/**
* Adds this tree recursively to the buffer.
*
* @param id the unqiue id for the method
* @param buffer the buffer to add the source code to
* @return the last ID being used
* @throws Exception if something goes wrong
*/
protected int toSource(int id, StringBuffer buffer) throws Exception {
int result;
int i;
int newID;
StringBuffer[] subBuffers;
buffer.append("\n");
buffer.append(" protected static double node" + id + "(Object[] i) {\n");
// leaf?
if (m_Attribute == null) {
result = id;
if (Double.isNaN(m_ClassValue)) buffer.append(" return Double.NaN;");
else buffer.append(" return " + m_ClassValue + ";");
if (m_ClassAttribute != null)
buffer.append(" // " + m_ClassAttribute.value((int) m_ClassValue));
buffer.append("\n");
buffer.append(" }\n");
} else {
buffer.append(" // " + m_Attribute.name() + "\n");
// subtree calls
subBuffers = new StringBuffer[m_Attribute.numValues()];
newID = id;
for (i = 0; i < m_Attribute.numValues(); i++) {
newID++;
buffer.append(" ");
if (i > 0) buffer.append("else ");
buffer.append(
"if (((String) i["
+ m_Attribute.index()
+ "]).equals(\""
+ m_Attribute.value(i)
+ "\"))\n");
buffer.append(" return node" + newID + "(i);\n");
subBuffers[i] = new StringBuffer();
newID = m_Successors[i].toSource(newID, subBuffers[i]);
}
buffer.append(" else\n");
buffer.append(
" throw new IllegalArgumentException(\"Value '\" + i["
+ m_Attribute.index()
+ "] + \"' is not allowed!\");\n");
buffer.append(" }\n");
// output subtree code
for (i = 0; i < m_Attribute.numValues(); i++) {
buffer.append(subBuffers[i].toString());
}
subBuffers = null;
result = newID;
}
return result;
}
public double classifyInstance(Instance inst) throws Exception {
if (m_attribute == null) {
return m_intercept;
} else {
if (inst.isMissing(m_attribute.index())) {
throw new Exception("UnivariateLinearRegression: No missing values!");
}
return m_intercept + m_slope * inst.value(m_attribute.index());
}
}
/**
* Method for building an Id3 tree.
*
* @param data the training data
* @exception Exception if decision tree can't be built successfully
*/
private void makeTree(Instances data) throws Exception {
// Check if no instances have reached this node.
if (data.numInstances() == 0) {
m_Attribute = null;
m_ClassValue = Utils.missingValue();
m_Distribution = new double[data.numClasses()];
return;
}
// Compute attribute with maximum information gain.
double[] infoGains = new double[data.numAttributes()];
Enumeration attEnum = data.enumerateAttributes();
while (attEnum.hasMoreElements()) {
Attribute att = (Attribute) attEnum.nextElement();
infoGains[att.index()] = computeInfoGain(data, att);
}
m_Attribute = data.attribute(Utils.maxIndex(infoGains));
// Make leaf if information gain is zero.
// Otherwise create successors.
if (Utils.eq(infoGains[m_Attribute.index()], 0)) {
m_Attribute = null;
m_Distribution = new double[data.numClasses()];
Enumeration instEnum = data.enumerateInstances();
while (instEnum.hasMoreElements()) {
Instance inst = (Instance) instEnum.nextElement();
m_Distribution[(int) inst.classValue()]++;
}
Utils.normalize(m_Distribution);
m_ClassValue = Utils.maxIndex(m_Distribution);
m_ClassAttribute = data.classAttribute();
} else {
Instances[] splitData = splitData(data, m_Attribute);
m_Successors = new Id3[m_Attribute.numValues()];
for (int j = 0; j < m_Attribute.numValues(); j++) {
m_Successors[j] = new Id3();
m_Successors[j].makeTree(splitData[j]);
}
}
}
/**
