/**
* Process a classifier's prediction for an instance and update a set of plotting instances and
* additional plotting info. m_PlotShape for nominal class datasets holds shape types (actual data
* points have automatic shape type assignment; classifier error data points have box shape type).
* For numeric class datasets, the actual data points are stored in m_PlotInstances and m_PlotSize
* stores the error (which is later converted to shape size values).
*
* @param toPredict the actual data point
* @param classifier the classifier
* @param eval the evaluation object to use for evaluating the classifier on the instance to
* predict
* @see #m_PlotShapes
* @see #m_PlotSizes
* @see #m_PlotInstances
*/
public void process(Instance toPredict, Classifier classifier, Evaluation eval) {
double pred;
double[] values;
int i;
try {
pred = eval.evaluateModelOnceAndRecordPrediction(classifier, toPredict);
if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) {
toPredict =
((weka.classifiers.misc.InputMappedClassifier) classifier)
.constructMappedInstance(toPredict);
}
if (!m_SaveForVisualization) return;
if (m_PlotInstances != null) {
values = new double[m_PlotInstances.numAttributes()];
for (i = 0; i < m_PlotInstances.numAttributes(); i++) {
if (i < toPredict.classIndex()) {
values[i] = toPredict.value(i);
} else if (i == toPredict.classIndex()) {
values[i] = pred;
values[i + 1] = toPredict.value(i);
i++;
} else {
values[i] = toPredict.value(i - 1);
}
}
m_PlotInstances.add(new DenseInstance(1.0, values));
if (toPredict.classAttribute().isNominal()) {
if (toPredict.isMissing(toPredict.classIndex()) || Utils.isMissingValue(pred)) {
m_PlotShapes.addElement(new Integer(Plot2D.MISSING_SHAPE));
} else if (pred != toPredict.classValue()) {
// set to default error point shape
m_PlotShapes.addElement(new Integer(Plot2D.ERROR_SHAPE));
} else {
// otherwise set to constant (automatically assigned) point shape
m_PlotShapes.addElement(new Integer(Plot2D.CONST_AUTOMATIC_SHAPE));
}
m_PlotSizes.addElement(new Integer(Plot2D.DEFAULT_SHAPE_SIZE));
} else {
// store the error (to be converted to a point size later)
Double errd = null;
if (!toPredict.isMissing(toPredict.classIndex()) && !Utils.isMissingValue(pred)) {
errd = new Double(pred - toPredict.classValue());
m_PlotShapes.addElement(new Integer(Plot2D.CONST_AUTOMATIC_SHAPE));
} else {
// missing shape if actual class not present or prediction is missing
m_PlotShapes.addElement(new Integer(Plot2D.MISSING_SHAPE));
}
m_PlotSizes.addElement(errd);
}
}
} catch (Exception ex) {
ex.printStackTrace();
}
}
/**
* Use <code> classifyInstance </code> from <code> OSDLCore </code> and assign probability one to
* the chosen label. The implementation is heavily based on the same method in the <code>
* Classifier </code> class.
*
* @param instance the instance to be classified
* @return an array containing a single '1' on the index that <code> classifyInstance </code>
* returns.
*/
public double[] distributionForInstance(Instance instance) {
// based on the code from the Classifier class
double[] dist = new double[instance.numClasses()];
int classification = 0;
switch (instance.classAttribute().type()) {
case Attribute.NOMINAL:
try {
classification = (int) Math.round(classifyInstance(instance));
} catch (Exception e) {
System.out.println("There was a problem with classifyIntance");
System.out.println(e.getMessage());
e.printStackTrace();
}
if (Utils.isMissingValue(classification)) {
return dist;
}
dist[classification] = 1.0;
return dist;
case Attribute.NUMERIC:
try {
dist[0] = classifyInstance(instance);
} catch (Exception e) {
System.out.println("There was a problem with classifyIntance");
System.out.println(e.getMessage());
e.printStackTrace();
}
return dist;
default:
return dist;
}
}
/**
* Convert an input instance
*
* @param current the input instance to convert
* @return a transformed instance
* @throws Exception if a problem occurs
*/
protected Instance convertInstance(Instance current) throws Exception {
double[] vals = new double[getOutputFormat().numAttributes()];
int index = 0;
for (int j = 0; j < current.numAttributes(); j++) {
if (j != current.classIndex()) {
if (m_unchanged != null && m_unchanged.attribute(current.attribute(j).name()) != null) {
vals[index++] = current.value(j);
} else {
Estimator[] estForAtt = m_estimatorLookup.get(current.attribute(j).name());
for (int k = 0; k < current.classAttribute().numValues(); k++) {
if (current.isMissing(j)) {
vals[index++] = Utils.missingValue();
} else {
double e = estForAtt[k].getProbability(current.value(j));
vals[index++] = e;
}
}
}
}
}
vals[vals.length - 1] = current.classValue();
DenseInstance instNew = new DenseInstance(current.weight(), vals);
return instNew;
}
/**
* Classifies the given test instance. The instance has to belong to a dataset when it's being
* classified. Note that a classifier MUST implement either this or distributionForInstance().
