Examples with TimeLagGraph edu.cmu.tetrad.graph.TimeLagGraph used on opensource projects

Example 6 with TimeLagGraph

use of edu.cmu.tetrad.graph.TimeLagGraph in project tetrad by cmu-phil.

the class MlBayesIm method simulateTimeSeries.

private DataSet simulateTimeSeries(int sampleSize) {
    TimeLagGraph timeSeriesGraph = getBayesPm().getDag().getTimeLagGraph();
    List<Node> variables = new ArrayList<>();
    for (Node node : timeSeriesGraph.getLag0Nodes()) {
        final DiscreteVariable e = new DiscreteVariable(timeSeriesGraph.getNodeId(node).getName());
        e.setNodeType(node.getNodeType());
        variables.add(e);
    }
    List<Node> lag0Nodes = timeSeriesGraph.getLag0Nodes();
    // DataSet fullData = new ColtDataSet(sampleSize, variables);
    DataSet fullData = new BoxDataSet(new VerticalIntDataBox(sampleSize, variables.size()), variables);
    Graph contemporaneousDag = timeSeriesGraph.subgraph(lag0Nodes);
    List<Node> tierOrdering = contemporaneousDag.getCausalOrdering();
    int[] tiers = new int[tierOrdering.size()];
    for (int i = 0; i < tierOrdering.size(); i++) {
        tiers[i] = getNodeIndex(tierOrdering.get(i));
    }
    // Construct the sample.
    int[] combination = new int[tierOrdering.size()];
    for (int i = 0; i < sampleSize; i++) {
        int[] point = new int[nodes.length];
        for (int nodeIndex : tiers) {
            double cutoff = RandomUtil.getInstance().nextDouble();
            for (int k = 0; k < getNumParents(nodeIndex); k++) {
                combination[k] = point[getParent(nodeIndex, k)];
            }
            int rowIndex = getRowIndex(nodeIndex, combination);
            double sum = 0.0;
            for (int k = 0; k < getNumColumns(nodeIndex); k++) {
                double probability = getProbability(nodeIndex, rowIndex, k);
                if (Double.isNaN(probability)) {
                    throw new IllegalStateException("Some probability " + "values in the BayesIm are not filled in; " + "cannot simulate data.");
                }
                sum += probability;
                if (sum >= cutoff) {
                    point[nodeIndex] = k;
                    break;
                }
            }
        }
    }
    return fullData;
}