List of usage examples for org.apache.commons.cli2 CommandLine getValue
Object getValue(final Option option) throws IllegalStateException;
From source file:org.apache.mahout.classifier.df.tools.Frequencies.java
@Override public int run(String[] args) throws IOException, ClassNotFoundException, InterruptedException { DefaultOptionBuilder obuilder = new DefaultOptionBuilder(); ArgumentBuilder abuilder = new ArgumentBuilder(); GroupBuilder gbuilder = new GroupBuilder(); Option dataOpt = obuilder.withLongName("data").withShortName("d").withRequired(true) .withArgument(abuilder.withName("path").withMinimum(1).withMaximum(1).create()) .withDescription("Data path").create(); Option datasetOpt = obuilder.withLongName("dataset").withShortName("ds").withRequired(true) .withArgument(abuilder.withName("path").withMinimum(1).create()).withDescription("dataset path") .create();/*from w w w. j a v a2s .c om*/ Option helpOpt = obuilder.withLongName("help").withDescription("Print out help").withShortName("h") .create(); Group group = gbuilder.withName("Options").withOption(dataOpt).withOption(datasetOpt).withOption(helpOpt) .create(); try { Parser parser = new Parser(); parser.setGroup(group); CommandLine cmdLine = parser.parse(args); if (cmdLine.hasOption(helpOpt)) { CommandLineUtil.printHelp(group); return 0; } String dataPath = cmdLine.getValue(dataOpt).toString(); String datasetPath = cmdLine.getValue(datasetOpt).toString(); log.debug("Data path : {}", dataPath); log.debug("Dataset path : {}", datasetPath); runTool(dataPath, datasetPath); } catch (OptionException e) { log.warn(e.toString(), e); CommandLineUtil.printHelp(group); } return 0; }
From source file:org.apache.mahout.classifier.df.tools.UDistrib.java
/** * Launch the uniform distribution tool. Requires the following command line arguments:<br> * //from w w w.ja v a 2s . com * data : data path dataset : dataset path numpartitions : num partitions output : output path * * @throws java.io.IOException */ public static void main(String[] args) throws IOException { DefaultOptionBuilder obuilder = new DefaultOptionBuilder(); ArgumentBuilder abuilder = new ArgumentBuilder(); GroupBuilder gbuilder = new GroupBuilder(); Option dataOpt = obuilder.withLongName("data").withShortName("d").withRequired(true) .withArgument(abuilder.withName("data").withMinimum(1).withMaximum(1).create()) .withDescription("Data path").create(); Option datasetOpt = obuilder.withLongName("dataset").withShortName("ds").withRequired(true) .withArgument(abuilder.withName("dataset").withMinimum(1).create()).withDescription("Dataset path") .create(); Option outputOpt = obuilder.withLongName("output").withShortName("o").withRequired(true) .withArgument(abuilder.withName("output").withMinimum(1).withMaximum(1).create()) .withDescription("Path to generated files").create(); Option partitionsOpt = obuilder.withLongName("numpartitions").withShortName("p").withRequired(true) .withArgument(abuilder.withName("numparts").withMinimum(1).withMinimum(1).create()) .withDescription("Number of partitions to create").create(); Option helpOpt = obuilder.withLongName("help").withDescription("Print out help").withShortName("h") .create(); Group group = gbuilder.withName("Options").withOption(dataOpt).withOption(outputOpt).withOption(datasetOpt) .withOption(partitionsOpt).withOption(helpOpt).create(); try { Parser parser = new Parser(); parser.setGroup(group); CommandLine cmdLine = parser.parse(args); if (cmdLine.hasOption(helpOpt)) { CommandLineUtil.printHelp(group); return; } String data = cmdLine.getValue(dataOpt).toString(); String dataset = cmdLine.getValue(datasetOpt).toString(); int numPartitions = Integer.parseInt(cmdLine.getValue(partitionsOpt).toString()); String output = cmdLine.getValue(outputOpt).toString(); runTool(data, dataset, output, numPartitions); } catch (OptionException e) { log.warn(e.toString(), e); CommandLineUtil.printHelp(group); } }
From source file:org.apache.mahout.classifier.mlp.TrainMultilayerPerceptron.java
static Double getDouble(CommandLine commandLine, Option option) { Object val = commandLine.getValue(option); if (val != null) { return Double.parseDouble(val.toString()); }/* w ww.j a v a 2 s . co m*/ return null; }
From source file:org.apache.mahout.classifier.mlp.TrainMultilayerPerceptron.java