* Constructs an instance suitable for passing to the model for scoring
*
* @param incoming the incoming instance
* @return an instance with values mapped to be consistent with what the model is expecting
*/
protected Instance mapIncomingFieldsToModelFields(Instance incoming) {
Instances modelHeader = m_model.getHeader();
double[] vals = new double[modelHeader.numAttributes()];
for (int i = 0; i < modelHeader.numAttributes(); i++) {
if (m_attributeMap[i] < 0) {
// missing or type mismatch
vals[i] = Utils.missingValue();
continue;
}
Attribute modelAtt = modelHeader.attribute(i);
Attribute incomingAtt = incoming.dataset().attribute(m_attributeMap[i]);
if (incoming.isMissing(incomingAtt.index())) {
vals[i] = Utils.missingValue();
continue;
}
if (modelAtt.isNumeric()) {
vals[i] = incoming.value(m_attributeMap[i]);
} else if (modelAtt.isNominal()) {
String incomingVal = incoming.stringValue(m_attributeMap[i]);
int modelIndex = modelAtt.indexOfValue(incomingVal);
if (modelIndex < 0) {
vals[i] = Utils.missingValue();
} else {
vals[i] = modelIndex;
}
} else if (modelAtt.isString()) {
vals[i] = 0;
modelAtt.setStringValue(incoming.stringValue(m_attributeMap[i]));
}
}
if (modelHeader.classIndex() >= 0) {
// set class to missing value
vals[modelHeader.classIndex()] = Utils.missingValue();
}
Instance newInst = null;
if (incoming instanceof SparseInstance) {
newInst = new SparseInstance(incoming.weight(), vals);
} else {
newInst = new DenseInstance(incoming.weight(), vals);
}
newInst.setDataset(modelHeader);
return newInst;
}
/**
* Builds a mapping between the header for the incoming data to be scored and the header used to
* train the model. Uses attribute names to match between the two. Also constructs a list of
* missing attributes and a list of type mismatches.
*
* @param modelHeader the header of the data used to train the model
* @param incomingHeader the header of the incoming data
* @throws DistributedWekaException if more than 50% of the attributes expected by the model are
* missing or have a type mismatch with the incoming data
*/
protected void buildAttributeMap(Instances modelHeader, Instances incomingHeader)
throws DistributedWekaException {
m_attributeMap = new int[modelHeader.numAttributes()];
int problemCount = 0;
for (int i = 0; i < modelHeader.numAttributes(); i++) {
Attribute modAtt = modelHeader.attribute(i);
Attribute incomingAtt = incomingHeader.attribute(modAtt.name());
if (incomingAtt == null) {
// missing model attribute
m_attributeMap[i] = -1;
m_missingMismatch.put(modAtt.name(), "missing from incoming data");
problemCount++;
} else if (modAtt.type() != incomingAtt.type()) {
// type mismatch
m_attributeMap[i] = -1;
m_missingMismatch.put(
modAtt.name(),
"type mismatch - "
+ "model: "
+ Attribute.typeToString(modAtt)
+ " != incoming: "
+ Attribute.typeToString(incomingAtt));
problemCount++;
} else {
m_attributeMap[i] = incomingAtt.index();
}
}
// -1 for the class (if set)
int adjustForClass = modelHeader.classIndex() >= 0 ? 1 : 0;
if (problemCount > (modelHeader.numAttributes() - adjustForClass) / 2) {
throw new DistributedWekaException(
"More than 50% of the attributes that the model "
+ "is expecting to see are either missing or have a type mismatch in the "
+ "incoming data.");
}
}
/**
* Sets the format of the input instances.
*
* @param instanceInfo an Instances object containing the input instance structure (any instances
* contained in the object are ignored - only the structure is required).
