private void setSuitableParameterDistribution(String parameter) throws ParseException {
boolean found = false;
for (String prefix : startsWithParametersTemplates.keySet()) {
if (parameter.startsWith(prefix)) {
if (parameterExpressions.get(parameter) == null) {
setParameterExpression(parameter, startsWithParametersTemplates.get(prefix));
}
if (parameterEstimationInitializationExpressions.get(parameter) == null) {
setParameterEstimationInitializationExpression(
parameter, startsWithParametersTemplates.get(prefix));
}
found = true;
}
}
if (!found) {
if (parameterExpressions.get(parameter) == null) {
setParameterExpression(parameter, getParametersTemplate());
}
if (parameterEstimationInitializationExpressions.get(parameter) == null) {
setParameterEstimationInitializationExpression(parameter, getParametersTemplate());
}
}
}
@Test
public void test8() {
RandomUtil.getInstance().setSeed(29999483L);
Node x = new GraphNode("X");
Node y = new GraphNode("Y");
List<Node> nodes = new ArrayList<>();
nodes.add(x);
nodes.add(y);
Graph graph = new EdgeListGraphSingleConnections(nodes);
graph.addDirectedEdge(x, y);
SemPm spm = new SemPm(graph);
SemIm sim = new SemIm(spm);
sim.setEdgeCoef(x, y, 20);
sim.setErrVar(x, 1);
sim.setErrVar(y, 1);
GeneralizedSemPm pm = new GeneralizedSemPm(spm);
GeneralizedSemIm im = new GeneralizedSemIm(pm, sim);
print(im);
try {
pm.setParameterEstimationInitializationExpression("b1", "U(10, 30)");
pm.setParameterEstimationInitializationExpression("T1", "U(.1, 3)");
pm.setParameterEstimationInitializationExpression("T2", "U(.1, 3)");
} catch (ParseException e) {
e.printStackTrace();
}
DataSet data = im.simulateDataRecursive(1000, false);
GeneralizedSemEstimator estimator = new GeneralizedSemEstimator();
GeneralizedSemIm estIm = estimator.estimate(pm, data);
print(estIm);
// System.out.println(estimator.getReport());
double aSquaredStar = estimator.getaSquaredStar();
assertEquals(0.69, aSquaredStar, 0.01);
}
/** Constructs a new SemPm from the given SemGraph. */
public GeneralizedSemPm(SemGraph graph) {
if (graph == null) {
throw new NullPointerException("Graph must not be null.");
}
// if (graph.existsDirectedCycle()) {
// throw new IllegalArgumentExcneption("Cycles are not supported.");
// }
// Cannot afford to allow error terms on this graph to be shown or hidden from the outside; must
// make a
// hidden copy of it and make sure error terms are shown.
this.graph = new SemGraph(graph);
this.graph.setShowErrorTerms(true);
for (Edge edge : this.graph.getEdges()) {
if (Edges.isBidirectedEdge(edge)) {
throw new IllegalArgumentException(
"The generalized SEM PM cannot currently deal with bidirected " + "edges. Sorry.");
}
}
this.nodes = Collections.unmodifiableList(this.graph.getNodes());
for (Node node : nodes) {
namesToNodes.put(node.getName(), node);
}
this.variableNodes = new ArrayList<>();
this.measuredNodes = new ArrayList<>();
for (Node variable : this.nodes) {
if (variable.getNodeType() == NodeType.MEASURED
|| variable.getNodeType() == NodeType.LATENT) {
variableNodes.add(variable);
}
if (variable.getNodeType() == NodeType.MEASURED) {
measuredNodes.add(variable);
}
}
this.errorNodes = new ArrayList<>();
for (Node variable : this.variableNodes) {
List<Node> parents = this.graph.getParents(variable);
boolean added = false;
for (Node _node : parents) {
if (_node.getNodeType() == NodeType.ERROR) {
errorNodes.add(_node);
added = true;
break;
}
}
if (!added) {
errorNodes.add(null);
}
}
this.referencedParameters = new HashMap<>();
this.referencedNodes = new HashMap<>();
this.nodeExpressions = new HashMap<>();
this.nodeExpressionStrings = new HashMap<>();
this.parameterExpressions = new HashMap<>();
this.parameterExpressionStrings = new HashMap<>();
this.parameterEstimationInitializationExpressions = new HashMap<>();
this.parameterEstimationInitializationExpressionStrings = new HashMap<>();
this.startsWithParametersTemplates = new HashMap<>();
this.startsWithParametersEstimationInitializationTemplates = new HashMap<>();
this.variableNames = new ArrayList<>();
for (Node _node : variableNodes) variableNames.add(_node.getName());
for (Node _node : errorNodes) variableNames.add(_node.getName());
try {
