///////////////////////////////////////////////////////////////////////////////
// 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 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.tetrad.bayes;

import edu.cmu.tetrad.data.ColtDataSet;
import edu.cmu.tetrad.data.DataSet;
import edu.cmu.tetrad.data.DiscreteVariable;
import edu.cmu.tetrad.graph.*;
import edu.cmu.tetrad.util.RandomUtil;

import java.io.IOException;
import java.io.ObjectInputStream;
import java.util.*;

/**
 * Stores a table of probabilities for a Bayes net and, together with BayesPm
 * and Dag, provides methods to manipulate this table.  The division of labor is
 * as follows.  The Dag is responsible for manipulating the basic graphical
 * structure of the Bayes net. Dag also stores and manipulates the names of the
 * nodes in the graph; there are no method in either BayesPm or BayesIm to do
 * this. BayesPm stores and manipulates the *categories* of each node in a DAG,
 * considered as a variable in a Bayes net.  The number of categories for a
 * variable can be changed there as well as the names for those categories. This
 * class, BayesIm, stores the actual probability tables which are implied by the
 * structures in the other two classes. The implied parameters take the form of
 * conditional probabilities--e.g., P(N=v0|P1=v1, P2=v2, ...), for all nodes and
 * all combinations of their parent categories.  The set of all such
 * probabilities is organized in this class as a three-dimensional table of
 * double values.  The first dimension corresponds to the nodes in the Bayes
 * net.  For each such node, the second dimension corresponds to a flat list of
 * combinations of parent categories for that node.  The third dimension
 * corresponds to the list of categories for that node itself.  Two methods
 * allow these values to be set and retrieved: <ul> <li>getWordRatio(int
 * nodeIndex, int rowIndex, int colIndex); and, <li>setProbability(int
 * nodeIndex, int rowIndex, int colIndex, int probability). </ul> To determine
 * the index of the node in question, use the method <ul> <li> getNodeIndex(Node
 * node). </ul> To determine the index of the row in question, use the method
 * <ul> <li>getRowIndex(int[] parentVals). </ul> To determine the order of the
 * parent values for a given node so that you can build the parentVals[] array,
 * use the method <ul> <li> getParents(int nodeIndex) </ul> To determine the
 * index of a category, use the method <ul> <li> getCategoryIndex(Node node)
 * </ul> in BayesPm.  The rest of the methods in this class are easily
 * understood as variants of the methods above. </p> <p>Thanks to Pucktada
 * Treeratpituk, Frank Wimberly, and Willie Wheeler for advise and earlier
 * versions.</p>
 *
 * @author Joseph Ramsey jdramsey@andrew.cmu.edu
 */
public final class MlBayesImObs implements BayesIm {
    static final long serialVersionUID = 23L;
    private static final double ALLOWABLE_DIFFERENCE = 1.0e-10;

    /**
     * Inidicates that new rows in this BayesIm should be initialized as
     * unknowns, forcing them to be specified manually. This is the default.
     */
    private static final int MANUAL = 0;

    /**
     * Indicates that new rows in this BayesIm should be initialized randomly.
     */
    private static final int RANDOM = 1;

    /**
     * The associated Bayes PM model.
     *
     * @serial
     */
    private BayesPm bayesPm;

    /**
     * The array of nodes from the graph.  Order is important.
     *
     * @serial
     */
    private Node[] nodes;

    /**
     * The list of parents for each node from the graph.  Order or nodes
     * corresponds to the order of nodes in 'nodes', and order in subarrays is
     * important.
     *
     * @serial
     */
    private int[][] parents;

    /**
     * The array of dimensionality (number of categories for each node) for each
     * of the subarrays of 'parents'.
     *
     * @serial
     */
    private int[][] parentDims;

    /**
     * The main data structure; stores the values of all of the conditional
     * probabilities for the Bayes net of the form P(N=v0 | P1=v1, P2=v2,...).
     * The first dimension is the node N, in the order of 'nodes'.  The second
     * dimension is the row index for the table of parameters associated with
     * node N; the third dimension is the column index.  The row index is
     * calculated by the function getRowIndex(int[] values) where 'values' is an
     * array of numerical indices for each of the parent values; the order of
     * the values in this array is the same as the order of node in 'parents';
     * the value indices are obtained from the Bayes PM for each node.  The
     * column is the index of the value of N, where this index is obtained from
     * the Bayes PM.
     *
     * @serial
     */
    private double[][][] probs;  // this is left in for compatibility

    // joint probability table
    private StoredCellProbsObs jpd;


    // a regular MlBayesIm used to create randomized CPDs
    // so that the values can be marginalized to a consistent observed jpd
    private BayesIm bayesImRandomize;

    // BayesIm containing only the observed variables.  Only used to
    // 1) construct propositions (mapped from the original allowUnfaithfulness bayesIm)
    //    in Identifiability
    // 2) to avoid summing over rows in jpd when only the latent variables
    //    have changed values (only sum when all the latent variables have
    //    value 0)
    // This is a MlBayesIm instead of a MlBayesImObs because otherwise
    // there will be an infinite loop attempting to creating the MlBayesImObs
    private BayesIm bayesImObs;

    //===============================CONSTRUCTORS=========================//

    /**
     * Constructs a new BayesIm from the given BayesPm, initializing all values
     * as Double.NaN ("?").
     *
     * @param bayesPm the given Bayes PM.  Carries with it the underlying graph
     *                model.
     * @throws IllegalArgumentException if the array of nodes provided is not a
     *                                  permutation of the nodes contained in
     *                                  the bayes parametric model provided.
     */
    public MlBayesImObs(BayesPm bayesPm) throws IllegalArgumentException {
        //this(bayesPm, null, MANUAL);
        this(bayesPm, MANUAL);
    }

