/** @return a string representation of the coefficients and variances of the model. */
  public String toString() {
    StringBuilder buf = new StringBuilder();
    NumberFormat nf = NumberFormatUtil.getInstance().getNumberFormat();

    buf.append("\nStandardized SEM:");
    buf.append("\n\nEdge coefficients (parameters):\n");

    for (Edge edge : edgeParameters.keySet()) {
      if (!Edges.isDirectedEdge(edge)) {
        continue;
      }

      buf.append("\n" + edge + " " + nf.format(edgeParameters.get(edge)));
    }

    buf.append("\n\nError covariances (parameters):\n");

    for (Edge edge : edgeParameters.keySet()) {
      if (!Edges.isBidirectedEdge(edge)) {
        continue;
      }

      buf.append("\n" + edge + " " + nf.format(edgeParameters.get(edge)));
    }

    buf.append("\n\nError variances (calculated):\n");

    for (Node error : getErrorNodes()) {
      double variance = getErrorVariance(error);
      buf.append("\n" + error + " " + (Double.isNaN(variance) ? "Undefined" : nf.format(variance)));
    }

    buf.append("\n");

    return buf.toString();
  }
Exemple #2
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  private String reportIfContinuous(Graph dag, DataSet dataSet) {
    SemPm semPm = new SemPm(dag);

    SemEstimator estimator = new SemEstimator(dataSet, semPm);
    estimator.estimate();
    SemIm semIm = estimator.getEstimatedSem();

    NumberFormat nf = NumberFormat.getInstance();
    nf.setMaximumFractionDigits(4);

    StringBuilder buf = new StringBuilder();
    buf.append("\nDegrees of Freedom = ")
        .append(semPm.getDof())
        .append("Chi-Square = ")
        .append(nf.format(semIm.getChiSquare()))
        .append("\nP Value = ")
        .append(nf.format(semIm.getPValue()))
        .append("\nBIC Score = ")
        .append(nf.format(semIm.getBicScore()));

    buf.append(
        "\n\nThe above chi square test assumes that the maximum "
            + "likelihood function over the measured variables has been "
            + "maximized. Under that assumption, the null hypothesis for "
            + "the test is that the population covariance matrix over all "
            + "of the measured variables is equal to the estimated covariance "
            + "matrix over all of the measured variables written as a function "
            + "of the free model parameters--that is, the unfixed parameters "
            + "for each directed edge (the linear coefficient for that edge), "
            + "each exogenous variable (the variance for the error term for "
            + "that variable), and each bidirected edge (the covariance for "
            + "the exogenous variables it connects).  The model is explained "
            + "in Bollen, Structural Equations with Latent Variable, 110. ");

    return buf.toString();
  }
    public String toString() {
      StringBuilder buf = new StringBuilder();

      buf.append("\n\nRange for " + edge);
      buf.append("\nCurrent value = " + coef);
      buf.append("\nLow end of range = " + low);
      buf.append("\nHigh end of range = " + high);

      return buf.toString();
    }
Exemple #4
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  /**
   * @return a relatively brief String representation of this SEM PM--the equations and
   *     distributions of the model. Initial value distributions for freeParameters are not printed.
   */
  public String toString() {
    StringBuilder buf = new StringBuilder();
    buf.append("\nEquations:\n");

    for (Node node : variableNodes) {
      buf.append("\n").append(node).append(" = ").append(nodeExpressionStrings.get(node));
    }

    buf.append("\n\nErrors:\n");

    for (Node node : errorNodes) {
      buf.append("\n").append(node).append(" ~ ").append(nodeExpressionStrings.get(node));
    }

    buf.append("\n\nParameters:\n");

    for (String param : getParameters()) {
      buf.append("\n").append(param).append(" ~ ").append(getParameterExpressionString(param));
    }

    return buf.toString();
  }
Exemple #5
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  private String reportIfDiscrete(Graph dag, DataSet dataSet) {
    List vars = dataSet.getVariables();
    Map<String, DiscreteVariable> nodesToVars = new HashMap<String, DiscreteVariable>();
    for (int i = 0; i < dataSet.getNumColumns(); i++) {
      DiscreteVariable var = (DiscreteVariable) vars.get(i);
      String name = var.getName();
      Node node = new GraphNode(name);
      nodesToVars.put(node.getName(), var);
    }

    BayesPm bayesPm = new BayesPm(new Dag(dag));
    List<Node> nodes = bayesPm.getDag().getNodes();

    for (Node node : nodes) {
      Node var = nodesToVars.get(node.getName());

      if (var instanceof DiscreteVariable) {
        DiscreteVariable var2 = nodesToVars.get(node.getName());
        int numCategories = var2.getNumCategories();
        List<String> categories = new ArrayList<String>();
        for (int j = 0; j < numCategories; j++) {
          categories.add(var2.getCategory(j));
        }
        bayesPm.setCategories(node, categories);
      }
    }