*
* @param instance the instance to be classified
* @return the predicted most likely class for the instance or Utils.missingValue() if no
* prediction is made
* @exception Exception if an error occurred during the prediction
*/
@Override
public double classifyInstance(Instance instance) throws Exception {
double[] dist = distributionForInstance(instance);
if (dist == null) {
throw new Exception("Null distribution predicted");
}
switch (instance.classAttribute().type()) {
case Attribute.NOMINAL:
double max = 0;
int maxIndex = 0;
for (int i = 0; i < dist.length; i++) {
if (dist[i] > max) {
maxIndex = i;
max = dist[i];
}
}
if (max > 0) {
return maxIndex;
} else {
return Utils.missingValue();
}
case Attribute.NUMERIC:
case Attribute.DATE:
return dist[0];
default:
return Utils.missingValue();
}
}
private void updateInstanceAndPutInField(
Instance instance, int correctClass, int predictedClassIndex) {
Segment segment = toSegment(instance);
String classAsString = instance.classAttribute().value(predictedClassIndex);
int predictedLabelId = Integer.parseInt(classAsString);
segment.setPredictedLabelDetail(labelMapReader.getSchema().get(predictedLabelId));
putInField(segment, correctClass, predictedClassIndex);
}
// use the TriTrainer Classifier to classify Instance;
public double classifyInstance(Instance instance) throws Exception {
double result;
double[] dist;
int index;
dist = distributionForInstance(instance); // 分类概率
if (instance.classAttribute().isNominal()) {
index = Utils.maxIndex(dist); // 返回概率最大的
if (dist[index] == 0) result = Instance.missingValue();
else result = dist[index];
} else if (instance.classAttribute().isNumeric()) {
result = dist[0];
} else {
result = Instance.missingValue();
}
return result;
}
@Override
public double[] distributionForInstance(Instance instance) throws Exception {
double[] distribution = getClassifier().distributionForInstance(instance);
int maxIndex = 0;
for (int i = 0; i < distribution.length; i++) {
if (distribution[maxIndex] < distribution[i]) maxIndex = i;
}
final String maxLabel = instance.classAttribute().value(maxIndex);
if (sureClasses.contains(maxLabel)) {
Arrays.fill(distribution, 0.0);
distribution[maxIndex] = 1.0;
System.err.println("INFO: Hacked confidence of '" + maxLabel + "'.");
} else {
Arrays.fill(distribution, 1.0d / instance.numClasses());
}
return distribution;
}
/**
* Calculates the class membership probabilities for the given test instance.