    /**
     * Constructs a new BayesIm from the given BayesPm, initializing values
     * either as MANUAL or RANDOM. If initialized manually, all values will be
     * set to Double.NaN ("?") in each row; if initialized randomly, all values
     * will distributed randomly in each row.
     *
     * @param bayesPm              the given Bayes PM.  Carries with it the
     *                             underlying graph model.
     * @param initializationMethod either MANUAL or RANDOM.
     * @throws IllegalArgumentException if the array of nodes provided is not a
     *                                  permutation of the nodes contained in
     *                                  the bayes parametric model provided.
     */
    public MlBayesImObs(BayesPm bayesPm, int initializationMethod)
            throws IllegalArgumentException {
        //this(bayesPm, null, initializationMethod);
        if (bayesPm == null) {
            throw new NullPointerException("BayesPm must not be null.");
        }

        this.bayesPm = new BayesPm(bayesPm);

        // Get the nodes from the BayesPm. This fixes the order of the nodes
        // in the BayesIm, independently of any change to the BayesPm.
        // (This order must be maintained.)
        Graph graph = bayesPm.getDag();
        this.nodes = graph.getNodes().toArray(new Node[graph.getNodes().size()]);

        // Initialize.
        initialize(null, initializationMethod);

    }

    /**
     * Constructs a new BayesIm from the given BayesPm, initializing values
     * either as MANUAL or RANDOM, but using values from the old BayesIm
     * provided where posssible. If initialized manually, all values that cannot
     * be retrieved from oldBayesIm will be set to Double.NaN ("?") in each such
     * row; if initialized randomly, all values that cannot be retrieved from
     * oldBayesIm will distributed randomly in each such row.
     *
     * @param bayesPm              the given Bayes PM.  Carries with it the
     *                             underlying graph model.
     * @param oldBayesIm           an already-constructed BayesIm whose values
     *                             may be used where possible to initialize this
     *                             BayesIm. May be null.
     * @param initializationMethod either MANUAL or RANDOM.
     * @throws IllegalArgumentException if the array of nodes provided is not a
     *                                  permutation of the nodes contained in
     *                                  the bayes parametric model provided.
     */
    public MlBayesImObs(BayesPm bayesPm, BayesIm oldBayesIm,
                        int initializationMethod) throws IllegalArgumentException {
        if (bayesPm == null) {
            throw new NullPointerException("BayesPm must not be null.");
        }

        this.bayesPm = new BayesPm(bayesPm);

        // Get the nodes from the BayesPm. This fixes the order of the nodes
        // in the BayesIm, independently of any change to the BayesPm.
        // (This order must be maintained.)
        Graph graph = bayesPm.getDag();
        this.nodes = graph.getNodes().toArray(new Node[graph.getNodes().size()]);

        // Initialize.
        initialize(oldBayesIm, initializationMethod);
    }

    /*
     * construct from a allowUnfaithfulness MlBayesIm using marginalized probaiblities,
	 * or copy from another MlBayesImObs
     */
    public MlBayesImObs(BayesIm bayesIm) throws IllegalArgumentException {
        if (bayesIm == null) {
            throw new NullPointerException("BayesIm must not be null.");
        }

        this.bayesPm = bayesIm.getBayesPm();

        // Get the nodes from the BayesPm, fixing on an order. (This is
        // important; the nodes must always be in the same order for this
        // BayesIm.)
        this.nodes = new Node[bayesIm.getNumNodes()];

        for (int i = 0; i < bayesIm.getNumNodes(); i++) {
            this.nodes[i] = bayesIm.getNode(i);
        }

        // Copy all the old values over.
        //initialize(bayesIm, MlBayesIm.MANUAL);
        initialize(bayesIm, MANUAL);
    }


    /**
     * Generates a simple exemplar of this class to test serialization.
     */
    public static MlBayesImObs serializableInstance() {
        return new MlBayesImObs(BayesPm.serializableInstance());
    }

    //===============================PUBLIC METHODS========================//

    /**
     * @return this PM.
     */
    public BayesPm getBayesPm() {
        return bayesPm;
    }

    /**
     * @return the DAG.
     */
    public Graph getDag() {
        return bayesPm.getDag();
    }

    /**
     * @return the number of nodes in the model.
     */
    public int getNumNodes() {
        return nodes.length;
    }

    /**
     * @return this node.
     */
    public Node getNode(int nodeIndex) {
        return nodes[nodeIndex];
    }

    /**
     * @param name the name of the node.
     * @return the node.
     */
    public Node getNode(String name) {
        return getDag().getNode(name);
    }

    /**
     * @param node the given node.
     * @return the index for that node, or -1 if the node is not in the
     * BayesIm.
     */
    public int getNodeIndex(Node node) {
        for (int i = 0; i < nodes.length; i++) {
            if (node == nodes[i]) {
                return i;
            }
        }

        return -1;
    }

    public List<Node> getVariables() {
        List<Node> variables = new LinkedList<>();

        for (int i = 0; i < getNumNodes(); i++) {
            Node node = getNode(i);
            variables.add(bayesPm.getVariable(node));
        }

        return variables;
    }

    /**
     * @return the list of measured variableNodes.
     */
    public List<Node> getMeasuredNodes() {
        return bayesPm.getMeasuredNodes();
    }

    public List<String> getVariableNames() {
        List<String> variableNames = new LinkedList<>();

        for (int i = 0; i < getNumNodes(); i++) {
            Node node = getNode(i);
            variableNames.add(bayesPm.getVariable(node).getName());
        }

        return variableNames;
    }

    /**
     * @return this number.
     * @see #getNumRows
     */
    public int getNumColumns(int nodeIndex) {
        return probs[nodeIndex][0].length;
    }

    /**
     * @return this number.
     * @see #getRowIndex
     * @see #getNumColumns
     */
    public int getNumRows(int nodeIndex) {
        return probs[nodeIndex].length;
    }

    /**
     * @param nodeIndex the given node.
     * @return the number of parents for this node.
     */
    public int getNumParents(int nodeIndex) {
        return parents[nodeIndex].length;
    }

    /**
     * @return the given parent of the given node.
     */
    public int getParent(int nodeIndex, int parentIndex) {
        return parents[nodeIndex][parentIndex];
    }

