///////////////////////////////////////////////////////////////////////////////
// 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.search;

//import com.mathworks.toolbox.javabuilder.MWException;
//import com.mathworks.toolbox.javabuilder.MWLogicalArray;

import edu.cmu.tetrad.data.DataSet;
import edu.cmu.tetrad.data.ICovarianceMatrix;
import edu.cmu.tetrad.graph.Node;
import edu.cmu.tetrad.util.NumberFormatUtil;
import edu.cmu.tetrad.util.TetradLogger;
import edu.cmu.tetrad.util.TetradMatrix;

import java.text.NumberFormat;
import java.util.*;

//import kci.Kci;
//import kci.*;
//import com.mathworks.toolbox.javabuilder.*;

/**
 * Checks conditional independence of variable in a continuous data set using a conditional correlation test
 * for the nonlinear nonGaussian case.
 *
 * @author Joseph Ramsey
 * @deprecated
 */
public final class IndTestKciMatlab implements IndependenceTest {

    private final double[][] _data;
    /**
     * The variables of the covariance data, in order. (Unmodifiable list.)
     */
    private List<Node> variables;

    /**
     * The significance level of the independence tests.
     */
    private double alpha;

    /**
     * Formats as 0.0000.
     */
    private static NumberFormat nf = NumberFormatUtil.getInstance().getNumberFormat();

    /**
     * Stores a reference to the dataset being analyzed.
     */
    private DataSet dataSet;
    private TetradMatrix data;
    private Map<Node, Integer> nodeMap;
    private int numTests;
    private boolean verbose = false;

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

    /**
     * Constructs a new Independence test which checks independence facts based on the correlation data implied by the
     * given data set (must be continuous). The given significance level is used.
     *
     * @param dataSet A data set containing only continuous columns.
     * @param alpha   The alpha level of the test.
     */
    public IndTestKciMatlab(DataSet dataSet, double alpha) {
        if (!(dataSet.isContinuous())) {
            throw new IllegalArgumentException("Data set must be continuous.");
        }

        List<Node> nodes = dataSet.getVariables();

        this.variables = Collections.unmodifiableList(nodes);
        setAlpha(alpha);

        this.dataSet = dataSet;
        data = this.dataSet.getDoubleData();

//        _data = data.transpose().toArray();
        _data = data.toArray();

        nodeMap = new HashMap<>();

        for (int i = 0; i < nodes.size(); i++) {
            nodeMap.put(nodes.get(i), i);
        }

        numTests = 0;
    }

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

    /**
     * Creates a new IndTestCramerT instance for a subset of the variables.
     */
    public IndependenceTest indTestSubset(List<Node> vars) {
        throw new UnsupportedOperationException();
    }

    public boolean isIndependent(Node x, Node y, List<Node> z) {
        boolean independent = checkIndependent(x, y, z);

        if (verbose) {
            if (independent) {
                TetradLogger.getInstance().log("independencies",
                        SearchLogUtils.independenceFactMsg(x, y, z, getPValue()));
            } else {
                TetradLogger.getInstance().log("dependencies",
                        SearchLogUtils.dependenceFactMsg(x, y, z, getPValue()));
            }
        }

        if (verbose) {
            SearchLogUtils.independenceFactMsg(x, y, z, getPValue());
        }

        return independent;
    }

    public boolean isIndependent(Node x, Node y, Node... z) {
        return isIndependent(x, y, Arrays.asList(z));
    }

    public boolean isDependent(Node x, Node y, List<Node> z) {
        boolean independent = checkIndependent(x, y, z);

        if (verbose) {
            if (independent) {
                TetradLogger.getInstance().log("independencies",
                        SearchLogUtils.independenceFactMsg(x, y, z, getPValue()));
            } else {
                TetradLogger.getInstance().log("dependencies",
                        SearchLogUtils.dependenceFactMsg(x, y, z, getPValue()));
            }
        }

        return !independent;
    }

    public boolean isDependent(Node x, Node y, Node... z) {
        List<Node> zList = Arrays.asList(z);
        return isDependent(x, y, zList);
    }

    /**
     * @return the probability associated with the most recently computed independence test.
     */
    public double getPValue() {
        return 0.0;
    }

    /**
     * Sets the significance level at which independence judgments should be made.  Affects the cutoff for partial
     * correlations to be considered statistically equal to zero.
     */
    public void setAlpha(double alpha) {
        if (alpha < 0.0 || alpha > 1.0) {
            throw new IllegalArgumentException("Significance out of range.");
        }

        this.alpha = alpha;
    }

    /**
     * Gets the getModel significance level.
     */
    public double getAlpha() {
        return this.alpha;
    }

    /**
     * @return the list of variables over which this independence checker is capable of determinine independence
     * relations-- that is, all the variables in the given graph or the given data set.
     */
    public List<Node> getVariables() {
        return this.variables;
    }

    /**
     * @return the variable with the given name.
     */
    public Node getVariable(String name) {
        for (Node node : variables) {
            if (node.getName().equals(name)) {
                return node;
            }
        }

        throw new IllegalArgumentException("Node a node in this test.");
    }

    /**
     * @return the list of variable varNames.
     */
    public List<String> getVariableNames() {
        List<Node> variables = getVariables();
        List<String> variableNames = new ArrayList<>();
        for (Node variable1 : variables) {
            variableNames.add(variable1.getName());
        }
        return variableNames;
    }

    /**
     * If <code>isDeterminismAllowed()</code>, deters to IndTestFisherZD; otherwise throws
     * UnsupportedOperationException.
     */
    public boolean determines(List<Node> z, Node x) throws UnsupportedOperationException {
        throw new UnsupportedOperationException();
    }

    /**
     * @return the data set being analyzed.
     */
    public DataSet getData() {
        return dataSet;
    }

    @Override
    public ICovarianceMatrix getCov() {
        return null;
    }

    @Override
    public List<DataSet> getDataSets() {
        return null;
    }

    @Override
    public int getSampleSize() {
        return 0;
    }

    @Override
    public List<TetradMatrix> getCovMatrices() {
        return null;
    }

    @Override
    public double getScore() {
        return getPValue();
    }

    /**
     * @return a string representation of this test.
     */
    public String toString() {
        return "Conditional Correlation, alpha = " + nf.format(getAlpha());
    }

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

    private boolean checkIndependent(Node x, Node y, List<Node> z) {
//        numTests++;
//
//        int xIndex = dataSet.getColumn(x) + 1;
//        int yIndex = dataSet.getColumn(y) + 1;
//
//        int[] zIndices = new int[z.size()];
//
//        for (int i = 0; i < z.size(); i++) {
//            zIndices[i] = dataSet.getColumn(z.get(i)) + 1;
//        }
//
//        try {
//            Kci _kci = new Kci();
//            Object[] output = _kci.indtest(1, xIndex, yIndex, zIndices, _data);
//
//            MWLogicalArray arr = (MWLogicalArray) output[0];
//            boolean out = arr.getBoolean(1);
//
//            return out;
//        } catch (MWException e) {
//            e.printStackTrace();
//        }
//
//        throw new IllegalStateException();
        return true;
    }

    public int getNumTests() {
        return numTests;
    }

    public boolean isVerbose() {
        return verbose;
    }

    public void setVerbose(boolean verbose) {
        this.verbose = verbose;
    }
}




