nominalAttrRange.deleteCharAt(nominalAttrRange.lastIndexOf(rangeDelimiter)); try { nominalToBinaryFilter = new NominalToBinary(); nominalToBinaryFilter.setAttributeIndices(nominalAttrRange.toString()); nominalToBinaryFilter.setInputFormat(dataSet); dataSet = Filter.useFilter(dataSet, nominalToBinaryFilter); } catch (Exception exception) {
/** * Filters an instance. */ protected Instance filterInstance(Instance inst) throws Exception { if (!m_checksTurnedOff) { m_Missing.input(inst); m_Missing.batchFinished(); inst = m_Missing.output(); } if (m_NominalToBinary != null) { m_NominalToBinary.input(inst); m_NominalToBinary.batchFinished(); inst = m_NominalToBinary.output(); } if (m_Filter != null) { m_Filter.input(inst); m_Filter.batchFinished(); inst = m_Filter.output(); } return inst; }
/** * Main method for testing this class. * * @param argv should contain arguments to the filter: use -h for help */ public static void main(String[] argv) { runFilter(new NominalToBinary(), argv); } }
/** * Returns class probabilities for an instance. * * @param inst the instance to compute the probabilities for * @return the probabilities * @throws Exception if distribution can't be computed successfully */ public double[] distributionForInstance(Instance inst) throws Exception { // replace missing values / convert nominal atts m_ReplaceMissingValues.input(inst); inst = m_ReplaceMissingValues.output(); m_NominalToBinary.input(inst); inst = m_NominalToBinary.output(); // obtain probs from logistic model return m_boostedModel.distributionForInstance(inst); }
public void setOptions(String[] options) throws Exception { setBinaryAttributesNominal(Utils.getFlag('N', options)); setTransformAllValues(Utils.getFlag('A', options)); setAttributeIndices(convertList); } else { setAttributeIndices("first-last"); setInvertSelection(Utils.getFlag('V', options)); if (getInputFormat() != null) { setInputFormat(getInputFormat()); setSpreadAttributeWeight(Utils.getFlag("spread-attribute-weight", options));
nominalToBinaryFilter = new NominalToBinary(); nominalToBinaryFilter.setInputFormat(data); data = Filter.useFilter(data, nominalToBinaryFilter);
/** Creates an example NominalToBinary */ public Filter getFilter() { NominalToBinary f = new NominalToBinary(); return f; }
/** Constructor - initialises the filter */ public NominalToBinary() { setAttributeIndices("first-last"); }
m_ReplaceMissingFilter); m_NominalToBinaryFilter = new NominalToBinary(); m_NominalToBinaryFilter.setInputFormat(m_TrainInstances); m_TrainInstances = Filter.useFilter(m_TrainInstances, m_NominalToBinaryFilter);
/** Creates an example NominalToBinary */ public Filter getFilter() { NominalToBinary f = new NominalToBinary(); return f; }
public void setOptions(String[] options) throws Exception { setBinaryAttributesNominal(Utils.getFlag('N', options)); setTransformAllValues(Utils.getFlag('A', options)); setAttributeIndices(convertList); } else { setAttributeIndices("first-last"); setInvertSelection(Utils.getFlag('V', options)); if (getInputFormat() != null) { setInputFormat(getInputFormat()); setSpreadAttributeWeight(Utils.getFlag("spread-attribute-weight", options));
/** * Returns class probabilities for an instance. * * @param inst the instance to compute the probabilities for * @return the probabilities * @throws Exception if distribution can't be computed successfully */ public double[] distributionForInstance(Instance inst) throws Exception { // replace missing values / convert nominal atts m_ReplaceMissingValues.input(inst); inst = m_ReplaceMissingValues.output(); m_NominalToBinary.input(inst); inst = m_NominalToBinary.output(); // obtain probs from logistic model return m_boostedModel.distributionForInstance(inst); }
/** * Main method for testing this class. * * @param argv should contain arguments to the filter: use -h for help */ public static void main(String[] argv) { runFilter(new NominalToBinary(), argv); } }
/** Constructor - initialises the filter */ public NominalToBinary() { setAttributeIndices("first-last"); }
m_nominalToBinaryFilter = new NominalToBinary(); m_nominalToBinaryFilter.setInputFormat(m_instances); m_instances = Filter.useFilter(m_instances, m_nominalToBinaryFilter);
/** * Filters an instance. */ protected Instance filterInstance(Instance inst) throws Exception { if (!m_checksTurnedOff) { m_Missing.input(inst); m_Missing.batchFinished(); inst = m_Missing.output(); } if (m_NominalToBinary != null) { m_NominalToBinary.input(inst); m_NominalToBinary.batchFinished(); inst = m_NominalToBinary.output(); } if (m_Filter != null) { m_Filter.input(inst); m_Filter.batchFinished(); inst = m_Filter.output(); } return inst; }
nomToBinFilter = new NominalToBinary(); try { nomToBinFilter = new NominalToBinary(); nomToBinFilter.setAttributeIndices(nominalAttrRange.toString()); nomToBinFilter.setInputFormat(dataSet); } catch (Exception exception) { nomToBinFilter = null;
m_numeric = false; m_random = null; m_nominalToBinaryFilter = new NominalToBinary(); m_sigmoidUnit = new SigmoidUnit(); m_linearUnit = new LinearUnit();
m_ReplaceMissingFilter); m_NominalToBinaryFilter = new NominalToBinary(); m_NominalToBinaryFilter.setInputFormat(m_TrainInstances); m_TrainInstances = Filter.useFilter(m_TrainInstances, m_NominalToBinaryFilter);