    /**
     * @return the dimension of the given parent for the given node.
     */
    public int getParentDim(int nodeIndex, int parentIndex) {
        return parentDims[nodeIndex][parentIndex];
    }

    /**
     * @return this array of parent dimensions.
     * @see #getParents
     */
    public int[] getParentDims(int nodeIndex) {
        int[] dims = parentDims[nodeIndex];
        int[] copy = new int[dims.length];
        System.arraycopy(dims, 0, copy, 0, dims.length);
        return copy;
    }

    /**
     * @return (a defensive copy of) the array containing all of the parents of
     * a given node in the order in which they are stored internally.
     * @see #getParentDims
     */
    public int[] getParents(int nodeIndex) {
        int[] nodeParents = parents[nodeIndex];
        int[] copy = new int[nodeParents.length];
        System.arraycopy(nodeParents, 0, copy, 0, nodeParents.length);
        return copy;
    }

    /**
     * @param nodeIndex the index of the node.
     * @param rowIndex  the index of the row in question.
     * @return the array representing the combination of parent values for this
     * row.
     * @see #getNodeIndex
     * @see #getRowIndex
     */
    public int[] getParentValues(int nodeIndex, int rowIndex) {
        int[] dims = getParentDims(nodeIndex);
        int[] values = new int[dims.length];

        for (int i = dims.length - 1; i >= 0; i--) {
            values[i] = rowIndex % dims[i];
            rowIndex /= dims[i];
        }

        return values;
    }

    /**
     * @return the value in the probability table for the given node, at the
     * given row and column.
     */
    public int getParentValue(int nodeIndex, int rowIndex, int colIndex) {
        return getParentValues(nodeIndex, rowIndex)[colIndex];
    }

    /**
     * @param nodeIndex the index of the node in question.
     * @param rowIndex  the row in the table for this for node which represents
     *                  the combination of parent values in question.
     * @param colIndex  the column in the table for this node which represents
     *                  the value of the node in question.
     * @return the probability stored for this parameter.
     * @see #getNodeIndex
     * @see #getRowIndex
     */
    public double getProbability(int nodeIndex, int rowIndex, int colIndex) {
        return probs[nodeIndex][rowIndex][colIndex];
    }

    /**
     * @return the row in the table for the given node and combination of parent
     * values.
     * @see #getParentValues
     */
    public int getRowIndex(int nodeIndex, int[] values) {
        int[] dim = getParentDims(nodeIndex);
        int rowIndex = 0;

        for (int i = 0; i < dim.length; i++) {
            rowIndex *= dim[i];
            rowIndex += values[i];
        }

        return rowIndex;
    }

    /**
     * Normalizes all rows in the tables associated with each of node in turn.
     */
    public void normalizeAll() {
        for (int nodeIndex = 0; nodeIndex < nodes.length; nodeIndex++) {
            normalizeNode(nodeIndex);
        }
    }

    /**
     * Normalizes all rows in the table associated with a given node.
     */
    public void normalizeNode(int nodeIndex) {
        for (int rowIndex = 0; rowIndex < getNumRows(nodeIndex); rowIndex++) {
            normalizeRow(nodeIndex, rowIndex);
        }
    }

    /**
     * Normalizes the given row.
     */
    public void normalizeRow(int nodeIndex, final int rowIndex) {
        final int numColumns = getNumColumns(nodeIndex);
        double total = 0.0;

        for (int colIndex = 0; colIndex < numColumns; colIndex++) {
            total += getProbability(nodeIndex, rowIndex, colIndex);
        }

        if (total != 0.0) {
            for (int colIndex = 0; colIndex < numColumns; colIndex++) {
                double probability =
                        getProbability(nodeIndex, rowIndex, colIndex);
                double prob = probability / total;
                setProbability(nodeIndex, rowIndex, colIndex, prob);
            }
        } else {
            double prob = 1.0 / numColumns;

            for (int colIndex = 0; colIndex < numColumns; colIndex++) {
                setProbability(nodeIndex, rowIndex, colIndex, prob);
            }
        }
    }

    /**
     * Sets the probability for the given node at a given row and column in the
     * table for that node.  To get the node index, use getNodeIndex().  To get
     * the row index, use getRowIndex().  To get the column index, use
     * getCategoryIndex() from the underlying BayesPm().  The value returned
     * will represent a conditional probability of the form P(N=v0 | P1=v1,
     * P2=v2, ... , Pn=vn), where N is the node referenced by nodeIndex, v0 is
     * the value referenced by colIndex, and the combination of parent values
     * indicated is the combination indicated by rowIndex.
     *
     * @param nodeIndex the index of the node in question.
     * @param rowIndex  the row in the table for this for node which represents
     *                  the combination of parent values in question.
     * @param colIndex  the column in the table for this node which represents
     *                  the value of the node in question.
     * @param value     the desired probability to be set.
     * @see #getProbability
     */
    public void setProbability(int nodeIndex, int rowIndex, int colIndex,
                               double value) {
        if (colIndex >= getNumColumns(nodeIndex)) {
            throw new IllegalArgumentException("Column out of range: " +
                    colIndex + " >= " + getNumColumns(nodeIndex));
        }

        if (!(0.0 <= value && value <= 1.0) && !Double.isNaN(value)) {
            throw new IllegalArgumentException("Probability value must be " +
                    "between 0.0 and 1.0 or Double.NaN.");
        }

        probs[nodeIndex][rowIndex][colIndex] = value;
    }

    /**
     * @return the index of the node with the given name in the specified
     * BayesIm.
     */
    public int getCorrespondingNodeIndex(int nodeIndex, BayesIm otherBayesIm) {
        String nodeName = getNode(nodeIndex).getName();
        Node oldNode = otherBayesIm.getNode(nodeName);
        return otherBayesIm.getNodeIndex(oldNode);
    }

