public Instances getPredictionInstancesSingleLabel(Instances testData, Classifier cl) throws Exception { StringBuffer classVals = new StringBuffer(); for (int i = 0; i < testData.classAttribute().numValues(); i++) { if (classVals.length() > 0) { classVals.append(","); } classVals.append(testData.classAttribute().value(i)); } // get predictions List<Double> labelPredictionList = new ArrayList<Double>(); for (int i = 0; i < testData.size(); i++) { labelPredictionList.add(cl.classifyInstance(testData.instance(i))); } // add an attribute with the predicted values at the end off the attributes Add filter = new Add(); filter.setAttributeName(WekaTestTask.PREDICTION_CLASS_LABEL_NAME); if (classVals.length() > 0) { filter.setAttributeType(new SelectedTag(Attribute.NOMINAL, Add.TAGS_TYPE)); filter.setNominalLabels(classVals.toString()); } filter.setInputFormat(testData); testData = Filter.useFilter(testData, filter); // fill predicted values for each instance for (int i = 0; i < labelPredictionList.size(); i++) { testData.instance(i).setValue(testData.classIndex() + 1, labelPredictionList.get(i)); } return testData; }
public Instances getPredictionInstancesSingleLabel(Instances testData, Classifier cl) throws Exception { StringBuffer classVals = new StringBuffer(); for (int i = 0; i < testData.classAttribute().numValues(); i++) { if (classVals.length() > 0) { classVals.append(","); } classVals.append(testData.classAttribute().value(i)); } // get predictions List<Double> labelPredictionList = new ArrayList<Double>(); for (int i = 0; i < testData.size(); i++) { labelPredictionList.add(cl.classifyInstance(testData.instance(i))); } // add an attribute with the predicted values at the end off the attributes Add filter = new Add(); filter.setAttributeName(WekaTestTask.PREDICTION_CLASS_LABEL_NAME); if (classVals.length() > 0) { filter.setAttributeType(new SelectedTag(Attribute.NOMINAL, Add.TAGS_TYPE)); filter.setNominalLabels(classVals.toString()); } filter.setInputFormat(testData); testData = Filter.useFilter(testData, filter); // fill predicted values for each instance for (int i = 0; i < labelPredictionList.size(); i++) { testData.instance(i).setValue(testData.classIndex() + 1, labelPredictionList.get(i)); } return testData; }
filter.setAttributeName(TestTask.PREDICTION_CLASS_LABEL_NAME); if (classVals.length() > 0) { filter.setAttributeType(new SelectedTag(Attribute.NOMINAL, Add.TAGS_TYPE)); filter.setNominalLabels(classVals.toString());
public void testAddDate() { m_Filter = getFilter(); ((Add) m_Filter).setAttributeType(new SelectedTag(Attribute.DATE, Add.TAGS_TYPE)); testBuffered(); testType(Attribute.DATE); }
public void testAddString() { m_Filter = getFilter(); ((Add) m_Filter).setAttributeType(new SelectedTag(Attribute.STRING, Add.TAGS_TYPE)); testBuffered(); testType(Attribute.STRING); }
public void testAddDate() { m_Filter = getFilter(); ((Add) m_Filter).setAttributeType(new SelectedTag(Attribute.DATE, Add.TAGS_TYPE)); testBuffered(); testType(Attribute.DATE); }
public void testAddString() { m_Filter = getFilter(); ((Add) m_Filter).setAttributeType(new SelectedTag(Attribute.STRING, Add.TAGS_TYPE)); testBuffered(); testType(Attribute.STRING); }
setAttributeType(new SelectedTag(tmpStr, TAGS_TYPE)); } else { setAttributeType(new SelectedTag(Attribute.NUMERIC, TAGS_TYPE));
setAttributeType(new SelectedTag(tmpStr, TAGS_TYPE)); } else { setAttributeType(new SelectedTag(Attribute.NUMERIC, TAGS_TYPE));
add.setAttributeIndex("first"); add.setAttributeType(new SelectedTag(Attribute.STRING, Add.TAGS_TYPE)); add.setInputFormat(newData); filteredData = Filter.useFilter(newData, add);
add.setAttributeIndex("first"); add.setAttributeType(new SelectedTag(Attribute.STRING, Add.TAGS_TYPE)); add.setInputFormat(newData); filteredData = Filter.useFilter(newData, add);
add.setAttributeIndex("first"); add.setAttributeType(new SelectedTag(Attribute.STRING, Add.TAGS_TYPE)); add.setInputFormat(newData); filteredData = Filter.useFilter(newData, add);