/** * Classifies a given instance. * * @param instance the instance to be assigned to a cluster * @return the number of the assigned cluster as an integer if the class is * enumerated, otherwise the predicted value * @throws Exception if instance could not be classified successfully */ @Override public int clusterInstance(Instance instance) throws Exception { m_ReplaceMissingFilter.input(instance); m_ReplaceMissingFilter.batchFinished(); Instance inst = m_ReplaceMissingFilter.output(); return clusterProcessedInstance(inst); }
/** * Classifies a given instance. * * @param instance the instance to be assigned to a cluster * @return the number of the assigned cluster as an integer if the class is * enumerated, otherwise the predicted value * @throws Exception if instance could not be classified successfully */ @Override public int clusterInstance(Instance instance) throws Exception { m_ReplaceMissingFilter.input(instance); m_ReplaceMissingFilter.batchFinished(); Instance inst = m_ReplaceMissingFilter.output(); return clusterProcessedInstance(inst); }
/** * Classifies a given instance. * * @param instance the instance to be assigned to a cluster * @return the number of the assigned cluster as an interger if the class is * enumerated, otherwise the predicted value * @throws Exception if instance could not be classified successfully */ @Override public int clusterInstance(Instance instance) throws Exception { m_ReplaceMissingFilter.input(instance); m_ReplaceMissingFilter.batchFinished(); Instance inst = m_ReplaceMissingFilter.output(); return clusterProcessedInstance(inst, false); }
/** * Classifies a given instance. * * @param instance the instance to be assigned to a cluster * @return the number of the assigned cluster as an interger if the class is * enumerated, otherwise the predicted value * @throws Exception if instance could not be classified successfully */ @Override public int clusterInstance(Instance instance) throws Exception { Instance inst = null; if (!m_dontReplaceMissing) { m_ReplaceMissingFilter.input(instance); m_ReplaceMissingFilter.batchFinished(); inst = m_ReplaceMissingFilter.output(); } else { inst = instance; } return clusterProcessedInstance(inst, false, true, null); }
/** * Classifies a given instance. * * @param instance the instance to be assigned to a cluster * @return the number of the assigned cluster as an interger if the class is * enumerated, otherwise the predicted value * @throws Exception if instance could not be classified successfully */ @Override public int clusterInstance(Instance instance) throws Exception { Instance inst = null; if (!m_dontReplaceMissing) { m_ReplaceMissingFilter.input(instance); m_ReplaceMissingFilter.batchFinished(); inst = m_ReplaceMissingFilter.output(); } else { inst = instance; } return clusterProcessedInstance(inst, false, true, null); }
/** * 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 */ @Override public double classifyInstance(Instance instance) throws Exception { // Transform the input instance Instance transformedInstance = instance; if (!m_checksTurnedOff && !m_isZeroR) { m_TransformFilter.input(transformedInstance); m_TransformFilter.batchFinished(); transformedInstance = m_TransformFilter.output(); m_MissingFilter.input(transformedInstance); m_MissingFilter.batchFinished(); transformedInstance = m_MissingFilter.output(); } // Calculate the dependent variable from the regression model return regressionPrediction(transformedInstance, m_SelectedAttributes, m_Coefficients); }
/** * 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 */ @Override public double classifyInstance(Instance instance) throws Exception { // Transform the input instance Instance transformedInstance = instance; if (!m_checksTurnedOff && !m_isZeroR) { m_TransformFilter.input(transformedInstance); m_TransformFilter.batchFinished(); transformedInstance = m_TransformFilter.output(); m_MissingFilter.input(transformedInstance); m_MissingFilter.batchFinished(); transformedInstance = m_MissingFilter.output(); } // Calculate the dependent variable from the regression model return regressionPrediction(transformedInstance, m_SelectedAttributes, m_Coefficients); }
m_ReplaceMissingValues.batchFinished(); inst = m_ReplaceMissingValues.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; }
m_ReplaceMissingValues.batchFinished(); inst = m_ReplaceMissingValues.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; }
/** * 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_Missing.batchFinished(); inst = m_Missing.output();
m_Missing.batchFinished(); inst = m_Missing.output();
m_ReplaceMissingValues.batchFinished(); instance = m_ReplaceMissingValues.output();
m_ReplaceMissingFilter.batchFinished(); tempInst = m_ReplaceMissingFilter.output();
m_ReplaceMissingFilter.batchFinished(); tempInst = m_ReplaceMissingFilter.output();
m_Missing.batchFinished(); inst = m_Missing.output();
m_Missing.batchFinished(); inst = m_Missing.output();