static String getString(CommandLine commandLine, Option option) { Object val = commandLine.getValue(option); if (val != null) { return val.toString(); }//www . j a v a 2 s . com return null; }
From source file:org.apache.mahout.classifier.sequencelearning.hmm.BaumWelchTrainer.java
public static void main(String[] args) throws IOException { DefaultOptionBuilder optionBuilder = new DefaultOptionBuilder(); ArgumentBuilder argumentBuilder = new ArgumentBuilder(); Option inputOption = DefaultOptionCreator.inputOption().create(); Option outputOption = DefaultOptionCreator.outputOption().create(); Option stateNumberOption = optionBuilder.withLongName("nrOfHiddenStates") .withDescription("Number of hidden states").withShortName("nh") .withArgument(argumentBuilder.withMaximum(1).withMinimum(1).withName("number").create()) .withRequired(true).create(); Option observedStateNumberOption = optionBuilder.withLongName("nrOfObservedStates") .withDescription("Number of observed states").withShortName("no") .withArgument(argumentBuilder.withMaximum(1).withMinimum(1).withName("number").create()) .withRequired(true).create(); Option epsilonOption = optionBuilder.withLongName("epsilon").withDescription("Convergence threshold") .withShortName("e") .withArgument(argumentBuilder.withMaximum(1).withMinimum(1).withName("number").create()) .withRequired(true).create(); Option iterationsOption = optionBuilder.withLongName("max-iterations") .withDescription("Maximum iterations number").withShortName("m") .withArgument(argumentBuilder.withMaximum(1).withMinimum(1).withName("number").create()) .withRequired(true).create(); Group optionGroup = new GroupBuilder().withOption(inputOption).withOption(outputOption) .withOption(stateNumberOption).withOption(observedStateNumberOption).withOption(epsilonOption) .withOption(iterationsOption).withName("Options").create(); try {// ww w .j a v a2 s.c om Parser parser = new Parser(); parser.setGroup(optionGroup); CommandLine commandLine = parser.parse(args); String input = (String) commandLine.getValue(inputOption); String output = (String) commandLine.getValue(outputOption); int nrOfHiddenStates = Integer.parseInt((String) commandLine.getValue(stateNumberOption)); int nrOfObservedStates = Integer.parseInt((String) commandLine.getValue(observedStateNumberOption)); double epsilon = Double.parseDouble((String) commandLine.getValue(epsilonOption)); int maxIterations = Integer.parseInt((String) commandLine.getValue(iterationsOption)); //constructing random-generated HMM HmmModel model = new HmmModel(nrOfHiddenStates, nrOfObservedStates, new Date().getTime()); List<Integer> observations = Lists.newArrayList(); //reading observations Scanner scanner = new Scanner(new FileInputStream(input), "UTF-8"); try { while (scanner.hasNextInt()) { observations.add(scanner.nextInt()); } } finally { scanner.close(); } int[] observationsArray = new int[observations.size()]; for (int i = 0; i < observations.size(); ++i) { observationsArray[i] = observations.get(i); } //training HmmModel trainedModel = HmmTrainer.trainBaumWelch(model, observationsArray, epsilon, maxIterations, true); //serializing trained model DataOutputStream stream = new DataOutputStream(new FileOutputStream(output)); try { LossyHmmSerializer.serialize(trainedModel, stream); } finally { Closeables.close(stream, false); } //printing tranied model System.out.println("Initial probabilities: "); for (int i = 0; i < trainedModel.getNrOfHiddenStates(); ++i) { System.out.print(i + " "); } System.out.println(); for (int i = 0; i < trainedModel.getNrOfHiddenStates(); ++i) { System.out.print(trainedModel.getInitialProbabilities().get(i) + " "); } System.out.println(); System.out.println("Transition matrix:"); System.out.print(" "); for (int i = 0; i < trainedModel.getNrOfHiddenStates(); ++i) { System.out.print(i + " "); } System.out.println(); for (int i = 0; i < trainedModel.getNrOfHiddenStates(); ++i) { System.out.print(i + " "); for (int j = 0; j < trainedModel.getNrOfHiddenStates(); ++j) { System.out.print(trainedModel.getTransitionMatrix().get(i, j) + " "); } System.out.println(); } System.out.println("Emission matrix: "); System.out.print(" "); for (int i = 0; i < trainedModel.getNrOfOutputStates(); ++i) { System.out.print(i + " "); } System.out.println(); for (int i = 0; i < trainedModel.getNrOfHiddenStates(); ++i) { System.out.print(i + " "); for (int j = 0; j < trainedModel.getNrOfOutputStates(); ++j) { System.out.print(trainedModel.getEmissionMatrix().get(i, j) + " "); } System.out.println(); } } catch (OptionException e) { CommandLineUtil.printHelp(optionGroup); } }