    /**
     * Assigns random probability values to the child values of this row that
     * add to 1.
     *
     * @param nodeIndex the node for the table that this row belongs to.
     * @param rowIndex  the index of the row.
     */
    public void clearRow(int nodeIndex, int rowIndex) {
        for (int colIndex = 0; colIndex < getNumColumns(nodeIndex); colIndex++) {
            setProbability(nodeIndex, rowIndex, colIndex, Double.NaN);
        }
    }


    /**
     * Assigns random probability values to the child values of this row that
     * add to 1.
     *
     * @param nodeIndex the node for the table that this row belongs to.
     * @param rowIndex  the index of the row.
     */
    public void randomizeRow(int nodeIndex, int rowIndex) {
        final int size = getNumColumns(nodeIndex);
        probs[nodeIndex][rowIndex] = getRandomWeights(size);
    }

    /**
     * Randomizes any row in the table for the given node index that has a
     * Double.NaN value in it.
     *
     * @param nodeIndex the node for the table whose incomplete rows are to be
     *                  randomized.
     */
    public void randomizeIncompleteRows(int nodeIndex) {
        for (int rowIndex = 0; rowIndex < getNumRows(nodeIndex); rowIndex++) {
            if (isIncomplete(nodeIndex, rowIndex)) {
                randomizeRow(nodeIndex, rowIndex);
            }
        }
    }

    /**
     * Randomizes every row in the table for the given node index.
     *
     * @param nodeIndex the node for the table to be randomized.
     */
    public void randomizeTable(int nodeIndex) {
        for (int rowIndex = 0; rowIndex < getNumRows(nodeIndex); rowIndex++) {
            randomizeRow(nodeIndex, rowIndex);
        }
        //        randomizeTable2(nodeIndex);
    }

//    private void randomizeTable2(int nodeIndex) {
//        for (int rowIndex = 0; rowIndex < getNumRows(nodeIndex); rowIndex++) {
//            if (isIncomplete(nodeIndex, rowIndex)) {
//                break;
//            }
//        }
//
//        // Trying for some more power ..jdramsey 5/7/10
//        List<Integer> rowIndices = new ArrayList<>();
//
//        for (int i = 0; i < getNumRows(nodeIndex); i++) {
//            rowIndices.add(i);
//        }
//
//        Collections.shuffle(rowIndices);
//
//        randomizeRow(nodeIndex, rowIndices.get(0));
//        double[][] values = new double[getNumRows(nodeIndex)][getNumColumns(nodeIndex)];
//
//        for (int row = 0; row < getNumRows(nodeIndex); row++) {
//            double bestNorm = 0.0;
//
//            for (int trial = 0; trial < 100; trial++) {
//                randomizeRow(nodeIndex, rowIndices.get(row));
//                double totalNorm = 0.0;
//
//                for (int _row = row - 1; _row < row; _row++) {
//                    double norm = norm(nodeIndex, rowIndices.get(row),
//                            rowIndices.get(_row));
//                    totalNorm += norm;
//                }
//
//                if (totalNorm > bestNorm) {
//                    bestNorm = totalNorm;
//
//                    for (int _row = 0; _row < getNumRows(nodeIndex); _row++) {
//                        for (int col = 0; col < getNumColumns(nodeIndex); col++) {
//                            values[_row][col] = getProbability(nodeIndex, _row, col);
//                        }
//                    }
//                }
//            }
//
//            for (int _row = 0; _row < getNumRows(nodeIndex); _row++) {
//                for (int col = 0; col < getNumColumns(nodeIndex); col++) {
//                    setProbability(nodeIndex, _row, col, values[_row][col]);
//                }
//            }
//        }
//    }

//    private double totalNorm(int nodeIndex, int parent, int cat1, int cat2) {
//        double[] sumProbs1 = new double[getNumColumns(nodeIndex)];
//        double[] sumProbs2 = new double[getNumColumns(nodeIndex)];
//
//        for (int row = 0; row < getNumRows(nodeIndex); row++) {
//            for (int col = 0; col < getNumColumns(nodeIndex); col++) {
//                if (getParentValues(nodeIndex, row)[parent] == cat1) {
//                    sumProbs1[col] += getProbability(nodeIndex, row, col);
//                }
//            }
//        }
//
//        for (int row = 0; row < getNumRows(nodeIndex); row++) {
//            for (int col = 0; col < getNumColumns(nodeIndex); col++) {
//                if (getParentValues(nodeIndex, row)[parent] == cat2) {
//                    sumProbs2[col] += getProbability(nodeIndex, row, col);
//                }
//            }
//        }
//
//        double norm = 0.0;
//
//        for (int col = 0; col < getNumColumns(nodeIndex); col++) {
//            double value1 = sumProbs1[col];
//            double value2 = sumProbs2[col];
//            double diff = value1 - value2;
//            double absNorm = Math.abs(diff);
//            norm += absNorm;
//        }
//
//        return norm;
//    }

//    private double norm(int nodeIndex, int row1, int row2) {
//        double norm = 0.0;
//
//        for (int col = 0; col < getNumColumns(nodeIndex); col++) {
//            double value1 = getProbability(nodeIndex, row1, col);
//            double value2 = getProbability(nodeIndex, row2, col);
//            double diff = value1 - value2;
//            double absNorm = Math.abs(diff);
//            //            norm += diff * diff;
//            norm += absNorm;
//        }
//        return norm;
//    }

    /**
     * Randomizes every row in the table for the given node index.
     *
     * @param nodeIndex the node for the table to be randomized.
     */
    public void clearTable(int nodeIndex) {
        for (int rowIndex = 0; rowIndex < getNumRows(nodeIndex); rowIndex++) {
            clearRow(nodeIndex, rowIndex);
        }
    }