*
* @param instance the instance to be classified
* @return predicted class probability distribution
* @exception Exception if distribution can't be computed successfully
*/
public double[] distributionForInstance(Instance instance) throws Exception {
if (instance.classAttribute().isNumeric()) {
throw new UnsupportedClassTypeException("Decorate can't handle a numeric class!");
}
double[] sums = new double[instance.numClasses()], newProbs;
Classifier curr;
for (int i = 0; i < m_Committee.size(); i++) {
curr = (Classifier) m_Committee.get(i);
newProbs = curr.distributionForInstance(instance);
for (int j = 0; j < newProbs.length; j++) sums[j] += newProbs[j];
}
if (Utils.eq(Utils.sum(sums), 0)) {
return sums;
} else {
Utils.normalize(sums);
return sums;
}
}
public double classifyInstance(Instance sample) throws Exception {
// transform instance to sequence
MonoDoubleItemSet[] sequence = new MonoDoubleItemSet[sample.numAttributes() - 1];
int shift = (sample.classIndex() == 0) ? 1 : 0;
for (int t = 0; t < sequence.length; t++) {
sequence[t] = new MonoDoubleItemSet(sample.value(t + shift));
}
Sequence seq = new Sequence(sequence);
double minD = Double.MAX_VALUE;
String classValue = null;
for (ClassedSequence s : prototypes) {
double tmpD = seq.distance(s.sequence);
if (tmpD < minD) {
minD = tmpD;
classValue = s.classValue;
}
}
// System.out.println(prototypes.size());
return sample.classAttribute().indexOfValue(classValue);
}
/**
* Predicts the class memberships for a given instance. If an instance is unclassified, the
* returned array elements must be all zero. If the class is numeric, the array must consist of
* only one element, which contains the predicted value. Note that a classifier MUST implement
* either this or classifyInstance().
*
* @param instance the instance to be classified
* @return an array containing the estimated membership probabilities of the test instance in each
* class or the numeric prediction
* @exception Exception if distribution could not be computed successfully
*/
@Override
public double[] distributionForInstance(Instance instance) throws Exception {
double[] dist = new double[instance.numClasses()];
switch (instance.classAttribute().type()) {
case Attribute.NOMINAL:
double classification = classifyInstance(instance);
if (Utils.isMissingValue(classification)) {
return dist;
} else {
dist[(int) classification] = 1.0;
}
return dist;
case Attribute.NUMERIC:
case Attribute.DATE:
dist[0] = classifyInstance(instance);
return dist;
default:
return dist;
}
}
// this method MajorityVoting to decide the probs of the Instance;
//
protected double[] distributionForInstanceMajorityVoting(Instance instance) throws Exception {
double[] probs = new double[instance.classAttribute().numValues()];
double[] votes = new double[probs.length];
for (int i = 0; i < class_Array.length; i++) {
probs = class_Array[i].distributionForInstance(instance);
int maxIndex = 0;
for (int j = 0; j < probs.length; j++) {
if (probs[j] > probs[maxIndex]) maxIndex = j;
}
// Consider the cases when multiple classes happen to have the same probability
for (int j = 0; j < probs.length; j++) {
if (probs[j] == probs[maxIndex]) votes[j]++;
}
}
int tmpMajorityIndex = 0;
for (int k = 1; k < votes.length; k++) {
if (votes[k] > votes[tmpMajorityIndex]) tmpMajorityIndex = k;
}
// Consider the cases when multiple classes receive the same amount of votes
Vector<Integer> majorityIndexes = new Vector<Integer>();
for (int k = 0; k < votes.length; k++) {
if (votes[k] == votes[tmpMajorityIndex]) majorityIndexes.add(k);
}
// System.out.println("forth");
// Resolve the ties according to a uniform random distribution
int majorityIndex = majorityIndexes.get(m_Random.nextInt(majorityIndexes.size()));
// set the probs of the classes which have not been voted to 0
for (int k = 0; k < probs.length; k++) probs[k] = 0;
// the class that have been voted the most receives 1
probs[majorityIndex] = 1;
return probs;
}
/**
* test on one sample
*
* @param sample
* @return p(y|sample) forall y
* @throws Exception
*/
public double classifyInstance(Instance sample) throws Exception {
// transform instance to sequence
MonoDoubleItemSet[] sequence = new MonoDoubleItemSet[sample.numAttributes() - 1];
