///////////////////////////////////////////////////////////////////////////////
// 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.                               //
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// 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      //
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// GNU General Public License for more details.                              //
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package edu.cmu.tetrad.test;

import edu.cmu.tetrad.data.DataSet;
import edu.cmu.tetrad.graph.EdgeListGraph;
import edu.cmu.tetrad.graph.Graph;
import edu.cmu.tetrad.graph.GraphNode;
import edu.cmu.tetrad.graph.Node;
import edu.cmu.tetrad.sem.Parameter;
import edu.cmu.tetrad.sem.SemEstimator;
import edu.cmu.tetrad.sem.SemIm;
import edu.cmu.tetrad.sem.SemPm;
import edu.cmu.tetrad.util.RandomUtil;
import org.junit.Test;

import java.util.List;

import static org.junit.Assert.assertEquals;

/**
 * Tests the use of variable means in the SEM classes.  Instantiates a SemPm and
 * SemIm from it. Values of the means of the variables are set in the IM. Then
 * the simulateData is used to create a continuous dataset.  The dataset is used
 * to SemEstimator and, from that, a new SemIm.  The means of the two SemIm's
 * are compared to see if they fall within a tolerance.
 *
 * @author Frank Wimberly
 */
public class TestSemVarMeans {

    @Test
    public void testMeansRecursive() {
        Graph graph = constructGraph1();
        SemPm semPm1 = new SemPm(graph);

        List<Parameter> parameters = semPm1.getParameters();

        for (Parameter p : parameters) {
            p.setInitializedRandomly(false);
        }

        SemIm semIm1 = new SemIm(semPm1);

        double[] means = {5.0, 4.0, 3.0, 2.0, 1.0};

        RandomUtil.getInstance().setSeed(-379467L);

        for (int i = 0; i < semIm1.getVariableNodes().size(); i++) {
            Node node = semIm1.getVariableNodes().get(i);
            semIm1.setMean(node, means[i]);
        }

        DataSet dataSet = semIm1.simulateDataRecursive(1000, false);

        SemEstimator semEst = new SemEstimator(dataSet, semPm1);
        semEst.estimate();
        SemIm estSemIm = semEst.getEstimatedSem();
        List<Node> nodes = semPm1.getVariableNodes();

        for (Node node : nodes) {
            double mean = semIm1.getMean(node);
            assertEquals(mean, estSemIm.getMean(node), 0.5);
        }
    }

    @Test
    public void testMeansReducedForm() {
        Graph graph = constructGraph1();
        SemPm semPm1 = new SemPm(graph);

        List<Parameter> parameters = semPm1.getParameters();

        for (Parameter p : parameters) {
            p.setInitializedRandomly(false);
        }

        SemIm semIm1 = new SemIm(semPm1);

        double[] means = {5.0, 4.0, 3.0, 2.0, 1.0};

        RandomUtil.getInstance().setSeed(-379467L);

        for (int i = 0; i < semIm1.getVariableNodes().size(); i++) {
            Node node = semIm1.getVariableNodes().get(i);
            semIm1.setMean(node, means[i]);
        }

        DataSet dataSet = semIm1.simulateDataReducedForm(1000, false);

        SemEstimator semEst = new SemEstimator(dataSet, semPm1);
        semEst.estimate();
        SemIm estSemIm = semEst.getEstimatedSem();
        List<Node> nodes = semPm1.getVariableNodes();

        for (Node node : nodes) {
            double mean = semIm1.getMean(node);
            assertEquals(mean, estSemIm.getMean(node), 0.5);
        }
    }

    @Test
    public void testMeansCholesky() {
        Graph graph = constructGraph1();
        SemPm semPm1 = new SemPm(graph);

        List<Parameter> parameters = semPm1.getParameters();

        for (Parameter p : parameters) {
            p.setInitializedRandomly(false);
        }

        SemIm semIm1 = new SemIm(semPm1);

        double[] means = {5.0, 4.0, 3.0, 2.0, 1.0};

        RandomUtil.getInstance().setSeed(-379467L);

        for (int i = 0; i < semIm1.getVariableNodes().size(); i++) {
            Node node = semIm1.getVariableNodes().get(i);
            semIm1.setMean(node, means[i]);
        }

        DataSet dataSet = semIm1.simulateDataCholesky(1000, false);

        SemEstimator semEst = new SemEstimator(dataSet, semPm1);
        semEst.estimate();
        SemIm estSemIm = semEst.getEstimatedSem();
        List<Node> nodes = semPm1.getVariableNodes();

        for (Node node : nodes) {
            double mean = semIm1.getMean(node);
            assertEquals(mean, estSemIm.getMean(node), 0.6);
        }
    }

    private Graph constructGraph1() {
        Graph graph = new EdgeListGraph();

        Node x1 = new GraphNode("X1");
        Node x2 = new GraphNode("X2");
        Node x3 = new GraphNode("X3");
        Node x4 = new GraphNode("X4");
        Node x5 = new GraphNode("X5");

        graph.addNode(x1);
        graph.addNode(x2);
        graph.addNode(x3);
        graph.addNode(x4);
        graph.addNode(x5);

        graph.addDirectedEdge(x1, x2);
        graph.addDirectedEdge(x2, x3);
        graph.addDirectedEdge(x3, x4);
        graph.addDirectedEdge(x1, x4);
        graph.addDirectedEdge(x4, x5);

        return graph;
    }
}