    /**
     * @return true iff one of the values in the given row is Double.NaN.
     */
    public boolean isIncomplete(int nodeIndex, int rowIndex) {
        for (int colIndex = 0; colIndex < getNumColumns(nodeIndex); colIndex++) {
            double p = getProbability(nodeIndex, rowIndex, colIndex);

            if (Double.isNaN(p)) {
                return true;
            }
        }

        return false;
    }

    /**
     * @return true iff any value in the table for the given node is
     * Double.NaN.
     */
    public boolean isIncomplete(int nodeIndex) {
        for (int rowIndex = 0; rowIndex < getNumRows(nodeIndex); rowIndex++) {
            if (isIncomplete(nodeIndex, rowIndex)) {
                return true;
            }
        }

        return false;
    }

    /**
     * Simulates a sample with the given sample size.
     *
     * @param sampleSize      the sample size.
     * @return the simulated sample as a DataSet.
     */
    public DataSet simulateData(int sampleSize, boolean latentDataSaved) {
        if (getBayesPm().getDag().isTimeLagModel()) {
            return simulateTimeSeries(sampleSize);
        }

        return simulateDataHelper(sampleSize, latentDataSaved);
    }

    public DataSet simulateData(DataSet dataSet, boolean latentDataSaved) {
        return simulateDataHelper(dataSet, latentDataSaved);
    }

    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;
    }

    /**
     * Simulates a sample with the given sample size.
     *
     * @param sampleSize      the sample size.
     * @param seed            the random number generator seed allows you
     *                        recreate the simulated data by passing in the same
     *                        seed (so you don't have to store the sample data
     * @return the simulated sample as a DataSet.
     */
    public DataSet simulateData(int sampleSize, long seed, boolean latentDataSaved) {
        RandomUtil random = RandomUtil.getInstance();
        long _seed = random.getSeed();
        random.setSeed(seed);
        DataSet dataSet = simulateData(sampleSize, latentDataSaved);
        random.revertSeed(_seed);
        return dataSet;
    }

    public DataSet simulateData(DataSet dataSet, long seed, boolean latentDataSaved) {
        RandomUtil random = RandomUtil.getInstance();
        random.setSeed(seed);
        return simulateDataHelper(dataSet, latentDataSaved);
    }

    /**
     * Simulates a sample with the given sample size.
     *
     * @param sampleSize      the sample size.
     * @return the simulated sample as a DataSet.
     */
    private DataSet simulateDataHelper(int sampleSize, boolean latentDataSaved) {
        int numMeasured = 0;
        int[] map = new int[nodes.length];
        List<Node> variables = new LinkedList<>();

        for (int j = 0; j < nodes.length; j++) {
            if (!latentDataSaved && nodes[j].getNodeType() != NodeType.MEASURED) {
                continue;
            }

            int numCategories = bayesPm.getNumCategories(nodes[j]);
            List<String> categories = new LinkedList<>();

            for (int k = 0; k < numCategories; k++) {
                categories.add(bayesPm.getCategory(nodes[j], k));
            }

            DiscreteVariable var =
                    new DiscreteVariable(nodes[j].getName(), categories);
            variables.add(var);
            int index = ++numMeasured - 1;
            map[index] = j;
        }

        DataSet dataSet = new ColtDataSet(sampleSize, variables);
        constructSample(sampleSize, numMeasured, dataSet, map);
        return dataSet;
    }

    /**
     * Constructs a random sample using the given already allocated data set, to
     * avoid allocating more memory.
     */
    private DataSet simulateDataHelper(DataSet dataSet, boolean latentDataSaved) {
        if (dataSet.getNumColumns() != nodes.length) {
            throw new IllegalArgumentException("When rewriting the old data set, " +
                    "number of variables in data set must equal number of variables " +
                    "in Bayes net.");
        }

        int sampleSize = dataSet.getNumRows();

        int numMeasured = 0;
        int[] map = new int[nodes.length];
        List<Node> variables = new LinkedList<>();

        for (int j = 0; j < nodes.length; j++) {
            if (!latentDataSaved && nodes[j].getNodeType() != NodeType.MEASURED) {
                continue;
            }

            int numCategories = bayesPm.getNumCategories(nodes[j]);
            List<String> categories = new LinkedList<>();

            for (int k = 0; k < numCategories; k++) {
                categories.add(bayesPm.getCategory(nodes[j], k));
            }

            DiscreteVariable var =
                    new DiscreteVariable(nodes[j].getName(), categories);
            variables.add(var);
            int index = ++numMeasured - 1;
            map[index] = j;
        }

        for (int i = 0; i < variables.size(); i++) {
            Node node = dataSet.getVariable(i);
            Node _node = variables.get(i);
            dataSet.changeVariable(node, _node);
        }

        constructSample(sampleSize, numMeasured, dataSet, map);
        return dataSet;
    }

    private void constructSample(int sampleSize,
                                 int numMeasured, DataSet dataSet,
                                 int[] map) {
        // Get a tier ordering and convert it to an int array.
        Graph graph = getBayesPm().getDag();
        Dag dag = new Dag(graph);
        List<Node> tierOrdering = dag.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[nodes.length];

        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;
                    }
                }
            }

            for (int j = 0; j < numMeasured; j++) {
                dataSet.setInt(i, j, point[map[j]]);
            }
        }
    }

    public boolean equals(Object o) {
        if (o == this) {
            return true;
        }

        if (!(o instanceof BayesIm)) {
            return false;
        }

        BayesIm otherIm = (BayesIm) o;

        if (getNumNodes() != otherIm.getNumNodes()) {
            return false;
        }

        for (int i = 0; i < getNumNodes(); i++) {
            int otherIndex = otherIm.getCorrespondingNodeIndex(i, otherIm);

            if (otherIndex == -1) {
                return false;
            }

            if (getNumColumns(i) != otherIm.getNumColumns(otherIndex)) {
                return false;
            }

            if (getNumRows(i) != otherIm.getNumRows(otherIndex)) {
                return false;
            }

            for (int j = 0; j < getNumRows(i); j++) {
                for (int k = 0; k < getNumColumns(i); k++) {
                    double prob = getProbability(i, j, k);
                    double otherProb = otherIm.getProbability(i, j, k);

                    if (Double.isNaN(prob) && Double.isNaN(otherProb)) {
                        continue;
                    }

                    if (Math.abs(prob - otherProb) > ALLOWABLE_DIFFERENCE) {
                        return false;
                    }
                }
            }
        }

        return true;
    }

    /**
     * Prints out the probability table for each variable.
     */
    public String toString() {

