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///////////////////////////////////////////////////////////////////////////////
// For information as to what this class does, see the Javadoc, below. //
// Copyright (C) 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, //
// 2007, 2008, 2009, 2010, 2014, 2015, 2022 by Peter Spirtes, Richard //
// Scheines, Joseph Ramsey, and Clark Glymour. //
// //
// This program is free software; you can redistribute it and/or modify //
// it under the terms of the GNU General Public License as published by //
// the Free Software Foundation; either version 2 of the License, or //
// (at your option) any later version. //
// //
// This program is distributed in the hope that it will be useful, //
// but WITHOUT ANY WARRANTY; without even the implied warranty of //
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the //
// GNU General Public License for more details. //
// //
// You should have received a copy of the GNU General Public License //
// along with this program; if not, write to the Free Software //
// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA //
///////////////////////////////////////////////////////////////////////////////
package edu.cmu.tetradapp.editor;
import edu.cmu.tetrad.bayes.BayesIm;
import edu.cmu.tetrad.bayes.BayesPm;
import edu.cmu.tetrad.graph.Node;
import edu.cmu.tetrad.util.JOptionUtils;
import edu.cmu.tetrad.util.NumberFormatUtil;
import org.apache.commons.math3.util.FastMath;
import org.jetbrains.annotations.NotNull;
import javax.swing.*;
import javax.swing.table.AbstractTableModel;
import javax.swing.table.TableCellEditor;
import javax.swing.table.TableColumn;
import javax.swing.table.TableModel;
import java.awt.*;
import java.awt.event.MouseAdapter;
import java.awt.event.MouseEvent;
import java.beans.PropertyChangeListener;
import java.beans.PropertyChangeSupport;
import java.text.NumberFormat;
/**
* This is the JTable which displays the getModel parameter set (an Model).
*
* @author josephramsey
*/
class BayesEstimatorNodeEditingTable extends JTable {
private int focusRow;
private int focusCol;
private int lastX;
private int lastY;
/**
* Constructs a new editing table from a given editing table model.
*
* @param node a {@link edu.cmu.tetrad.graph.Node} object
* @param bayesIm a {@link edu.cmu.tetrad.bayes.BayesIm} object
*/
public BayesEstimatorNodeEditingTable(Node node, BayesIm bayesIm) {
if (node == null) {
return;
}
if (bayesIm == null) {
throw new NullPointerException();
}
if (bayesIm.getNodeIndex(node) < 0) {
throw new IllegalArgumentException("Node " + node +
" is not a node" + " for BayesIm " + bayesIm + ".");
}
Model model = new Model(node, bayesIm, this);
model.addPropertyChangeListener(evt -> {
if ("modelChanged".equals(evt.getPropertyName())) {
firePropertyChange("modelChanged", null, null);
}
});
setModel(model);
setDefaultEditor(Number.class, new NumberCellEditor());
setDefaultRenderer(Number.class, new NumberCellRenderer());
getTableHeader().setReorderingAllowed(false);
getTableHeader().setResizingAllowed(true);
setAutoResizeMode(JTable.AUTO_RESIZE_OFF);
setCellSelectionEnabled(true);
ListSelectionModel rowSelectionModel = getSelectionModel();
rowSelectionModel.addListSelectionListener(e -> {
ListSelectionModel m = (ListSelectionModel) (e.getSource());
setFocusRow(m.getAnchorSelectionIndex());
});
ListSelectionModel columnSelectionModel = getColumnModel()
.getSelectionModel();
columnSelectionModel.addListSelectionListener(
e -> {
ListSelectionModel m =
(ListSelectionModel) (e.getSource());
setFocusColumn(m.getAnchorSelectionIndex());
});
addMouseListener(new MouseAdapter() {
public void mousePressed(MouseEvent e) {
if (SwingUtilities.isRightMouseButton(e) || e.isControlDown()) {
showPopup(e);
}
}
});
setFocusRow(0);
setFocusColumn(0);
}
/**
* createDefaultColumnsFromModel.
