/** * 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 Center(), args); } }
/** * default constructor */ public PLSFilter() { super(); // setup pre-processing m_Missing = new ReplaceMissingValues(); m_Filter = new Center(); }
/** * 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 Center(), args); } }
/** Creates a default Center */ public Filter getFilter() { return new Center(); }
/** Creates a default Center */ public Filter getFilter() { return new Center(); }
east=new East(frame); west=new West(frame); center=new Center(frame);
/** Creates a configured MultiFilter (variant) */ public Filter getConfiguredFilterVariant() { MultiFilter result = new MultiFilter(); Filter[] filters = new Filter[2]; filters[0] = new ReplaceMissingValues(); filters[1] = new Center(); result.setFilters(filters); return result; }
/** Creates a configured MultiFilter (variant) */ public Filter getConfiguredFilterVariant() { MultiFilter result = new MultiFilter(); Filter[] filters = new Filter[2]; filters[0] = new ReplaceMissingValues(); filters[1] = new Center(); result.setFilters(filters); return result; }
protected void fillCovariance() throws Exception { // just center the data or standardize it? if (m_center) { m_centerFilter = new Center(); m_centerFilter.setInputFormat(m_TrainInstances); m_TrainInstances = Filter.useFilter(m_TrainInstances, m_centerFilter); } else { m_standardizeFilter = new Standardize(); m_standardizeFilter.setInputFormat(m_TrainInstances); m_TrainInstances = Filter.useFilter(m_TrainInstances, m_standardizeFilter); } // now compute the covariance matrix m_Correlation = new UpperSymmDenseMatrix(m_NumAttribs); for (int i = 0; i < m_NumAttribs; i++) { for (int j = i; j < m_NumAttribs; j++) { double cov = 0; for (Instance inst: m_TrainInstances) { cov += inst.value(i) * inst.value(j); } cov /= m_TrainInstances.numInstances() - 1; m_Correlation.set(i, j, cov); } } }
protected void fillCovariance() throws Exception { // just center the data or standardize it? if (m_center) { m_centerFilter = new Center(); m_centerFilter.setInputFormat(m_TrainInstances); m_TrainInstances = Filter.useFilter(m_TrainInstances, m_centerFilter); } else { m_standardizeFilter = new Standardize(); m_standardizeFilter.setInputFormat(m_TrainInstances); m_TrainInstances = Filter.useFilter(m_TrainInstances, m_standardizeFilter); } // now compute the covariance matrix m_Correlation = new UpperSymmDenseMatrix(m_NumAttribs); for (int i = 0; i < m_NumAttribs; i++) { for (int j = i; j < m_NumAttribs; j++) { double cov = 0; for (Instance inst: m_TrainInstances) { cov += inst.value(i) * inst.value(j); } cov /= m_TrainInstances.numInstances() - 1; m_Correlation.set(i, j, cov); } } }
m_centerFilter = new Center(); m_centerFilter.setInputFormat(m_trainInstances); m_trainInstances = Filter.useFilter(m_trainInstances, m_centerFilter);
m_centerFilter = new Center(); m_centerFilter.setInputFormat(m_trainInstances); m_trainInstances = Filter.useFilter(m_trainInstances, m_centerFilter);
setPreprocessing((Filter) Utils.forName(Filter.class, tmpStr, tmpOptions)); } else { setPreprocessing(new Center());
setPreprocessing((Filter) Utils.forName(Filter.class, tmpStr, tmpOptions)); } else { setPreprocessing(new Center());
m_ClassMean = instances.meanOrMode(instances.classIndex()); m_ClassStdDev = 1; m_Filter = new Center(); ((Center) m_Filter).setIgnoreClass(true); break;