		/*
        for (int i = 0; i < getNumNodes(); i++) {
            buf.append("\n\nNode: ").append(getNode(i));

            if (getNumParents(i) == 0) {
                buf.append("\n");
            } else {
                buf.append("\n\n");
                for (int k = 0; k < getNumParents(i); k++) {
                    buf.append(getNode(getParent(i, k))).append("\t");
                }
            }

            for (int j = 0; j < getNumRows(i); j++) {
                buf.append("\n");
                for (int k = 0; k < getNumParents(i); k++) {
                    buf.append(getParentValue(i, j, k));

                    if (k < getNumParents(i) - 1) {
                        buf.append("\t");
                    }
                }

                if (getNumParents(i) > 0) {
                    buf.append("\t");
                }

                for (int k = 0; k < getNumColumns(i); k++) {
                    buf.append(nf.format(getProbability(i, j, k))).append("\t");
                }
            }
        }
		 */

        return "MlBayesImObs\n";
    }

    ///////////////////////////////////////////////////////
    // methods added for MlBayesImObs
    ///////////////////////////////////////////////////////

//    public BayesIm getBayesImRandomize() {
//        return bayesImRandomize;
//    }

    public BayesIm getBayesImObs() {
        return bayesImObs;
    }

    public StoredCellProbsObs getJPD() {
        return jpd;
    }

    public int getNumRows() {
        return jpd.getNumRows();
    }

    // translate rowIndex into the variable values
    public int[] getRowValues(int rowIndex) {
        return jpd.getVariableValues(rowIndex);
    }

    public double getProbability(int rowIndex) {
        return jpd.getCellProb(getRowValues(rowIndex));
    }

    public void setProbability(int rowIndex, double value) {
        if (!(0.0 <= value && value <= 1.0) && !Double.isNaN(value)) {
            throw new IllegalArgumentException("Probability value must be " +
                    "between 0.0 and 1.0 or Double.NaN.");
        }
        jpd.setCellProbability(getRowValues(rowIndex), value);
    }

    public void createRandomCellTable() {
        for (int nodeIndex = 0; nodeIndex < nodes.length; nodeIndex++) {

            bayesImRandomize.randomizeTable(nodeIndex);
        }
        jpd.createCellTable((MlBayesIm) bayesImRandomize);
    }


    //=============================PRIVATE METHODS=======================//

    ///////////////////////////////////////
    // initialization: the JPD and a BayesIm with only the observed variables
    // the data structure for the CPD are left in for compatibility
    private void initialize(BayesIm oldBayesIm, int initializationMethod) {
        parents = new int[this.nodes.length][];
        parentDims = new int[this.nodes.length][];
        probs = new double[this.nodes.length][][];

        // initialize parents, parentDims, probs, even if probs is not used
        for (int nodeIndex = 0; nodeIndex < this.nodes.length; nodeIndex++) {
            initializeNode(nodeIndex);
        }

        ///////////////////////////////////////////////////////////////////////
        // used for randomizing the jpd
        this.bayesImRandomize = new MlBayesIm(bayesPm);

        ///////////////////////////////////////////////////////////////////////
        // construct a BayesIm with only observed variables
        // This is used for making Proposition with only the observed variables

        Dag dag = new Dag(bayesPm.getDag());
        for (Node node : this.nodes) {
            if (node.getNodeType() == NodeType.LATENT) {
                dag.removeNode(node);
            }
        }
        BayesPm bayesPmObs = new BayesPm(dag, bayesPm);

        // not a MlBayesImObs to avoid an infinite loop of constructing
        // an MlBayesImObs inside an MlBayesImObs
        this.bayesImObs = new MlBayesIm(bayesPmObs);

        ///////////////////////////////////////////////////////////////////////
        // construct the jpd

        List<Node> obsNodes = new ArrayList<>();
        for (Node node1 : nodes) {
            Node node = bayesPm.getVariable(node1);
            if (node.getNodeType() == NodeType.MEASURED) {
                obsNodes.add(node);
            }
        }
        // this does not work: different ordering of nodes
        // graph is the DAG restricted to only observed variables
        //List<Node> obsNodes = bayesPmObs.getVariable();

        this.jpd = new StoredCellProbsObs(obsNodes);
        // this does not work: different ordering of nodes
        //jpd = new StoredCellProbsObs(getMeasuredNodes());

        ///////////////////////////////////////////////////////////////////////
        // initialize the jpd

        if (initializationMethod == RANDOM) {
            // this does not work: assigning arbitrary random values to the jpd
            // will violate the constraints imposed by the graphical structure
            //jpd.createRandomCellTable();

            if (oldBayesIm == null) {
                createRandomCellTable();
            } else if (oldBayesIm.getClass().getSimpleName().equals("MlBayesIm")) {
                jpd.createCellTable((MlBayesIm) oldBayesIm);
            } else if (oldBayesIm.getClass().getSimpleName().equals("MlBayesImObs")) {
                if (bayesPm.equals(oldBayesIm.getBayesPm())) {
                    this.jpd.createCellTable((MlBayesImObs) oldBayesIm);
                } else {
                    createRandomCellTable();
                }
            }
        } else if (initializationMethod == MANUAL) {
            if (oldBayesIm == null) {
                jpd.clearCellTable();
            } else if (oldBayesIm.getClass().getSimpleName().equals("MlBayesIm")) {
                jpd.createCellTable((MlBayesIm) oldBayesIm);
            } else if (oldBayesIm.getClass().getSimpleName().equals("MlBayesImObs")) {
                if (bayesPm.equals(oldBayesIm.getBayesPm())) {
                    this.jpd.createCellTable((MlBayesImObs) oldBayesIm);
                } else {
                    jpd.clearCellTable();
                }
            }

        } else {
            throw new IllegalArgumentException("Unrecognized state.");
        }

    }

    /**
     * This method initializes the node indicated.
     */
    private void initializeNode(int nodeIndex) {
        Node node = nodes[nodeIndex];

