List of usage examples for weka.core Instance insertAttributeAt
public void insertAttributeAt(int position);
From source file:mulan.transformations.multiclass.Copy.java
License:Open Source License
/** * Transforms a multi-label instance to a list of single-label instances, * one for each of the labels that annotate the instance, by copying the * feature vector/*ww w . ja v a 2s . c o m*/ * * @param instance a multi-label instance * @return a list with the transformed single-label instances */ List<Instance> transformInstance(Instance instance) { List<Instance> result = new ArrayList<Instance>(); for (int counter = 0; counter < numOfLabels; counter++) { if (instance.attribute(labelIndices[counter]).value((int) instance.value(labelIndices[counter])) .equals("1")) { Instance transformed = null; try { transformed = RemoveAllLabels.transformInstance(instance, labelIndices); transformed.setDataset(null); transformed.insertAttributeAt(transformed.numAttributes()); transformed.setValue(transformed.numAttributes() - 1, counter); } catch (Exception ex) { Logger.getLogger(Copy.class.getName()).log(Level.SEVERE, null, ex); } result.add(transformed); } } return result; }
From source file:mulan.transformations.multiclass.Ignore.java
License:Open Source License
/** * Transforms a multi-label example with a single annotation to a * single-label example and ignores multi-label example with more * annotations//from w w w .j av a 2 s. c o m * * @param instance a multi-label example * @return a list that is either empty or contains the transformed * single-label example */ List<Instance> transformInstance(Instance instance) { List<Instance> result = new ArrayList<Instance>(); int indexOfSingleLabel = -1; int counter = 0; for (int labelCounter = 0; labelCounter < numOfLabels; labelCounter++) { int index = labelIndices[labelCounter]; if (instance.attribute(index).value((int) instance.value(index)).equals("1")) { counter++; indexOfSingleLabel = labelCounter; } if (counter > 1) { break; } } if (counter > 1 || counter == 0) { return result; } Instance transformedInstance; try { transformedInstance = RemoveAllLabels.transformInstance(instance, labelIndices); transformedInstance.setDataset(null); transformedInstance.insertAttributeAt(transformedInstance.numAttributes()); transformedInstance.setValue(transformedInstance.numAttributes() - 1, indexOfSingleLabel); result.add(transformedInstance); } catch (Exception ex) { Logger.getLogger(Ignore.class.getName()).log(Level.SEVERE, null, ex); } return result; }
From source file:mulan.transformations.multiclass.SelectBasedOnFrequency.java
License:Open Source License
/** * Transforms a multi-label example to a list containing a single-label * multi-class example by selecting the most/least frequent label in the * training set/*from w w w . j av a2 s .c o m*/ * * @param instance * @return */ List<Instance> transformInstance(Instance instance) { int value = labelOccurance[0]; int labelSelected = 0; for (int counter = 1; counter < numOfLabels; counter++) { if (instance.attribute(labelIndices[counter]).value((int) instance.value(labelIndices[counter])) .equals("1")) { boolean test = false; switch (type) { case MIN: test = labelOccurance[counter] < value ? true : false; break; case MAX: test = labelOccurance[counter] > value ? true : false; break; } if (test) { value = labelOccurance[counter]; labelSelected = counter; } } } Instance transformed = null; try { transformed = RemoveAllLabels.transformInstance(instance, labelIndices); transformed.setDataset(null); transformed.insertAttributeAt(transformed.numAttributes()); transformed.setValue(transformed.numAttributes() - 1, labelSelected); } catch (Exception ex) { Logger.getLogger(Copy.class.getName()).log(Level.SEVERE, null, ex); } List<Instance> result = new ArrayList<Instance>(); result.add(transformed); return result; }
From source file:mulan.transformations.multiclass.SelectRandom.java
License:Open Source License
/** * Transforms a multi-label example to a list containing a single-label * multi-class example by randomly selecting one of the labels * //from w w w . j a v a 2 s . c o m * @param instance the multi-label example * @return the list with the single-label multi-class example */ List<Instance> transformInstance(Instance instance) { ArrayList<Integer> labels = new ArrayList<Integer>(); for (int counter = 0; counter < numOfLabels; counter++) { if (instance.attribute(labelIndices[counter]).value((int) instance.value(labelIndices[counter])) .equals("1")) { labels.add(counter); } } int randomLabel = labels.get((int) (Math.random() * labels.size())); Instance transformed = null; try { transformed = RemoveAllLabels.transformInstance(instance, labelIndices); transformed.setDataset(null); transformed.insertAttributeAt(transformed.numAttributes()); transformed.setValue(transformed.numAttributes() - 1, randomLabel); } catch (Exception ex) { Logger.getLogger(Copy.class.getName()).log(Level.SEVERE, null, ex); } List<Instance> result = new ArrayList<Instance>(); result.add(transformed); return result; }
