Made a version of MlBayesim that doesn't store NaNs in the tables so that huge models can be estimated.

tetrad-gui/src/main/java/edu/cmu/tetradapp/editor/BayesImNodeEditingTable.java

@@ -23,6 +23,7 @@
 
 import edu.cmu.tetrad.bayes.BayesIm;
 import edu.cmu.tetrad.bayes.BayesPm;
+import edu.cmu.tetrad.bayes.MlBayesIm;
 import edu.cmu.tetrad.graph.Node;
 import edu.cmu.tetrad.util.JOptionUtils;
 import edu.cmu.tetrad.util.NumberFormatUtil;
@@ -536,8 +537,9 @@ public Object getValueAt(int tableRow, int tableCol) {
                 int colIndex = tableCol - parentVals.length;
 
                 if (colIndex < getBayesIm().getNumColumns(getNodeIndex())) {
-                    return getBayesIm().getProbability(getNodeIndex(), tableRow,
+                    double probability = getBayesIm().getProbability(getNodeIndex(), tableRow,
                             colIndex);
+                    return probability;
                 }
 
                 return "null";
@@ -555,10 +557,14 @@ public boolean isCellEditable(int row, int col) {
          * Sets the value of the cell at (row, col) to 'aValue'.
          */
         public void setValueAt(Object aValue, int row, int col) {
+            if (getBayesIm().getCptMapType() == MlBayesIm.CptMapType.COUNT_MAP) {
+                return;
+            }
+
             int numParents = getBayesIm().getNumParents(getNodeIndex());
             int colIndex = col - numParents;
 
-            if ("".equals(aValue) || aValue == null) {
+            if (getBayesIm().getCptMapType() == MlBayesIm.CptMapType.PROB_MAP && ("".equals(aValue) || aValue == null)) {
                 getBayesIm().setProbability(getNodeIndex(), row, colIndex,
                         Double.NaN);
                 fireTableRowsUpdated(row, row);
@@ -752,6 +758,8 @@ public void resetFailedRow() {
         public void resetFailedCol() {
             this.failedCol = -1;
         }
+
+
     }
 }
 

tetrad-gui/src/main/java/edu/cmu/tetradapp/editor/RandomGraphEditor.java

@@ -481,7 +481,7 @@ public void setEnabled(boolean enabled) {
      * @return a boolean
      */
     public boolean isRandomForward() {
-        return this.parameters.getBoolean("graphRandomFoward", true);
+        return this.parameters.getBoolean("graphRandomForward", true);
     }
 
     private void setRandomForward(boolean randomFoward) {

tetrad-gui/src/main/java/edu/cmu/tetradapp/model/BayesImWrapper.java

@@ -36,6 +36,7 @@
 import java.io.Serial;
 import java.util.ArrayList;
 import java.util.List;
+import java.util.function.IntBinaryOperator;
 
 /**
  * Wraps a Bayes Pm for use in the Tetrad application.
@@ -90,11 +91,11 @@ public BayesImWrapper(BayesPmWrapper bayesPmWrapper, BayesImWrapper oldBayesImwr
         BayesIm oldBayesIm = oldBayesImwrapper.getBayesIm();
 
         if (params.getString("initializationMode", "manualRetain").equals("manualRetain")) {
-            setBayesIm(bayesPm, oldBayesIm, MlBayesIm.MANUAL);
+            setBayesIm(bayesPm, oldBayesIm, MlBayesIm.InitializationMethod.MANUAL);
         } else if (params.getString("initializationMode", "manualRetain").equals("randomRetain")) {
-            setBayesIm(bayesPm, oldBayesIm, MlBayesIm.RANDOM);
+            setBayesIm(bayesPm, oldBayesIm, MlBayesIm.InitializationMethod.RANDOM);
         } else if (params.getString("initializationMode", "manualRetain").equals("randomOverwrite")) {
-            setBayesIm(new MlBayesIm(bayesPm, MlBayesIm.RANDOM));
+            setBayesIm(new MlBayesIm(bayesPm, MlBayesIm.InitializationMethod.RANDOM));
         }
     }
 