*/
public void createDefaultColumnsFromModel() {
super.createDefaultColumnsFromModel();
if (getModel() instanceof Model model) {
FontMetrics fontMetrics = getFontMetrics(getFont());
for (int i = 0; i < model.getColumnCount(); i++) {
TableColumn column = getColumnModel().getColumn(i);
String columnName = model.getColumnName(i);
int currentWidth = column.getPreferredWidth();
if (columnName != null) {
int minimumWidth = fontMetrics.stringWidth(columnName) + 8;
if (minimumWidth > currentWidth) {
column.setPreferredWidth(minimumWidth);
}
}
}
}
}
private void showPopup(MouseEvent e) {
JPopupMenu popup = new JPopupMenu();
JMenuItem randomizeRow = new JMenuItem("Randomize this row");
JMenuItem randomizeIncompleteRows =
new JMenuItem("Randomize incomplete rows in table");
JMenuItem randomizeEntireTable =
new JMenuItem("Randomize entire table");
JMenuItem randomizeAllTables = new JMenuItem("Randomize all tables");
JMenuItem clearRow = new JMenuItem("Clear this row");
JMenuItem clearEntireTable = new JMenuItem("Clear entire table");
randomizeRow.addActionListener(e1 -> {
int nodeIndex = getEditingTableModel().getNodeIndex();
BayesEstimatorNodeEditingTable editingTable =
BayesEstimatorNodeEditingTable.this;
TableCellEditor cellEditor = editingTable.getCellEditor();
if (cellEditor != null) {
cellEditor.cancelCellEditing();
}
Point point = new Point(getLastX(), getLastY());
int rowIndex = editingTable.rowAtPoint(point);
BayesIm bayesIm = getBayesIm();
bayesIm.randomizeRow(nodeIndex, rowIndex);
getEditingTableModel().fireTableDataChanged();
firePropertyChange("modelChanged", null, null);
});
randomizeIncompleteRows.addActionListener(e12 -> {
int nodeIndex = getEditingTableModel().getNodeIndex();
BayesIm bayesIm = getBayesIm();
if (!existsIncompleteRow(bayesIm, nodeIndex)) {
JOptionPane.showMessageDialog(JOptionUtils.centeringComp(),
"There are no incomplete rows in this table.");
return;
}
BayesEstimatorNodeEditingTable editingTable =
BayesEstimatorNodeEditingTable.this;
TableCellEditor cellEditor = editingTable.getCellEditor();
if (cellEditor != null) {
cellEditor.cancelCellEditing();
}
bayesIm.randomizeIncompleteRows(nodeIndex);
getEditingTableModel().fireTableDataChanged();
firePropertyChange("modelChanged", null, null);
});
randomizeEntireTable.addActionListener(e13 -> {
int nodeIndex = getEditingTableModel().getNodeIndex();
BayesIm bayesIm = getBayesIm();
if (existsCompleteRow(bayesIm, nodeIndex)) {
int ret = JOptionPane.showConfirmDialog(
JOptionUtils.centeringComp(),
"This will modify all values in the table. " +
"Continue?", "Warning",
JOptionPane.YES_NO_OPTION);
if (ret == JOptionPane.NO_OPTION) {
return;
}
}
BayesEstimatorNodeEditingTable editingTable =
BayesEstimatorNodeEditingTable.this;
TableCellEditor cellEditor = editingTable.getCellEditor();
if (cellEditor != null) {
cellEditor.cancelCellEditing();
}
bayesIm.randomizeTable(nodeIndex);
getEditingTableModel().fireTableDataChanged();
firePropertyChange("modelChanged", null, null);
});
randomizeAllTables.addActionListener(e14 -> {
int ret = JOptionPane.showConfirmDialog(
JOptionUtils.centeringComp(),
"This will modify all values in the entire Bayes model! " +
"Continue?", "Warning",
JOptionPane.YES_NO_OPTION);
if (ret == JOptionPane.NO_OPTION) {
return;
}
BayesIm bayesIm = getBayesIm();
for (int nodeIndex = 0;
nodeIndex < getBayesIm().getNumNodes(); nodeIndex++) {
BayesEstimatorNodeEditingTable editingTable =
BayesEstimatorNodeEditingTable.this;
TableCellEditor cellEditor = editingTable.getCellEditor();
if (cellEditor != null) {
cellEditor.cancelCellEditing();
}
bayesIm.randomizeTable(nodeIndex);
getEditingTableModel().fireTableDataChanged();
firePropertyChange("modelChanged", null, null);
}
});
clearRow.addActionListener(e15 -> {
int nodeIndex = getEditingTableModel().getNodeIndex();
BayesEstimatorNodeEditingTable editingTable =
BayesEstimatorNodeEditingTable.this;
TableCellEditor cellEditor = editingTable.getCellEditor();
if (cellEditor != null) {
cellEditor.cancelCellEditing();
}
Point point = new Point(getLastX(), getLastY());
int rowIndex = editingTable.rowAtPoint(point);