* @return true if the outputFormat may be collected immediately
* @throws Exception if the format couldn't be set successfully
*/
@Override
public boolean setInputFormat(Instances instanceInfo) throws Exception {
super.setInputFormat(instanceInfo);
int classIndex = instanceInfo.classIndex();
// setup the map
if (m_renameVals != null && m_renameVals.length() > 0) {
String[] vals = m_renameVals.split(",");
for (String val : vals) {
String[] parts = val.split(":");
if (parts.length != 2) {
throw new WekaException("Invalid replacement string: " + val);
}
if (parts[0].length() == 0 || parts[1].length() == 0) {
throw new WekaException("Invalid replacement string: " + val);
}
m_renameMap.put(
m_ignoreCase ? parts[0].toLowerCase().trim() : parts[0].trim(), parts[1].trim());
}
}
// try selected atts as a numeric range first
Range tempRange = new Range();
tempRange.setInvert(m_invert);
if (m_selectedColsString == null) {
m_selectedColsString = "";
}
try {
tempRange.setRanges(m_selectedColsString);
tempRange.setUpper(instanceInfo.numAttributes() - 1);
m_selectedAttributes = tempRange.getSelection();
m_selectedCols = tempRange;
} catch (Exception r) {
// OK, now try as named attributes
StringBuffer indexes = new StringBuffer();
String[] attNames = m_selectedColsString.split(",");
boolean first = true;
for (String n : attNames) {
n = n.trim();
Attribute found = instanceInfo.attribute(n);
if (found == null) {
throw new WekaException(
"Unable to find attribute '" + n + "' in the incoming instances'");
}
if (first) {
indexes.append("" + (found.index() + 1));
first = false;
} else {
indexes.append("," + (found.index() + 1));
}
}
tempRange = new Range();
tempRange.setRanges(indexes.toString());
tempRange.setUpper(instanceInfo.numAttributes() - 1);
m_selectedAttributes = tempRange.getSelection();
m_selectedCols = tempRange;
}
ArrayList<Attribute> attributes = new ArrayList<Attribute>();
for (int i = 0; i < instanceInfo.numAttributes(); i++) {
if (m_selectedCols.isInRange(i)) {
if (instanceInfo.attribute(i).isNominal()) {
List<String> valsForAtt = new ArrayList<String>();
for (int j = 0; j < instanceInfo.attribute(i).numValues(); j++) {
String origV = instanceInfo.attribute(i).value(j);
String replace =
m_ignoreCase ? m_renameMap.get(origV.toLowerCase()) : m_renameMap.get(origV);
if (replace != null && !valsForAtt.contains(replace)) {
valsForAtt.add(replace);
} else {
valsForAtt.add(origV);
}
}
Attribute newAtt = new Attribute(instanceInfo.attribute(i).name(), valsForAtt);
attributes.add(newAtt);
} else {
// ignore any selected attributes that are not nominal
Attribute att = (Attribute) instanceInfo.attribute(i).copy();
attributes.add(att);
}
} else {
Attribute att = (Attribute) instanceInfo.attribute(i).copy();
attributes.add(att);
}
}
Instances outputFormat = new Instances(instanceInfo.relationName(), attributes, 0);
outputFormat.setClassIndex(classIndex);
setOutputFormat(outputFormat);
return true;
}
@Override
public void init(Instances structure, Environment env) {
super.init(structure, env);
m_resolvedLhsName = m_lhsAttributeName;
m_resolvedRhsOperand = m_rhsOperand;
try {
m_resolvedLhsName = m_env.substitute(m_resolvedLhsName);
m_resolvedRhsOperand = m_env.substitute(m_resolvedRhsOperand);
} catch (Exception ex) {
}
Attribute lhs = null;
// try as an index or "special" label first
if (m_resolvedLhsName.toLowerCase().startsWith("/first")) {
lhs = structure.attribute(0);
} else if (m_resolvedLhsName.toLowerCase().startsWith("/last")) {
lhs = structure.attribute(structure.numAttributes() - 1);
} else {
// try as an index
try {
int indx = Integer.parseInt(m_resolvedLhsName);
indx--;
lhs = structure.attribute(indx);
} catch (NumberFormatException ex) {
}
}
if (lhs == null) {
lhs = structure.attribute(m_resolvedLhsName);
}
if (lhs == null) {
throw new IllegalArgumentException(
"Data does not contain attribute " + "\"" + m_resolvedLhsName + "\"");
}
m_lhsAttIndex = lhs.index();
if (m_rhsIsAttribute) {
Attribute rhs = null;
// try as an index or "special" label first
if (m_resolvedRhsOperand.toLowerCase().equals("/first")) {
rhs = structure.attribute(0);
} else if (m_resolvedRhsOperand.toLowerCase().equals("/last")) {
rhs = structure.attribute(structure.numAttributes() - 1);
} else {
// try as an index
try {
int indx = Integer.parseInt(m_resolvedRhsOperand);
indx--;
rhs = structure.attribute(indx);
