/** * 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); }
/** * 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); }
try { nomToBinFilter.input(inputInstance); inputInstance = nomToBinFilter.output(); inputInstance.setDataset(null); } catch (Exception ex) {
inst = m_nominalToBinary.output();
inst = m_NominalToBinary.output();
/** * Computes the distribution for a given instance * * @param instance the instance for which distribution is computed * @return the distribution * @throws Exception if the distribution can't be computed successfully */ @Override public double[] distributionForInstance(Instance instance) throws Exception { m_ReplaceMissingValues.input(instance); instance = m_ReplaceMissingValues.output(); m_AttFilter.input(instance); instance = m_AttFilter.output(); m_NominalToBinary.input(instance); instance = m_NominalToBinary.output(); // Extract the predictor columns into an array double[] instDat = new double[m_NumPredictors + 1]; int j = 1; instDat[0] = 1; for (int k = 0; k <= m_NumPredictors; k++) { if (k != m_ClassIndex) { instDat[j++] = instance.value(k); } } double[] distribution = evaluateProbability(instDat); return distribution; }
/** * 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; }
inst = m_NominalToBinary.output();
/** * 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; }
/** * Computes the distribution for a given instance * * @param instance the instance for which distribution is computed * @return the distribution * @throws Exception if the distribution can't be computed successfully */ @Override public double[] distributionForInstance(Instance instance) throws Exception { m_ReplaceMissingValues.input(instance); instance = m_ReplaceMissingValues.output(); m_AttFilter.input(instance); instance = m_AttFilter.output(); m_NominalToBinary.input(instance); instance = m_NominalToBinary.output(); // Extract the predictor columns into an array double[] instDat = new double[m_NumPredictors + 1]; int j = 1; instDat[0] = 1; for (int k = 0; k <= m_NumPredictors; k++) { if (k != m_ClassIndex) { instDat[j++] = instance.value(k); } } double[] distribution = evaluateProbability(instDat); return distribution; }
try { nominalToBinaryFilter.input(instance); inputInstance = nominalToBinaryFilter.output(); inputInstance.setDataset(null); } catch (Exception ex) {
/** * Classifies the given instance using the linear regression function. * * @param instance the test instance * @return the classification * @throws Exception if classification can't be done successfully */ public double classifyInstance(Instance instance) throws Exception { // Filter instance m_Missing.input(instance); m_Missing.batchFinished(); instance = m_Missing.output(); if (!m_onlyNumeric && m_NominalToBinary != null) { m_NominalToBinary.input(instance); m_NominalToBinary.batchFinished(); instance = m_NominalToBinary.output(); } if (m_Filter != null) { m_Filter.input(instance); m_Filter.batchFinished(); instance = m_Filter.output(); } double result = m_optimizer.SVMOutput(instance); return result * m_x1 + m_x0; }
/** * Classifies the given instance using the linear regression function. * * @param instance the test instance * @return the classification * @throws Exception if classification can't be done successfully */ public double classifyInstance(Instance instance) throws Exception { // Filter instance m_Missing.input(instance); m_Missing.batchFinished(); instance = m_Missing.output(); if (!m_onlyNumeric && m_NominalToBinary != null) { m_NominalToBinary.input(instance); m_NominalToBinary.batchFinished(); instance = m_NominalToBinary.output(); } if (m_Filter != null) { m_Filter.input(instance); m_Filter.batchFinished(); instance = m_Filter.output(); } double result = m_optimizer.SVMOutput(instance); return result * m_x1 + m_x0; }
m_NominalToBinary.input(inst); m_NominalToBinary.batchFinished(); inst = m_NominalToBinary.output();
m_NominalToBinary.input(inst); m_NominalToBinary.batchFinished(); inst = m_NominalToBinary.output();
m_currentInstance = m_nominalToBinaryFilter.output(); } else { m_currentInstance = i;
m_currentInstance = m_nominalToBinaryFilter.output(); } else { m_currentInstance = i;
tempInst = m_NominalToBinaryFilter.output();
tempInst = m_NominalToBinaryFilter.output();
m_NominalToBinary.input(inst); m_NominalToBinary.batchFinished(); inst = m_NominalToBinary.output();