List of usage examples for org.apache.commons.configuration Configuration getDouble
Double getDouble(String key, Double defaultValue);
From source file:keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus.java
/** * <p>//from ww w . j a va2 s .com * @param settings Settings Configuration * </p> */ public void configure(Configuration settings) { initialStepSize = settings.getDouble("initial-step-size[@value]", 0.0125); minimumDelta = settings.getDouble("minimum-delta[@value]", 0.0); maximumDelta = settings.getDouble("maximum-delta[@value]", 50.0); positiveEta = settings.getDouble("positive-eta[@value]", 1.2); negativeEta = settings.getDouble("negative-eta[@value]", 0.2); epochs = settings.getInt("cycles[@value]", 25); }
From source file:net.sf.jclal.activelearning.singlelabel.querystrategy.VarianceReductionQueryStrategy.java
/** * * @param configuration Configuration for variance reduction strategy. * *The XML labels supported are://www. ja v a2 s . c o m * * <ul> * <li>epsilon= double</li> * <li>epsilon-iteration= int</li> * <li>factor-regularization= double</li> * </ul> */ @Override public void configure(Configuration configuration) { super.configure(configuration); //Set epsilon double currentEpsilon = configuration.getDouble("epsilon", epsilon); setEpsilon(currentEpsilon); //Set epsilon iteration int currentMaxEpsilonI = configuration.getInt("epsilon-iteration", maxEpsilonIteration); setMaxEpsilonIteration(currentMaxEpsilonI); //Set factor regularization double currentFactorRegularization = configuration.getDouble("factor-regularization", factorRegularization); setFactorRegularization(currentFactorRegularization); }
From source file:edu.berkeley.sparrow.examples.HeterogeneousFrontend.java
public void run(String[] args) { try {//from w ww .ja v a2 s . c o m OptionParser parser = new OptionParser(); parser.accepts("c", "configuration file").withRequiredArg().ofType(String.class); parser.accepts("help", "print help statement"); OptionSet options = parser.parse(args); if (options.has("help")) { parser.printHelpOn(System.out); System.exit(-1); } // Logger configuration: log to the console BasicConfigurator.configure(); LOG.setLevel(Level.DEBUG); Configuration conf = new PropertiesConfiguration(); if (options.has("c")) { String configFile = (String) options.valueOf("c"); conf = new PropertiesConfiguration(configFile); } double warmupLambda = conf.getDouble("warmup_job_arrival_rate_s", DEFAULT_WARMUP_JOB_ARRIVAL_RATE_S); int warmupDurationS = conf.getInt("warmup_s", DEFAULT_WARMUP_S); int postWarmupS = conf.getInt("post_warmup_s", DEFAULT_POST_WARMUP_S); double lambda = conf.getDouble("job_arrival_rate_s", DEFAULT_JOB_ARRIVAL_RATE_S); int experimentDurationS = conf.getInt("experiment_s", DEFAULT_EXPERIMENT_S); LOG.debug("Using arrival rate of " + lambda + " tasks per second and running experiment for " + experimentDurationS + " seconds."); int tasksPerJob = conf.getInt("tasks_per_job", DEFAULT_TASKS_PER_JOB); int numPreferredNodes = conf.getInt("num_preferred_nodes", DEFAULT_NUM_PREFERRED_NODES); LOG.debug("Using " + numPreferredNodes + " preferred nodes for each task."); int benchmarkIterations = conf.getInt("benchmark.iterations", DEFAULT_BENCHMARK_ITERATIONS); int benchmarkId = conf.getInt("benchmark.id", DEFAULT_TASK_BENCHMARK); List<String> backends = new ArrayList<String>(); if (numPreferredNodes > 0) { /* Attempt to parse the list of slaves, which we'll need to (randomly) select preferred * nodes. */ if (!conf.containsKey(BACKENDS)) { LOG.fatal("Missing configuration backend list, which is needed to randomly select " + "preferred nodes (num_preferred_nodes set to " + numPreferredNodes + ")"); } for (String node : conf.getStringArray(BACKENDS)) { backends.add(node); } if (backends.size() < numPreferredNodes) { LOG.fatal("Number of backends smaller than number of preferred nodes!"); } } List<UserInfo> users = new ArrayList<UserInfo>(); if (conf.containsKey(USERS)) { for (String userSpecification : conf.getStringArray(USERS)) { LOG.debug("Reading user specification: " + userSpecification); String[] parts = userSpecification.split(":"); if (parts.length != 3) { LOG.error("Unexpected user specification string: " + userSpecification + "; ignoring user"); continue; } users.add(new UserInfo(parts[0], Integer.parseInt(parts[1]), Integer.parseInt(parts[2]))); } } if (users.size() == 0) { // Add a dummy user. users.add(new UserInfo("defaultUser", 1, 0)); } SparrowFrontendClient client = new SparrowFrontendClient(); int schedulerPort = conf.getInt("scheduler_port", SchedulerThrift.DEFAULT_SCHEDULER_THRIFT_PORT); client.initialize(new InetSocketAddress("localhost", schedulerPort), APPLICATION_ID, this); if (warmupDurationS > 0) { LOG.debug("Warming up for " + warmupDurationS + " seconds at arrival rate of " + warmupLambda + " jobs per second"); launchTasks(users, warmupLambda, warmupDurationS, tasksPerJob, numPreferredNodes, benchmarkIterations, benchmarkId, backends, client); LOG.debug("Waiting for queues to drain after warmup (waiting " + postWarmupS + " seconds)"); Thread.sleep(postWarmupS * 1000); } LOG.debug("Launching experiment for " + experimentDurationS + " seconds"); launchTasks(users, lambda, experimentDurationS, tasksPerJob, numPreferredNodes, benchmarkIterations, benchmarkId, backends, client); } catch (Exception e) { LOG.error("Fatal exception", e); } }
From source file:edu.berkeley.sparrow.examples.FairnessTestingFrontend.java
public void run(String[] args) { try {/*ww w. j a v a 2s . c o m*/ OptionParser parser = new OptionParser(); parser.accepts("c", "configuration file").withRequiredArg().ofType(String.class); parser.accepts("help", "print help statement"); OptionSet options = parser.parse(args); if (options.has("help")) { parser.printHelpOn(System.out); System.exit(-1); } // Logger configuration: log to the console BasicConfigurator.configure(); LOG.setLevel(Level.DEBUG); Configuration conf = new PropertiesConfiguration(); if (options.has("c")) { String configFile = (String) options.valueOf("c"); conf = new PropertiesConfiguration(configFile); } double warmup_lambda = conf.getDouble("warmup_job_arrival_rate_s", DEFAULT_WARMUP_JOB_ARRIVAL_RATE_S); int warmup_duration_s = conf.getInt("warmup_s", DEFAULT_WARMUP_S); int post_warmup_s = conf.getInt("post_warmup_s", DEFAULT_POST_WARMUP_S); // We use this to represent the the rate to fully load the cluster. This is a hack. double lambda = conf.getDouble("job_arrival_rate_s", DEFAULT_JOB_ARRIVAL_RATE_S); int experiment_duration_s = conf.getInt("experiment_s", DEFAULT_EXPERIMENT_S); LOG.debug("Using arrival rate of " + lambda + " tasks per second and running experiment for " + experiment_duration_s + " seconds."); int tasksPerJob = conf.getInt("tasks_per_job", DEFAULT_TASKS_PER_JOB); int numPreferredNodes = conf.getInt("num_preferred_nodes", DEFAULT_NUM_PREFERRED_NODES); LOG.debug("Using " + numPreferredNodes + " preferred nodes for each task."); int benchmarkIterations = conf.getInt("benchmark.iterations", DEFAULT_BENCHMARK_ITERATIONS); int benchmarkId = conf.getInt("benchmark.id", DEFAULT_TASK_BENCHMARK); List<String> backends = new ArrayList<String>(); if (numPreferredNodes > 0) { /* Attempt to parse the list of slaves, which we'll need to (randomly) select preferred * nodes. */ if (!conf.containsKey(BACKENDS)) { LOG.fatal("Missing configuration backend list, which is needed to randomly select " + "preferred nodes (num_preferred_nodes set to " + numPreferredNodes + ")"); } for (String node : conf.getStringArray(BACKENDS)) { backends.add(node); } if (backends.size() < numPreferredNodes) { LOG.fatal("Number of backends smaller than number of preferred nodes!"); } } List<SubExperiment> experiments = new ArrayList<SubExperiment>(); double