/**
   * Randomly picks the location of a new window, such that it fits completely on the screen.
   *
   * @param desktopPane the desktop pane that the frame is being added to.
   * @param frame the JInternalFrame which is being added.
   * @param desiredSize the desired dimensions of the frame.
   */
  private static void setGoodBounds(
      JInternalFrame frame, JDesktopPane desktopPane, Dimension desiredSize) {
    RandomUtil randomUtil = RandomUtil.getInstance();
    Dimension desktopSize = desktopPane.getSize();

    Dimension d = new Dimension(desiredSize);
    int tx = desktopSize.width - d.width;
    int ty = desktopSize.height - d.height;

    if (tx < 0) {
      tx = 0;
      d.width = desktopSize.width;
    } else {
      tx = (int) (randomUtil.nextDouble() * tx);
    }

    if (ty < 0) {
      ty = 0;
      d.height = desktopSize.height;
    } else {
      ty = (int) (randomUtil.nextDouble() * ty);
    }

    frame.setBounds(tx, ty, d.width, d.height);
  }
Esempio n. 2
0
  /**
   * Takes a Cholesky decomposition from the Cholesky.cholesky method and a set of data simulated
   * using the information in that matrix. Written by Don Crimbchin. Modified June 8, Matt
   * Easterday: added a random # seed so that data can be recalculated with the same result in
   * Causality lab
   *
   * @param cholesky the result from cholesky above.
   * @param randomUtil a random number generator, if null the method will make a new generator for
   *     each random number needed
   * @return an array the same length as the width or length (cholesky should have the same width
   *     and length) containing a randomly generate data set.
   */
  private double[] exogenousData(TetradMatrix cholesky, RandomUtil randomUtil) {

    // Step 1. Generate normal samples.
    double exoData[] = new double[cholesky.rows()];

    for (int i = 0; i < exoData.length; i++) {
      exoData[i] = randomUtil.nextNormal(0, 1);
    }

    // Step 2. Multiply by cholesky to get correct covariance.
    double point[] = new double[exoData.length];

    for (int i = 0; i < exoData.length; i++) {
      double sum = 0.0;

      for (int j = 0; j <= i; j++) {
        sum += cholesky.get(i, j) * exoData[j];
      }

      point[i] = sum;
    }

    return point;
  }
Esempio n. 3
0
  /**
   * @param sampleSize The sample size of the desired data set.
   * @param latentDataSaved True if latent variables should be included in the data set.
   * @return This returns a standardized data set simulated from the model, using the reduced form
   *     method.
   */
  public DataSet simulateDataReducedForm(int sampleSize, boolean latentDataSaved) {
    int numVars = getVariableNodes().size();

    // Calculate inv(I - edgeCoef)
    TetradMatrix edgeCoef = edgeCoef().copy().transpose();

    //        TetradMatrix iMinusB = TetradAlgebra.identity(edgeCoef.rows());
    //        iMinusB.assign(edgeCoef, Functions.minus);

    TetradMatrix iMinusB = TetradAlgebra.identity(edgeCoef.rows()).minus(edgeCoef);

    TetradMatrix inv = iMinusB.inverse();

    // Pick error values e, for each calculate inv * e.
    TetradMatrix sim = new TetradMatrix(sampleSize, numVars);

    // Generate error data with the right variances and covariances, then override this
    // with error data for varaibles that have special distributions defined. Not ideal,
    // but not sure what else to do at the moment. It's better than not taking covariances
    // into account!
    TetradMatrix cholesky = MatrixUtils.choleskyC(errCovar(errorVariances()));

    for (int i = 0; i < sampleSize; i++) {
      TetradVector e = new TetradVector(exogenousData(cholesky, RandomUtil.getInstance()));
      TetradVector ePrime = inv.times(e);
      sim.assignRow(i, ePrime); // sim.viewRow(i).assign(ePrime);
    }

    DataSet fullDataSet = ColtDataSet.makeContinuousData(getVariableNodes(), sim);

    if (latentDataSaved) {
      return fullDataSet;
    } else {
      return DataUtils.restrictToMeasured(fullDataSet);
    }
  }
Esempio n. 4
0
  private void resolveOneEdgeMax(Graph graph, Node x, Node y, boolean strong, Graph oldGraph) {
    if (RandomUtil.getInstance().nextDouble() > 0.5) {
      Node temp = x;
      x = y;
      y = temp;
    }

    TetradLogger.getInstance().log("info", "\nEDGE " + x + " --- " + y);

    SortedMap<Double, String> scoreReports = new TreeMap<Double, String>();

    List<Node> neighborsx = graph.getAdjacentNodes(x);
    neighborsx.remove(y);

    double max = Double.NEGATIVE_INFINITY;
    boolean left = false;
    boolean right = false;

    DepthChoiceGenerator genx = new DepthChoiceGenerator(neighborsx.size(), neighborsx.size());
    int[] choicex;

    while ((choicex = genx.next()) != null) {
      List<Node> condxMinus = GraphUtils.asList(choicex, neighborsx);

      List<Node> condxPlus = new ArrayList<Node>(condxMinus);
      condxPlus.add(y);

      double xPlus = score(x, condxPlus);
      double xMinus = score(x, condxMinus);

      List<Node> neighborsy = graph.getAdjacentNodes(y);
      neighborsy.remove(x);

      DepthChoiceGenerator geny = new DepthChoiceGenerator(neighborsy.size(), neighborsy.size());
      int[] choicey;

      while ((choicey = geny.next()) != null) {
        List<Node> condyMinus = GraphUtils.asList(choicey, neighborsy);

        //                List<Node> parentsY = oldGraph.getParents(y);
        //                parentsY.remove(x);
        //                if (!condyMinus.containsAll(parentsY)) {
        //                    continue;
        //                }

