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());
}
}
}
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);
}
}
/** 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();
}
}
@Test
public void test14() {
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 * tan(X1) + E_X2");
pm.setNodeExpression(x3, "a2 * tan(X2) + E_X3");
pm.setNodeExpression(x4, "a3 * tan(X1) + a4 * tan(X3) ^ 2 + E_X4");
pm.setNodeExpression(pm.getErrorNode(x1), "N(0, c1)");
pm.setNodeExpression(pm.getErrorNode(x2), "N(0, c2)");
pm.setNodeExpression(pm.getErrorNode(x3), "N(0, c3)");
pm.setNodeExpression(pm.getErrorNode(x4), "N(0, c4)");
pm.setParameterExpression("a1", "1");
pm.setParameterExpression("a2", "1");
pm.setParameterExpression("a3", "1");
pm.setParameterExpression("a4", "1");
pm.setParameterExpression("c1", "4");
pm.setParameterExpression("c2", "4");
pm.setParameterExpression("c3", "4");
pm.setParameterExpression("c4", "4");
GeneralizedSemIm im = new GeneralizedSemIm(pm);
print("True model: ");
print(im);
DataSet data = im.simulateDataRecursive(1000, false);
GeneralizedSemIm imInit = new GeneralizedSemIm(pm);
imInit.setParameterValue("c1", 8);
imInit.setParameterValue("c2", 8);
imInit.setParameterValue("c3", 8);
imInit.setParameterValue("c4", 8);
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(71.25, aSquaredStar, 0.01);
} catch (ParseException e) {
e.printStackTrace();
}
}
@Test
public void test1() {
GeneralizedSemPm pm = makeTypicalPm();
print(pm);
Node x1 = pm.getNode("X1");
Node x2 = pm.getNode("X2");
Node x3 = pm.getNode("X3");
Node x4 = pm.getNode("X4");
Node x5 = pm.getNode("X5");
SemGraph graph = pm.getGraph();
List<Node> variablesNodes = pm.getVariableNodes();
print(variablesNodes);
List<Node> errorNodes = pm.getErrorNodes();
print(errorNodes);
try {
pm.setNodeExpression(x1, "cos(B1) + E_X1");
print(pm);
String b1 = "B1";
String b2 = "B2";
String b3 = "B3";
Set<Node> nodes = pm.getReferencingNodes(b1);
assertTrue(nodes.contains(x1));
assertTrue(!nodes.contains(x2) && !nodes.contains(x2));
Set<String> referencedParameters = pm.getReferencedParameters(x3);
print("Parameters referenced by X3 are: " + referencedParameters);
assertTrue(referencedParameters.contains(b1) && referencedParameters.contains(b2));
assertTrue(!(referencedParameters.contains(b1) && referencedParameters.contains(b3)));
Node e_x3 = pm.getNode("E_X3");
//
for (Node node : pm.getNodes()) {
Set<Node> referencingNodes = pm.getReferencingNodes(node);
print("Nodes referencing " + node + " are: " + referencingNodes);
}
for (Node node : pm.getVariableNodes()) {
Set<Node> referencingNodes = pm.getReferencedNodes(node);
print("Nodes referenced by " + node + " are: " + referencingNodes);
}
Set<Node> referencingX3 = pm.getReferencingNodes(x3);
assertTrue(referencingX3.contains(x4));
assertTrue(!referencingX3.contains(x5));
Set<Node> referencedByX3 = pm.getReferencedNodes(x3);
assertTrue(
referencedByX3.contains(x1)
&& referencedByX3.contains(x2)
&& referencedByX3.contains(e_x3)
&& !referencedByX3.contains(x4));
pm.setNodeExpression(x5, "a * E^X2 + X4 + E_X5");
Node e_x5 = pm.getErrorNode(x5);
graph.setShowErrorTerms(true);
assertTrue(e_x5.equals(graph.getExogenous(x5)));
pm.setNodeExpression(e_x5, "Beta(3, 5)");
print(pm);
assertEquals("Split(-1.5,-.5,.5,1.5)", pm.getParameterExpressionString(b1));
pm.setParameterExpression(b1, "N(0, 2)");
assertEquals("N(0, 2)", pm.getParameterExpressionString(b1));
GeneralizedSemIm im = new GeneralizedSemIm(pm);
print(im);
DataSet dataSet = im.simulateDataAvoidInfinity(10, false);
print(dataSet);
} catch (ParseException e) {
e.printStackTrace();
}
}
@Test
public void test4() {
// For X3
Map<String, String[]> templates = new HashMap<>();
templates.put(
"NEW(b) + NEW(b) + NEW(c) + NEW(c) + NEW(c)", new String[] {"X1", "X2", "X3", "X4", "X5"});
templates.put("NEW(X1) + NEW(b) + NEW(c) + NEW(c) + NEW(c)", new String[] {});
templates.put("$", new String[] {});
templates.put("TSUM($)", new String[] {"X1", "X2", "X3", "X4", "X5"});
templates.put("TPROD($)", new String[] {"X1", "X2", "X3", "X4", "X5"});
templates.put("TPROD($) + X2", new String[] {"X1", "X2", "X3", "X4", "X5"});
templates.put("TPROD($) + TSUM($)", new String[] {"X1", "X2", "X3", "X4", "X5"});
templates.put("tan(TSUM(NEW(a)*$))", new String[] {"X1", "X2", "X3", "X4", "X5"});
templates.put("Normal(0, 1)", new String[] {"X1", "X2", "X3", "X4", "X5"});
templates.put("Normal(m, s)", new String[] {"X1", "X2", "X3", "X4", "X5"});
templates.put("Normal(NEW(m), s)", new String[] {"X1", "X2", "X3", "X4", "X5"});
templates.put("Normal(NEW(m), NEW(s)) + m1 + s6", new String[] {"X1", "X2", "X3", "X4", "X5"});
templates.put("TSUM($) + a", new String[] {"X1", "X2", "X3", "X4", "X5"});
templates.put("TSUM($) + TSUM($) + TSUM($) + 1", new String[] {"X1", "X2", "X3", "X4", "X5"});
for (String template : templates.keySet()) {
GeneralizedSemPm semPm = makeTypicalPm();
print(semPm.getGraph().toString());
Set<Node> shouldWork = new HashSet<>();
for (String name : templates.get(template)) {
shouldWork.add(semPm.getNode(name));
}
Set<Node> works = new HashSet<>();
for (int i = 0; i < semPm.getNodes().size(); i++) {
print("-----------");
print(semPm.getNodes().get(i).toString());
print("Trying template: " + template);
String _template = template;
Node node = semPm.getNodes().get(i);
try {
_template = TemplateExpander.getInstance().expandTemplate(_template, semPm, node);
} catch (Exception e) {
print("Couldn't expand template: " + template);
continue;
}
try {
semPm.setNodeExpression(node, _template);
print("Set formula " + _template + " for " + node);
if (semPm.getVariableNodes().contains(node)) {
works.add(node);
}
} catch (Exception e) {
print("Couldn't set formula " + _template + " for " + node);
}
}
for (String parameter : semPm.getParameters()) {
print("-----------");
print(parameter);
print("Trying template: " + template);
String _template = template;
try {
_template = TemplateExpander.getInstance().expandTemplate(_template, semPm, null);
} catch (Exception e) {
print("Couldn't expand template: " + template);
continue;
}
try {
semPm.setParameterExpression(parameter, _template);
print("Set formula " + _template + " for " + parameter);
} catch (Exception e) {
print("Couldn't set formula " + _template + " for " + parameter);
}
}
assertEquals(shouldWork, works);
}
}