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

Example 31 with DiscreteVariable

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

the class BayesUtils method ensureVarsInData.

/**
 * Ensures that the discrete variables in the given list are compatible with
 * the variables in the data set by the same names. If a variable x in the
 * list has a superlist of the categories for x in the data, the data
 * variables is changed.
 *
 * @throws IllegalArgumentException if a variable exists in the list for
 *                                  which no variable occurs in the data by
 *                                  the same name or if the variable in the
 *                                  data by the same name does not have a
 *                                  subset of its categories.
 */
public static void ensureVarsInData(List<Node> pmVars, DataSet dataSet) {
    for (Node pmVar1 : pmVars) {
        DiscreteVariable pmVar = (DiscreteVariable) pmVar1;
        String name = pmVar.getName();
        DiscreteVariable from = (DiscreteVariable) dataSet.getVariable(name);
        if (from == null) {
            throw new IllegalArgumentException("Variable " + pmVar + " was not in the data.");
        }
        List<String> pmCategories = pmVar.getCategories();
        List<String> dataCategories = from.getCategories();
        if (!pmCategories.equals(dataCategories)) {
            if (pmCategories.containsAll(dataCategories)) {
                DiscreteVariable to = new DiscreteVariable(pmVar);
                dataSet.changeVariable(from, to);
            } else {
                throw new IllegalArgumentException("Variable '" + name + "' " + "has more categories in the data than in the model." + "\n\tIn the model, the categories are: " + pmCategories + "." + "\n\tIn the data, the categories are: " + dataCategories + ".");
            }
        }
    }
}

Example 32 with DiscreteVariable

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

the class TabularDataTable method pasteIntoColumn.

/**
 * The row and column indices are JTable indices.
 */
private void pasteIntoColumn(int row, int col, Object value) {
    int dataRow = row - getNumLeadingRows();
    int dataCol = col - getNumLeadingCols();
    Node variable = dataSet.getVariable(dataCol);
    if (variable instanceof ContinuousVariable && value instanceof Number) {
        dataSet.setObject(dataRow, dataCol, value);
        // dataSet.setDouble(dataRow, dataCol, (Double) value);
        return;
    }
    if ("".equals(value) || value == null) {
        return;
    }
    String valueTrimmed = ((String) value).trim();
    boolean quoted = false;
    if (valueTrimmed.startsWith("\"") && valueTrimmed.endsWith("\"")) {
        value = valueTrimmed.substring(1, valueTrimmed.length() - 1);
        quoted = true;
    }
    if (!(variable instanceof DiscreteVariable) && isEmpty(dataSet, dataCol) && (quoted || !isNumber((String) value))) {
        variable = swapDiscreteColumnForContinuous(col);
    }
    if (value instanceof String && ((String) value).trim().equals("*")) {
        value = ((Variable) variable).getMissingValueMarker();
    }
    dataSet.setObject(dataRow, dataCol, value);
    pcs.firePropertyChange("modelChanged", null, null);
}

Example 33 with DiscreteVariable

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

the class TabularDataTable method getValueAt.

/**
 * @return the value at the given (row, col) coordinates of the table as an
 * Object. If the variable for the col is a DiscreteVariable, the String
 * value (as opposed to the integer index value) is extracted and returned.
 * If the coordinates are out of range of the wrapped table model, 'null' is
 * returned. Otherwise, the value stored in the wrapped table model at the
 * given coordinates is returned.
 */
@Override
public Object getValueAt(int row, int col) {
    int columnIndex = col - getNumLeadingCols();
    int rowIndex = row - 2;
    // else
    if (col >= getNumLeadingCols() && col < dataSet.getNumColumns() + getNumLeadingCols()) {
        Node variable = dataSet.getVariable(columnIndex);
        if (row == 0) {
            boolean discrete = variable instanceof DiscreteVariable;
            return "C" + Integer.toString(columnIndex + 1) + (discrete ? "-T" : "");
        } else if (row == 1) {
            return dataSet.getVariable(columnIndex).getName();
        } else if (rowIndex >= dataSet.getNumRows()) {
            return null;
        } else {
            if (variable instanceof DiscreteVariable) {
                ((DiscreteVariable) variable).setCategoryNamesDisplayed(isCategoryNamesShown());
            }
            Object value = dataSet.getObject(rowIndex, columnIndex);
            if (((Variable) variable).isMissingValue(value)) {
                return "*";
            } else {
                return value;
            }
        }
    } else if (col >= dataSet.getNumColumns() + getNumLeadingCols()) {
        if (row == 0) {
            return "C" + Integer.toString(columnIndex + 1);
        }
    }
    return null;
}

Example 34 with DiscreteVariable

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

the class MNLRLikelihood method getDoF.

public double getDoF(int child_index, int[] parents) {
    double dof = 0;
    Node c = variables.get(child_index);
    List<ContinuousVariable> continuous_parents = new ArrayList<>();
    List<DiscreteVariable> discrete_parents = new ArrayList<>();
    for (int p : parents) {
        Node parent = variables.get(p);
        if (parent instanceof ContinuousVariable) {
            continuous_parents.add((ContinuousVariable) parent);
        } else {
            discrete_parents.add((DiscreteVariable) parent);
        }
    }
    int p = continuous_parents.size();
    List<List<Integer>> cells = adTree.getCellLeaves(discrete_parents);
    // List<List<Integer>> cells = partition(discrete_parents, 0).cells;
    int[] continuousCols = new int[p];
    for (int j = 0; j < p; j++) continuousCols[j] = nodesHash.get(continuous_parents.get(j));
    for (List<Integer> cell : cells) {
        int r = cell.size();
        if (r > 0) {
            int degree = fDegree;
            if (fDegree < 1) {
                degree = (int) Math.floor(Math.log(r));
            }
            if (c instanceof ContinuousVariable) {
                dof += degree * continuous_parents.size() + 1;
            } else {
                dof += ((degree * continuous_parents.size()) + 1) * (((DiscreteVariable) c).getNumCategories() - 1);
            }
        }
    }
    return dof;
}