} catch (NumberFormatException ex) {
}
}
if (rhs == null) {
rhs = structure.attribute(m_resolvedRhsOperand);
}
if (rhs == null) {
throw new IllegalArgumentException(
"Data does not contain attribute " + "\"" + m_resolvedRhsOperand + "\"");
}
m_rhsAttIndex = rhs.index();
} else if (m_operator != ExpressionType.CONTAINS
&& m_operator != ExpressionType.STARTSWITH
&& m_operator != ExpressionType.ENDSWITH
&& m_operator != ExpressionType.REGEX
&& m_operator != ExpressionType.ISMISSING) {
// make sure the operand is parseable as a number (unless missing has
// been specified - equals only)
if (lhs.isNominal()) {
m_numericOperand = lhs.indexOfValue(m_resolvedRhsOperand);
if (m_numericOperand < 0) {
throw new IllegalArgumentException(
"Unknown nominal value '"
+ m_resolvedRhsOperand
+ "' for attribute '"
+ lhs.name()
+ "'");
}
} else {
try {
m_numericOperand = Double.parseDouble(m_resolvedRhsOperand);
} catch (NumberFormatException e) {
throw new IllegalArgumentException(
"\"" + m_resolvedRhsOperand + "\" is not parseable as a number!");
}
}
}
if (m_operator == ExpressionType.REGEX) {
m_regexPattern = Pattern.compile(m_resolvedRhsOperand);
}
}
private void readHeader() throws IOException {
m_rowCount = 1;
m_incrementalReader = null;
m_current = new ArrayList<Object>();
openTempFiles();
m_rowBuffer = new ArrayList<String>();
String firstRow = m_sourceReader.readLine();
if (firstRow == null) {
throw new IOException("No data in the file!");
}
if (m_noHeaderRow) {
m_rowBuffer.add(firstRow);
}
ArrayList<Attribute> attribNames = new ArrayList<Attribute>();
// now tokenize to determine attribute names (or create att names if
// no header row
StringReader sr = new StringReader(firstRow + "\n");
// System.out.print(firstRow + "\n");
m_st = new StreamTokenizer(sr);
initTokenizer(m_st);
m_st.ordinaryChar(m_FieldSeparator.charAt(0));
int attNum = 1;
StreamTokenizerUtils.getFirstToken(m_st);
if (m_st.ttype == StreamTokenizer.TT_EOF) {
StreamTokenizerUtils.errms(m_st, "premature end of file");
}
boolean first = true;
boolean wasSep;
while (m_st.ttype != StreamTokenizer.TT_EOL && m_st.ttype != StreamTokenizer.TT_EOF) {
// Get next token
if (!first) {
StreamTokenizerUtils.getToken(m_st);
}
if (m_st.ttype == m_FieldSeparator.charAt(0) || m_st.ttype == StreamTokenizer.TT_EOL) {
wasSep = true;
} else {
wasSep = false;
String attName = null;
if (m_noHeaderRow) {
attName = "att" + attNum;
attNum++;
} else {
attName = m_st.sval;
}
attribNames.add(new Attribute(attName, (java.util.List<String>) null));
}
if (!wasSep) {
StreamTokenizerUtils.getToken(m_st);
}
first = false;
}
String relationName;
if (m_sourceFile != null) {
relationName = (m_sourceFile.getName()).replaceAll("\\.[cC][sS][vV]$", "");
} else {
relationName = "stream";
}
m_structure = new Instances(relationName, attribNames, 0);
m_NominalAttributes.setUpper(m_structure.numAttributes() - 1);
m_StringAttributes.setUpper(m_structure.numAttributes() - 1);
m_dateAttributes.setUpper(m_structure.numAttributes() - 1);
m_numericAttributes.setUpper(m_structure.numAttributes() - 1);
m_nominalVals = new HashMap<Integer, LinkedHashSet<String>>();
m_types = new TYPE[m_structure.numAttributes()];
for (int i = 0; i < m_structure.numAttributes(); i++) {
if (m_NominalAttributes.isInRange(i)) {
m_types[i] = TYPE.NOMINAL;
LinkedHashSet<String> ts = new LinkedHashSet<String>();
m_nominalVals.put(i, ts);
} else if (m_StringAttributes.isInRange(i)) {
m_types[i] = TYPE.STRING;
} else if (m_dateAttributes.isInRange(i)) {
m_types[i] = TYPE.DATE;
} else if (m_numericAttributes.isInRange(i)) {
m_types[i] = TYPE.NUMERIC;
} else {
m_types[i] = TYPE.UNDETERMINED;
}
}
if (m_nominalLabelSpecs.size() > 0) {
for (String spec : m_nominalLabelSpecs) {
String[] attsAndLabels = spec.split(":");
if (attsAndLabels.length == 2) {
String[] labels = attsAndLabels[1].split(",");
try {
// try as a range string first
Range tempR = new Range();
tempR.setRanges(attsAndLabels[0].trim());
tempR.setUpper(m_structure.numAttributes() - 1);
int[] rangeIndexes = tempR.getSelection();
for (int i = 0; i < rangeIndexes.length; i++) {
m_types[rangeIndexes[i]] = TYPE.NOMINAL;
LinkedHashSet<String> ts = new LinkedHashSet<String>();
for (String lab : labels) {
ts.add(lab);
}
m_nominalVals.put(rangeIndexes[i], ts);
}
} catch (IllegalArgumentException e) {
// one or more named attributes?