@@ -193,9 +194,9 @@ public BayesImWrapper(BayesPmWrapper bayesPmWrapper, Parameters params) {
         if (params.getString("initializationMode", "manualRetain").equals("manualRetain")) {
             setBayesIm(new MlBayesIm(bayesPm));
         } else if (params.getString("initializationMode", "manualRetain").equals("randomRetain")) {
-            setBayesIm(new MlBayesIm(bayesPm, MlBayesIm.RANDOM));
+            setBayesIm(new MlBayesIm(bayesPm, MlBayesIm.InitializationMethod.RANDOM));
         } else if (params.getString("initializationMode", "manualRetain").equals("randomOverwrite")) {
-            setBayesIm(new MlBayesIm(bayesPm, MlBayesIm.RANDOM));
+            setBayesIm(new MlBayesIm(bayesPm, MlBayesIm.InitializationMethod.RANDOM));
         }
     }
 
@@ -342,9 +343,9 @@ public String getModelSourceName() {
 
     //============================== private methods ============================//
 
-    private void setBayesIm(BayesPm bayesPm, BayesIm oldBayesIm, int manual) {
+    private void setBayesIm(BayesPm bayesPm, BayesIm oldBayesIm, MlBayesIm.InitializationMethod initializationMethod) {
         this.bayesIms = new ArrayList<>();
-        this.bayesIms.add(new MlBayesIm(bayesPm, oldBayesIm, manual));
+        this.bayesIms.add(new MlBayesIm(bayesPm, oldBayesIm, initializationMethod));
     }
 
     /**

tetrad-gui/src/main/java/edu/cmu/tetradapp/model/BayesPmWrapper.java

@@ -206,8 +206,8 @@ public BayesPmWrapper(GraphWrapper graphWrapper, Parameters params) {
             lowerBound = upperBound = 3;
             setBayesPm(graph, lowerBound, upperBound);
         } else if (params.getString("bayesPmInitializationMode", "range").equals("range")) {
-            lowerBound = params.getInt("minCategories", 2);
-            upperBound = params.getInt("maxCategories", 4);
+            lowerBound = params.getInt("lowerBoundNumVals", 2);
+            upperBound = params.getInt("upperBoundNumVals", 4);
             setBayesPm(graph, lowerBound, upperBound);
         } else {
             throw new IllegalStateException("Unrecognized type.");

tetrad-gui/src/main/java/edu/cmu/tetradapp/session/SessionNode.java

@@ -1173,6 +1173,13 @@ public int compareTo(Node node) {
      * @return a boolean
      */
     public boolean existsParameterizedConstructor(Class modelClass) {
+        if (modelClass == null) {
+
+            // If the model class is null, then there is no constructor, so display a dialog to the users by
+            // return false here.
+            return false;
+        }
+
         Object param = getParam(modelClass);
         List parentModels = listParentModels();
         parentModels.add(param);

tetrad-lib/src/main/java/edu/cmu/tetrad/algcomparison/simulation/BayesNetSimulation.java

@@ -238,9 +238,9 @@ private DataSet simulate(Graph graph, Parameters parameters) {
                     int minCategories = parameters.getInt(Params.MIN_CATEGORIES);
                     int maxCategories = parameters.getInt(Params.MAX_CATEGORIES);
                     pm = new BayesPm(graph, minCategories, maxCategories);
-                    im = new MlBayesIm(pm, MlBayesIm.RANDOM);
+                    im = new MlBayesIm(pm, MlBayesIm.InitializationMethod.RANDOM);
                 } else {
-                    im = new MlBayesIm(pm, MlBayesIm.RANDOM);
+                    im = new MlBayesIm(pm, MlBayesIm.InitializationMethod.RANDOM);
                     this.im = im;
                 }
             }

tetrad-lib/src/main/java/edu/cmu/tetrad/algcomparison/simulation/ConditionalGaussianSimulation.java

@@ -320,7 +320,7 @@ private DataSet simulate(Graph G, Parameters parameters) {
         }
 
