/** * get a Boolean parameter * @param key * @param defaultValue * @return */ public boolean getBooleanParameter(String key, boolean defaultValue) { return getBooleanParameter(null, key, defaultValue); }
public void init(TrainingParameters trainingParameters, Map<String,String> reportMap) { this.trainingParameters = trainingParameters; if (reportMap == null) reportMap = new HashMap<>(); this.reportMap = reportMap; printMessages = trainingParameters.getBooleanParameter(VERBOSE_PARAM, VERBOSE_DEFAULT); }
/** * Use the PluggableParameters directly... * @param key * @param defaultValue */ @Deprecated protected boolean getBooleanParam(String key, boolean defaultValue) { return trainingParameters.getBooleanParameter(key, defaultValue); }
public void init(TrainingParameters indexingParameters,Map<String, String> reportMap) { this.reportMap = reportMap; if (this.reportMap == null) reportMap = new HashMap<>(); trainingParameters = indexingParameters; printMessages = trainingParameters.getBooleanParameter(AbstractTrainer.VERBOSE_PARAM, AbstractTrainer.VERBOSE_DEFAULT); }
public AbstractModel doTrain(SequenceStream events) throws IOException { int iterations = getIterations(); int cutoff = getCutoff(); boolean useAverage = trainingParameters.getBooleanParameter("UseAverage", true); return trainModel(iterations, events, cutoff, useAverage); }
public AbstractModel doTrain(DataIndexer indexer) throws IOException { int iterations = getIterations(); int cutoff = getCutoff(); AbstractModel model; boolean useAverage = trainingParameters.getBooleanParameter("UseAverage", true); boolean useSkippedAveraging = trainingParameters.getBooleanParameter("UseSkippedAveraging", false); // overwrite otherwise it might not work if (useSkippedAveraging) useAverage = true; double stepSizeDecrease = trainingParameters.getDoubleParameter("StepSizeDecrease", 0); double tolerance = trainingParameters.getDoubleParameter("Tolerance", PerceptronTrainer.TOLERANCE_DEFAULT); this.setSkippedAveraging(useSkippedAveraging); if (stepSizeDecrease > 0) this.setStepSizeDecrease(stepSizeDecrease); this.setTolerance(tolerance); model = this.trainModel(iterations, indexer, cutoff, useAverage); return model; }
@Override public void index(ObjectStream<Event> eventStream) throws IOException { int cutoff = trainingParameters.getIntParameter(CUTOFF_PARAM, CUTOFF_DEFAULT); boolean sort = trainingParameters.getBooleanParameter(SORT_PARAM, SORT_DEFAULT);
@Override public void index(ObjectStream<Event> eventStream) throws IOException { int cutoff = trainingParameters.getIntParameter(CUTOFF_PARAM, CUTOFF_DEFAULT); boolean sort = trainingParameters.getBooleanParameter(SORT_PARAM, SORT_DEFAULT); long start = System.currentTimeMillis(); display("Indexing events with OnePass using cutoff of " + cutoff + "\n\n"); display("\tComputing event counts... "); Map<String, Integer> predicateIndex = new HashMap<>(); List<Event> events = computeEventCounts(eventStream, predicateIndex, cutoff); display("done. " + events.size() + " events\n"); display("\tIndexing... "); List<ComparableEvent> eventsToCompare = index(ObjectStreamUtils.createObjectStream(events), predicateIndex); display("done.\n"); display("Sorting and merging events... "); sortAndMerge(eventsToCompare, sort); display(String.format("Done indexing in %.2f s.\n", (System.currentTimeMillis() - start) / 1000d)); }
LOG_LIKELIHOOD_THRESHOLD_DEFAULT); useSimpleSmoothing = trainingParameters.getBooleanParameter(SMOOTHING_PARAM, SMOOTHING_DEFAULT); if (useSimpleSmoothing) { _smoothingObservation = trainingParameters.getBooleanParameter(GAUSSIAN_SMOOTHING_PARAM, GAUSSIAN_SMOOTHING_DEFAULT); if (useGaussianSmoothing) { sigma = trainingParameters.getDoubleParameter(
/** * get a Boolean parameter * @param key * @param defaultValue * @return */ public boolean getBooleanParameter(String key, boolean defaultValue) { return getBooleanParameter(null, key, defaultValue); }
