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

Example 11 with DiscreteVariable

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

the class BayesUpdaterClassifierEditor method getToolbar.

private Component getToolbar() {
    JButton classifyButton = new JButton("Classify");
    classifyButton.addActionListener(new ActionListener() {

        public void actionPerformed(ActionEvent e) {
            Window owner = (Window) getTopLevelAncestor();
            new WatchedProcess(owner) {

                public void watch() {
                    doClassify();
                    showClassification();
                    showRocCurve();
                    showConfusionMatrix();
                }
            };
        }
    });
    List<Node> nodes = getClassifier().getBayesImVars();
    Node[] variables = nodes.toArray(new Node[0]);
    this.variableDropdown = new JComboBox(variables);
    getVariableDropdown().setBackground(Color.WHITE);
    getVariableDropdown().setMaximumSize(new Dimension(200, 50));
    DiscreteVariable variable = (DiscreteVariable) getVariableDropdown().getSelectedItem();
    this.categoryDropdown = new JComboBox(variable.getCategories().toArray(new String[0]));
    getCategoryDropdown().setBackground(Color.WHITE);
    getCategoryDropdown().setMaximumSize(new Dimension(200, 50));
    this.variableDropdown.addItemListener(new ItemListener() {

        public void itemStateChanged(ItemEvent e) {
            JComboBox comboBox = (JComboBox) e.getSource();
            Object selectedItem = comboBox.getSelectedItem();
            DiscreteVariable variable = (DiscreteVariable) selectedItem;
            List<String> categories = variable.getCategories();
            DefaultComboBoxModel newModel = new DefaultComboBoxModel(categories.toArray(new String[0]));
            getCategoryDropdown().setModel(newModel);
        // if (categories.size() == 2) {
        // getBinaryCutoffField().setEnabled(true);
        // getBinaryCutoffField().setEditable(true);
        // }
        // else {
        // getBinaryCutoffField().setEnabled(false);
        // getBinaryCutoffField().setEditable(false);
        // }
        }
    });
    this.categoryDropdown.addItemListener(new ItemListener() {

        public void itemStateChanged(ItemEvent e) {
            showRocCurve();
        }
    });
    // this.binaryCutoffField = new DoubleTextField(getBinaryCutoff(), 5,
    // NumberFormatUtil.getInstance().getNumberFormat());
    // this.binaryCutoffField.setFilter(new DoubleTextField.Filter() {
    // public double filter(double value, double oldValue) {
    // if (value >= 0.0 && value <= 1.0) {
    // setBinaryCutoff(value);
    // return value;
    // }
    // 
    // return oldValue;
    // }
    // });
    // DiscreteVariable selectedVar =
    // (DiscreteVariable) this.variableDropdown.getSelectedItem();
    // List<String> categories = selectedVar.getCategories();
    // 
    // if (categories.size() == 2) {
    // getBinaryCutoffField().setEnabled(true);
    // getBinaryCutoffField().setEditable(true);
    // }
    // else {
    // getBinaryCutoffField().setEnabled(false);
    // getBinaryCutoffField().setEditable(false);
    // }
    Box toolbar = Box.createVerticalBox();
    Box row1 = Box.createHorizontalBox();
    row1.add(Box.createHorizontalStrut(5));
    row1.add(new JLabel("Target = "));
    row1.add(getVariableDropdown());
    row1.add(Box.createHorizontalStrut(5));
    row1.add(new JLabel("Category for ROC ="));
    row1.add(getCategoryDropdown());
    row1.add(Box.createHorizontalStrut(10));
    row1.add(classifyButton);
    row1.add(Box.createHorizontalGlue());
    toolbar.add(row1);
    toolbar.add(Box.createVerticalStrut(5));
    // Box row2 = Box.createHorizontalBox();
    // row2.add(Box.createHorizontalStrut(5));
    // row2.add(new JLabel("(Cutoff for binary target = "));
    // row2.add(getBinaryCutoffField());
    // row2.add(new JLabel(" )"));
    // row2.add(Box.createHorizontalGlue());
    // toolbar.add(row2);
    toolbar.setBorder(new EmptyBorder(2, 2, 2, 2));
    return toolbar;
}

Example 12 with DiscreteVariable

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

the class MGM method runTests1.

