List of usage examples for weka.core Option name
public String name()
From source file:com.rapidminer.tools.WekaTools.java
License:Open Source License
/** * Tries to guess the type of the given option. If the number of arguments * is zero, than a boolean type is assumed. In other cases it will be tried * to parse the default value in the options array as a number and on * success a Double type is returned. If this fails, a ParameterTypeString * is returned./*from w w w .j a v a 2 s . c o m*/ */ public static ParameterType guessParameterType(Option option, String[] options) { if (option.numArguments() == 0) { String defaultString = getStringDefault(option.name(), options); if (defaultString == null) { return new ParameterTypeBoolean(option.name(), option.description(), getBooleanDefault(option.name(), options)); } else { return new ParameterTypeString(option.name(), option.description(), defaultString); } } else { String defaultString = getStringDefault(option.name(), options); if (defaultString == null) { return new ParameterTypeString(option.name(), option.description()); } else { try { double defaultValue = Double.parseDouble(defaultString); return new ParameterTypeDouble(option.name(), option.description(), Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY, defaultValue); } catch (NumberFormatException e) { return new ParameterTypeString(option.name(), option.description(), defaultString); } } } }
From source file:com.rapidminer.tools.WekaTools.java
License:Open Source License
/** Add the parameter type for the options of a Weka option handler. */ public static void addParameterTypes(OptionHandler handler, List<ParameterType> types, List<ParameterType> wekaParameters, boolean meta, String metaParameter) { String[] defaultOptions = removeMetaOptions(handler.getOptions()); Enumeration options = handler.listOptions(); while (options.hasMoreElements()) { Option option = (Option) options.nextElement(); if (option.name().trim().length() == 0) break; // necessary to prevent adding of parameters of children // of meta learners // prevent adding the meta learning scheme options if (meta && option.name().trim().toLowerCase().equals(metaParameter.toLowerCase())) { continue; }/*from w w w. jav a 2s . co m*/ ParameterType type = guessParameterType(option, defaultOptions); type.setExpert(false); // all Weka paras as non expert paras types.add(type); wekaParameters.add(type); } }
From source file:meka.classifiers.multilabel.Evaluation.java
License:Open Source License
public static void printOptions(Enumeration e) { // Evaluation Options StringBuffer text = new StringBuffer(); text.append("\n\nEvaluation Options:\n\n"); text.append("-h\n"); text.append("\tOutput help information.\n"); text.append("-t <name of training file>\n"); text.append("\tSets training file.\n"); text.append("-T <name of test file>\n"); text.append("\tSets test file.\n"); text.append("-x <number of folds>\n"); text.append("\tDo cross-validation with this many folds.\n"); text.append("-R\n"); text.append("\tRandomize the order of instances in the dataset.\n"); text.append("-split-percentage <percentage>\n"); text.append("\tSets the percentage for the train/test set split, e.g., 66.\n"); text.append("-split-number <number>\n"); text.append("\tSets the number of training examples, e.g., 800\n"); text.append("-i\n"); text.append("\tInvert the specified train/test split.\n"); text.append("-s <random number seed>\n"); text.append("\tSets random number seed (use with -R, for different CV or train/test splits).\n"); text.append("-threshold <threshold>\n"); text.append(//from w ww . j ava 2 s . c om "\tSets the type of thresholding; where\n\t\t'PCut1' automatically calibrates a threshold (the default);\n\t\t'PCutL' automatically calibrates one threshold for each label;\n\t\tany number, e.g. '0.5', specifies that threshold.\n"); text.append("-C <number of labels>\n"); text.append("\tSets the number of target variables (labels) to assume (indexed from the beginning).\n"); //text.append("-f <results_file>\n"); //text.append("\tSpecify a file to output results and evaluation statistics into.\n"); text.append("-d <classifier_file>\n"); text.append("\tSpecify a file to dump classifier into.\n"); text.append("-l <classifier_file>\n"); text.append("\tSpecify a file to load classifier from.\n"); text.append("-verbosity <verbosity level>\n"); text.append("\tSpecify more/less evaluation output\n"); // Multilabel Options text.append("\n\nClassifier Options:\n\n"); while (e.hasMoreElements()) { Option o = (Option) (e.nextElement()); text.append("-" + o.name() + '\n'); text.append("" + o.description() + '\n'); } System.out.println(text); }
From source file:meka.classifiers.multilabel.incremental.IncrementalEvaluation.java
License:Open Source License
public static void printOptions(Enumeration e) { // Evaluation Options StringBuffer text = new StringBuffer(); text.append("\n\nEvaluation Options:\n\n"); text.append("-t\n"); text.append("\tSpecify the dataset (required)\n"); //text.append("-split-percentage <percentage>\n"); //text.append("\tSets the percentage of data to use for the initial training, e.g., 10.\n"); text.append("-x <number of windows>\n"); text.append("\tSets the number of samples to take (at evenly space intervals); default: 10.\n"); text.append("-supervision <ratio labelled>\n"); text.append("\tSets the ratio of labelled instances; default: 1.\n"); text.append("-threshold <threshold>\n"); text.append("\tSets the threshold to use.\n"); text.append("-verbosity <verbosity level>\n"); text.append("\tSpecify more/less evaluation output.\n"); // Multilabel Options text.append("\n\nClassifier Options:\n\n"); while (e.hasMoreElements()) { Option o = (Option) (e.nextElement()); text.append("-" + o.name() + '\n'); text.append("" + o.description() + '\n'); }// ww w.j a va2 s.co m System.out.println(text); }