use of edu.cmu.tetrad.algcomparison.independence.ChiSquare in project tetrad by cmu-phil.
the class TestGeneralBootstrapTest method testFCId.
@Test
public void testFCId() {
double structurePrior = 1, samplePrior = 1;
int depth = -1;
int maxPathLength = -1;
int numVars = 20;
int edgesPerNode = 2;
int numLatentConfounders = 4;
int numCases = 50;
int numBootstrapSamples = 5;
boolean verbose = true;
long seed = 123;
Graph dag = makeDiscreteDAG(numVars, numLatentConfounders, edgesPerNode);
DagToPag dagToPag = new DagToPag(dag);
Graph truePag = dagToPag.convert();
System.out.println("Truth PAG_of_the_true_DAG Graph:");
System.out.println(truePag.toString());
BayesPm pm = new BayesPm(dag, 2, 3);
BayesIm im = new MlBayesIm(pm, MlBayesIm.RANDOM);
DataSet data = im.simulateData(numCases, seed, false);
Parameters parameters = new Parameters();
parameters.set("structurePrior", structurePrior);
parameters.set("samplePrior", samplePrior);
parameters.set("depth", depth);
parameters.set("maxPathLength", maxPathLength);
parameters.set("numPatternsToStore", 0);
parameters.set("verbose", verbose);
IndependenceWrapper test = new ChiSquare();
Algorithm algorithm = new Fci(test);
GeneralBootstrapTest bootstrapTest = new GeneralBootstrapTest(data, algorithm, numBootstrapSamples);
bootstrapTest.setVerbose(verbose);
bootstrapTest.setParameters(parameters);
bootstrapTest.setEdgeEnsemble(BootstrapEdgeEnsemble.Highest);
Graph resultGraph = bootstrapTest.search();
System.out.println("Estimated Bootstrapped PAG_of_the_true_DAG Graph:");
System.out.println(resultGraph.toString());
// Adjacency Confusion Matrix
int[][] adjAr = GeneralBootstrapTest.getAdjConfusionMatrix(truePag, resultGraph);
printAdjConfusionMatrix(adjAr);
// Edge Type Confusion Matrix
int[][] edgeAr = GeneralBootstrapTest.getEdgeTypeConfusionMatrix(truePag, resultGraph);
printEdgeTypeConfusionMatrix(edgeAr);
}
use of edu.cmu.tetrad.algcomparison.independence.ChiSquare in project tetrad by cmu-phil.
the class TestGeneralBootstrapTest method testGFCId.
@Test
public void testGFCId() {
double structurePrior = 1, samplePrior = 1;
boolean faithfulnessAssumed = false;
int maxDegree = -1;
int numVars = 20;
int edgesPerNode = 2;
int numLatentConfounders = 4;
int numCases = 50;
int numBootstrapSamples = 5;
boolean verbose = true;
long seed = 123;
Graph dag = makeDiscreteDAG(numVars, numLatentConfounders, edgesPerNode);
DagToPag dagToPag = new DagToPag(dag);
Graph truePag = dagToPag.convert();
System.out.println("Truth PAG_of_the_true_DAG Graph:");
System.out.println(truePag.toString());
BayesPm pm = new BayesPm(dag, 2, 3);
BayesIm im = new MlBayesIm(pm, MlBayesIm.RANDOM);
DataSet data = im.simulateData(numCases, seed, false);
Parameters parameters = new Parameters();
parameters.set("structurePrior", structurePrior);
parameters.set("samplePrior", samplePrior);
parameters.set("faithfulnessAssumed", faithfulnessAssumed);
parameters.set("maxDegree", maxDegree);
parameters.set("numPatternsToStore", 0);
parameters.set("verbose", verbose);
ScoreWrapper score = new BdeuScore();
IndependenceWrapper test = new ChiSquare();
Algorithm algorithm = new Gfci(test, score);
GeneralBootstrapTest bootstrapTest = new GeneralBootstrapTest(data, algorithm, numBootstrapSamples);
bootstrapTest.setVerbose(verbose);
bootstrapTest.setParameters(parameters);
bootstrapTest.setEdgeEnsemble(BootstrapEdgeEnsemble.Highest);
Graph resultGraph = bootstrapTest.search();
System.out.println("Estimated Bootstrapped PAG_of_the_true_DAG Graph:");
System.out.println(resultGraph.toString());
// Adjacency Confusion Matrix
int[][] adjAr = GeneralBootstrapTest.getAdjConfusionMatrix(truePag, resultGraph);
printAdjConfusionMatrix(adjAr);
// Edge Type Confusion Matrix
int[][] edgeAr = GeneralBootstrapTest.getEdgeTypeConfusionMatrix(truePag, resultGraph);
printEdgeTypeConfusionMatrix(edgeAr);
}
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