List<Node> variableNodes = getVariableNodes();
for (Node node : variableNodes) {
if (!this.graph.isParameterizable(node)) continue;
if (nodeExpressions.get(node) != null) {
continue;
}
String variablestemplate = getVariablesTemplate();
String formula =
TemplateExpander.getInstance().expandTemplate(variablestemplate, this, node);
setNodeExpression(node, formula);
Set<String> parameters = getReferencedParameters(node);
String parametersTemplate = getParametersTemplate();
for (String parameter : parameters) {
if (parameterExpressions.get(parameter) == null) {
if (parametersTemplate != null) {
setParameterExpression(parameter, parametersTemplate);
} else if (this.graph.isTimeLagModel()) {
String expressionString = "Split(-0.9, -.1, .1, 0.9)";
setParameterExpression(parameter, expressionString);
setParametersTemplate(expressionString);
} else {
String expressionString = "Split(-1.5, -.5, .5, 1.5)";
setParameterExpression(parameter, expressionString);
setParametersTemplate(expressionString);
}
}
}
for (String parameter : parameters) {
if (parameterEstimationInitializationExpressions.get(parameter) == null) {
if (parametersTemplate != null) {
setParameterEstimationInitializationExpression(parameter, parametersTemplate);
} else if (this.graph.isTimeLagModel()) {
String expressionString = "Split(-0.9, -.1, .1, 0.9)";
setParameterEstimationInitializationExpression(parameter, expressionString);
} else {
String expressionString = "Split(-1.5, -.5, .5, 1.5)";
setParameterEstimationInitializationExpression(parameter, expressionString);
}
}
setStartsWithParametersTemplate("s", "Split(-1.5, -.5, .5, 1.5)");
setStartsWithParametersEstimationInitializaationTemplate(
"s", "Split(-1.5, -.5, .5, 1.5)");
}
}
for (Node node : errorNodes) {
if (node == null) continue;
String template = getErrorsTemplate();
String formula = TemplateExpander.getInstance().expandTemplate(template, this, node);
setNodeExpression(node, formula);
Set<String> parameters = getReferencedParameters(node);
setStartsWithParametersTemplate("s", "U(1, 3)");
setStartsWithParametersEstimationInitializaationTemplate("s", "U(1, 3)");
for (String parameter : parameters) {
setParameterExpression(parameter, "U(1, 3)");
}
}
} catch (ParseException e) {
throw new IllegalStateException("Parse error in constructing initial model.", e);
}
}
@Test
public void test15() {
RandomUtil.getInstance().setSeed(29999483L);
try {
Node x1 = new GraphNode("X1");
Node x2 = new GraphNode("X2");
Node x3 = new GraphNode("X3");
Node x4 = new GraphNode("X4");
Graph g = new EdgeListGraphSingleConnections();
g.addNode(x1);
g.addNode(x2);
g.addNode(x3);
g.addNode(x4);
g.addDirectedEdge(x1, x2);
g.addDirectedEdge(x2, x3);
g.addDirectedEdge(x3, x4);
g.addDirectedEdge(x1, x4);
GeneralizedSemPm pm = new GeneralizedSemPm(g);
pm.setNodeExpression(x1, "E_X1");
pm.setNodeExpression(x2, "a1 * X1 + E_X2");
pm.setNodeExpression(x3, "a2 * X2 + E_X3");
pm.setNodeExpression(x4, "a3 * X1 + a4 * X3 ^ 2 + E_X4");
pm.setNodeExpression(pm.getErrorNode(x1), "Gamma(c1, c2)");
pm.setNodeExpression(pm.getErrorNode(x2), "ChiSquare(c3)");
pm.setNodeExpression(pm.getErrorNode(x3), "ChiSquare(c4)");
pm.setNodeExpression(pm.getErrorNode(x4), "ChiSquare(c5)");
pm.setParameterExpression("c1", "5");
pm.setParameterExpression("c2", "2");
pm.setParameterExpression("c3", "10");
pm.setParameterExpression("c4", "10");
pm.setParameterExpression("c5", "10");
pm.setParameterEstimationInitializationExpression("c1", "U(1, 5)");
pm.setParameterEstimationInitializationExpression("c2", "U(1, 5)");
pm.setParameterEstimationInitializationExpression("c3", "U(1, 5)");
pm.setParameterEstimationInitializationExpression("c4", "U(1, 5)");
pm.setParameterEstimationInitializationExpression("c5", "U(1, 5)");
GeneralizedSemIm im = new GeneralizedSemIm(pm);
print("True model: ");
print(im);
DataSet data = im.simulateDataRecursive(1000, false);
GeneralizedSemEstimator estimator = new GeneralizedSemEstimator();
GeneralizedSemIm estIm = estimator.estimate(pm, data);
print("\n\n\nEstimated model: ");
print(estIm);
print(estimator.getReport());
double aSquaredStar = estimator.getaSquaredStar();
assertEquals(.79, aSquaredStar, 0.01);
} catch (ParseException e) {
e.printStackTrace();
}
}
