Instances train = ... // from somewhere Instances test = ... // from somewhere Standardize filter = new Standardize(); filter.setInputFormat(train); // initializing the filter once with training set Instances newTrain = Filter.useFilter(train, filter); // configures the Filter based on train instances and returns filtered instances Instances newTest = Filter.useFilter(test, filter); // create new test se
Instances train = ... // from somewhere Instances test = ... // from somewhere Standardize filter = new Standardize(); filter.setInputFormat(train); // initializing the filter once with training set Instances newTrain = Filter.useFilter(train, filter); // configures the Filter based on train instances and returns filtered instances Instances newTest = Filter.useFilter(test, filter); // create new test set
/** * Main method for testing this class. * * @param argv should contain arguments to the filter: * use -h for help */ public static void main(String [] argv) { runFilter(new Standardize(), argv); } }
/** Creates an example Standardize */ public Filter getFilter() { Standardize f = new Standardize(); return f; }
/** * Main method for testing this class. * * @param argv should contain arguments to the filter: * use -h for help */ public static void main(String [] argv) { runFilter(new Standardize(), argv); } }
/** Creates an example Standardize */ public Filter getFilter() { Standardize f = new Standardize(); return f; }
m_Filter = new Standardize(); } else if (m_filterType == FILTER_NORMALIZE) { m_Filter = new Normalize();
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_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);
m_Filter = new Standardize(); } else if (m_filterType == FILTER_NORMALIZE) { m_Filter = new Normalize();
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);
filter = new Standardize(); filter.setOptions(new String[]{"-unset-class-temporarily"}); filter.setInputFormat(data);
m_ClassStdDev = StrictMath.sqrt(instances.variance(instances .classIndex())); m_Filter = new Standardize(); ((Standardize) m_Filter).setIgnoreClass(true); break;
m_Filter = new Standardize(); ((Standardize)m_Filter).setIgnoreClass(true); m_Filter.setInputFormat(instances);
m_Filter = new Standardize(); ((Standardize)m_Filter).setIgnoreClass(true); m_Filter.setInputFormat(instances);
m_Filter = new Standardize(); m_Filter.setInputFormat(insts); insts = Filter.useFilter(insts, m_Filter);
m_Filter = new Standardize(); m_Filter.setInputFormat(insts); insts = Filter.useFilter(insts, m_Filter);
m_Filter = new Standardize(); ((Standardize) m_Filter).setIgnoreClass(true); m_Filter.setInputFormat(insts);
m_Filter = new Standardize(); ((Standardize) m_Filter).setIgnoreClass(true); m_Filter.setInputFormat(insts);