BayesIm bayesIm = getBayesIm();
bayesIm.clearRow(nodeIndex, rowIndex);
getEditingTableModel().fireTableRowsUpdated(rowIndex, rowIndex);
firePropertyChange("modelChanged", null, null);
});
clearEntireTable.addActionListener(e16 -> {
int nodeIndex = getEditingTableModel().getNodeIndex();
BayesIm bayesIm = getBayesIm();
if (existsCompleteRow(bayesIm, nodeIndex)) {
int ret = JOptionPane.showConfirmDialog(
JOptionUtils.centeringComp(),
"This will delete all values in the table. " +
"Continue?", "Warning",
JOptionPane.YES_NO_OPTION);
if (ret == JOptionPane.NO_OPTION) {
return;
}
}
BayesEstimatorNodeEditingTable editingTable =
BayesEstimatorNodeEditingTable.this;
TableCellEditor cellEditor = editingTable.getCellEditor();
if (cellEditor != null) {
cellEditor.cancelCellEditing();
}
bayesIm.clearTable(nodeIndex);
getEditingTableModel().fireTableDataChanged();
firePropertyChange("modelChanged", null, null);
});
popup.add(randomizeRow);
popup.add(randomizeIncompleteRows);
popup.add(randomizeEntireTable);
popup.add(randomizeAllTables);
popup.addSeparator();
popup.add(clearRow);
popup.add(clearEntireTable);
this.lastX = e.getX();
this.lastY = e.getY();
popup.show((Component) e.getSource(), e.getX(), e.getY());
}
private boolean existsCompleteRow(BayesIm bayesIm, int nodeIndex) {
boolean existsCompleteRow = false;
for (int rowIndex = 0;
rowIndex < bayesIm.getNumRows(nodeIndex); rowIndex++) {
if (!bayesIm.isIncomplete(nodeIndex, rowIndex)) {
existsCompleteRow = true;
break;
}
}
return existsCompleteRow;
}
private boolean existsIncompleteRow(BayesIm bayesIm, int nodeIndex) {
boolean existsCompleteRow = false;
for (int rowIndex = 0;
rowIndex < bayesIm.getNumRows(nodeIndex); rowIndex++) {
if (bayesIm.isIncomplete(nodeIndex, rowIndex)) {
existsCompleteRow = true;
break;
}
}
return existsCompleteRow;
}
/**
* {@inheritDoc}
*/
public void setModel(@NotNull TableModel model) {
super.setModel(model);
}
/**
* Sets the focus row to the anchor row currently being selected.
*/
private void setFocusRow(int row) {
if (row == -1) {
return;
}
Model editingTableModel = (Model) getModel();
int failedRow = editingTableModel.getFailedRow();
if (failedRow != -1) {
row = failedRow;
editingTableModel.resetFailedRow();
}
this.focusRow = row;
if (this.focusCol < getRowCount()) {
setRowSelectionInterval(this.focusRow, this.focusRow);
editCellAt(this.focusRow, this.focusCol);
}
}
/**
* Sets the focus column to the anchor column currently being selected.
*/
private void setFocusColumn(int col) {
Model editingTableModel = (Model) getModel();
int failedCol = editingTableModel.getFailedCol();
if (failedCol != -1) {
col = failedCol;
editingTableModel.resetFailedCol();
}
if (col < getNumParents()) {
col = getNumParents();
}
this.focusCol = FastMath.max(col, getNumParents());
if (this.focusCol >= getNumParents() &&
this.focusCol < getColumnCount()) {
setColumnSelectionInterval(this.focusCol, this.focusCol);
editCellAt(this.focusRow, this.focusCol);
}
}
private int getNumParents() {
Model editingTableModel = (Model) getModel();
BayesIm bayesIm = editingTableModel.getBayesIm();
int nodeIndex = editingTableModel.getNodeIndex();
return bayesIm.getNumParents(nodeIndex);
}
private Model getEditingTableModel() {
return (Model) getModel();
}
private BayesIm getBayesIm() {
return getEditingTableModel().getBayesIm();
}
private int getLastX() {
return this.lastX;
}
private int getLastY() {
return this.lastY;
}
/**
* The abstract table model containing the parameters to be edited for a given node. Parameters for a given node N
* with parents P1, P2, ..., are of the form P(N=v0 | P1=v1, P2=v2, ..., Pn = vn). The first n columns of this
* table for each row contains a combination of values for (P1, P2, ... Pn), such as (v0, v1, ..., vn). If there
* are m values for N, the next m columns contain numbers in the range [0.0, 1.0] representing conditional
* probabilities that N takes on that corresponding value given this combination of parent values. These
* conditional probabilities may be edited. As they are being edited for a given row, the only condition is that
* they be greater than or equal to 0.0.
