/** * Main method for running this filter. * * @param args should contain arguments to the filter, use -h for help */ public static void main(String[] args) { runFilter(new Normalize(), args); } }
/** * Main method for running this filter. * * @param args should contain arguments to the filter, use -h for help */ public static void main(String[] args) { runFilter(new Normalize(), args); } }
/** Creates an example Normalize */ public Filter getFilter() { Normalize f = new Normalize(); return f; }
/** Creates an example Normalize */ public Filter getFilter() { Normalize f = new Normalize(); return f; }
Normalize norm = new Normalize(); norm.setInputFormat(train); Instances processed_train = Filter.useFilter(train, norm);
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;
m_Filter = new Standardize(); } else if (m_filterType == FILTER_NORMALIZE) { m_Filter = new Normalize(); } else { m_Filter = null;
data = Filter.useFilter(data, filter); } else if (filterType == FILTER_NORMALIZE) { filter = new Normalize(); filter.setOptions(new String[]{"-unset-class-temporarily"}); filter.setInputFormat(data);
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 Normalize(); ((Normalize)m_Filter).setIgnoreClass(true); // Normalize class as well m_Filter.setInputFormat(insts);
m_normalize = new Normalize(); m_normalize.setInputFormat(data); data = Filter.useFilter(data, m_normalize);
m_normalize = new Normalize(); m_normalize.setInputFormat(data); data = Filter.useFilter(data, m_normalize);
m_normalize = new Normalize(); m_normalize.setInputFormat(data); data = Filter.useFilter(data, m_normalize);
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); } else if (m_filterType == FILTER_NORMALIZE) { m_Filter = new Normalize(); ((Normalize)m_Filter).setIgnoreClass(true); m_Filter.setInputFormat(instances);
insts = Filter.useFilter(insts, m_Filter); } else if (m_filterType == FILTER_NORMALIZE) { m_Filter = new Normalize(); m_Filter.setInputFormat(insts); insts = Filter.useFilter(insts, m_Filter);
insts = Filter.useFilter(insts, m_Filter); } else if (m_filterType == FILTER_NORMALIZE) { m_Filter = new Normalize(); m_Filter.setInputFormat(insts); insts = Filter.useFilter(insts, m_Filter);
insts = Filter.useFilter(insts, m_Filter); } else if (m_filterType == FILTER_NORMALIZE) { m_Filter = new Normalize(); ((Normalize) m_Filter).setIgnoreClass(true); m_Filter.setInputFormat(insts);
insts = Filter.useFilter(insts, m_Filter); } else if (m_filterType == FILTER_NORMALIZE) { m_Filter = new Normalize(); ((Normalize) m_Filter).setIgnoreClass(true); m_Filter.setInputFormat(insts);