fullyUtilizedArrivalRate = lambda; // For the first twenty seconds, the first user submits at a rate to fully utilize the cluster. List<UserInfo> onlyUser0 = new ArrayList<UserInfo>(); onlyUser0.add(new UserInfo("user0", 1, 0)); experiments.add(new SubExperiment(onlyUser0, 20, fullyUtilizedArrivalRate)); // For the next 10 seconds, user1 increases her rate to 25% of the cluster. List<UserInfo> user1QuarterDemand = new ArrayList<UserInfo>(); user1QuarterDemand.add(new UserInfo("user0", 4, 0)); user1QuarterDemand.add(new UserInfo("user1", 5, 0)); experiments.add(new SubExperiment(user1QuarterDemand, 10, 1.25 * fullyUtilizedArrivalRate)); // For the next 10 seconds, user 1 increases her rate to 50% of the cluster (using exactly // her share, but no more). List<UserInfo> user1HalfDemand = new ArrayList<UserInfo>(); user1HalfDemand.add(new UserInfo("user0", 2, 0)); user1HalfDemand.add(new UserInfo("user1", 3, 0)); experiments.add(new SubExperiment(user1HalfDemand, 10, 1.5 * fullyUtilizedArrivalRate)); // Next user 1 goes back down to 25%. experiments.add(new SubExperiment(user1QuarterDemand, 10, 1.25 * fullyUtilizedArrivalRate)); // Finally user 1 goes back to 0. experiments.add(new SubExperiment(onlyUser0, 20, fullyUtilizedArrivalRate)); SparrowFrontendClient client = new SparrowFrontendClient(); int schedulerPort = conf.getInt("scheduler_port", SchedulerThrift.DEFAULT_SCHEDULER_THRIFT_PORT); client.initialize(new InetSocketAddress("localhost", schedulerPort), APPLICATION_ID, this); if (warmup_duration_s > 0) { List<SubExperiment> warmupExperiment = new ArrayList<SubExperiment>(); List<UserInfo> warmupUsers = new ArrayList<UserInfo>(); warmupUsers.add(new UserInfo("warmupUser", 1, 0)); warmupExperiment.add(new SubExperiment(warmupUsers, warmup_duration_s, warmup_lambda)); LOG.debug("Warming up for " + warmup_duration_s + " seconds at arrival rate of " + warmup_lambda + " jobs per second"); launchTasks(warmupExperiment, tasksPerJob, numPreferredNodes, benchmarkIterations, benchmarkId, backends, client); LOG.debug("Waiting for queues to drain after warmup (waiting " + post_warmup_s + " seconds)"); Thread.sleep(post_warmup_s * 1000); } LOG.debug("Launching experiment for " + experiment_duration_s + " seconds"); launchTasks(experiments, tasksPerJob, numPreferredNodes, benchmarkIterations, benchmarkId, backends, client); } catch (Exception e) { LOG.error("Fatal exception", e); } }
From source file:com.hello2morrow.sonarplugin.SonargraphSensor.java
void analyse(IProject project, SensorContext sensorContext, ReportContext report) { this.sensorContext = sensorContext; Configuration configuration = project.getConfiguration(); this.indexCost = configuration.getDouble(SonargraphPluginBase.COST_PER_INDEX_POINT, SonargraphPluginBase.COST_PER_INDEX_POINT_DEFAULT); XsdBuildUnits buildUnits = report.getBuildUnits(); List<XsdAttributeRoot> buildUnitList = buildUnits.getBuildUnit(); if (buildUnitList.size() == 1) { XsdAttributeRoot sonarBuildUnit = buildUnitList.get(0); String buName = getBuildUnitName(sonarBuildUnit.getName()); analyse(project, sonarBuildUnit, buName, report); } else if (buildUnitList.size() > 1) { boolean foundMatchingBU = false; for (XsdAttributeRoot sonarBuildUnit : buildUnitList) { String buName = getBuildUnitName(sonarBuildUnit.getName()); if (buildUnitMatchesAnalyzedProject(buName, project)) { analyse(project, sonarBuildUnit, buName, report); foundMatchingBU = true;/*from w w w . java 2 s . c o m*/ break; } } if (!foundMatchingBU) { LOG.warn("Project " + project.getName() + " could not be mapped to a build unit. The project will not be analyzed. Check the build unit configuration of your Sonargraph system."); } } else { LOG.error("No build units found in report file!"); } }
From source file:com.hello2morrow.sonarplugin.SonarJSensor.java