From source file:org.apache.mahout.classifier.sequencelearning.hmm.hadoop.BaumWelchDriver.java
@Override public int run(String[] args) throws Exception { DefaultOptionBuilder optionBuilder = new DefaultOptionBuilder(); ArgumentBuilder argumentBuilder = new ArgumentBuilder(); Option inputOption = optionBuilder.withLongName("input") .withDescription("Sequence file containing VectorWritables as training sequence").withShortName("i") .withArgument(argumentBuilder.withMaximum(1).withMinimum(1).withName("path").create()) .withRequired(true).create(); Option outputOption = optionBuilder.withLongName("output") .withDescription("Output path to store the trained model encoded as Sequence Files") .withShortName("o") .withArgument(argumentBuilder.withMaximum(1).withMinimum(1).withName("path").create()) .withRequired(true).create(); Option modelOption = optionBuilder.withLongName("model") .withDescription("Initial HmmModel encoded as a Sequence File. " + "Will be constructed with a random distribution if the 'buildRandom' option is set to true.") .withShortName("im") .withArgument(argumentBuilder.withMaximum(1).withMinimum(1).withName("path").create()) .withRequired(false).create(); Option hiddenStateMapPath = optionBuilder.withLongName("hiddenStateToIDMap") .withDescription("Hidden states to ID map path.").withShortName("hmap") .withArgument(argumentBuilder.withMaximum(1).withMinimum(1).withName("path").create()) .withRequired(true).create(); Option emitStateMapPath = optionBuilder.withLongName("emittedStateToIDMap") .withDescription("Emitted states to ID map path.").withShortName("smap") .withArgument(argumentBuilder.withMaximum(1).withMinimum(1).withName("path").create()) .withRequired(true).create(); Option randomOption = optionBuilder.withLongName("buildRandom") .withDescription(//from w w w. j a v a 2 s . c om "Optional argument to generate a random initial HmmModel and store it in 'model' directory") .withShortName("r") .withArgument(argumentBuilder.withMaximum(1).withMinimum(1).withName("boolean").create()) .withRequired(false).create(); Option scalingOption = optionBuilder.withLongName("Scaling") .withDescription("Optional argument to invoke scaled training").withShortName("l") .withArgument(argumentBuilder.withMaximum(1).withMinimum(1).withName("string").create()) .withRequired(true).create(); Option stateNumberOption = optionBuilder.withLongName("nrOfHiddenStates") .withDescription("Number of hidden states").withShortName("nh") .withArgument(argumentBuilder.withMaximum(1).withMinimum(1).withName("number").create()) .withRequired(true).create(); Option observedStateNumberOption = optionBuilder.withLongName("nrOfObservedStates") .withDescription("Number of observed states").withShortName("no") .withArgument(argumentBuilder.withMaximum(1).withMinimum(1).withName("number").create()) .withRequired(true).create(); Option epsilonOption = optionBuilder.withLongName("epsilon").withDescription("Convergence threshold") .withShortName("e") .withArgument(argumentBuilder.withMaximum(1).withMinimum(1).withName("number").create()) .withRequired(true).create(); Option iterationsOption = optionBuilder.withLongName("maxIterations") .withDescription("Maximum iterations number").withShortName("m") .withArgument(argumentBuilder.withMaximum(1).withMinimum(1).withName("number").create()) .withRequired(true).create(); Group optionGroup = new GroupBuilder().withOption(inputOption).withOption(outputOption) .withOption(modelOption).withOption(hiddenStateMapPath).withOption(emitStateMapPath) .withOption(randomOption).withOption(scalingOption).withOption(stateNumberOption) .withOption(observedStateNumberOption).withOption(epsilonOption).withOption(iterationsOption) .withName("Options").create(); try { Parser parser = new Parser(); parser.setGroup(optionGroup); CommandLine commandLine = parser.parse(args); String input = (String) commandLine.getValue(inputOption); String output = (String) commandLine.getValue(outputOption); String modelIn = (String) commandLine.getValue(modelOption); String hiddenStateToIdMap = (String) commandLine.getValue(hiddenStateMapPath); String emittedStateToIdMap = (String) commandLine.getValue(emitStateMapPath); Boolean buildRandom = commandLine.hasOption(randomOption); String scaling = (String) commandLine.getValue(scalingOption); int numHidden = Integer.parseInt((String) commandLine.getValue(stateNumberOption)); int numObserved = Integer.parseInt((String) commandLine.getValue(observedStateNumberOption)); double convergenceDelta = Double.parseDouble((String) commandLine.getValue(epsilonOption)); int maxIterations = Integer.parseInt((String) commandLine.getValue(iterationsOption)); if (getConf() == null) { setConf(new Configuration()); } if (buildRandom) { BaumWelchUtils.buildRandomModel(numHidden, numObserved, new Path(modelIn), getConf()); } run(getConf(), new Path(input), new Path(modelIn), new Path(output), new Path(hiddenStateToIdMap), new Path(emittedStateToIdMap), numHidden, numObserved, convergenceDelta, scaling, maxIterations); } catch (OptionException e) { CommandLineUtil.printHelp(optionGroup); } return 0; }
From source file:org.apache.mahout.classifier.sequencelearning.hmm.RandomSequenceGenerator.java
public static void main(String[] args) throws IOException { DefaultOptionBuilder optionBuilder = new DefaultOptionBuilder(); ArgumentBuilder argumentBuilder = new ArgumentBuilder(); Option outputOption = optionBuilder.withLongName("output") .withDescription("Output file with sequence of observed states").withShortName("o") .withArgument(argumentBuilder.withMaximum(1).withMinimum(1).withName("path").create()) .withRequired(false).create(); Option modelOption = optionBuilder.withLongName("model").withDescription("Path to serialized HMM model") .withShortName("m") .withArgument(argumentBuilder.withMaximum(1).withMinimum(1).withName("path").create()) .withRequired(true).create(); Option lengthOption = optionBuilder.withLongName("length").withDescription("Length of generated sequence") .withShortName("l") .withArgument(argumentBuilder.withMaximum(1).withMinimum(1).withName("number").create()) .withRequired(true).create(); Group optionGroup = new GroupBuilder().withOption(outputOption).withOption(modelOption) .withOption(lengthOption).withName("Options").create(); try {// w w w. jav a 2 s. c om Parser parser = new Parser(); parser.setGroup(optionGroup); CommandLine commandLine = parser.parse(args); String output = (String) commandLine.getValue(outputOption); String modelPath = (String) commandLine.getValue(modelOption); int length = Integer.parseInt((String) commandLine.getValue(lengthOption)); //reading serialized HMM DataInputStream modelStream = new DataInputStream(new FileInputStream(modelPath)); HmmModel model; try { model = LossyHmmSerializer.deserialize(modelStream); } finally { Closeables.close(modelStream, true); } //generating observations int[] observations = HmmEvaluator.predict(model, length, System.currentTimeMillis()); //writing output PrintWriter writer = new PrintWriter( new OutputStreamWriter(new FileOutputStream(output), Charsets.UTF_8), true); try { for (int observation : observations) { writer.print(observation); writer.print(' '); } } finally { Closeables.close(writer, false); } } catch (OptionException e) { CommandLineUtil.printHelp(optionGroup); } }
From source file:org.apache.mahout.classifier.sequencelearning.hmm.ViterbiEvaluator.java
public static void main(String[] args) throws IOException { DefaultOptionBuilder optionBuilder = new DefaultOptionBuilder(); ArgumentBuilder argumentBuilder = new ArgumentBuilder(); Option inputOption = DefaultOptionCreator.inputOption().create(); Option outputOption = DefaultOptionCreator.outputOption().create(); Option modelOption = optionBuilder.withLongName("model").withDescription("Path to serialized HMM model") .withShortName("m") .withArgument(argumentBuilder.withMaximum(1).withMinimum(1).withName("path").create()) .withRequired(true).create(); Option likelihoodOption = optionBuilder.withLongName("likelihood") .withDescription("Compute likelihood of observed sequence").withShortName("l").withRequired(false) .create();//from w w w . ja v a 2s.c o m Group optionGroup = new GroupBuilder().withOption(inputOption).withOption(outputOption) .withOption(modelOption).withOption(likelihoodOption).withName("Options").create(); try { Parser parser = new Parser(); parser.setGroup(optionGroup); CommandLine commandLine = parser.parse(args); String input = (String) commandLine.getValue(inputOption); String output = (String) commandLine.getValue(outputOption); String modelPath = (String) commandLine.getValue(modelOption); boolean computeLikelihood = commandLine.hasOption(likelihoodOption); //reading serialized HMM DataInputStream modelStream = new DataInputStream(new FileInputStream(modelPath)); HmmModel model; try { model = LossyHmmSerializer.deserialize(modelStream); } finally { Closeables.close(modelStream, true); } //reading observations List<Integer> observations = Lists.newArrayList(); Scanner scanner = new Scanner(new FileInputStream(input), "UTF-8"); try { while (scanner.hasNextInt()) { observations.add(scanner.nextInt()); } } finally { scanner.close(); } int[] observationsArray = new int[observations.size()]; for (int i = 0; i < observations.size(); ++i) { observationsArray[i] = observations.get(i); } //decoding int[] hiddenStates = HmmEvaluator.decode(model, observationsArray, true); //writing output PrintWriter writer = new PrintWriter( new OutputStreamWriter(new FileOutputStream(output), Charsets.UTF_8), true); try { for (int hiddenState : hiddenStates) { writer.print(hiddenState); writer.print(' '); } } finally { Closeables.close(writer, false); } if (computeLikelihood) { System.out.println("Likelihood: " + HmmEvaluator.modelLikelihood(model, observationsArray, true)); } } catch (OptionException e) { CommandLineUtil.printHelp(optionGroup); } }
From source file:org.apache.mahout.classifier.sgd.TestASFEmail.java
boolean parseArgs(String[] args) { DefaultOptionBuilder builder = new DefaultOptionBuilder(); Option help = builder.withLongName("help").withDescription("print this list").create(); ArgumentBuilder argumentBuilder = new ArgumentBuilder(); Option inputFileOption = builder.withLongName("input").withRequired(true) .withArgument(argumentBuilder.withName("input").withMaximum(1).create()) .withDescription("where to get training data").create(); Option modelFileOption = builder.withLongName("model").withRequired(true) .withArgument(argumentBuilder.withName("model").withMaximum(1).create()) .withDescription("where to get a model").create(); Group normalArgs = new GroupBuilder().withOption(help).withOption(inputFileOption) .withOption(modelFileOption).create(); Parser parser = new Parser(); parser.setHelpOption(help);/*w w w .j a v a 2 s .c om*/ parser.setHelpTrigger("--help"); parser.setGroup(normalArgs); parser.setHelpFormatter(new HelpFormatter(" ", "", " ", 130)); CommandLine cmdLine = parser.parseAndHelp(args); if (cmdLine == null) { return false; } inputFile = (String) cmdLine.getValue(inputFileOption); modelFile = (String) cmdLine.getValue(modelFileOption); return true; }
From source file:org.apache.mahout.classifier.sgd.TrainAdaptiveLogistic.java
private static boolean parseArgs(String[] args) { DefaultOptionBuilder builder = new DefaultOptionBuilder(); Option help = builder.withLongName("help").withDescription("print this list").create(); Option quiet = builder.withLongName("quiet").withDescription("be extra quiet").create(); ArgumentBuilder argumentBuilder = new ArgumentBuilder(); Option showperf = builder.withLongName("showperf") .withDescription("output performance measures during training").create(); Option inputFile = builder.withLongName("input").withRequired(true) .withArgument(argumentBuilder.withName("input").withMaximum(1).create()) .withDescription("where to get training data").create(); Option outputFile = builder.withLongName("output").withRequired(true) .withArgument(argumentBuilder.withName("output").withMaximum(1).create()) .withDescription("where to write the model content").create(); Option threads = builder.withLongName("threads") .withArgument(argumentBuilder.withName("threads").withDefault("4").create()) .withDescription("the number of threads AdaptiveLogisticRegression uses").create(); Option predictors = builder.withLongName("predictors").withRequired(true) .withArgument(argumentBuilder.withName("predictors").create()) .withDescription("a list of predictor variables").create(); Option types = builder.withLongName("types").withRequired(true) .withArgument(argumentBuilder.withName("types").create()) .withDescription("a list of predictor variable types (numeric, word, or text)").create(); Option target = builder.withLongName("target").withDescription("the name of the target variable") .withRequired(true).withArgument(argumentBuilder.withName("target").withMaximum(1).create()) .create();/*from www . java2s . c o m*/ Option targetCategories = builder.withLongName("categories") .withDescription("the number of target categories to be considered").withRequired(true) .withArgument(argumentBuilder.withName("categories").withMaximum(1).create()).create(); Option features = builder.withLongName("features") .withDescription("the number of internal hashed features to use") .withArgument(argumentBuilder.withName("numFeatures").withDefault("1000").withMaximum(1).create()) .create(); Option passes = builder.withLongName("passes") .withDescription("the number of times to pass over the input data") .withArgument(argumentBuilder.withName("passes").withDefault("2").withMaximum(1).create()).create(); Option interval = builder.withLongName("interval") .withArgument(argumentBuilder.withName("interval").withDefault("500").create()) .withDescription("the interval property of AdaptiveLogisticRegression").create(); Option window = builder.withLongName("window") .withArgument(argumentBuilder.withName("window").withDefault("800").create()) .withDescription("the average propery of AdaptiveLogisticRegression").create(); Option skipperfnum = builder.withLongName("skipperfnum") .withArgument(argumentBuilder.withName("skipperfnum").withDefault("99").create()) .withDescription("show performance measures every (skipperfnum + 1) rows").create(); Option prior = builder.withLongName("prior") .withArgument(argumentBuilder.withName("prior").withDefault("L1").create()) .withDescription("the prior algorithm to use: L1, L2, ebp, tp, up").create(); Option priorOption = builder.withLongName("prioroption") .withArgument(argumentBuilder.withName("prioroption").create()) .withDescription("constructor parameter for ElasticBandPrior and TPrior").create(); Option auc = builder.withLongName("auc") .withArgument(argumentBuilder.withName("auc").withDefault("global").create()) .withDescription("the auc to use: global or grouped").create(); Group normalArgs = new GroupBuilder().withOption(help).withOption(quiet).withOption(inputFile) .withOption(outputFile).withOption(target).withOption(targetCategories).withOption(predictors) .withOption(types).withOption(passes).withOption(interval).withOption(window).withOption(threads) .withOption(prior).withOption(features).withOption(showperf).withOption(skipperfnum) .withOption(priorOption).withOption(auc).create(); Parser parser = new Parser(); parser.setHelpOption(help); parser.setHelpTrigger("--help"); parser.setGroup(normalArgs); parser.setHelpFormatter(new HelpFormatter(" ", "", " ", 130)); CommandLine cmdLine = parser.parseAndHelp(args); if (cmdLine == null) { return false; } TrainAdaptiveLogistic.inputFile = getStringArgument(cmdLine, inputFile); TrainAdaptiveLogistic.outputFile = getStringArgument(cmdLine, outputFile); List<String> typeList = Lists.newArrayList(); for (Object x : cmdLine.getValues(types)) { typeList.add(x.toString()); } List<String> predictorList = Lists.newArrayList(); for (Object x : cmdLine.getValues(predictors)) { predictorList.add(x.toString()); } lmp = new AdaptiveLogisticModelParameters(); lmp.setTargetVariable(getStringArgument(cmdLine, target)); lmp.setMaxTargetCategories(getIntegerArgument(cmdLine, targetCategories)); lmp.setNumFeatures(getIntegerArgument(cmdLine, features)); lmp.setInterval(getIntegerArgument(cmdLine, interval)); lmp.setAverageWindow(getIntegerArgument(cmdLine, window)); lmp.setThreads(getIntegerArgument(cmdLine, threads)); lmp.setAuc(getStringArgument(cmdLine, auc)); lmp.setPrior(getStringArgument(cmdLine, prior)); if (cmdLine.getValue(priorOption) != null) { lmp.setPriorOption(getDoubleArgument(cmdLine, priorOption)); } lmp.setTypeMap(predictorList, typeList); TrainAdaptiveLogistic.showperf = getBooleanArgument(cmdLine, showperf); TrainAdaptiveLogistic.skipperfnum = getIntegerArgument(cmdLine, skipperfnum); TrainAdaptiveLogistic.passes = getIntegerArgument(cmdLine, passes); lmp.checkParameters(); return true; }