@Test
public void test7() {
RandomUtil.getInstance().setSeed(29999483L);
List<Node> nodes = new ArrayList<>();
int numVars = 10;
for (int i = 0; i < numVars; i++) nodes.add(new ContinuousVariable("X" + (i + 1)));
Graph graph =
GraphUtils.randomGraphRandomForwardEdges(nodes, 0, numVars, 30, 15, 15, false, true);
GeneralizedSemPm pm = new GeneralizedSemPm(graph);
GeneralizedSemIm im = new GeneralizedSemIm(pm);
print(im);
DataSet data = im.simulateDataRecursive(1000, false);
GeneralizedSemEstimator estimator = new GeneralizedSemEstimator();
GeneralizedSemIm estIm = estimator.estimate(pm, data);
print(estIm);
print(estimator.getReport());
double aSquaredStar = estimator.getaSquaredStar();
assertEquals(0.67, aSquaredStar, 0.01);
}
@Test
public void test6() {
RandomUtil.getInstance().setSeed(29999483L);
int numVars = 5;
List<Node> nodes = new ArrayList<>();
for (int i = 0; i < numVars; i++) nodes.add(new ContinuousVariable("X" + (i + 1)));
Graph graph =
GraphUtils.randomGraphRandomForwardEdges(nodes, 0, numVars, 30, 15, 15, false, true);
SemPm spm = new SemPm(graph);
SemImInitializationParams params = new SemImInitializationParams();
params.setCoefRange(0.5, 1.5);
params.setVarRange(1, 3);
SemIm sim = new SemIm(spm, params);
GeneralizedSemPm pm = new GeneralizedSemPm(spm);
GeneralizedSemIm im = new GeneralizedSemIm(pm, sim);
DataSet data = im.simulateData(1000, false);
print(im);
GeneralizedSemEstimator estimator = new GeneralizedSemEstimator();
GeneralizedSemIm estIm = estimator.estimate(pm, data);
print(estIm);
print(estimator.getReport());
double aSquaredStar = estimator.getaSquaredStar();
assertEquals(0.59, aSquaredStar, 0.01);
}
@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();
}
}