/**
* Select the best value for k by hold-one-out cross-validation. If the class attribute is
* nominal, classification error is minimised. If the class attribute is numeric, mean absolute
* error is minimised
*/
protected void crossValidate() {
try {
if (m_NNSearch instanceof weka.core.neighboursearch.CoverTree)
throw new Exception(
"CoverTree doesn't support hold-one-out "
+ "cross-validation. Use some other NN "
+ "method.");
double[] performanceStats = new double[m_kNNUpper];
double[] performanceStatsSq = new double[m_kNNUpper];
for (int i = 0; i < m_kNNUpper; i++) {
performanceStats[i] = 0;
performanceStatsSq[i] = 0;
}
m_kNN = m_kNNUpper;
Instance instance;
Instances neighbours;
double[] origDistances, convertedDistances;
for (int i = 0; i < m_Train.numInstances(); i++) {
if (m_Debug && (i % 50 == 0)) {
System.err.print("Cross validating " + i + "/" + m_Train.numInstances() + "\r");
}
instance = m_Train.instance(i);
neighbours = m_NNSearch.kNearestNeighbours(instance, m_kNN);
origDistances = m_NNSearch.getDistances();
for (int j = m_kNNUpper - 1; j >= 0; j--) {
// Update the performance stats
convertedDistances = new double[origDistances.length];
System.arraycopy(origDistances, 0, convertedDistances, 0, origDistances.length);
double[] distribution = makeDistribution(neighbours, convertedDistances);
double thisPrediction = Utils.maxIndex(distribution);
if (m_Train.classAttribute().isNumeric()) {
thisPrediction = distribution[0];
double err = thisPrediction - instance.classValue();
performanceStatsSq[j] += err * err; // Squared error
performanceStats[j] += Math.abs(err); // Absolute error
} else {
if (thisPrediction != instance.classValue()) {
performanceStats[j]++; // Classification error
}
}
if (j >= 1) {
neighbours = pruneToK(neighbours, convertedDistances, j);
}
}
}
// Display the results of the cross-validation
for (int i = 0; i < m_kNNUpper; i++) {
if (m_Debug) {
System.err.print("Hold-one-out performance of " + (i + 1) + " neighbors ");
}
if (m_Train.classAttribute().isNumeric()) {
if (m_Debug) {
if (m_MeanSquared) {
System.err.println(
"(RMSE) = " + Math.sqrt(performanceStatsSq[i] / m_Train.numInstances()));
} else {
System.err.println("(MAE) = " + performanceStats[i] / m_Train.numInstances());
}
}
} else {
if (m_Debug) {
System.err.println("(%ERR) = " + 100.0 * performanceStats[i] / m_Train.numInstances());
}
}
}
// Check through the performance stats and select the best
// k value (or the lowest k if more than one best)
double[] searchStats = performanceStats;
if (m_Train.classAttribute().isNumeric() && m_MeanSquared) {
searchStats = performanceStatsSq;
}
double bestPerformance = Double.NaN;
int bestK = 1;
for (int i = 0; i < m_kNNUpper; i++) {
if (Double.isNaN(bestPerformance) || (bestPerformance > searchStats[i])) {
bestPerformance = searchStats[i];
bestK = i + 1;
}
}
m_kNN = bestK;
if (m_Debug) {
System.err.println("Selected k = " + bestK);
}
m_kNNValid = true;
} catch (Exception ex) {
throw new Error("Couldn't optimize by cross-validation: " + ex.getMessage());
}
}
/**
* Calculates the class membership probabilities for the given test instance.
*
* @param instance the instance to be classified
* @return preedicted class probability distribution
* @throws Exception if distribution can't be computed successfully
*/
public double[] distributionForInstance(Instance instance) throws Exception {
// default model?
if (m_ZeroR != null) {
return m_ZeroR.distributionForInstance(instance);
}
if (m_Train.numInstances() == 0) {
throw new Exception("No training instances!");
}
m_NNSearch.addInstanceInfo(instance);
int k = m_Train.numInstances();
if ((!m_UseAllK && (m_kNN < k)) /*&&
!(m_WeightKernel==INVERSE ||
m_WeightKernel==GAUSS)*/) {
k = m_kNN;
}
Instances neighbours = m_NNSearch.kNearestNeighbours(instance, k);
double distances[] = m_NNSearch.getDistances();
if (m_Debug) {
System.out.println("Test Instance: " + instance);
System.out.println(
"For "
+ k
+ " kept "
+ neighbours.numInstances()
+ " out of "
+ m_Train.numInstances()
+ " instances.");
}
// IF LinearNN has skipped so much that <k neighbours are remaining.