/** * get a Boolean parameter * @param key * @param defaultValue * @return */ public boolean getBooleanParameter(String key, boolean defaultValue) { return getBooleanParameter(null, key, defaultValue); }
public void init(TrainingParameters indexingParameters,Map<String, String> reportMap) { this.reportMap = reportMap; if (this.reportMap == null) reportMap = new HashMap<>(); trainingParameters = indexingParameters; printMessages = trainingParameters.getBooleanParameter(AbstractTrainer.VERBOSE_PARAM, AbstractTrainer.VERBOSE_DEFAULT); }
/** * Use the PluggableParameters directly... * @param key * @param defaultValue */ @Deprecated protected boolean getBooleanParam(String key, boolean defaultValue) { return trainingParameters.getBooleanParameter(key, defaultValue); }
public void init(TrainingParameters trainingParameters, Map<String,String> reportMap) { this.trainingParameters = trainingParameters; if (reportMap == null) reportMap = new HashMap<>(); this.reportMap = reportMap; printMessages = trainingParameters.getBooleanParameter(VERBOSE_PARAM, VERBOSE_DEFAULT); }
public void init(TrainingParameters trainingParameters, Map<String,String> reportMap) { this.trainingParameters = trainingParameters; if (reportMap == null) reportMap = new HashMap<>(); this.reportMap = reportMap; printMessages = trainingParameters.getBooleanParameter(VERBOSE_PARAM, VERBOSE_DEFAULT); }
public void init(TrainingParameters indexingParameters,Map<String, String> reportMap) { this.reportMap = reportMap; if (this.reportMap == null) reportMap = new HashMap<>(); trainingParameters = indexingParameters; printMessages = trainingParameters.getBooleanParameter(AbstractTrainer.VERBOSE_PARAM, AbstractTrainer.VERBOSE_DEFAULT); }
/** * Use the PluggableParameters directly... * @param key * @param defaultValue */ @Deprecated protected boolean getBooleanParam(String key, boolean defaultValue) { return trainingParameters.getBooleanParameter(key, defaultValue); }
@Test public void testPutGet() { TrainingParameters tp = build("k1=v1,int.k2=123,str.k2=v3,str.k3=v4,boolean.k4=false,double.k5=123.45,k21=234.5"); Assert.assertEquals("v1", tp.getStringParameter("k1", "def")); Assert.assertEquals("def", tp.getStringParameter("k2", "def")); Assert.assertEquals("v3", tp.getStringParameter("str", "k2", "def")); Assert.assertEquals("def", tp.getStringParameter("str", "k4", "def")); Assert.assertEquals(-100, tp.getIntParameter("k11", -100)); tp.put("k11", 234); Assert.assertEquals(234, tp.getIntParameter("k11", -100)); Assert.assertEquals(123, tp.getIntParameter("int", "k2", -100)); Assert.assertEquals(-100, tp.getIntParameter("int", "k4", -100)); Assert.assertEquals(234.5, tp.getDoubleParameter("k21", -100), 0.001); tp.put("k21", 345.6); Assert.assertEquals(345.6, tp.getDoubleParameter("k21", -100), 0.001); // should be changed tp.putIfAbsent("k21", 456.7); Assert.assertEquals(345.6, tp.getDoubleParameter("k21", -100), 0.001); // should be unchanged Assert.assertEquals(123.45, tp.getDoubleParameter("double", "k5", -100), 0.001); Assert.assertEquals(true, tp.getBooleanParameter("k31", true)); tp.put("k31", false); Assert.assertEquals(false, tp.getBooleanParameter("k31", true)); Assert.assertEquals(false, tp.getBooleanParameter("boolean", "k4", true)); }
public AbstractModel doTrain(SequenceStream events) throws IOException { int iterations = getIterations(); int cutoff = getCutoff(); boolean useAverage = trainingParameters.getBooleanParameter("UseAverage", true); return trainModel(iterations, events, cutoff, useAverage); }
public AbstractModel doTrain(SequenceStream events) throws IOException { int iterations = getIterations(); int cutoff = getCutoff(); boolean useAverage = trainingParameters.getBooleanParameter("UseAverage", true); return trainModel(iterations, events, cutoff, useAverage); }