int shift = (sample.classIndex() == 0) ? 1 : 0;
for (int t = 0; t < sequence.length; t++) {
sequence[t] = new MonoDoubleItemSet(sample.value(t + shift));
}
Sequence seq = new Sequence(sequence);
// for each class
String classValue = null;
double maxProb = 0.0;
double[] pr = new double[classedData.keySet().size()];
for (String clas : classedData.keySet()) {
int c = trainingData.classAttribute().indexOfValue(clas);
double prob = 0.0;
for (int k = 0; k < centroidsPerClass[c].length; k++) {
// compute P(Q|k_c)
if (sigmasPerClass[c][k] == Double.NaN || sigmasPerClass[c][k] == 0) {
System.err.println("sigma=NAN||sigma=0");
continue;
}
double dist = seq.distanceEuc(centroidsPerClass[c][k]);
double p = computeProbaForQueryAndCluster(sigmasPerClass[c][k], dist);
prob += p / centroidsPerClass[c].length;
// prob += p*prior[c][k];
if (p > maxProb) {
maxProb = p;
classValue = clas;
}
}
// if (prob > maxProb) {
// maxProb = prob;
// classValue = clas;
// }
}
// System.out.println(Arrays.toString(pr));
// System.out.println(classValue);
return sample.classAttribute().indexOfValue(classValue);
}
@Override
public void updateNode(Instance inst) throws Exception {
super.updateDistribution(inst);
for (int i = 0; i < inst.numAttributes(); i++) {
Attribute a = inst.attribute(i);
if (i != inst.classIndex()) {
ConditionalSufficientStats stats = m_nodeStats.get(a.name());
if (stats == null) {
if (a.isNumeric()) {
stats = new GaussianConditionalSufficientStats();
} else {
stats = new NominalConditionalSufficientStats();
}
m_nodeStats.put(a.name(), stats);
}
stats.update(
inst.value(a), inst.classAttribute().value((int) inst.classValue()), inst.weight());
}
}
}
public double crearMetodo(Instances pTrain, Instances pTest, Instances pEvaluar) {
// Cargamos las instancias clasificadas
Instances train = pTrain;
Instances test = pTest;
double bestAccuracy = 0;
double accuracy = 0;
KNN bestKNN = null;
int bestL = 0;
int indice = 0;
// Movemos el valor de L para cambiar la cantidad de las muestras
for (int i = 2; i < 16; i++) {
ArrayList<ArrayList<Instance>> L = crearArrays(i);
// Le metemos las instancias a cada ArrayList
for (ArrayList<Instance> grupoInstancias : L) {
grupoInstancias.addAll(rellenarConInstancias(train));
}
// Creamos los clasificadores
ArrayList<KNN> clasificadores = new ArrayList<KNN>(i);
for (int j = 0; j < i; j++) {
try {
clasificadores.set(j, new KNN(L.get(j)));
} catch (IndexOutOfBoundsException e) {
clasificadores.add(new KNN(L.get(j)));
}
}
// Array de resultados
ArrayList<Instance> Resultados = new ArrayList<Instance>();
// Clasificamos las instancias
for (int j = 0; j < test.numInstances(); j++) {
// Creo un Array de Instancias para guardar los resultados
ArrayList<Instance> clasificadas = new ArrayList<Instance>();
// Clasificamos la instancia con todo el grupo de KNN
for (KNN knn : clasificadores) {
clasificadas.add(knn.clasificarInstacia(2, 2, test.instance(j)));
}
// Escogemos la clase mayoritaria del Array de clasificadas.
Integer[] clases = new Integer[test.classAttribute().numValues()];
// Inicio el array a cero
for (int k = 0; k < clases.length; k++) {
clases[k] = 0;
}
// Asigno el numero de instancias con esa clase.
for (Instance instancia : clasificadas) {
clases[
instancia
.classAttribute()
.indexOfValue(instancia.stringValue(instancia.classAttribute()))] +=
1;
}
// Elegir la clase mayoritaria y introducir a resultado
int numero = 0; // numero
indice = 0; // indice
for (int k = 0; k < clases.length; k++) {
if (clases[k] > numero) {
numero = clases[k];
indice = k;
}
}
clasificadas.get(0).setClassValue(clasificadas.get(0).classAttribute().value(indice));
Resultados.add(clasificadas.get(0));
Evaluador ev = new Evaluador(pEvaluar);
accuracy = ev.EvaluateModel(Resultados);
if (accuracy > bestAccuracy) {
bestAccuracy = accuracy;
bestL = i;
}
}
}
System.out.println("Mejor L: " + bestL);
System.out.println("Mejor accuracy: " + bestAccuracy);
return accuracy;
}
public double classifyInstance(Instance instance) {
int classAttribute = instance.classAttribute().index();
Object classValue = classifier.classifyInstance(convert(instance), classAttribute);
return instance.classAttribute().indexOfValue(classValue.toString());
}