        // Set up parents array.  Should store the parents of
        // each node as ints in a particular order.
        Graph graph = getBayesPm().getDag();
        List<Node> parentList = graph.getParents(node);
        int[] parentArray = new int[parentList.size()];

        for (int i = 0; i < parentList.size(); i++) {
            parentArray[i] = getNodeIndex(parentList.get(i));
        }

        // Sort parent array.
        Arrays.sort(parentArray);

        parents[nodeIndex] = parentArray;

        // Setup dimensions array for parents.
        int[] dims = new int[parentArray.length];

        for (int i = 0; i < dims.length; i++) {
            Node parNode = nodes[parentArray[i]];
            dims[i] = getBayesPm().getNumCategories(parNode);
        }

        // Calculate dimensions of table.
        int numRows = 1;

        for (int dim : dims) {
            if (numRows > 1000000 /* Integer.MAX_VALUE / dim*/) {
                throw new IllegalArgumentException(
                        "The number of rows in the " +
                                "conditional probability table for " +
                                nodes[nodeIndex] +
                                " is greater than 1,000,000 and cannot be " +
                                "represented.");
            }

            numRows *= dim;
        }

        int numCols = getBayesPm().getNumCategories(node);

        parentDims[nodeIndex] = dims;
        probs[nodeIndex] = new double[numRows][numCols];

		/*
        // Initialize each row.
        for (int rowIndex = 0; rowIndex < numRows; rowIndex++) {
            if (oldBayesIm == null ) {
                overwriteRow(nodeIndex, rowIndex, initializationMethod);
            } else {
                retainOldRowIfPossible(nodeIndex, rowIndex, oldBayesIm,
									   initializationMethod);
            }
        }
		 */
    }

//    private void overwriteRow(int nodeIndex, int rowIndex,
//                              int initializationMethod) {
//        if (initializationMethod == RANDOM) {
//            randomizeRow(nodeIndex, rowIndex);
//        } else if (initializationMethod == MANUAL) {
//            initializeRowAsUnknowns(nodeIndex, rowIndex);
//        } else {
//            throw new IllegalArgumentException("Unrecognized state.");
//        }
//    }

    /**
     * This method chooses random probabilities for a row which add up to 1.0.
     * Random doubles are drawn from a random distribution, and the final row is
     * then normalized.
     *
     * @param size the length of the row.
     * @return an array with randomly distributed probabilities of this length.
     * @see #randomizeRow
     */
    private static double[] getRandomWeights(int size) {
        assert size >= 0;

        double[] row = new double[size];
        double sum = 0.0;

        // If I put most of the mass in each row on one of the categories,
        // I get lovely classification results for Bayes nets with all
        // 4-category variables. To include a bias, set 'bias' to a positive
        // number.
        double bias = 0;

        int randomCell = RandomUtil.getInstance().nextInt(size);

        //        if (RandomUtil.getInstance().nextDouble() < 0.5) {
        //            randomCell = -1;
        //        }

        for (int i = 0; i < size; i++) {
            row[i] = RandomUtil.getInstance().nextDouble();

            if (i == randomCell) {
                row[i] += bias;
            }

            sum += row[i];
        }

        for (int i = 0; i < size; i++) {
            row[i] /= sum;
        }

        return row;
    }

//    private void initializeRowAsUnknowns(int nodeIndex, int rowIndex) {
//        final int size = getNumColumns(nodeIndex);
//        double[] row = new double[size];
//        Arrays.fill(row, Double.NaN);
//        probs[nodeIndex][rowIndex] = row;
//    }

//    /**
//     * This method initializes the node indicated.
//     */
//    private void retainOldRowIfPossible(int nodeIndex, int rowIndex,
//                                        BayesIm oldBayesIm, int initializationMethod) {
//        //        Set<Node> newParents = new HashSet<Node>(getBayesPm().getDag().getParents(node));
//        //        Set<Node> oldParents = new HashSet<Node>(oldBayesIm.getBayesPm().getDag().getParents(node));
//        //        int method = newParents == oldParents ? initializationMethod : MlBayesIm.MANUAL;
//
//
//        int oldNodeIndex = getCorrespondingNodeIndex(nodeIndex, oldBayesIm);
//
//        if (oldNodeIndex == -1) {
//            overwriteRow(nodeIndex, rowIndex, initializationMethod);
//        } else if (getNumColumns(nodeIndex) != oldBayesIm.getNumColumns(oldNodeIndex)) {
//            overwriteRow(nodeIndex, rowIndex, initializationMethod);
//            //        } else if (parentsChanged(nodeIndex, this, oldBayesIm)) {
//            //            overwriteRow(nodeIndex, rowIndex, initializationMethod);
//        } else {
//            int oldRowIndex = getUniqueCompatibleOldRow(nodeIndex, rowIndex, oldBayesIm);
//
//            if (oldRowIndex >= 0) {
//                copyValuesFromOldToNew(oldNodeIndex, oldRowIndex, nodeIndex,
//                        rowIndex, oldBayesIm);
//            } else {
//                overwriteRow(nodeIndex, rowIndex, initializationMethod);
//            }
//        }
//    }
//
    //    private boolean parentsChanged(int nodeIndex, BayesIm bayesIm, BayesIm oldBayesIm) {
    //        int[] dims = bayesIm.getParents(nodeIndex);
    //        int[] oldDims = oldBayesIm.getParents(nodeIndex);
    //
    //        if (dims.length != oldDims.length) {
    //            return false;
    //        }
    //
    //        for (int i = 0; i < dims.length; i++) {
    //            if (dims[i] != oldDims[i]) {
    //                return false;
    //            }
    //        }
    //
    //        return true;
    //    }

