use of beast.core.State in project beast2 by CompEvol.
the class UniformIntegerOperatorTest method testIntegerParameterBound.
public void testIntegerParameterBound() {
try {
// 4 vaules {0, 1, 2, 3}
count = new int[dimension][4];
IntegerParameter parameter = new IntegerParameter(new Integer[] { 1, 0, 3 });
parameter.setLower(0);
parameter.setUpper(3);
State state = new State();
state.initByName("stateNode", parameter);
state.initialise();
UniformOperator uniformOperator = new UniformOperator();
uniformOperator.initByName("parameter", parameter, "howMany", 3, "weight", 1.0);
for (int i = 0; i < 400; i++) {
uniformOperator.proposal();
Integer[] values = parameter.getValues();
for (int k = 0; k < values.length; k++) {
int j = values[k];
count[k][j] += 1;
}
}
System.out.println("Integer distributions lower = 0, upper = 3");
for (int j = 0; j < count.length; j++) {
System.out.println("x[" + j + "] = " + Arrays.toString(count[j]));
}
assertTrue("Expected count[0][0-3] > 0", count[0][0] > 0 && count[0][1] > 0 && count[0][2] > 0 && count[0][3] > 0);
assertTrue("Expected count[1][0-3] > 0", count[1][0] > 0 && count[1][1] > 0 && count[1][2] > 0 && count[1][3] > 0);
assertTrue("Expected count[2][0-3] > 0", count[2][0] > 0 && count[2][1] > 0 && count[2][2] > 0 && count[2][3] > 0);
} catch (Exception e) {
e.printStackTrace();
}
}
use of beast.core.State in project beast2 by CompEvol.
the class MRCAPriorInputEditor method customConnector.
public static void customConnector(BeautiDoc doc) {
Object o0 = doc.pluginmap.get("prior");
if (o0 != null && o0 instanceof CompoundDistribution) {
CompoundDistribution p = (CompoundDistribution) o0;
for (Distribution p0 : p.pDistributions.get()) {
if (p0 instanceof MRCAPrior) {
MRCAPrior prior = (MRCAPrior) p0;
if (prior.treeInput.get() != null) {
boolean isInState = false;
for (BEASTInterface o : prior.treeInput.get().getOutputs()) {
if (o instanceof State) {
isInState = true;
break;
}
}
if (!isInState) {
doc.disconnect(prior, "prior", "distribution");
doc.disconnect(prior, "tracelog", "log");
if (prior.onlyUseTipsInput.get()) {
disableTipSampling(prior, doc);
}
doc.unregisterPlugin(prior);
return;
}
}
}
}
}
}
use of beast.core.State in project MultiTypeTree by tgvaughan.
the class NSR_Test method test.
@Test
public void test() throws Exception {
System.out.println("NSR test");
// Fix seed.
Randomizer.setSeed(42);
// Assemble migration model:
RealParameter rateMatrix = new RealParameter("0.1 0.1");
RealParameter popSizes = new RealParameter("7.0 7.0");
SCMigrationModel migModel = new SCMigrationModel();
migModel.initByName("rateMatrix", rateMatrix, "popSizes", popSizes, "typeSet", new TypeSet("A", "B"));
// Assemble initial MultiTypeTree
MultiTypeTree mtTree = new StructuredCoalescentMultiTypeTree();
mtTree.initByName("typeLabel", "deme", "migrationModel", migModel, "leafTypes", "1 0");
// Set up state:
State state = new State();
state.initByName("stateNode", mtTree);
// Assemble distribution:
StructuredCoalescentTreeDensity distribution = new StructuredCoalescentTreeDensity();
distribution.initByName("migrationModel", migModel, "multiTypeTree", mtTree);
// Set up operators:
Operator operatorNSR = new NodeShiftRetype();
operatorNSR.initByName("weight", 1.0, "multiTypeTree", mtTree, "migrationModel", migModel);
// Set up stat analysis logger:
MultiTypeTreeStatLogger logger = new MultiTypeTreeStatLogger();
logger.initByName("multiTypeTree", mtTree, "burninFrac", 0.1, "logEvery", 100);
// Set up MCMC:
MCMC mcmc = new MCMC();
mcmc.initByName("chainLength", "1000000", "state", state, "distribution", distribution, "operator", operatorNSR, "logger", logger);
// Run MCMC:
mcmc.run();
System.out.format("height mean = %s\n", logger.getHeightMean());
System.out.format("height var = %s\n", logger.getHeightVar());
System.out.format("height ESS = %s\n", logger.getHeightESS());
// Compare analysis results with truth:
boolean withinTol = (logger.getHeightESS() > 2000) && (Math.abs(logger.getHeightMean() - 19.0) < 0.5) && (Math.abs(logger.getHeightVar() - 291) < 30);
Assert.assertTrue(withinTol);
}
use of beast.core.State in project MultiTypeTree by tgvaughan.
the class STXR_NRR_MTU_TS_Test method test.
@Test
public void test() throws Exception {
System.out.println("STXR_NRR_MTU_TS test");
// Fix seed.
