/** * default constructor */ public PLSFilter() { super(); // setup pre-processing m_Missing = new ReplaceMissingValues(); m_Filter = new Center(); }
/** * 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 StringToWordVector(), argv); } }
/** * Main method for testing this class. * * @param args should contain arguments to the filter: use -h for help */ public static void main(String[] args) { runFilter(new AddExpression(), args); } }
public void testAddNominal() { m_Filter = getFilter(); ((Add)m_Filter).setNominalLabels("hello,there,bob"); testBuffered(); testType(Attribute.NOMINAL); }
/** Creates a default RemoveUseless */ public Filter getFilter() { return getFilter(new RemoveUseless().getMaximumVariancePercentageAllowed()); }
/** Creates a default AddCluster, with SimpleKMeans as cluster * @see #getClusterer */ public Filter getFilter() { AddCluster f = new AddCluster(); f.setClusterer(getClusterer()); return f; }
/** * Runs filter with different variance. */ public void testVariance() { m_Filter = getFilter(); ((PrincipalComponents) m_Filter).setVarianceCovered(0.8); performTest(); }
/** Creates a default RandomProjection */ public Filter getFilter() { return getFilter(new RandomProjection().getNumberOfAttributes()); }
/** * Runs filter with a maximum number of attributes. */ public void testMaxAttributes() { m_Filter = getFilter(); ((PrincipalComponents) m_Filter).setMaximumAttributeNames(2); performTest(); }
/** * Runs filter with covariance matrix + centering rather than correlation * + standardizing. */ public void testCovariance() { m_Filter = getFilter(); ((PrincipalComponents) m_Filter).setCenterData(true); performTest(); }
/** Creates a default Discretize */ public Filter getFilter() { Discretize f= new Discretize(); return f; }
/** Creates a default ClusterMembership */ public Filter getFilter() { ClusterMembership f = new ClusterMembership(); return f; }
/** * Tests a percentage. */ public void testPercentage() { performTest(0.5, 4); }
public void testAddNominal() { m_Filter = getFilter(); ((Add)m_Filter).setNominalLabels("hello,there,bob"); testBuffered(); testType(Attribute.NOMINAL); }
/** Creates a default AddCluster, with SimpleKMeans as cluster * @see #getClusterer */ public Filter getFilter() { AddCluster f = new AddCluster(); f.setClusterer(getClusterer()); return f; }
/** * Runs filter with different variance. */ public void testVariance() { m_Filter = getFilter(); ((PrincipalComponents) m_Filter).setVarianceCovered(0.8); performTest(); }
/** * Runs filter with a maximum number of attributes. */ public void testMaxAttributes() { m_Filter = getFilter(); ((PrincipalComponents) m_Filter).setMaximumAttributeNames(2); performTest(); }
/** * Runs filter with covariance matrix + centering rather than correlation * + standardizing. */ public void testCovariance() { m_Filter = getFilter(); ((PrincipalComponents) m_Filter).setCenterData(true); performTest(); }
/** * Tests an absolute number. */ public void testAbsolute() { performTest(5, 5); }