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

Example 21 with ColtDataSet

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

the class TestTransform method testSingleTransforms.

@Test
public void testSingleTransforms() {
    // build a dataset.
    List<Node> list = Arrays.asList((Node) new ContinuousVariable("x"), new ContinuousVariable("y"), new ContinuousVariable("z"));
    DataSet data = new ColtDataSet(2, list);
    data.setDouble(0, 0, 2);
    data.setDouble(1, 0, 3);
    data.setDouble(2, 0, 4);
    data.setDouble(0, 1, 1);
    data.setDouble(1, 1, 6);
    data.setDouble(2, 1, 5);
    data.setDouble(0, 2, 8);
    data.setDouble(1, 2, 8);
    data.setDouble(2, 2, 8);
    DataSet copy = new ColtDataSet((ColtDataSet) data);
    // test transforms on it.
    try {
        String eq = "z = (x + y)";
        Transformation.transform(copy, eq);
        assertTrue(copy.getDouble(0, 2) == 3.0);
        assertTrue(copy.getDouble(1, 2) == 9.0);
        assertTrue(copy.getDouble(2, 2) == 9.0);
        copy = new ColtDataSet((ColtDataSet) data);
        eq = "x = x + 3";
        Transformation.transform(copy, eq);
        assertTrue(copy.getDouble(0, 0) == 5.0);
        assertTrue(copy.getDouble(1, 0) == 6.0);
        assertTrue(copy.getDouble(2, 0) == 7.0);
        copy = new ColtDataSet((ColtDataSet) data);
        eq = "x = pow(x, 2) + y + z";
        Transformation.transform(copy, eq);
        assertTrue(copy.getDouble(0, 0) == 13.0);
        assertTrue(copy.getDouble(1, 0) == 23.0);
        assertTrue(copy.getDouble(2, 0) == 29.0);
    } catch (ParseException ex) {
        fail(ex.getMessage());
    }
}

Example 22 with ColtDataSet

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

the class TestLingamPattern method simulateDataNonNormal.

/**
 * This simulates data by picking random values for the exogenous terms and percolating this information down
 * through the SEM, assuming it is acyclic. Fast for large simulations but hangs for cyclic models.
 *
 * @param sampleSize    > 0.
 * @return the simulated data set.
 */
private DataSet simulateDataNonNormal(SemIm semIm, int sampleSize, List<Distribution> distributions) {
    List<Node> variables = new LinkedList<>();
    List<Node> variableNodes = semIm.getSemPm().getVariableNodes();
    for (Node node : variableNodes) {
        ContinuousVariable var = new ContinuousVariable(node.getName());
        variables.add(var);
    }
    DataSet dataSet = new ColtDataSet(sampleSize, variables);
    // Create some index arrays to hopefully speed up the simulation.
    SemGraph graph = semIm.getSemPm().getGraph();
    List<Node> tierOrdering = graph.getCausalOrdering();
    int[] tierIndices = new int[variableNodes.size()];
    for (int i = 0; i < tierIndices.length; i++) {
        tierIndices[i] = variableNodes.indexOf(tierOrdering.get(i));
    }
    int[][] _parents = new int[variables.size()][];
    for (int i = 0; i < variableNodes.size(); i++) {
        Node node = variableNodes.get(i);
        List<Node> parents = graph.getParents(node);
        for (Iterator<Node> j = parents.iterator(); j.hasNext(); ) {
            Node _node = j.next();
            if (_node.getNodeType() == NodeType.ERROR) {
                j.remove();
            }
        }
        _parents[i] = new int[parents.size()];
        for (int j = 0; j < parents.size(); j++) {
            Node _parent = parents.get(j);
            _parents[i][j] = variableNodes.indexOf(_parent);
        }
    }
    // Do the simulation.
    for (int row = 0; row < sampleSize; row++) {
        for (int i = 0; i < tierOrdering.size(); i++) {
            int col = tierIndices[i];
            Distribution distribution = distributions.get(col);
            // System.out.println(distribution);
            double value = distribution.nextRandom();
            for (int j = 0; j < _parents[col].length; j++) {
                int parent = _parents[col][j];
                value += dataSet.getDouble(row, parent) * semIm.getEdgeCoef().get(parent, col);
            }
            value += semIm.getMeans()[col];
            dataSet.setDouble(row, col, value);
        }
    }
    return dataSet;
}

Example 23 with ColtDataSet

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

the class TestKernelGaussian method testMedianBandwidth.

/**
 * Tests the bandwidth setting to the median distance between points in the sample
 */
@Test
public void testMedianBandwidth() {
    Node X = new ContinuousVariable("X");
    DataSet dataset = new ColtDataSet(5, Arrays.asList(X));
    dataset.setDouble(0, 0, 1);
    dataset.setDouble(1, 0, 2);
    dataset.setDouble(2, 0, 3);
    dataset.setDouble(3, 0, 4);
    dataset.setDouble(4, 0, 5);
    KernelGaussian kernel = new KernelGaussian(dataset, X);
    assertTrue(kernel.getBandwidth() == 2);
}

Example 24 with ColtDataSet

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

the class BayesUpdaterClassifierEditor method showClassification.

private void showClassification() {
    int tabIndex = -1;
    for (int i = 0; i < getTabbedPane().getTabCount(); i++) {
        if ("Classification".equals(getTabbedPane().getTitleAt(i))) {
            getTabbedPane().remove(i);
            tabIndex = i;
        }
    }
    // Put the class information into a DataSet.
    int[] classifications = getClassifier().getClassifications();
    double[][] marginals = getClassifier().getMarginals();
    int maxCategory = 0;
    for (int classification : classifications) {
        if (classification > maxCategory) {
            maxCategory = classification;
        }
    }
    List<Node> variables = new LinkedList<>();
    DiscreteVariable targetVariable = classifier.getTargetVariable();
    DiscreteVariable classVar = new DiscreteVariable(targetVariable.getName(), maxCategory + 1);
    variables.add(classVar);
    for (int i = 0; i < marginals.length; i++) {
        String name = "P(" + targetVariable + "=" + i + ")";
        ContinuousVariable scoreVar = new ContinuousVariable(name);
        variables.add(scoreVar);
    }
    classVar.setName("Result");
    DataSet dataSet = new ColtDataSet(classifications.length, variables);
    for (int i = 0; i < classifications.length; i++) {
        dataSet.setInt(i, 0, classifications[i]);
        for (int j = 0; j < marginals.length; j++) {
            dataSet.setDouble(i, j + 1, marginals[j][i]);
        }
    }
    DataDisplay jTable = new DataDisplay(dataSet);
    JScrollPane scroll = new JScrollPane(jTable);
    if (tabIndex == -1) {
        getTabbedPane().add("Classification", scroll);
    } else {
        getTabbedPane().add(scroll, tabIndex);
        getTabbedPane().setTitleAt(tabIndex, "Classification");
    }
}

Example 25 with ColtDataSet

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

the class MlBayesImObs method simulateTimeSeries.

private DataSet simulateTimeSeries(int sampleSize) {
    TimeLagGraph timeSeriesGraph = getBayesPm().getDag().getTimeLagGraph();
    List<Node> variables = new ArrayList<>();
    for (Node node : timeSeriesGraph.getLag0Nodes()) {
        variables.add(new DiscreteVariable(timeSeriesGraph.getNodeId(node).getName()));
    }
    List<Node> lag0Nodes = timeSeriesGraph.getLag0Nodes();
    DataSet fullData = new ColtDataSet(sampleSize, 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;
}