Normalize norm = new Normalize(); norm.setInputFormat(train); Instances processed_train = Filter.useFilter(train, norm);
/** * Input an instance for filtering. Filter requires all training instances be * read before producing output. * * @param instance the input instance * @return true if the filtered instance may now be collected with output(). * @throws Exception if an error occurs * @throws IllegalStateException if no input format has been set. */ @Override public boolean input(Instance instance) throws Exception { if (getInputFormat() == null) { throw new IllegalStateException("No input instance format defined"); } if (m_NewBatch) { resetQueue(); m_NewBatch = false; } if (m_MinArray == null) { bufferInput(instance); return false; } else { convertInstance(instance); return true; } }
/** Creates an example Normalize */ public Filter getFilter() { Normalize f = new Normalize(); return f; }
m_Filter = new Normalize(); ((Normalize)m_Filter).setIgnoreClass(true); // Normalize class as well m_Filter.setInputFormat(insts); insts = Filter.useFilter(insts, m_Filter);
setScale(Double.parseDouble(tmpStr)); } else { setScale(1.0); setTranslation(Double.parseDouble(tmpStr)); } else { setTranslation(0.0); if (getInputFormat() != null) { setInputFormat(getInputFormat());
m_RemoveUseless.batchFinished(); m_Normalize.input(instance); instance =m_Normalize.output(); m_Normalize.batchFinished();
/** Creates an example Normalize */ public Filter getFilter() { Normalize f = new Normalize(); return f; }
instances = Filter.useFilter(instances, m_Filter); } else if (m_filterType == FILTER_NORMALIZE) { m_Filter = new Normalize(); ((Normalize)m_Filter).setIgnoreClass(true); m_Filter.setInputFormat(instances); instances = Filter.useFilter(instances, m_Filter);
setScale(Double.parseDouble(tmpStr)); } else { setScale(1.0); setTranslation(Double.parseDouble(tmpStr)); } else { setTranslation(0.0); if (getInputFormat() != null) { setInputFormat(getInputFormat());
Normalize norm = new Normalize(); norm.setInputFormat(train); train = Filter.useFilter(train, norm); RemoveUseless ru = new RemoveUseless(); ru.setInputFormat(train); train = Filter.useFilter(train, ru); Ranker rank = new Ranker(); InfoGainAttributeEval eval = new InfoGainAttributeEval(); eval.buildEvaluator(train);
m_Filter = new Standardize(); } else if (m_filterType == FILTER_NORMALIZE) { m_Filter = new Normalize(); } else { m_Filter = null;
instances = Filter.useFilter(instances, m_Filter); } else if (m_filterType == FILTER_NORMALIZE) { m_Filter = new Normalize(); ((Normalize)m_Filter).setIgnoreClass(true); m_Filter.setInputFormat(instances); instances = Filter.useFilter(instances, m_Filter);
/** * Input an instance for filtering. Filter requires all training instances be * read before producing output. * * @param instance the input instance * @return true if the filtered instance may now be collected with output(). * @throws Exception if an error occurs * @throws IllegalStateException if no input format has been set. */ @Override public boolean input(Instance instance) throws Exception { if (getInputFormat() == null) { throw new IllegalStateException("No input instance format defined"); } if (m_NewBatch) { resetQueue(); m_NewBatch = false; } if (m_MinArray == null) { bufferInput(instance); return false; } else { convertInstance(instance); return true; } }
m_data = Filter.useFilter(data, m_RemoveUseless); m_Normalize = new Normalize(); m_Normalize.setInputFormat(m_data); m_data = Filter.useFilter(m_data, m_Normalize);
m_Filter = new Standardize(); } else if (m_filterType == FILTER_NORMALIZE) { m_Filter = new Normalize(); } else { m_Filter = null;