if (k > distances.length) k = distances.length;
if (m_Debug) {
System.out.println("Instance Distances");
for (int i = 0; i < distances.length; i++) {
System.out.println("" + distances[i]);
}
}
// Determine the bandwidth
double bandwidth = distances[k - 1];
// Check for bandwidth zero
if (bandwidth <= 0) {
// if the kth distance is zero than give all instances the same weight
for (int i = 0; i < distances.length; i++) distances[i] = 1;
} else {
// Rescale the distances by the bandwidth
for (int i = 0; i < distances.length; i++) distances[i] = distances[i] / bandwidth;
}
// Pass the distances through a weighting kernel
for (int i = 0; i < distances.length; i++) {
switch (m_WeightKernel) {
case LINEAR:
distances[i] = 1.0001 - distances[i];
break;
case EPANECHNIKOV:
distances[i] = 3 / 4D * (1.0001 - distances[i] * distances[i]);
break;
case TRICUBE:
distances[i] = Math.pow((1.0001 - Math.pow(distances[i], 3)), 3);
break;
case CONSTANT:
// System.err.println("using constant kernel");
distances[i] = 1;
break;
case INVERSE:
distances[i] = 1.0 / (1.0 + distances[i]);
break;
case GAUSS:
distances[i] = Math.exp(-distances[i] * distances[i]);
break;
}
}
if (m_Debug) {
System.out.println("Instance Weights");
for (int i = 0; i < distances.length; i++) {
System.out.println("" + distances[i]);
}
}
// Set the weights on the training data
double sumOfWeights = 0, newSumOfWeights = 0;
for (int i = 0; i < distances.length; i++) {
double weight = distances[i];
Instance inst = (Instance) neighbours.instance(i);
sumOfWeights += inst.weight();
newSumOfWeights += inst.weight() * weight;
inst.setWeight(inst.weight() * weight);
// weightedTrain.add(newInst);
}
// Rescale weights
for (int i = 0; i < neighbours.numInstances(); i++) {
Instance inst = neighbours.instance(i);
inst.setWeight(inst.weight() * sumOfWeights / newSumOfWeights);
}
// Create a weighted classifier
m_Classifier.buildClassifier(neighbours);
if (m_Debug) {
System.out.println("Classifying test instance: " + instance);
System.out.println("Built base classifier:\n" + m_Classifier.toString());
}
// Return the classifier's predictions
return m_Classifier.distributionForInstance(instance);
}
/**
* Calculates the class membership probabilities for the given test instance.
*
* @param instance the instance to be classified
* @return predicted class probability distribution
* @throws Exception if an error occurred during the prediction
*/
public double[] distributionForInstance(Instance instance) throws Exception {
NaiveBayes nb = new NaiveBayes();
// System.out.println("number of instances "+m_Train.numInstances());
if (m_Train.numInstances() == 0) {
// throw new Exception("No training instances!");
return m_defaultModel.distributionForInstance(instance);
}
if ((m_WindowSize > 0) && (m_Train.numInstances() > m_WindowSize)) {
m_kNNValid = false;
boolean deletedInstance = false;
while (m_Train.numInstances() > m_WindowSize) {
m_Train.delete(0);
}
// rebuild datastructure KDTree currently can't delete
if (deletedInstance == true) m_NNSearch.setInstances(m_Train);
}
// Select k by cross validation
if (!m_kNNValid && (m_CrossValidate) && (m_kNNUpper >= 1)) {
crossValidate();
}
m_NNSearch.addInstanceInfo(instance);
m_kNN = 1000;
Instances neighbours = m_NNSearch.kNearestNeighbours(instance, m_kNN);
double[] distances = m_NNSearch.getDistances();
// System.out.println("--------------classify instance--------- ");
// System.out.println("neighbours.numInstances"+neighbours.numInstances());
// System.out.println("distances.length"+distances.length);
// System.out.println("--------------classify instance--------- ");
/* for (int k = 0; k < distances.length; k++) {
//System.out.println("-------");
//System.out.println("distance of "+k+" "+distances[k]);
//System.out.println("instance of "+k+" "+neighbours.instance(k));
//distances[k] = distances[k]+0.1;
//System.out.println("------- after add 0.1");
//System.out.println("distance of "+k+" "+distances[k]);
}
*/
Instances instances = new Instances(m_Train);
// int attrnum = instances.numAttributes();
instances.deleteWithMissingClass();
Instances newm_Train = new Instances(instances, 0, instances.numInstances());
for (int k = 0; k < neighbours.numInstances(); k++) {
// System.out.println("-------");
// Instance in = new Instance();
Instance insk = neighbours.instance(k);
// System.out.println("instance "+k+" "+neighbours.instance(k));
// System.out.println("-------");
double dis = distances[k] + 0.1;
// System.out.println("dis "+dis);
dis = (1 / dis) * 10;
// System.out.println("1/dis "+dis);
int weightnum = (int) dis;
// System.out.println("weightnum "+weightnum);
for (int s = 0; s < weightnum; s++) {
newm_Train.add(insk);
}
}
// System.out.println("number of instances "+newm_Train.numInstances());
/* for (int k = 0; k < newm_Train.numInstances(); k++) {
System.out.println("-------");
System.out.println("instance "+k+" "+newm_Train.instance(k));
System.out.println("-------");
}
/*
for (int k = 0; k < distances.length; k++) {
System.out.println("-------");
System.out.println("distance of "+k+" "+distances[k]);
System.out.println("-------");
}*/
nb.buildClassifier(newm_Train);
double[] dis = nb.distributionForInstance(instance);
// double[] distribution = makeDistribution(neighbours, distances);
return dis;
// return distribution;
}