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

Example 91 with Node

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

the class BooleanGlassSimulation method createData.

@Override
public void createData(Parameters parameters) {
    this.graph = randomGraph.createGraph(parameters);
    LagGraphParams params = new LagGraphParams(parameters);
    params.setIndegree(2);
    params.setMlag(1);
    RandomActiveLagGraph _graph = new RandomActiveLagGraph(params);
    BooleanGlassGenePm pm = new BooleanGlassGenePm(_graph);
    BooleanGlassGeneIm im = new BooleanGlassGeneIm(pm, parameters);
    DataModelList data = im.simulateData();
    List<DataSet> dataSets = new ArrayList<>();
    for (DataModel model : data) {
        dataSets.add((DataSet) model);
    }
    this.dataSets = dataSets;
    List<String> factors = new ArrayList<>(_graph.getFactors());
    Map<String, Node> nodes = new HashMap<>();
    for (String factor : factors) {
        nodes.put(factor, new ContinuousVariable(factor));
    }
    TimeLagGraph graph = new TimeLagGraph();
    graph.setMaxLag(_graph.getMaxLag());
    for (String factor : factors) {
        graph.addNode(nodes.get(factor));
    }
    for (String factor : factors) {
        for (Object o : _graph.getParents(factor)) {
            LaggedFactor laggedFactor = (LaggedFactor) o;
            String _factor = laggedFactor.getFactor();
            int lag = laggedFactor.getLag();
            Node node1 = graph.getNode(_factor + ":" + lag);
            Node node2 = graph.getNode(factor);
            graph.addDirectedEdge(node1, node2);
        }
    }
    topToBottomLayout(graph);
    this.graph = graph;
}

Example 92 with Node

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

the class GeneralSemSimulation method getPm.

private GeneralizedSemPm getPm(Graph graph, Parameters parameters) {
    GeneralizedSemPm pm = new GeneralizedSemPm(graph);
    List<Node> variablesNodes = pm.getVariableNodes();
    List<Node> errorNodes = pm.getErrorNodes();
    String measuredFunction = parameters.getString("generalSemFunctionTemplateMeasured");
    String latentFunction = parameters.getString("generalSemFunctionTemplateLatent");
    String error = parameters.getString("generalSemErrorTemplate");
    try {
        for (Node node : variablesNodes) {
            if (node.getNodeType() == NodeType.LATENT) {
                String _template = TemplateExpander.getInstance().expandTemplate(latentFunction, pm, node);
                pm.setNodeExpression(node, _template);
            } else {
                String _template = TemplateExpander.getInstance().expandTemplate(measuredFunction, pm, node);
                pm.setNodeExpression(node, _template);
            }
        }
        for (Node node : errorNodes) {
            String _template = TemplateExpander.getInstance().expandTemplate(error, pm, node);
            pm.setNodeExpression(node, _template);
        }
    } catch (ParseException e) {
        System.out.println(e);
    }
    return pm;
}

Example 93 with Node

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

the class LinearSineSimulation method simulate.

