if (m_ASEvaluator instanceof AttributeTransformer) { m_trainInstances = ((AttributeTransformer) m_ASEvaluator).transformedHeader(); m_transformer = (AttributeTransformer) m_ASEvaluator; ((AttributeTransformer) tempEval).transformedHeader(); m_transformer = (AttributeTransformer) tempEval;
if (m_ASEvaluator instanceof AttributeTransformer) { m_trainInstances = ((AttributeTransformer) m_ASEvaluator).transformedHeader(); m_transformer = (AttributeTransformer) m_ASEvaluator; ((AttributeTransformer) tempEval).transformedHeader(); m_transformer = (AttributeTransformer) tempEval;
/** * reduce the dimensionality of a set of instances to include only those * attributes chosen by the last run of attribute selection. * * @param in the instances to be reduced * @return a dimensionality reduced set of instances * @exception Exception if the instances can't be reduced */ public Instances reduceDimensionality(Instances in) throws Exception { if (m_attributeFilter == null) { throw new Exception("No feature selection has been performed yet!"); } if (m_transformer != null) { Instances transformed = new Instances(m_transformer.transformedHeader(), in.numInstances()); for (int i = 0; i < in.numInstances(); i++) { transformed.add(m_transformer.convertInstance(in.instance(i))); } return Filter.useFilter(transformed, m_attributeFilter); } return Filter.useFilter(in, m_attributeFilter); }
informat = ((AttributeTransformer) m_ASEvaluator).transformedHeader(); } else { informat = getInputFormat();
/** * reduce the dimensionality of a set of instances to include only those * attributes chosen by the last run of attribute selection. * * @param in the instances to be reduced * @return a dimensionality reduced set of instances * @exception Exception if the instances can't be reduced */ public Instances reduceDimensionality(Instances in) throws Exception { if (m_attributeFilter == null) { throw new Exception("No feature selection has been performed yet!"); } if (m_transformer != null) { Instances transformed = new Instances(m_transformer.transformedHeader(), in.numInstances()); for (int i = 0; i < in.numInstances(); i++) { transformed.add(m_transformer.convertInstance(in.instance(i))); } return Filter.useFilter(transformed, m_attributeFilter); } return Filter.useFilter(in, m_attributeFilter); }
informat = ((AttributeTransformer) m_ASEvaluator).transformedHeader(); } else { informat = getInputFormat();
data = ((AttributeTransformer)ASEval).transformedHeader(); if (m_classIndex >= 0 && data.classIndex() >= 0) { m_classIndex = data.classIndex();
data = ((AttributeTransformer) ASEval).transformedHeader(); if (m_classIndex >= 0 && data.classIndex() >= 0) { m_classIndex = data.classIndex();
data = ((AttributeTransformer) ASEval).transformedHeader(); if (m_classIndex >= 0 && data.classIndex() >= 0) { m_classIndex = data.classIndex();
if (transformer != null) { reduced = new Instances(transformer.transformedHeader(), data.numInstances()); for (int i = 0; i < data.numInstances(); i++) { reduced.add(transformer.convertInstance(data.instance(i)));
if (transformer != null) { reduced = new Instances(transformer.transformedHeader(), data.numInstances()); for (int i = 0; i < data.numInstances(); i++) { reduced.add(transformer.convertInstance(data.instance(i)));