public SonarJSensor(Configuration config, RulesManager rulesManager, RulesProfile rulesProfile) { indexCost = config.getDouble(COST_PER_INDEX_POINT, 12.0); this.rulesManager = rulesManager; this.rulesProfile = rulesProfile; if (rulesManager == null) { LOG.warn("No RulesManager provided to sensor"); }/* ww w.j a v a 2 s .c o m*/ if (rulesProfile == null) { LOG.warn("No RulesProfile given to sensor"); } }
From source file:keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator.java
/** * <p>/*from w ww.j a v a 2 s.c o m*/ * Configuration parameters for ParametricMutator are: * </p> * <ul> * <li> * <code>[@selective] boolean (default=false)</code></p> * If this parameter is set to <code>true</true> only certain randomly * selected nodes are parametrically mutated. * </li> * <li> * <code>temperature-exponent[@value] double (default=1)</code></p> * Temperature exponent to be used for obtaining temperature * of each indivual mutated. * </li> * <li> * <code>amplitude[@value] double (default=5)</code></p> * Amplitude factor to increase the range of parametric variations * of mutated weights. * </li> * <li> * <code>fitness-difference[@value] double (default=0.0000001)</code></p> * Difference between two fitnesses that we consider * enoung to say that the fitness has improved * </li> * <li> * <code>initial-alpha-values: complex</code></p> * Initial values of alpha parameters. * <ul> * <li> * <code>initial-alpha-values[@input] double (default=0.5)</code></p> * Initial value of alpha parameter used for input weights. * </li> * <li> * <code>initial-alpha-values[@ouput] double (default=1)</code></p> * Initial value of alpha parameter used for output weights. * </li> * </ul> * </li> * </ul> */ public void configure(Configuration settings) { // Setup selective selective = settings.getBoolean("[@selective]", false); // Setup temperExponent temperExponent = settings.getDouble("temperature-exponent[@value]", 1); // Setup amplitude amplitude = settings.getDouble("amplitude[@value]", 5); // Setup fitDif fitDif = settings.getDouble("fitness-difference[@value]", 0.0000001); // Setup alphaInput initialAlphaInput = settings.getDouble("initial-alpha-values[@input]", 0.5); // Setup alphaOutput initialAlphaOutput = settings.getDouble("initial-alpha-values[@output]", 1); }
From source file:net.sf.jclal.activelearning.multilabel.querystrategy.MultiLabelDensityDiversityQueryStrategy.java
/** * * @param configuration Configuration object for density diversity strategy. * * The XML labels supported are://from w w w . j a va2 s . c o m * <ul> * <li><b>importance-density= double</b></li> * <li> * <b>distance-function type= class</b> * <p> * Package: net.sf.jclal.util.distancefunction * </p> * <p> * Class: All * </p> * <p> * Package: weka.core * </p> * <p> * Class: EuclideanDistance || ManhattanDistance || MinkowskiDistance...</p> * </li> * <li>matrix-file= boolean</li> * <li> * <b>sub-query-strategy type= class</b> * <p> * Package: net.sf.jclal.activelearning.multilabel.querystrategy</p> * <p> * Class: All</p> * </li> * </ul> */ @Override public void configure(Configuration configuration) { try { super.configure(configuration); } catch (Exception e) { } // Set relativeImportanceOfDensity double currentImportance = configuration.getDouble("importance-density", relativeImportanceOfDensity); setRelativeImportanceOfDensity(currentImportance); String distanceError = "distance-function type= "; try { // Set the distance classname String distanceClassname = configuration.getString("distance-function[@type]"); distanceError += distanceClassname; // the distance class Class<? extends NormalizableDistance> distance = (Class<? extends NormalizableDistance>) Class .forName(distanceClassname); // the distance instance NormalizableDistance currentDistance = distance.newInstance(); // Configure the distance if (currentDistance instanceof IConfigure) { ((IConfigure) currentDistance).configure(configuration.subset("distance-function")); } // Set