private DataSet simulate(Graph G, Parameters parameters) {
    HashMap<String, Integer> nd = new HashMap<>();
    List<Node> nodes = G.getNodes();
    Collections.shuffle(nodes);
    if (this.shuffledOrder == null) {
        List<Node> shuffledNodes = new ArrayList<>(nodes);
        Collections.shuffle(shuffledNodes);
        this.shuffledOrder = shuffledNodes;
    }
    for (int i = 0; i < nodes.size(); i++) {
        nd.put(shuffledOrder.get(i).getName(), 0);
    }
    G = makeMixedGraph(G, nd);
    nodes = G.getNodes();
    DataSet mixedData = new BoxDataSet(new MixedDataBox(nodes, parameters.getInt("sampleSize")), nodes);
    List<Node> tierOrdering = G.getCausalOrdering();
    int[] tiers = new int[tierOrdering.size()];
    for (int t = 0; t < tierOrdering.size(); t++) {
        tiers[t] = nodes.indexOf(tierOrdering.get(t));
    }
    for (int mixedIndex : tiers) {
        ContinuousVariable child = (ContinuousVariable) nodes.get(mixedIndex);
        ArrayList<ContinuousVariable> continuousParents = new ArrayList<>();
        for (Node node : G.getParents(child)) {
            continuousParents.add((ContinuousVariable) node);
        }
        HashMap<String, double[]> intercept = new HashMap<>();
        HashMap<String, double[]> linear = new HashMap<>();
        HashMap<String, double[]> beta = new HashMap<>();
        HashMap<String, double[]> gamma = new HashMap<>();
        HashMap<String, double[]> bounds = new HashMap<>();
        for (int j = 1; j <= continuousParents.size(); j++) {
            String key = continuousParents.get(j - 1).toString();
            if (!bounds.containsKey(key)) {
                double m0 = mixedData.getDouble(0, mixedData.getColumn(continuousParents.get(j - 1)));
                double m1 = mixedData.getDouble(0, mixedData.getColumn(continuousParents.get(j - 1)));
                for (int i = 1; i < parameters.getInt("sampleSize"); i++) {
                    m0 = Math.min(m0, mixedData.getDouble(i, mixedData.getColumn(continuousParents.get(j - 1))));
                    m1 = Math.max(m1, mixedData.getDouble(i, mixedData.getColumn(continuousParents.get(j - 1))));
                }
                double[] temp = new double[3];
                temp[0] = m0;
                temp[1] = (m1 - m0) / 2;
                temp[2] = m1;
                bounds.put(key, temp);
            }
        }
        double mean = 0;
        double var = 0;
        for (int i = 0; i < parameters.getInt("sampleSize"); i++) {
            double[] parents = new double[continuousParents.size()];
            double value = 0;
            String key = "";
            for (int j = 1; j <= continuousParents.size(); j++) parents[j - 1] = mixedData.getDouble(i, mixedData.getColumn(continuousParents.get(j - 1)));
            if (!intercept.containsKey(key)) {
                double[] interceptCoefficients = new double[1];
                interceptCoefficients[0] = randSign() * RandomUtil.getInstance().nextUniform(interceptLow, interceptHigh);
                intercept.put(key, interceptCoefficients);
            }
            if (!linear.containsKey(key) && !continuousParents.isEmpty()) {
                double[] linearCoefficients = new double[parents.length];
                for (int j = 0; j < parents.length; j++) linearCoefficients[j] = randSign() * RandomUtil.getInstance().nextUniform(linearLow, linearHigh);
                linear.put(key, linearCoefficients);
            }
            if (!beta.containsKey(key) && !continuousParents.isEmpty()) {
                double[] betaCoefficients = new double[parents.length];
                for (int j = 0; j < parents.length; j++) betaCoefficients[j] = randSign() * RandomUtil.getInstance().nextUniform(betaLow, betaHigh);
                beta.put(key, betaCoefficients);
            }
            if (!gamma.containsKey(key) && !continuousParents.isEmpty()) {
                double[] gammaCoefficients = new double[parents.length];
                for (int j = 0; j < parents.length; j++) {
                    String key2 = continuousParents.get(j).toString();
                    gammaCoefficients[j] = (bounds.get(key2)[1] - bounds.get(key2)[0]) / (2 * Math.PI * RandomUtil.getInstance().nextUniform(gammaLow, gammaHigh));
                }
                gamma.put(key, gammaCoefficients);
            }
            value += intercept.get(key)[0];
            if (!continuousParents.isEmpty()) {
                for (int x = 0; x < parents.length; x++) {
                    String key2 = continuousParents.get(x).toString();
                    value += linear.get(key)[x] * parents[x] + beta.get(key)[x] * Math.sin(parents[x] / (gamma.get(key)[x]));
                }
            }
            mixedData.setDouble(i, mixedIndex, value);
            mean += value;
            var += Math.pow(value, 2);
        }
        if (continuousParents.size() == 0) {
            var = 1;
        } else {
            mean /= mixedData.getNumRows();
            var /= mixedData.getNumRows();
            var -= Math.pow(mean, 2);
            var = Math.sqrt(var);
        }
        double noiseVar = RandomUtil.getInstance().nextUniform(varLow, varHigh);
        for (int i = 0; i < parameters.getInt("sampleSize"); i++) {
            mixedData.setDouble(i, mixedIndex, mixedData.getDouble(i, mixedIndex) + var * RandomUtil.getInstance().nextNormal(0, noiseVar));
        }
    }
    return mixedData;
}