*
* @author josephramsey
*/
static final class Model extends AbstractTableModel {
/**
* The BayesIm being edited.
*/
private final BayesIm bayesIm;
/**
* This table can only display conditional probabilities for one node at at time. This is the node.
*/
private final int nodeIndex;
private int failedRow = -1;
private int failedCol = -1;
private PropertyChangeSupport pcs;
/**
* Constructs a new editing table model for a given a node in a given bayesIm.
*/
public Model(Node node, BayesIm bayesIm, JComponent messageAnchor) {
if (node == null) {
throw new NullPointerException("Node must not be null.");
}
if (bayesIm == null) {
throw new NullPointerException("Bayes IM must not be null.");
}
if (messageAnchor == null) {
throw new NullPointerException(
"Message anchor must not be null.");
}
this.bayesIm = bayesIm;
this.nodeIndex = bayesIm.getNodeIndex(node);
}
/**
* @return the name of the given column.
*/
public String getColumnName(int col) {
Node node = getBayesIm().getNode(getNodeIndex());
if (col < getBayesIm().getNumParents(getNodeIndex())) {
int parent = getBayesIm().getParent(getNodeIndex(), col);
return getBayesIm().getNode(parent).getName();
} else {
int numNodeVals = getBayesIm().getNumColumns(getNodeIndex());
int valIndex = col - getBayesIm().getNumParents(getNodeIndex());
if (valIndex < numNodeVals) {
String value = getBayesIm().getBayesPm().getCategory(node,
valIndex);
return node.getName() + "=" + value;
}
return null;
}
}
/**
* @return the number of rows in the table.
*/
public int getRowCount() {
return getBayesIm().getNumRows(getNodeIndex());
}
/**
* @return the total number of columns in the table, which is equal to the number of parents for the node plus
* the number of values for the node.
*/
public int getColumnCount() {
int numParents = getBayesIm().getNumParents(getNodeIndex());
int numColumns = getBayesIm().getNumColumns(getNodeIndex());
return numParents + numColumns;
}
/**
* @return the value of the table at the given row and column. The type of value returned depends on the column.
* If there are n parent values and m node values, then the first n columns have String values representing the
* values of the parent nodes for a particular combination (row) and the next m columns have Double values
* representing conditional probabilities of node values given parent value combinations.
*/
public Object getValueAt(int tableRow, int tableCol) {
int[] parentVals =
getBayesIm().getParentValues(getNodeIndex(), tableRow);
if (tableCol < parentVals.length) {
Node columnNode = getBayesIm().getNode(
getBayesIm().getParent(getNodeIndex(), tableCol));
BayesPm bayesPm = getBayesIm().getBayesPm();
return bayesPm.getCategory(columnNode, parentVals[tableCol]);
} else {
int colIndex = tableCol - parentVals.length;
if (colIndex < getBayesIm().getNumColumns(getNodeIndex())) {
return getBayesIm().getProbability(getNodeIndex(), tableRow,
colIndex);
}
return "null";
}
}
/**
* Determines whether a cell is in the column range to allow for editing.
*/
public boolean isCellEditable(int row, int col) {
return !(col < getBayesIm().getNumParents(getNodeIndex()));
}
/**
* Sets the value of the cell at (row, col) to 'aValue'.
