private Stats getStats() { return new Stats(); }
private Stats getStats() { return new Stats(); }
Stats ps = new Stats(); java.io.LineNumberReader r = new java.io.LineNumberReader( new java.io.InputStreamReader(System.in));
Stats ps = new Stats(); java.io.LineNumberReader r = new java.io.LineNumberReader( new java.io.InputStreamReader(System.in));
Stats[] stats = new Stats[resultTypes.length]; for (int i = 0; i < stats.length; i++) { stats[i] = new Stats();
Stats[] stats = new Stats[resultTypes.length]; for (int i = 0; i < stats.length; i++) { stats[i] = new Stats();
/** * Unweighted macro-averaged F-measure. If some classes not present in the * test set, they're just skipped (since recall is undefined there anyway) . * * @return unweighted macro-averaged F-measure. * */ public double unweightedMacroFmeasure() { weka.experiment.Stats rr = new weka.experiment.Stats(); for (int c = 0; c < m_NumClasses; c++) { // skip if no testing positive cases of this class if (numTruePositives(c) + numFalseNegatives(c) > 0) { rr.add(fMeasure(c)); } } rr.calculateDerived(); return rr.mean; }
/** * Unweighted macro-averaged F-measure. If some classes not present in the * test set, they're just skipped (since recall is undefined there anyway) . * * @return unweighted macro-averaged F-measure. * */ public double unweightedMacroFmeasure() { weka.experiment.Stats rr = new weka.experiment.Stats(); for (int c = 0; c < m_NumClasses; c++) { // skip if no testing positive cases of this class if (numTruePositives(c) + numFalseNegatives(c) > 0) { rr.add(fMeasure(c)); } } rr.calculateDerived(); return rr.mean; }
result.numericStats = new weka.experiment.Stats();
result.numericStats = new weka.experiment.Stats();
/** * Sets the format of the input instances. * * @param instanceInfo an Instances object containing the input instance * structure (any instances contained in the object are ignored - * only the structure is required). * @return true if the outputFormat may be collected immediately * @throws Exception if the input format can't be set successfully */ @Override public boolean setInputFormat(Instances instanceInfo) throws Exception { m_SelectCols.setUpper(instanceInfo.numAttributes() - 1); super.setInputFormat(instanceInfo); setOutputFormat(instanceInfo); m_attStats = new Stats[instanceInfo.numAttributes()]; for (int i = 0; i < instanceInfo.numAttributes(); i++) { if (m_SelectCols.isInRange(i) && instanceInfo.attribute(i).isNumeric() && (instanceInfo.classIndex() != i) || getIgnoreClass()) { m_attStats[i] = new Stats(); } } if (instanceInfo != null) compile(); return true; }
/** * Sets the format of the input instances. * * @param instanceInfo an Instances object containing the input instance * structure (any instances contained in the object are ignored - * only the structure is required). * @return true if the outputFormat may be collected immediately * @throws Exception if the input format can't be set successfully */ @Override public boolean setInputFormat(Instances instanceInfo) throws Exception { m_SelectCols.setUpper(instanceInfo.numAttributes() - 1); super.setInputFormat(instanceInfo); setOutputFormat(instanceInfo); m_attStats = new Stats[instanceInfo.numAttributes()]; for (int i = 0; i < instanceInfo.numAttributes(); i++) { if (m_SelectCols.isInRange(i) && instanceInfo.attribute(i).isNumeric() && (instanceInfo.classIndex() != i) || getIgnoreClass()) { m_attStats[i] = new Stats(); } } if (instanceInfo != null) compile(); return true; }
result.numericStats = new weka.experiment.Stats();
new int[m_clusterInstances.attribute(i).numValues()]; } else { m_attStats[i].numericStats = new Stats();
i).numValues()]; } else { m_attStats[i].numericStats = new Stats();
i).numValues()]; } else { m_attStats[i].numericStats = new Stats();