Example 94 with Node

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

the class GeneralSemSimulationSpecial1 method getPm.

private GeneralizedSemPm getPm(Graph graph) {
    GeneralizedSemPm pm = new GeneralizedSemPm(graph);
    List<Node> variablesNodes = pm.getVariableNodes();
    List<Node> errorNodes = pm.getErrorNodes();
    Map<String, String> paramMap = new HashMap<>();
    String[] funcs = { "TSUM(NEW(B)*$)", "TSUM(NEW(B)*$+NEW(C)*sin(NEW(T)*$+NEW(A)))", "TSUM(NEW(B)*(.5*$ + .5*(sqrt(abs(NEW(b)*$+NEW(exoErrorType))) ) ) )" };
    paramMap.put("s", "U(1,3)");
    paramMap.put("B", "Split(-1.5,-.5,.5,1.5)");
    paramMap.put("C", "Split(-1.5,-.5,.5,1.5)");
    paramMap.put("T", "U(.5,1.5)");
    paramMap.put("A", "U(0,.25)");
    paramMap.put("exoErrorType", "U(-.5,.5)");
    paramMap.put("funcType", "U(1,5)");
    String nonlinearStructuralEdgesFunction = funcs[0];
    String nonlinearFactorMeasureEdgesFunction = funcs[0];
    try {
        for (Node node : variablesNodes) {
            if (node.getNodeType() == NodeType.LATENT) {
                String _template = TemplateExpander.getInstance().expandTemplate(nonlinearStructuralEdgesFunction, pm, node);
                pm.setNodeExpression(node, _template);
            } else {
                String _template = TemplateExpander.getInstance().expandTemplate(nonlinearFactorMeasureEdgesFunction, pm, node);
                pm.setNodeExpression(node, _template);
            }
        }
        for (Node node : errorNodes) {
            String _template = TemplateExpander.getInstance().expandTemplate("U(-.5,.5)", pm, node);
            pm.setNodeExpression(node, _template);
        }
        Set<String> parameters = pm.getParameters();
        for (String parameter : parameters) {
            for (String type : paramMap.keySet()) {
                if (parameter.startsWith(type)) {
                    pm.setParameterExpression(parameter, paramMap.get(type));
                }
            }
        }
    } catch (ParseException e) {
        System.out.println(e);
    }
    return pm;
}

Example 95 with Node

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

the class LoadContinuousDataSmithSim method readGraph.

public Graph readGraph(File file) {
    try {
        DataReader reader = new DataReader();
        reader.setVariablesSupplied(false);
        reader.setDelimiter(DelimiterType.COMMA);
        DataSet data = reader.parseTabular(file);
        List<Node> variables = data.getVariables();
        Graph graph = new EdgeListGraph(variables);
        for (int i = 0; i < variables.size(); i++) {
            for (int j = 0; j < variables.size(); j++) {
                if (i == j)
                    continue;
                if (data.getDouble(i, j) != 0) {
                    graph.addDirectedEdge(variables.get(i), variables.get(j));
                }
            }
        }
        return graph;
    } catch (IOException e) {
        throw new RuntimeException(e);
    }
}