Randomizer.setSeed(42);
// Assemble migration model:
RealParameter rateMatrix = new RealParameter("0.1 0.1");
RealParameter popSizes = new RealParameter("7.0 7.0");
SCMigrationModel migModel = new SCMigrationModel();
migModel.initByName("rateMatrix", rateMatrix, "popSizes", popSizes, "typeSet", new TypeSet("A", "B"));
// Assemble initial MultiTypeTree
MultiTypeTree mtTree = new StructuredCoalescentMultiTypeTree();
mtTree.initByName("typeLabel", "deme", "migrationModel", migModel, "leafTypes", "1 1 0 0");
// Set up state:
State state = new State();
state.initByName("stateNode", mtTree);
// Assemble distribution:
StructuredCoalescentTreeDensity distribution = new StructuredCoalescentTreeDensity();
distribution.initByName("migrationModel", migModel, "multiTypeTree", mtTree);
// Set up operators:
Operator operatorSTXR = new TypedSubtreeExchangeRandom();
operatorSTXR.initByName("weight", 1.0, "multiTypeTree", mtTree, "migrationModel", migModel, "mu", 0.2);
Operator operatorNRR = new NodeRetypeRandom();
operatorNRR.initByName("weight", 1.0, "multiTypeTree", mtTree, "migrationModel", migModel, "mu", 0.2);
Operator operatorMTU = new MultiTypeUniform();
operatorMTU.initByName("weight", 1.0, "multiTypeTree", mtTree, "migrationModel", migModel);
Operator operatorMTTS = new MultiTypeTreeScale();
operatorMTTS.initByName("weight", 1.0, "multiTypeTree", mtTree, "migrationModel", migModel, "scaleFactor", 1.5, "useOldTreeScaler", false);
// Set up stat analysis logger:
MultiTypeTreeStatLogger logger = new MultiTypeTreeStatLogger();
logger.initByName("multiTypeTree", mtTree, "burninFrac", 0.1, "logEvery", 1000);
// Set up MCMC:
MCMC mcmc = new MCMC();
mcmc.initByName("chainLength", "10000000", "state", state, "distribution", distribution, "operator", operatorSTXR, "operator", operatorNRR, "operator", operatorMTU, "operator", operatorMTTS, "logger", logger);
// Run MCMC:
mcmc.run();
System.out.format("height mean = %s\n", logger.getHeightMean());
System.out.format("height var = %s\n", logger.getHeightVar());
System.out.format("height ESS = %s\n", logger.getHeightESS());
// Compare analysis results with truth:
boolean withinTol = (logger.getHeightESS() > 2000) && (Math.abs(logger.getHeightMean() - 25.8) < 0.5) && (Math.abs(logger.getHeightVar() - 320) < 30);
Assert.assertTrue(withinTol);
}
use of beast.core.State in project MultiTypeTree by tgvaughan.
the class TWBR_TS_Test method test2.
@Test
public void test2() throws Exception {
System.out.println("TWBR_test 2");
// Fix seed.
Randomizer.setSeed(42);
// Assemble initial MultiTypeTree
String newickStr = "((1[&deme=1]:1,2[&deme=0]:1)[&deme=0]:1," + "3[&deme=0]:2)[&deme=0]:0;";
MultiTypeTreeFromNewick mtTree = new MultiTypeTreeFromNewick();
mtTree.initByName("value", newickStr, "typeLabel", "deme");
// Assemble migration model:
RealParameter rateMatrix = new RealParameter("0.1 0.1");
RealParameter popSizes = new RealParameter("7.0 7.0");
SCMigrationModel migModel = new SCMigrationModel();
migModel.initByName("rateMatrix", rateMatrix, "popSizes", popSizes, "typeSet", new TypeSet("A", "B"));
// Assemble distribution:
StructuredCoalescentTreeDensity distribution = new StructuredCoalescentTreeDensity();
distribution.initByName("migrationModel", migModel, "multiTypeTree", mtTree);
// Set up state:
State state = new State();
state.initByName("stateNode", mtTree);
// Set up operator:
TypedWilsonBaldingRandom operatorTWBR = new TypedWilsonBaldingRandom();
operatorTWBR.initByName("weight", 1.0, "multiTypeTree", mtTree, "migrationModel", migModel, "mu", 0.2, "alpha", 0.2);
Operator operatorMTTS = new MultiTypeTreeScale();
operatorMTTS.initByName("weight", 1.0, "multiTypeTree", mtTree, "migrationModel", migModel, "scaleFactor", 0.8, "useOldTreeScaler", false);
// Set up stat analysis logger:
MultiTypeTreeStatLogger logger = new MultiTypeTreeStatLogger();
logger.initByName("multiTypeTree", mtTree, "burninFrac", 0.1, "logEvery", 1000);
// Set up MCMC:
MCMC mcmc = new MCMC();
mcmc.initByName("chainLength", "1000000", "state", state, "distribution", distribution, "operator", operatorTWBR, "operator", operatorMTTS, "logger", logger);
// Run MCMC:
mcmc.run();
System.out.format("height mean = %s\n", logger.getHeightMean());
System.out.format("height var = %s\n", logger.getHeightVar());
System.out.format("height ESS = %s\n", logger.getHeightESS());
// Compare analysis results with truth:
boolean withinTol = (logger.getHeightESS() > 200) && (Math.abs(logger.getHeightMean() - 23) < 1) && (Math.abs(logger.getHeightVar() - 300) < 30);
Assert.assertTrue(withinTol);
}
Aggregations