String[] attNames = attsAndLabels[0].split(",");
for (String attN : attNames) {
Attribute a = m_structure.attribute(attN.trim());
if (a != null) {
int attIndex = a.index();
m_types[attIndex] = TYPE.NOMINAL;
LinkedHashSet<String> ts = new LinkedHashSet<String>();
for (String lab : labels) {
ts.add(lab);
}
m_nominalVals.put(attIndex, ts);
}
}
}
}
}
}
// Prevents the first row from getting lost in the
// case where there is no header row and we're
// running in batch mode
if (m_noHeaderRow && getRetrieval() == BATCH) {
StreamTokenizer tempT = new StreamTokenizer(new StringReader(firstRow));
initTokenizer(tempT);
tempT.ordinaryChar(m_FieldSeparator.charAt(0));
String checked = getInstance(tempT);
dumpRow(checked);
}
m_st = new StreamTokenizer(m_sourceReader);
initTokenizer(m_st);
m_st.ordinaryChar(m_FieldSeparator.charAt(0));
// try and determine a more accurate structure from the first batch
readData(false || getRetrieval() == BATCH);
makeStructure();
}
/**
* The procedure implementing the SMOTE algorithm. The output instances are pushed onto the output
* queue for collection.
*
* @throws Exception if provided options cannot be executed on input instances
*/
protected void doSMOTE() throws Exception {
int minIndex = 0;
int min = Integer.MAX_VALUE;
if (m_DetectMinorityClass) {
// find minority class
int[] classCounts =
getInputFormat().attributeStats(getInputFormat().classIndex()).nominalCounts;
for (int i = 0; i < classCounts.length; i++) {
if (classCounts[i] != 0 && classCounts[i] < min) {
min = classCounts[i];
minIndex = i;
}
}
} else {
String classVal = getClassValue();
if (classVal.equalsIgnoreCase("first")) {
minIndex = 1;
} else if (classVal.equalsIgnoreCase("last")) {
minIndex = getInputFormat().numClasses();
} else {
minIndex = Integer.parseInt(classVal);
}
if (minIndex > getInputFormat().numClasses()) {
throw new Exception("value index must be <= the number of classes");
}
minIndex--; // make it an index
}
int nearestNeighbors;
if (min <= getNearestNeighbors()) {
nearestNeighbors = min - 1;
} else {
nearestNeighbors = getNearestNeighbors();
}
if (nearestNeighbors < 1) throw new Exception("Cannot use 0 neighbors!");
// compose minority class dataset
// also push all dataset instances
Instances sample = getInputFormat().stringFreeStructure();
Enumeration instanceEnum = getInputFormat().enumerateInstances();
while (instanceEnum.hasMoreElements()) {
Instance instance = (Instance) instanceEnum.nextElement();
push((Instance) instance.copy());
if ((int) instance.classValue() == minIndex) {
sample.add(instance);
}
}
// compute Value Distance Metric matrices for nominal features
Map vdmMap = new HashMap();
Enumeration attrEnum = getInputFormat().enumerateAttributes();
while (attrEnum.hasMoreElements()) {
Attribute attr = (Attribute) attrEnum.nextElement();
if (!attr.equals(getInputFormat().classAttribute())) {
if (attr.isNominal() || attr.isString()) {
double[][] vdm = new double[attr.numValues()][attr.numValues()];
vdmMap.put(attr, vdm);
int[] featureValueCounts = new int[attr.numValues()];
int[][] featureValueCountsByClass =
new int[getInputFormat().classAttribute().numValues()][attr.numValues()];
instanceEnum = getInputFormat().enumerateInstances();
while (instanceEnum.hasMoreElements()) {
Instance instance = (Instance) instanceEnum.nextElement();
int value = (int) instance.value(attr);
int classValue = (int) instance.classValue();
featureValueCounts[value]++;
featureValueCountsByClass[classValue][value]++;
}
for (int valueIndex1 = 0; valueIndex1 < attr.numValues(); valueIndex1++) {
for (int valueIndex2 = 0; valueIndex2 < attr.numValues(); valueIndex2++) {
double sum = 0;
for (int classValueIndex = 0;
classValueIndex < getInputFormat().numClasses();
classValueIndex++) {
double c1i = featureValueCountsByClass[classValueIndex][valueIndex1];