        List<Node> condyPlus = new ArrayList<Node>(condyMinus);
        condyPlus.add(x);

        double yPlus = score(y, condyPlus);
        double yMinus = score(y, condyMinus);

        // Checking them all at once is expensive but avoids lexical ordering problems in the
        // algorithm.
        if (normal(y, condyPlus)
            || normal(x, condxMinus)
            || normal(x, condxPlus)
            || normal(y, condyMinus)) {
          continue;
        }

        double delta = 0.0;

        if (strong) {
          if (yPlus <= xPlus + delta && xMinus <= yMinus + delta) {
            double score = combinedScore(xPlus, yMinus);

            if (yPlus <= yMinus + delta && xMinus <= xPlus + delta) {
              StringBuilder builder = new StringBuilder();

              builder.append("\nStrong " + y + "->" + x + " " + score);
              builder.append("\n   Parents(" + x + ") = " + condxMinus);
              builder.append("\n   Parents(" + y + ") = " + condyMinus);

              scoreReports.put(-score, builder.toString());

              if (score > max) {
                max = score;
                left = true;
                right = false;
              }
            } else {
              StringBuilder builder = new StringBuilder();

              builder.append("\nNo directed edge " + x + "--" + y + " " + score);
              builder.append("\n   Parents(" + x + ") = " + condxMinus);
              builder.append("\n   Parents(" + y + ") = " + condyMinus);

              scoreReports.put(-score, builder.toString());
            }
          } else if (xPlus <= yPlus + delta && yMinus <= xMinus + delta) {
            double score = combinedScore(yPlus, xMinus);

            if (yMinus <= yPlus + delta && xPlus <= xMinus + delta) {
              StringBuilder builder = new StringBuilder();

              builder.append("\nStrong " + x + "->" + y + " " + score);
              builder.append("\n   Parents(" + x + ") = " + condxMinus);
              builder.append("\n   Parents(" + y + ") = " + condyMinus);

              scoreReports.put(-score, builder.toString());

              if (score > max) {
                max = score;
                left = false;
                right = true;
              }
            } else {
              StringBuilder builder = new StringBuilder();

              builder.append("\nNo directed edge " + x + "--" + y + " " + score);
              builder.append("\n   Parents(" + x + ") = " + condxMinus);
              builder.append("\n   Parents(" + y + ") = " + condyMinus);

              scoreReports.put(-score, builder.toString());
            }
          } else if (yPlus <= xPlus + delta && yMinus <= xMinus + delta) {
            double score = combinedScore(yPlus, xMinus);

            StringBuilder builder = new StringBuilder();

            builder.append("\nNo directed edge " + x + "--" + y + " " + score);
            builder.append("\n   Parents(" + x + ") = " + condxMinus);
            builder.append("\n   Parents(" + y + ") = " + condyMinus);

            scoreReports.put(-score, builder.toString());
          } else if (xPlus <= yPlus + delta && xMinus <= yMinus + delta) {
            double score = combinedScore(yPlus, xMinus);

            StringBuilder builder = new StringBuilder();

            builder.append("\nNo directed edge " + x + "--" + y + " " + score);
            builder.append("\n   Parents(" + x + ") = " + condxMinus);
            builder.append("\n   Parents(" + y + ") = " + condyMinus);

            scoreReports.put(-score, builder.toString());
          }
        } else {
          if (yPlus <= xPlus + delta && xMinus <= yMinus + delta) {
            double score = combinedScore(xPlus, yMinus);

            StringBuilder builder = new StringBuilder();

            builder.append("\nWeak " + y + "->" + x + " " + score);
            builder.append("\n   Parents(" + x + ") = " + condxMinus);
            builder.append("\n   Parents(" + y + ") = " + condyMinus);

            scoreReports.put(-score, builder.toString());

            if (score > max) {
              max = score;
              left = true;
              right = false;
            }
          } else if (xPlus <= yPlus + delta && yMinus <= xMinus + delta) {
            double score = combinedScore(yPlus, xMinus);

            StringBuilder builder = new StringBuilder();

            builder.append("\nWeak " + x + "->" + y + " " + score);
            builder.append("\n   Parents(" + x + ") = " + condxMinus);
            builder.append("\n   Parents(" + y + ") = " + condyMinus);

            scoreReports.put(-score, builder.toString());

            if (score > max) {
              max = score;
              left = false;
              right = true;
            }
          } else if (yPlus <= xPlus + delta && yMinus <= xMinus + delta) {
            double score = combinedScore(yPlus, xMinus);

            StringBuilder builder = new StringBuilder();

            builder.append("\nNo directed edge " + x + "--" + y + " " + score);
            builder.append("\n   Parents(" + x + ") = " + condxMinus);
            builder.append("\n   Parents(" + y + ") = " + condyMinus);

            scoreReports.put(-score, builder.toString());
          } else if (xPlus <= yPlus + delta && xMinus <= yMinus + delta) {
            double score = combinedScore(yPlus, xMinus);

            StringBuilder builder = new StringBuilder();

            builder.append("\nNo directed edge " + x + "--" + y + " " + score);
            builder.append("\n   Parents(" + x + ") = " + condxMinus);
            builder.append("\n   Parents(" + y + ") = " + condyMinus);

            scoreReports.put(-score, builder.toString());
          }
        }
      }
    }

    for (double score : scoreReports.keySet()) {
      TetradLogger.getInstance().log("info", scoreReports.get(score));
    }

    graph.removeEdges(x, y);

    if (left) {
      graph.addDirectedEdge(y, x);
    }

    if (right) {
      graph.addDirectedEdge(x, y);
    }

    if (!graph.isAdjacentTo(x, y)) {
      graph.addUndirectedEdge(x, y);
    }
  }