the distance setTypeOfDistance(currentDistance); } catch (ClassNotFoundException e) { throw new ConfigurationRuntimeException("Illegal distance classname: " + distanceError, e); } catch (InstantiationException e) { throw new ConfigurationRuntimeException("Illegal distance classname: " + distanceError, e); } catch (IllegalAccessException e) { throw new ConfigurationRuntimeException("Illegal distance classname: " + distanceError, e); } // Set the sub query strategy String subError = "sub-query-strategy type= "; try { // sub Query strategy classname String strategyClassname = configuration.getString("sub-query-strategy[@type]"); subError += strategyClassname; // sub Query strategy class Class<? extends IQueryStrategy> strategyClass = (Class<? extends IQueryStrategy>) Class .forName(strategyClassname); // sub Query strategy instance IQueryStrategy currentSubStrategy = strategyClass.newInstance(); // Configure sub Query strategy (if necessary) if (currentSubStrategy instanceof IConfigure) { ((IConfigure) currentSubStrategy).configure(configuration.subset("sub-query-strategy")); } // Set the sub Query strategy setSubQueryStrategy(currentSubStrategy); } catch (ClassNotFoundException e) { throw new ConfigurationRuntimeException("Illegal sub-query-strategy classname: " + subError, e); } catch (InstantiationException e) { throw new ConfigurationRuntimeException("Illegal sub-query-strategy classname: " + subError, e); } catch (IllegalAccessException e) { throw new ConfigurationRuntimeException("Illegal sub-query-strategy classname: " + subError, e); } //Set if handle the matrix over a file and not over the main memory boolean matrixFile = configuration.getBoolean("matrix-file", matrixOverFile); setMatrixOverFile(matrixFile); }
From source file:net.sf.jclal.sampling.supervised.Resample.java
/** * * @param configuration The configuration object for Resample. * The XML labels supported are:// w w w.j a va2 s.c om * <ul> * <li><b>no-replacement= boolean</b></li> * <li><b>invert-selection= boolean</b></li> * <li><b>m_BiasToUniformClass= double</b></li> * </ul> */ @Override public void configure(Configuration configuration) { super.configure(configuration); boolean noReplacementT = configuration.getBoolean("no-replacement", noReplacement); setNoReplacement(noReplacementT); boolean invert = configuration.getBoolean("invert-selection", invertSelection); setInvertSelection(invert); double mBias = configuration.getDouble("bias-to-uniform-class", biasToUniformClass); setBiasToUniformClass(mBias); }
From source file:edu.berkeley.sparrow.daemon.scheduler.Scheduler.java
public void initialize(Configuration conf, InetSocketAddress socket) throws IOException { address = Network.socketAddressToThrift(socket); String mode = conf.getString(SparrowConf.DEPLYOMENT_MODE, "unspecified"); this.conf = conf; if (mode.equals("standalone")) { state = new StandaloneSchedulerState(); } else if (mode.equals("configbased")) { state = new ConfigSchedulerState(); } else {// w w w .j a va 2s .c om throw new RuntimeException("Unsupported deployment mode: " + mode); } state.initialize(conf); defaultProbeRatioUnconstrained = conf.getDouble(SparrowConf.SAMPLE_RATIO, SparrowConf.DEFAULT_SAMPLE_RATIO); defaultProbeRatioConstrained = conf.getDouble(SparrowConf.SAMPLE_RATIO_CONSTRAINED, SparrowConf.DEFAULT_SAMPLE_RATIO_CONSTRAINED); requestTaskPlacers = Maps.newConcurrentMap(); useCancellation = conf.getBoolean(SparrowConf.CANCELLATION, SparrowConf.DEFAULT_CANCELLATION); if (useCancellation) { LOG.debug("Initializing cancellation service"); cancellationService = new CancellationService(nodeMonitorClientPool); new Thread(cancellationService).start(); } else { LOG.debug("Not using cancellation"); } spreadEvenlyTaskSetSize = conf.getInt(SparrowConf.SPREAD_EVENLY_TASK_SET_SIZE, SparrowConf.DEFAULT_SPREAD_EVENLY_TASK_SET_SIZE); }