double c2i = featureValueCountsByClass[classValueIndex][valueIndex2];
double c1 = featureValueCounts[valueIndex1];
double c2 = featureValueCounts[valueIndex2];
double term1 = c1i / c1;
double term2 = c2i / c2;
sum += Math.abs(term1 - term2);
}
vdm[valueIndex1][valueIndex2] = sum;
}
}
}
}
}
// use this random source for all required randomness
Random rand = new Random(getRandomSeed());
// find the set of extra indices to use if the percentage is not evenly
// divisible by 100
List extraIndices = new LinkedList();
double percentageRemainder = (getPercentage() / 100) - Math.floor(getPercentage() / 100.0);
int extraIndicesCount = (int) (percentageRemainder * sample.numInstances());
if (extraIndicesCount >= 1) {
for (int i = 0; i < sample.numInstances(); i++) {
extraIndices.add(i);
}
}
Collections.shuffle(extraIndices, rand);
extraIndices = extraIndices.subList(0, extraIndicesCount);
Set extraIndexSet = new HashSet(extraIndices);
// the main loop to handle computing nearest neighbors and generating SMOTE
// examples from each instance in the original minority class data
Instance[] nnArray = new Instance[nearestNeighbors];
for (int i = 0; i < sample.numInstances(); i++) {
Instance instanceI = sample.instance(i);
// find k nearest neighbors for each instance
List distanceToInstance = new LinkedList();
for (int j = 0; j < sample.numInstances(); j++) {
Instance instanceJ = sample.instance(j);
if (i != j) {
double distance = 0;
attrEnum = getInputFormat().enumerateAttributes();
while (attrEnum.hasMoreElements()) {
Attribute attr = (Attribute) attrEnum.nextElement();
if (!attr.equals(getInputFormat().classAttribute())) {
double iVal = instanceI.value(attr);
double jVal = instanceJ.value(attr);
if (attr.isNumeric()) {
distance += Math.pow(iVal - jVal, 2);
} else {
distance += ((double[][]) vdmMap.get(attr))[(int) iVal][(int) jVal];
}
}
}
distance = Math.pow(distance, .5);
distanceToInstance.add(new Object[] {distance, instanceJ});
}
}
// sort the neighbors according to distance
Collections.sort(
distanceToInstance,
new Comparator() {
public int compare(Object o1, Object o2) {
double distance1 = (Double) ((Object[]) o1)[0];
double distance2 = (Double) ((Object[]) o2)[0];
return Double.compare(distance1, distance2);
}
});
// populate the actual nearest neighbor instance array
Iterator entryIterator = distanceToInstance.iterator();
int j = 0;
while (entryIterator.hasNext() && j < nearestNeighbors) {
nnArray[j] = (Instance) ((Object[]) entryIterator.next())[1];
j++;
}
// create synthetic examples
int n = (int) Math.floor(getPercentage() / 100);
while (n > 0 || extraIndexSet.remove(i)) {
double[] values = new double[sample.numAttributes()];
int nn = rand.nextInt(nearestNeighbors);
attrEnum = getInputFormat().enumerateAttributes();
while (attrEnum.hasMoreElements()) {
Attribute attr = (Attribute) attrEnum.nextElement();
if (!attr.equals(getInputFormat().classAttribute())) {
if (attr.isNumeric()) {
double dif = nnArray[nn].value(attr) - instanceI.value(attr);
double gap = rand.nextDouble();
values[attr.index()] = (instanceI.value(attr) + gap * dif);
} else if (attr.isDate()) {
double dif = nnArray[nn].value(attr) - instanceI.value(attr);
double gap = rand.nextDouble();
values[attr.index()] = (long) (instanceI.value(attr) + gap * dif);
} else {
int[] valueCounts = new int[attr.numValues()];
int iVal = (int) instanceI.value(attr);
valueCounts[iVal]++;
for (int nnEx = 0; nnEx < nearestNeighbors; nnEx++) {
int val = (int) nnArray[nnEx].value(attr);
valueCounts[val]++;
}
int maxIndex = 0;
int max = Integer.MIN_VALUE;
for (int index = 0; index < attr.numValues(); index++) {
if (valueCounts[index] > max) {
max = valueCounts[index];
maxIndex = index;
}
}
values[attr.index()] = maxIndex;
}
}
}
values[sample.classIndex()] = minIndex;
Instance synthetic = new Instance(1.0, values);
push(synthetic);
n--;
}
}
}