use of dr.inference.loggers.Logger in project beast-mcmc by beast-dev.
the class SMC method chain.
/**
* This method actually initiates the MCMC analysis.
*/
public void chain() {
currentState = 0;
timer.start();
if (loggers != null) {
for (Logger logger : loggers) {
logger.startLogging();
}
}
mc.addMarkovChainListener(chainListener);
for (StateLoaderSaver particleState : particleStates) {
// Don't need the savedLnL - it won't be there
particleState.loadState(mc, new double[1]);
// reset the current chain length to 0
mc.setCurrentLength(0);
mc.runChain(options.getChainLength(), true);
// Save state to file...
particleState.saveState(mc, mc.getCurrentLength(), mc.getCurrentScore());
}
mc.terminateChain();
mc.removeMarkovChainListener(chainListener);
timer.stop();
}
use of dr.inference.loggers.Logger in project beast-mcmc by beast-dev.
the class MCMCParser method parseXMLObject.
/**
* @return an mcmc object based on the XML element it was passed.
*/
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
MCMC mcmc = new MCMC(xo.getAttribute(NAME, "mcmc1"));
long chainLength = xo.getLongIntegerAttribute(CHAIN_LENGTH);
boolean useCoercion = xo.getAttribute(COERCION, true);
long coercionDelay = chainLength / 100;
if (xo.hasAttribute(PRE_BURNIN)) {
coercionDelay = xo.getIntegerAttribute(PRE_BURNIN);
}
coercionDelay = xo.getAttribute(COERCION_DELAY, coercionDelay);
double temperature = xo.getAttribute(TEMPERATURE, 1.0);
long fullEvaluationCount = xo.getAttribute(FULL_EVALUATION, 2000);
double evaluationTestThreshold = MarkovChain.EVALUATION_TEST_THRESHOLD;
if (System.getProperty("mcmc.evaluation.threshold") != null) {
evaluationTestThreshold = Double.parseDouble(System.getProperty("mcmc.evaluation.threshold"));
}
evaluationTestThreshold = xo.getAttribute(EVALUATION_THRESHOLD, evaluationTestThreshold);
int minOperatorCountForFullEvaluation = xo.getAttribute(MIN_OPS_EVALUATIONS, 1);
MCMCOptions options = new MCMCOptions(chainLength, fullEvaluationCount, minOperatorCountForFullEvaluation, evaluationTestThreshold, useCoercion, coercionDelay, temperature);
OperatorSchedule opsched = (OperatorSchedule) xo.getChild(OperatorSchedule.class);
Likelihood likelihood = (Likelihood) xo.getChild(Likelihood.class);
likelihood.setUsed();
if (Boolean.valueOf(System.getProperty("show_warnings", "false"))) {
// check that all models, parameters and likelihoods are being used
for (Likelihood l : Likelihood.FULL_LIKELIHOOD_SET) {
if (!l.isUsed()) {
java.util.logging.Logger.getLogger("dr.inference").warning("Likelihood, " + l.getId() + ", of class " + l.getClass().getName() + " is not being handled by the MCMC.");
}
}
for (Model m : Model.FULL_MODEL_SET) {
if (!m.isUsed()) {
java.util.logging.Logger.getLogger("dr.inference").warning("Model, " + m.getId() + ", of class " + m.getClass().getName() + " is not being handled by the MCMC.");
}
}
for (Parameter p : Parameter.FULL_PARAMETER_SET) {
if (!p.isUsed()) {
java.util.logging.Logger.getLogger("dr.inference").warning("Parameter, " + p.getId() + ", of class " + p.getClass().getName() + " is not being handled by the MCMC.");
}
}
}
ArrayList<Logger> loggers = new ArrayList<Logger>();
for (int i = 0; i < xo.getChildCount(); i++) {
Object child = xo.getChild(i);
if (child instanceof Logger) {
loggers.add((Logger) child);
}
}
mcmc.setShowOperatorAnalysis(true);
if (xo.hasAttribute(OPERATOR_ANALYSIS)) {
mcmc.setOperatorAnalysisFile(XMLParser.getLogFile(xo, OPERATOR_ANALYSIS));
}
Logger[] loggerArray = new Logger[loggers.size()];
loggers.toArray(loggerArray);
java.util.logging.Logger.getLogger("dr.inference").info("\nCreating the MCMC chain:" + "\n chainLength=" + options.getChainLength() + "\n autoOptimize=" + options.useCoercion() + (options.useCoercion() ? "\n autoOptimize delayed for " + options.getCoercionDelay() + " steps" : "") + (options.getFullEvaluationCount() == 0 ? "\n full evaluation test off" : ""));
mcmc.init(options, likelihood, opsched, loggerArray);
MarkovChain mc = mcmc.getMarkovChain();
double initialScore = mc.getCurrentScore();
if (initialScore == Double.NEGATIVE_INFINITY) {
String message = "The initial posterior is zero";
if (likelihood instanceof CompoundLikelihood) {
message += ": " + ((CompoundLikelihood) likelihood).getDiagnosis(2);
} else {
message += "!";
}
throw new IllegalArgumentException(message);
}
if (!xo.getAttribute(SPAWN, true))
mcmc.setSpawnable(false);
return mcmc;
}
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