperties method setGraph.

public final void setGraph(Graph graph) {
    if (graph == null) {
        throw new NullPointerException();
    }
    List<Node> vars = dataSet.getVariables();
    Map<String, DiscreteVariable> nodesToVars = new HashMap<>();
    for (int i = 0; i < dataSet.getNumColumns(); i++) {
        DiscreteVariable var = (DiscreteVariable) vars.get(i);
        String name = var.getName();
        Node node = new GraphNode(name);
        nodesToVars.put(node.getName(), var);
    }
    Dag dag = new Dag(graph);
    BayesPm bayesPm = new BayesPm(dag);
    List<Node> nodes = bayesPm.getDag().getNodes();
    for (Node node1 : nodes) {
        Node var = nodesToVars.get(node1.getName());
        if (var != null) {
            DiscreteVariable var2 = (DiscreteVariable) var;
            List<String> categories = var2.getCategories();
            bayesPm.setCategories(node1, categories);
        }
    }
    this.graph = graph;
    this.bayesPm = bayesPm;
    this.blankBayesIm = new MlBayesIm(bayesPm);
}

Example 30 with DiscreteVariable

use of edu.cmu.tetrad.data.DiscreteVariable in project tetrad by cmu-phil.

the class DirichletBayesIm method simulateDataHelper.

/**
 * Simulates a sample with the given sample size.
 *
 * @param sampleSize      the sample size.
 * @param randomUtil      optional random number generator to use when
 *                        creating the data
 * @param latentDataSaved true iff data for latent variables should be
 *                        saved.
 * @return the simulated sample as a DataSet.
 */
private DataSet simulateDataHelper(int sampleSize, RandomUtil randomUtil, boolean latentDataSaved) {
    int numMeasured = 0;
    int[] map = new int[nodes.length];
    List<Node> variables = new LinkedList<>();
    for (int j = 0; j < nodes.length; j++) {
        if (!latentDataSaved && nodes[j].getNodeType() != NodeType.MEASURED) {
            continue;
        }
        int numCategories = bayesPm.getNumCategories(nodes[j]);
        List<String> categories = new LinkedList<>();
        for (int k = 0; k < numCategories; k++) {
            categories.add(bayesPm.getCategory(nodes[j], k));
        }
        DiscreteVariable var = new DiscreteVariable(nodes[j].getName(), categories);
        variables.add(var);
        int index = ++numMeasured - 1;
        map[index] = j;
    }
    DataSet dataSet = new ColtDataSet(sampleSize, variables);
    constructSample(sampleSize, randomUtil, numMeasured, dataSet, map);
    return dataSet;
}