Causalbuilder tests extended #23

src/test/java/ch/idsia/credici/model/CausalBuilderTest.java

@@ -1,24 +1,27 @@
 package ch.idsia.credici.model;
 
+import ch.idsia.credici.factor.EquationBuilder;
 import ch.idsia.credici.model.builder.CausalBuilder;
 import ch.idsia.crema.factor.bayesian.BayesianFactor;
+import ch.idsia.crema.model.graphical.SparseDirectedAcyclicGraph;
 import ch.idsia.crema.model.graphical.specialized.BayesianNetwork;
+import ch.idsia.crema.utility.RandomUtil;
 import org.junit.Before;
 import org.junit.Test;
 
 import static org.junit.Assert.assertArrayEquals;
-import static org.junit.Assert.assertTrue;
 
 public class CausalBuilderTest {
 
 	BayesianNetwork bnet;
+	int x, y;
 
 
 	@Before
 	public void init(){
 		bnet = new BayesianNetwork();
-		int y = bnet.addVariable(2);
-		int x = bnet.addVariable(2);
+		y = bnet.addVariable(2);
+		x = bnet.addVariable(2);
 
 		bnet.setFactor(y, new BayesianFactor(bnet.getDomain(y), new double[]{0.3,0.7}));
 		bnet.setFactor(x, new BayesianFactor(bnet.getDomain(x,y), new double[]{0.6,0.5, 0.5,0.5}));
@@ -51,16 +54,73 @@ public void buildMarkovianEqless() {
 	@Test
 	public void builWithCausalDAG() {
 
-	}
+		StructuralCausalModel model =  CausalBuilder.of(bnet).build();
+
+
+		SparseDirectedAcyclicGraph causalDAG = new SparseDirectedAcyclicGraph();
+		causalDAG.addVariable(y);
+		causalDAG.addVariable(x);
+
+		int u = causalDAG.addVariable();
+
+		causalDAG.addLink(y,x);
+		causalDAG.addLink(u,x);
+		causalDAG.addLink(u,y);
+
+
+		RandomUtil.setRandomSeed(1);
+		StructuralCausalModel m =
+				CausalBuilder.of(bnet)
+						.setCausalDAG(causalDAG)
+						.setFillRandomEquations(true)
+						.setFillRandomExogenousFactors(4)
+						.setExoVarSizes(new int[]{4}).build();
+
+
+		assertArrayEquals(
+				((BayesianFactor)m.getEmpiricalMap().values().toArray()[0]).getData(),
+				new double[]{0.806, 0.0, 0.0, 0.194},
+				0.00001);
 
-	@Test
-	public void builFromEquations() {
 
 	}
 
 	@Test
-	public void buildFromExoVarSizes() {
+	public void buildEquations() {
+
+
+		StructuralCausalModel model =  CausalBuilder.of(bnet).build();
+		int ux = model.getExogenousParents(x)[0];
+		int uy = model.getExogenousParents(y)[0];
+
+
+	/*			u0	u1	u2	u4
+			x0	0	0	0	1
+		y0	x1	1	1	1	0
+			---------------------
+			x0	1	0	1	0
+		y1	x1	0	1	0	1
+
+	 */
+
+		BayesianFactor eq = null;
+		eq = EquationBuilder.of(model).fromVector(x, 1,0, 1,1, 1,0, 0,1);
+
+		assertArrayEquals(
+				eq.filter(x,0).filter(y, 0).getData(),
+				new double[]{0.0, 0.0, 0.0, 1.0},
+				0.000001
+		);
+
+		assertArrayEquals(
+				eq.filter(x,0).filter(y, 1).getData(),
+				new double[]{1.0, 0.0, 1.0, 0.0},
+				0.000001
+		);
+
+
 
 	}
 
+
 }