//    /**
//     * @return the unique rowIndex in the old BayesIm for the given node that is
//     * compatible with the given rowIndex in the new BayesIm for that node, if
//     * one exists. Otherwise, returns -1. A compatible rowIndex is one in which
//     * all the parents that the given node has in common between the old BayesIm
//     * and the new BayesIm are assigned the values they have in the new
//     * rowIndex. If a parent node is removed in the new BayesIm, there may be
//     * more than one such compatible rowIndex in the old BayesIm, in which case
//     * -1 is returned. Likewise, there may be no compatible rows, in which case
//     * -1 is returned.
//     */
//    private int getUniqueCompatibleOldRow(int nodeIndex, int rowIndex,
//                                          BayesIm oldBayesIm) {
//        int oldNodeIndex = getCorrespondingNodeIndex(nodeIndex, oldBayesIm);
//        int oldNumParents = oldBayesIm.getNumParents(oldNodeIndex);
//
//        int[] oldParentValues = new int[oldNumParents];
//        Arrays.fill(oldParentValues, -1);
//
//        int[] parentValues = getParentValues(nodeIndex, rowIndex);
//
//        // Go through each parent of the node in the new BayesIm.
//        for (int i = 0; i < getNumParents(nodeIndex); i++) {
//
//            // Get the index of the parent in the new graph and in the old
//            // graph. If it's no longer in the new graph, skip to the next
//            // parent.
//            int parentNodeIndex = getParent(nodeIndex, i);
//            int oldParentNodeIndex =
//                    getCorrespondingNodeIndex(parentNodeIndex, oldBayesIm);
//            int oldParentIndex = -1;
//
//            for (int j = 0; j < oldBayesIm.getNumParents(oldNodeIndex); j++) {
//                if (oldParentNodeIndex == oldBayesIm.getParent(oldNodeIndex, j)) {
//                    oldParentIndex = j;
//                    break;
//                }
//            }
//
//            if (oldParentIndex == -1 ||
//                    oldParentIndex >= oldBayesIm.getNumParents(oldNodeIndex)) {
//                return -1;
//            }
//
//            // Look up that value index for the new BayesIm for that parent.
//            // If it was a valid value index in the old BayesIm, record
//            // that value in oldParentValues. Otherwise return -1.
//            int newParentValue = parentValues[i];
//            int oldParentDim =
//                    oldBayesIm.getParentDim(oldNodeIndex, oldParentIndex);
//
//            if (newParentValue < oldParentDim) {
//                oldParentValues[oldParentIndex] = newParentValue;
//            } else {
//                return -1;
//            }
//        }
//
//        //        // Go through each parent of the node in the new BayesIm.
//        //        for (int i = 0; i < oldBayesIm.getNumParents(oldNodeIndex); i++) {
//        //
//        //            // Get the index of the parent in the new graph and in the old
//        //            // graph. If it's no longer in the new graph, skip to the next
//        //            // parent.
//        //            int oldParentNodeIndex = oldBayesIm.getParent(oldNodeIndex, i);
//        //            int parentNodeIndex =
//        //                    oldBayesIm.getCorrespondingNodeIndex(oldParentNodeIndex, this);
//        //            int parentIndex = -1;
//        //
//        //            for (int j = 0; j < this.getNumParents(nodeIndex); j++) {
//        //                if (parentNodeIndex == this.getParent(nodeIndex, j)) {
//        //                    parentIndex = j;
//        //                    break;
//        //                }
//        //            }
//        //
//        //            if (parentIndex == -1 ||
//        //                    parentIndex >= this.getNumParents(nodeIndex)) {
//        //                continue;
//        //            }
//        //
//        //            // Look up that value index for the new BayesIm for that parent.
//        //            // If it was a valid value index in the old BayesIm, record
//        //            // that value in oldParentValues. Otherwise return -1.
//        //            int parentValue = oldParentValues[i];
//        //            int parentDim =
//        //                    this.getParentDim(nodeIndex, parentIndex);
//        //
//        //            if (parentValue < parentDim) {
//        //                oldParentValues[parentIndex] = oldParentValue;
//        //            } else {
//        //                return -1;
//        //            }
//        //        }
//
//        // If there are any -1's in the combination at this point, return -1.
//        for (int oldParentValue : oldParentValues) {
//            if (oldParentValue == -1) {
//                return -1;
//            }
//        }
//
//        // Otherwise, return the combination, which will be a row in the
//        // old BayesIm.
//        return oldBayesIm.getRowIndex(oldNodeIndex, oldParentValues);
//    }

//    private void copyValuesFromOldToNew(int oldNodeIndex, int oldRowIndex,
//                                        int nodeIndex, int rowIndex, BayesIm oldBayesIm) {
//        if (getNumColumns(nodeIndex) != oldBayesIm.getNumColumns(oldNodeIndex)) {
//            throw new IllegalArgumentException("It's only possible to copy " +
//                    "one row of probability values to another in a Bayes IM " +
//                    "if the number of columns in the table are the same.");
//        }
//
//        for (int colIndex = 0; colIndex < getNumColumns(nodeIndex); colIndex++) {
//            double prob = oldBayesIm.getProbability(oldNodeIndex, oldRowIndex,
//                    colIndex);
//            setProbability(nodeIndex, rowIndex, colIndex, prob);
//        }
//    }


    /**
     * Adds semantic checks to the default deserialization method. This method
     * must have the standard signature for a readObject method, and the body of
     * the method must begin with "s.defaultReadObject();". Other than that, any
     * semantic checks can be specified and do not need to stay the same from
     * version to version. A readObject method of this form may be added to any
     * class, even if Tetrad sessions were previously saved out using a version
     * of the class that didn't include it. (That's what the
     * "s.defaultReadObject();" is for. See J. Bloch, Effective Java, for help.
     *
     * @throws java.io.IOException
     * @throws ClassNotFoundException
     */
    private void readObject(ObjectInputStream s)
            throws IOException, ClassNotFoundException {
        s.defaultReadObject();

        if (bayesPm == null) {
            throw new NullPointerException();
        }

        if (nodes == null) {
            throw new NullPointerException();
        }

        if (parents == null) {
            throw new NullPointerException();
        }

        if (parentDims == null) {
            throw new NullPointerException();
        }

        if (probs == null) {
            throw new NullPointerException();
        }
    }
}