         BayesPm bayesPm = new BayesPm(AG);
-        BayesIm bayesIm = new MlBayesIm(bayesPm, MlBayesIm.RANDOM);
+        BayesIm bayesIm = new MlBayesIm(bayesPm, MlBayesIm.InitializationMethod.RANDOM);
 
         SemPm semPm = new SemPm(XG);
 

tetrad-lib/src/main/java/edu/cmu/tetrad/algcomparison/statistic/Statistic.java

@@ -43,7 +43,7 @@ public interface Statistic extends Serializable {
 
     /**
      * Returns a mapping of the statistic to the interval [0, 1], with higher being better. This is used for a
-     * calculation of a utility for an algorithm.If the statistic is already between 0 and 1, you can just return the
+     * calculation of a utility for an algorithm. If the statistic is already between 0 and 1, you can just return the
      * statistic.
      *
      * @param value The value of the statistic.

tetrad-lib/src/main/java/edu/cmu/tetrad/bayes/ApproximateUpdater.java

@@ -344,7 +344,7 @@ private void doUpdate() {
     }
 
     private BayesIm createdUpdatedBayesIm(BayesPm updatedBayesPm) {
-        return new MlBayesIm(updatedBayesPm, this.bayesIm, MlBayesIm.RANDOM);
+        return new MlBayesIm(updatedBayesPm, this.bayesIm, MlBayesIm.InitializationMethod.RANDOM);
     }
 
     private BayesPm createUpdatedBayesPm(Dag updatedGraph) {

tetrad-lib/src/main/java/edu/cmu/tetrad/bayes/BayesIm.java

@@ -393,4 +393,8 @@ public interface BayesIm extends VariableSource, Im, Simulator {
      * @return a string representation for this Bayes net.
      */
     String toString();
+
+    default MlBayesIm.CptMapType getCptMapType() {
+        return MlBayesIm.CptMapType.PROB_MAP;
+    }
 }

tetrad-lib/src/main/java/edu/cmu/tetrad/bayes/CptInvariantUpdater.java

@@ -266,7 +266,7 @@ public String toString() {
     //==============================PRIVATE METHODS=======================//
 
     private BayesIm createdManipulatedBayesIm(BayesPm updatedBayesPm) {
-        return new MlBayesIm(updatedBayesPm, this.bayesIm, MlBayesIm.MANUAL);
+        return new MlBayesIm(updatedBayesPm, this.bayesIm, MlBayesIm.InitializationMethod.MANUAL);
     }
 
     private BayesPm createManipulatedBayesPm(Dag updatedGraph) {

tetrad-lib/src/main/java/edu/cmu/tetrad/bayes/CptMap.java

@@ -0,0 +1,41 @@
+package edu.cmu.tetrad.bayes;
+
+import edu.cmu.tetrad.util.TetradSerializable;
+
+/**
+ * An interface representing a map of probabilities or counts for nodes in a Bayesian network. Implementations of this
+ * interface should provide methods to get the probability or count for a node at a given row and column, as well as
+ * methods to retrieve the number of rows and columns in the map.
+ * <p>
+ * This interface extends the TetradSerializable interface, indicating that implementations should be serializable and
+ * follow certain guidelines for compatibility across different versions of Tetrad.
+ *
+ * @author josephramsey
+ * @see CptMapProbs
+ * @see CptMapCounts
+ */
+public interface CptMap extends TetradSerializable {
+
+    /**
+     * Retrieves the value at the specified row and column in the CptMap.
+     *
+     * @param row    the row index of the value to retrieve.
+     * @param column the column index of the value to retrieve.
+     * @return the value at the specified row and column in the CptMap.
+     */
+    double get(int row, int column);
+
+    /**
+     * Retrieves the number of rows in the CptMap.
+     *
+     * @return the number of rows in the CptMap.
+     */
+    int getNumRows();
+
+    /**
+     * Retrieves the number of columns in the CptMap.
+     *
+     * @return the number of columns in the CptMap.
+     */
+    int getNumColumns();
+}