private static void runTests1() {
    try {
        // DoubleMatrix2D xIn = DoubleFactory2D.dense.make(loadDataSelect("/Users/ajsedgewick/tetrad/test_data", "med_test_C.txt"));
        // DoubleMatrix2D yIn = DoubleFactory2D.dense.make(loadDataSelect("/Users/ajsedgewick/tetrad/test_data", "med_test_D.txt"));
        // String path = MGM.class.getResource("test_data").getPath();
        String path = "/Users/ajsedgewick/tetrad_master/tetrad/tetrad-lib/src/main/java/edu/pitt/csb/mgm/test_data";
        System.out.println(path);
        DoubleMatrix2D xIn = DoubleFactory2D.dense.make(MixedUtils.loadDelim(path, "med_test_C.txt").getDoubleData().toArray());
        DoubleMatrix2D yIn = DoubleFactory2D.dense.make(MixedUtils.loadDelim(path, "med_test_D.txt").getDoubleData().toArray());
        int[] L = new int[24];
        Node[] vars = new Node[48];
        for (int i = 0; i < 24; i++) {
            L[i] = 2;
            vars[i] = new ContinuousVariable("X" + i);
            vars[i + 24] = new DiscreteVariable("Y" + i);
        }
        double lam = .2;
        MGM model = new MGM(xIn, yIn, new ArrayList<>(Arrays.asList(vars)), L, new double[] { lam, lam, lam });
        MGM model2 = new MGM(xIn, yIn, new ArrayList<>(Arrays.asList(vars)), L, new double[] { lam, lam, lam });
        System.out.println("Weights: " + Arrays.toString(model.weights.toArray()));
        DoubleMatrix2D test = xIn.copy();
        DoubleMatrix2D test2 = xIn.copy();
        long t = System.currentTimeMillis();
        for (int i = 0; i < 50000; i++) {
            test2 = xIn.copy();
            test.assign(test2);
        }
        System.out.println("assign Time: " + (System.currentTimeMillis() - t));
        t = System.currentTimeMillis();
        double[][] xArr = xIn.toArray();
        for (int i = 0; i < 50000; i++) {
            if (Thread.currentThread().isInterrupted()) {
                break;
            }
            // test = DoubleFactory2D.dense.make(xArr);
            test2 = xIn.copy();
            test = test2;
        }
        System.out.println("equals Time: " + (System.currentTimeMillis() - t));
        System.out.println("Init nll: " + model.smoothValue(model.params.toMatrix1D()));
        System.out.println("Init reg term: " + model.nonSmoothValue(model.params.toMatrix1D()));
        t = System.currentTimeMillis();
        model.learnEdges(700);
        // model.learn(1e-7, 700);
        System.out.println("Orig Time: " + (System.currentTimeMillis() - t));
        System.out.println("nll: " + model.smoothValue(model.params.toMatrix1D()));
        System.out.println("reg term: " + model.nonSmoothValue(model.params.toMatrix1D()));
        System.out.println("params:\n" + model.params);
        System.out.println("adjMat:\n" + model.adjMatFromMGM());
    } catch (IOException ex) {
        ex.printStackTrace();
    }
}

Example 13 with DiscreteVariable

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

the class DataConvertUtils method toMixedDataBox.

public static DataModel toMixedDataBox(MixedTabularDataset mixedTabularDataset) {
    int numOfRows = mixedTabularDataset.getNumOfRows();
    MixedVarInfo[] mixedVarInfos = mixedTabularDataset.getMixedVarInfos();
    double[][] continuousData = mixedTabularDataset.getContinuousData();
    int[][] discreteData = mixedTabularDataset.getDiscreteData();
    List<Node> nodes = new LinkedList<>();
    for (MixedVarInfo mixedVarInfo : mixedVarInfos) {
        if (mixedVarInfo.isContinuous()) {
            nodes.add(new ContinuousVariable(mixedVarInfo.getName()));
        } else {
            nodes.add(new DiscreteVariable(mixedVarInfo.getName(), mixedVarInfo.getCategories()));
        }
    }
    return new BoxDataSet(new MixedDataBox(nodes, numOfRows, continuousData, discreteData), nodes);
}

Example 14 with DiscreteVariable

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

the class TestLogisticRegression method test1.

@Test
public void test1() {
    List<Node> nodes = new ArrayList<>();
    for (int i = 0; i < 5; i++) {
        nodes.add(new ContinuousVariable("X" + (i + 1)));
    }
    Graph graph = new Dag(GraphUtils.randomGraph(nodes, 0, 5, 3, 3, 3, false));
    System.out.println(graph);
    SemPm pm = new SemPm(graph);
    SemIm im = new SemIm(pm);
    DataSet data = im.simulateDataRecursive(1000, false);
    Node x1 = data.getVariable("X1");
    Node x2 = data.getVariable("X2");
    Node x3 = data.getVariable("X3");
    Node x4 = data.getVariable("X4");
    Node x5 = data.getVariable("X5");
    Discretizer discretizer = new Discretizer(data);
    discretizer.equalCounts(x1, 2);
    DataSet d2 = discretizer.discretize();
    LogisticRegression regression = new LogisticRegression(d2);
    List<Node> regressors = new ArrayList<>();
    regressors.add(x2);
    regressors.add(x3);
    regressors.add(x4);
    regressors.add(x5);
    DiscreteVariable x1b = (DiscreteVariable) d2.getVariable("X1");
    regression.regress(x1b, regressors);
    System.out.println(regression);
}

Example 15 with DiscreteVariable

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

the class AdLeafTree method getCellLeaves.

/**
 * Finds the set of indices into the leaves of the tree for the given variables.
 * Counts are the sizes of the index sets.
 *
 * @param A A list of discrete variables.
 * @return The list of index sets of the first variable varied by the second variable,
 * and so on, to the last variable.
 */
public List<List<Integer>> getCellLeaves(List<DiscreteVariable> A) {
    Collections.sort(A, new Comparator<DiscreteVariable>() {

        @Override
        public int compare(DiscreteVariable o1, DiscreteVariable o2) {
            return Integer.compare(nodesHash.get(o1), nodesHash.get(o2));
        }
    });
    if (baseCase == null) {
        Vary vary = new Vary();
        this.baseCase = new ArrayList<>();
        baseCase.add(vary);
    }
    List<Vary> varies = baseCase;
    for (DiscreteVariable v : A) {
        varies = getVaries(varies, nodesHash.get(v));
    }
    List<List<Integer>> rows = new ArrayList<>();
    for (Vary vary : varies) {
        rows.addAll(vary.getRows());
    }
    return rows;
}