Example 35 with DiscreteVariable

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

the class LogisticRegressionParamsPanel method actionPerformed.

public void actionPerformed(ActionEvent e) {
    String varName;
    int varSelectionIndex, predictorVarSelectionIndex;
    DefaultListModel varsModel = (DefaultListModel) availableVarsList.getModel();
    DefaultListModel predsModel = (DefaultListModel) predictorVarListbox.getModel();
    int startIndexVars = varsSelModel.getMinSelectionIndex();
    int endIndexVars = varsSelModel.getMaxSelectionIndex();
    int startIndexPreds = predsSelModel.getMinSelectionIndex();
    int endIndexPreds = predsSelModel.getMaxSelectionIndex();
    String[] varSelectedNames = new String[endIndexVars - startIndexVars + 1];
    String[] predSelectedNames = new String[endIndexPreds - startIndexPreds + 1];
    if (varsModel.size() != 0) {
        varName = (String) availableVarsList.getSelectedValue();
    } else {
        varName = "";
    }
    /* include/exclude response variable */
    if (e.getActionCommand().equals(INCLUDE_RESPONSE)) {
        if ((availableVarsList.isSelectionEmpty()) && (responseButton.getIsIncluded())) {
            return;
        }
        if (responseButton.getIsIncluded()) {
            // Make sure response variable is dichotomous
            DataSet cds = (DataSet) dataModel;
            int nrows = cds.getNumRows();
            Node variable = cds.getVariable(varName);
            int varIndex = cds.getVariables().indexOf(variable);
            // make sure the variable is binary.
            if (variable instanceof DiscreteVariable) {
                for (int i = 0; i < nrows; i++) {
                    int value = cds.getInt(i, varIndex);
                    if (value != 0 && value != 1) {
                        JOptionPane.showMessageDialog(this, "The given target was discrete but not binary");
                        return;
                    }
                }
            } else {
                for (int i = 0; i < nrows; i++) {
                    double value = cds.getDouble(i, varIndex);
                    if (value != 0.0d && value != 1.0d) {
                        JOptionPane.showMessageDialog(this, "Target must be a dictotomous variable");
                        return;
                    }
                }
            }
            responseVar.setText(varName);
            varsModel.removeElement(varName);
        } else {
            varsModel.addElement(responseVar.getText());
            responseVar.setText("");
            // Test
            responseButton.toggleInclude();
            // Test
            return;
        }
        responseButton.toggleInclude();
        String newTargetName = responseVar.getText();
        setTargetName(newTargetName);
        params().set("targetName", targetName());
    } else // include predictor variable.
    if (e.getActionCommand().equals(INCLUDE_PREDICTOR)) {
        if (availableVarsList.isSelectionEmpty()) {
            return;
        }
        int intervalSize = endIndexVars - startIndexVars + 1;
        for (int i = 0; i < intervalSize; i++) {
            varSelectedNames[i] = (String) availableVarsList.getSelectedValuesList().get(i);
        }
        for (int i = 0; i < intervalSize; i++) {
            varsModel.removeElement(varSelectedNames[i]);
            predsModel.addElement(varSelectedNames[i]);
        }
    } else // exclude predictor variable.
    if (e.getActionCommand().equals(EXCLUDE_PREDICTOR)) {
        if (predictorVarListbox.isSelectionEmpty()) {
            return;
        }
        int intervalSize = endIndexPreds - startIndexPreds + 1;
        for (int i = 0; i < intervalSize; i++) {
            predSelectedNames[i] = (String) predictorVarListbox.getSelectedValuesList().get(i);
        }
        for (int i = 0; i < intervalSize; i++) {
            predsModel.removeElement(predSelectedNames[i]);
            varsModel.addElement(predSelectedNames[i]);
        }
    } else {
        return;
    }
    // updates expt variables and predictor listbox.
    varSelectionIndex = availableVarsList.getSelectedIndex();
    predictorVarSelectionIndex = predictorVarListbox.getSelectedIndex();
    if (varSelectionIndex > 0) {
        varSelectionIndex--;
    }
    if (varSelectionIndex != -1) {
        availableVarsList.setSelectedIndex(varSelectionIndex);
    }
    if (predictorVarSelectionIndex > 0) {
        predictorVarSelectionIndex--;
    }
    if (predictorVarSelectionIndex != -1) {
        predictorVarListbox.setSelectedIndex(predictorVarSelectionIndex);
    }
    int numPredictors = predsModel.size();
    Object[] predictors = new Object[numPredictors];
    List<String> regNames = new ArrayList<>();
    for (int i = 0; i < numPredictors; i++) {
        predictors[i] = predsModel.getElementAt(i);
        regNames.add((String) predsModel.getElementAt(i));
    }
    setRegressorNames(regNames);
    params().set("regressorNames", regressorNames);
}