From source file:mulan.transformations.PT6Transformation.java
License:Open Source License
public Instances transformInstances(MultiLabelInstances mlData) throws Exception { int numLabels = mlData.getNumLabels(); labelIndices = mlData.getLabelIndices(); // remove all labels Instances transformed = RemoveAllLabels.transformInstances(mlData); // add at the end an attribute with values the label names ArrayList<String> labelNames = new ArrayList<String>(numLabels); for (int counter = 0; counter < numLabels; counter++) { labelNames.add(mlData.getDataSet().attribute(labelIndices[counter]).name()); }// w ww.ja v a 2s. co m Attribute attrLabel = new Attribute("Label", labelNames); transformed.insertAttributeAt(attrLabel, transformed.numAttributes()); // and at the end a binary attribute ArrayList<String> binaryValues = new ArrayList<String>(2); binaryValues.add("0"); binaryValues.add("1"); Attribute classAttr = new Attribute("Class", binaryValues); transformed.insertAttributeAt(classAttr, transformed.numAttributes()); // add instances transformed = new Instances(transformed, 0); transformed.setClassIndex(transformed.numAttributes() - 1); Instances data = mlData.getDataSet(); for (int instanceIndex = 0; instanceIndex < data.numInstances(); instanceIndex++) { for (int labelCounter = 0; labelCounter < numLabels; labelCounter++) { Instance temp; temp = RemoveAllLabels.transformInstance(data.instance(instanceIndex), labelIndices); temp.setDataset(null); temp.insertAttributeAt(temp.numAttributes()); temp.insertAttributeAt(temp.numAttributes()); temp.setDataset(transformed); temp.setValue(temp.numAttributes() - 2, (String) labelNames.get(labelCounter)); if (data.attribute(labelIndices[labelCounter]) .value((int) data.instance(instanceIndex).value(labelIndices[labelCounter])).equals("1")) { temp.setValue(temp.numAttributes() - 1, "1"); } else { temp.setValue(temp.numAttributes() - 1, "0"); } transformed.add(temp); } } return transformed; }
From source file:mulan.transformations.PT6Transformation.java
License:Open Source License
public Instance transformInstance(Instance instance) throws Exception { if (labelIndices == null) { System.out.println("Label Indices not set!!"); return null; }// ww w . j av a2 s. c o m Instance transformedInstance = RemoveAllLabels.transformInstance(instance, labelIndices); transformedInstance.setDataset(null); transformedInstance.insertAttributeAt(transformedInstance.numAttributes()); transformedInstance.insertAttributeAt(transformedInstance.numAttributes()); return transformedInstance; }
From source file:org.knime.knip.suise.ops.BuildTrainingData.java
License:Open Source License
/** * {@inheritDoc}//w w w . ja v a 2 s. com */ @Override public Instances compute(RandomAccessibleInterval<LabelingType<L>> lab, Img<T> img, Instances r) { Random rand = new Random(); double[] extent = new double[lab.numDimensions()]; for (int d = 0; d < m_dimIndices.length; d++) { extent[m_dimIndices[d]] = lab.max(m_dimIndices[d]); } RectangleRegionOfInterest roi = new RectangleRegionOfInterest(new double[lab.numDimensions()], extent); Cursor<LabelingType<L>> labCur = roi.getIterableIntervalOverROI(lab).localizingCursor(); OutOfBounds<T> imgRA = new OutOfBoundsBorder<T>(img); LabelRegions<L> regions = new LabelRegions<L>(lab); // get the class distributions Map<L, Double> classDistr = null; if (m_balanceInstancePerClass) { long sum = 0; long area; Collection<L> labels = regions.getExistingLabels(); classDistr = new HashMap<L, Double>(labels.size()); for (L label : labels) { area = regions.getLabelRegion(label).size(); sum += area; classDistr.put(label, new Double(area)); } // determine the new sampling rate for each class individually double instancesPerClass = (double) sum / (double) labels.size(); for (L label : labels) { Double sampleRate = instancesPerClass / classDistr.get(label) * m_samplingRate; classDistr.put(label, sampleRate); } } long[] tmpPos = new long[imgRA.numDimensions()]; while (labCur.hasNext()) { labCur.fwd(); for (int d = 0; d < m_dimIndices.length; d++) { imgRA.setPosition(labCur.getLongPosition(m_dimIndices[d]), m_dimIndices[d]); if (imgRA.isOutOfBounds()) { imgRA.localize(tmpPos); NodeLogger.getLogger(getClass()).warn("Labeling reaches beyond the feature image. Position " + Arrays.toString(tmpPos) + " skipped."); continue; } } if (!labCur.get().isEmpty()) { if (m_balanceInstancePerClass) { if (rand.nextDouble() >= classDistr.get(labCur.get().iterator().next())) { continue; } } else { if (rand.nextDouble() >= m_samplingRate) { continue; } } double[] featVec = new double[(int) img.dimension(m_featDim)]; for (int f = 0; f < img.dimension(m_featDim); f++) { imgRA.setPosition(f, m_featDim); featVec[f] = imgRA.get().getRealDouble(); } for (L classLabel : labCur.get()) { Instance instance = new DenseInstance(1.0, featVec); instance.insertAttributeAt(instance.numAttributes()); instance.setDataset(r); instance.setClassValue(classLabel.toString()); r.add(instance); } } } return r; }