*/
public void setValueAt(Object aValue, int row, int col) {
int numParents = getBayesIm().getNumParents(getNodeIndex());
int colIndex = col - numParents;
if ("".equals(aValue) || aValue == null) {
getBayesIm().setProbability(getNodeIndex(), row, colIndex,
Double.NaN);
fireTableRowsUpdated(row, row);
getPcs().firePropertyChange("modelChanged", null, null);
return;
}
try {
NumberFormat nf = NumberFormatUtil.getInstance().getNumberFormat();
double probability = Double.parseDouble((String) aValue);
// probability = Double.parseDouble(nf.format(probability));
double sumInRow = sumInRow(row, colIndex) + probability;
double oldProbability = getBayesIm().getProbability(this.nodeIndex, row, colIndex);
if (!Double.isNaN(oldProbability)) {
oldProbability = Double.parseDouble(nf.format(oldProbability));
}
if (probability == oldProbability) {
return;
}
if (probabilityOutOfRange(probability)) {
JOptionPane.showMessageDialog(JOptionUtils.centeringComp(),
"Probabilities must be in range [0.0, 1.0].");
this.failedRow = row;
this.failedCol = col;
} else if (numNanCols(row) == 0) {
if (sumInRow < 0.99995 || sumInRow > 1.00005) {
emptyRow(row);
getBayesIm().setProbability(getNodeIndex(), row,
colIndex, probability);
if (this.bayesIm.getNumColumns((this.nodeIndex)) == 2) {
fillInSingleRemainingColumn(row);
}
fireTableRowsUpdated(row, row);
getPcs().firePropertyChange("modelChanged", null,
null);
}
} else if (sumInRow > 1.00005) {
JOptionPane.showMessageDialog(JOptionUtils.centeringComp(),
"Sum of probabilities in row must not exceed 1.0.");
this.failedRow = row;
this.failedCol = col;
} else {
getBayesIm().setProbability(getNodeIndex(), row, colIndex,
probability);
fillInSingleRemainingColumn(row);
fillInZerosIfSumIsOne(row);
fireTableRowsUpdated(row, row);
getPcs().firePropertyChange("modelChanged", null,
null);
}
} catch (NumberFormatException e) {
JOptionPane.showMessageDialog(JOptionUtils.centeringComp(),
"Could not interpret '" + aValue + "'");
this.failedRow = row;
this.failedCol = col;
}
}
public void addPropertyChangeListener(PropertyChangeListener l) {
getPcs().addPropertyChangeListener(l);
}
private PropertyChangeSupport getPcs() {
if (this.pcs == null) {
this.pcs = new PropertyChangeSupport(this);
}
return this.pcs;
}
private void fillInSingleRemainingColumn(int rowIndex) {
int leftOverColumn = uniqueNanCol(rowIndex);
if (leftOverColumn != -1) {
double difference = 1.0 - sumInRow(rowIndex, leftOverColumn);
getBayesIm().setProbability(getNodeIndex(), rowIndex,
leftOverColumn, difference);
}
}
private void fillInZerosIfSumIsOne(int rowIndex) {
double sum = sumInRow(rowIndex, -1);
if (sum > 0.9995 && sum < 1.0005) {
int numColumns = getBayesIm().getNumColumns(getNodeIndex());
for (int i = 0; i < numColumns; i++) {
double probability = getBayesIm().getProbability(
getNodeIndex(), rowIndex, i);
if (Double.isNaN(probability)) {
getBayesIm().setProbability(getNodeIndex(), rowIndex, i,
0.0);
}
}
}
}
private boolean probabilityOutOfRange(double value) {
return value < 0.0 || value > 1.0;
}
private int uniqueNanCol(int rowIndex) {
int numNanCols = 0;
int lastNanCol = -1;
for (int i = 0; i < getBayesIm().getNumColumns(getNodeIndex()); i++) {
double probability = getBayesIm().getProbability(getNodeIndex(),
rowIndex, i);
if (Double.isNaN(probability)) {
numNanCols++;
lastNanCol = i;
}
}
return numNanCols == 1 ? lastNanCol : -1;
}
private int numNanCols(int rowIndex) {
int numNanCols = 0;
for (int i = 0; i < getBayesIm().getNumColumns(getNodeIndex()); i++) {
double probability = getBayesIm().getProbability(getNodeIndex(),
rowIndex, i);
if (Double.isNaN(probability)) {
numNanCols++;
}
}
return numNanCols;
}
private void emptyRow(int rowIndex) {
for (int i = 0; i < getBayesIm().getNumColumns(getNodeIndex()); i++) {
getBayesIm().setProbability(getNodeIndex(), rowIndex, i,
Double.NaN);
}
}
private double sumInRow(int rowIndex, int skipCol) {
double sum = 0.0;
for (int i = 0; i < getBayesIm().getNumColumns(getNodeIndex()); i++) {
double probability = getBayesIm().getProbability(getNodeIndex(),
rowIndex, i);
if (i != skipCol && !Double.isNaN(probability)) {
NumberFormat nf = NumberFormatUtil.getInstance().getNumberFormat();
probability = Double.parseDouble(nf.format(probability));
sum += probability;
}
}
return sum;
}
/**
* @return the class of the column.
*/
public Class getColumnClass(int col) {
boolean isParent = col < getBayesIm().getNumParents(getNodeIndex());
return isParent ? Object.class : Number.class;
}
public BayesIm getBayesIm() {
return this.bayesIm;
}
public int getNodeIndex() {
return this.nodeIndex;
}
public int getFailedRow() {
return this.failedRow;
}
public int getFailedCol() {
return this.failedCol;
}
public void resetFailedRow() {
this.failedRow = -1;
}
public void resetFailedCol() {
this.failedCol = -1;
}
}
}
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