    BayesProperties properties = new BayesProperties(dataSet, dag);
    properties.setGraph(dag);

    NumberFormat nf = NumberFormat.getInstance();
    nf.setMaximumFractionDigits(4);

    StringBuilder buf = new StringBuilder();
    buf.append("\nP-value = ").append(properties.getLikelihoodRatioP());
    buf.append("\nDf = ").append(properties.getPValueDf());
    buf.append("\nChi square = ").append(nf.format(properties.getPValueChisq()));
    buf.append("\nBIC score = ").append(nf.format(properties.getBic()));
    buf.append("\n\nH0: Completely disconnected graph.");

    return buf.toString();
  }
Exemple #6
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  private void saveKnowledge(Writer out) throws IOException {
    StringBuilder buf = new StringBuilder();
    buf.append("/knowledge");

    buf.append("\naddtemporal\n");

    for (int i = 0; i < tierSpecs.size(); i++) {
      String forbiddenWithin = isTierForbiddenWithin(i) ? "*" : "";

      buf.append("\n").append(i).append(forbiddenWithin).append(" ");

      List<String> tier = getTier(i);

      for (Object aTier : tier) {
        String name = (String) aTier;
        buf.append(name).append(" ");
      }
    }

    buf.append("\n");

    buf.append("\nforbiddirect\n\n");

    Set<OrderedPair<Set<MyNode>>> copy = new HashSet<>(forbiddenRulesSpecs);
    copy.removeAll(forbiddenTierRules());

    for (OrderedPair<Set<MyNode>> o : copy) {
      Set<MyNode> first = o.getFirst();
      Set<MyNode> second = o.getSecond();

      for (MyNode s : first) {
        buf.append(s).append(" ");
      }

      buf.append("==> ");

      for (MyNode s : second) {
        buf.append(s).append(" ");
      }

      buf.append("\n");
    }

    buf.append("requiredirect\n\n");

    for (OrderedPair<Set<MyNode>> o : requiredRulesSpecs) {
      Set<MyNode> first = o.getFirst();
      Set<MyNode> second = o.getSecond();

      for (MyNode s : first) {
        buf.append(s).append(" ");
      }

      buf.append("==> ");

      for (MyNode s : second) {
        buf.append(s).append(" ");
      }

      buf.append("\n");
    }

    out.write(buf.toString());
    out.flush();
  }
Exemple #7
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  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);
    }
  }
Exemple #8
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  public GeneralizedSemPm(SemPm semPm) {
    this(semPm.getGraph());

    // Write down equations.
    try {
      List<Node> variableNodes = getVariableNodes();

      for (int i = 0; i < variableNodes.size(); i++) {
        Node node = variableNodes.get(i);
        List<Node> parents = getVariableParents(node);

        StringBuilder buf = new StringBuilder();

        for (int j = 0; j < parents.size(); j++) {
          if (!(variableNodes.contains(parents.get(j)))) {
            continue;
          }

          Node parent = parents.get(j);

          Parameter _parameter = semPm.getParameter(parent, node);
          String parameter = _parameter.getName();
          Set<Node> nodes = new HashSet<>();
          nodes.add(node);

          referencedParameters.put(parameter, nodes);

          buf.append(parameter);
          buf.append("*");
          buf.append(parents.get(j).getName());

          setParameterExpression(parameter, "Split(-1.5, -.5, .5, 1.5)");
          setStartsWithParametersTemplate(parameter.substring(0, 1), "Split(-1.5, -.5, .5, 1.5)");
          setStartsWithParametersEstimationInitializaationTemplate(
              parameter.substring(0, 1), "Split(-1.5, -.5, .5, 1.5)");

          if (j < parents.size() - 1) {
            buf.append(" + ");
          }
        }

        if (buf.toString().trim().length() != 0) {
          buf.append(" + ");
        }

        buf.append(errorNodes.get(i));
        setNodeExpression(node, buf.toString());
      }

      for (Node node : variableNodes) {
        Parameter _parameter = semPm.getParameter(node, node);
        String parameter = _parameter.getName();

        String distributionFormula = "N(0," + parameter + ")";
        setNodeExpression(getErrorNode(node), distributionFormula);
        setParameterExpression(parameter, "U(0, 1)");
        setStartsWithParametersTemplate(parameter.substring(0, 1), "U(0, 1)");
        setStartsWithParametersEstimationInitializaationTemplate(
            parameter.substring(0, 1), "U(0, 1)");
      }

      variableNames = new ArrayList<>();
      for (Node _node : variableNodes) variableNames.add(_node.getName());
      for (Node _node : errorNodes) variableNames.add(_node.getName());

    } catch (ParseException e) {
      throw new IllegalStateException("Parse error in constructing initial model.", e);
    }
  }