/** @param args */
private void Init() {
testIns.setClassIndex(testIns.numAttributes() - 1);
labeledIns.setClassIndex(labeledIns.numAttributes() - 1);
unlabeledIns.setClassIndex(unlabeledIns.numAttributes() - 1);
class_Array[0] = classifier1;
class_Array[1] = classifier2;
class_Array[2] = classifier3;
}
protected void initMinMax(Instances data) {
m_Min = new double[data.numAttributes()];
m_Max = new double[data.numAttributes()];
for (int i = 0; i < data.numAttributes(); i++) {
m_Min[i] = m_Max[i] = Double.NaN;
}
for (int i = 0; i < data.numInstances(); i++) {
updateMinMax(data.instance(i));
}
}
/**
* Generate artificial training examples.
*
* @param artSize size of examples set to create
* @param data training data
* @return the set of unlabeled artificial examples
*/
protected Instances generateArtificialData(int artSize, Instances data) {
int numAttributes = data.numAttributes();
Instances artData = new Instances(data, artSize);
double[] att;
Instance artInstance;
for (int i = 0; i < artSize; i++) {
att = new double[numAttributes];
for (int j = 0; j < numAttributes; j++) {
if (data.attribute(j).isNominal()) {
// Select nominal value based on the frequency of occurence in the training data
double[] stats = (double[]) m_AttributeStats.get(j);
att[j] = (double) selectIndexProbabilistically(stats);
} else if (data.attribute(j).isNumeric()) {
// Generate numeric value from the Guassian distribution
// defined by the mean and std dev of the attribute
double[] stats = (double[]) m_AttributeStats.get(j);
att[j] = (m_Random.nextGaussian() * stats[1]) + stats[0];
} else System.err.println("Decorate can only handle numeric and nominal values.");
}
artInstance = new Instance(1.0, att);
artData.add(artInstance);
}
return artData;
}
/**
* Compute and store statistics required for generating artificial data.
*
* @param data training instances
* @exception Exception if statistics could not be calculated successfully
*/
protected void computeStats(Instances data) throws Exception {
int numAttributes = data.numAttributes();
m_AttributeStats = new Vector(numAttributes); // use to map attributes to their stats
for (int j = 0; j < numAttributes; j++) {
if (data.attribute(j).isNominal()) {
// Compute the probability of occurence of each distinct value
int[] nomCounts = (data.attributeStats(j)).nominalCounts;
double[] counts = new double[nomCounts.length];
if (counts.length < 2)
throw new Exception("Nominal attribute has less than two distinct values!");
// Perform Laplace smoothing
for (int i = 0; i < counts.length; i++) counts[i] = nomCounts[i] + 1;
Utils.normalize(counts);
double[] stats = new double[counts.length - 1];
stats[0] = counts[0];
// Calculate cumulative probabilities
for (int i = 1; i < stats.length; i++) stats[i] = stats[i - 1] + counts[i];
m_AttributeStats.add(j, stats);
} else if (data.attribute(j).isNumeric()) {
// Get mean and standard deviation from the training data
double[] stats = new double[2];
stats[0] = data.meanOrMode(j);
stats[1] = Math.sqrt(data.variance(j));
m_AttributeStats.add(j, stats);
} else System.err.println("Decorate can only handle numeric and nominal values.");
}
}
/**
* Calculates the distance between two instances
*
* @param test the first instance
* @param train the second instance
* @return the distance between the two given instances, between 0 and 1
*/
protected double distance(Instance first, Instance second) {
double distance = 0;
int firstI, secondI;
for (int p1 = 0, p2 = 0; p1 < first.numValues() || p2 < second.numValues(); ) {
if (p1 >= first.numValues()) {
firstI = m_instances.numAttributes();
} else {
firstI = first.index(p1);
}
if (p2 >= second.numValues()) {
secondI = m_instances.numAttributes();
} else {
secondI = second.index(p2);
}
if (firstI == m_instances.classIndex()) {
p1++;
continue;
}
if (secondI == m_instances.classIndex()) {
p2++;
continue;
}
double diff;
if (firstI == secondI) {
diff = difference(firstI, first.valueSparse(p1), second.valueSparse(p2));
p1++;
p2++;
} else if (firstI > secondI) {
diff = difference(secondI, 0, second.valueSparse(p2));
p2++;
} else {
diff = difference(firstI, first.valueSparse(p1), 0);
p1++;
}
distance += diff * diff;
}
return Math.sqrt(distance / m_instances.numAttributes());
}
/**
* Generates a clusterer by the mean of spectral clustering algorithm.
*
* @param data set of instances serving as training data
* @exception Exception if the clusterer has not been generated successfully
*/
public void buildClusterer(Instances data) throws java.lang.Exception {
m_Sequences = new Instances(data);
int n = data.numInstances();
int k = data.numAttributes();
DoubleMatrix2D w;
if (useSparseMatrix) w = DoubleFactory2D.sparse.make(n, n);
else w = DoubleFactory2D.dense.make(n, n);
double[][] v1 = new double[n][];
for (int i = 0; i < n; i++) v1[i] = data.instance(i).toDoubleArray();
v = DoubleFactory2D.dense.make(v1);
double sigma_sq = sigma * sigma;
// Sets up similarity matrix
for (int i = 0; i < n; i++)
for (int j = i; j < n; j++) {
/*double dist = distnorm2(v.viewRow(i), v.viewRow(j));
if((r == -1) || (dist < r)) {
double sim = Math.exp(- (dist * dist) / (2 * sigma_sq));
w.set(i, j, sim);
w.set(j, i, sim);
}*/
/* String [] key = {data.instance(i).stringValue(0), data.instance(j).stringValue(0)};
System.out.println(key[0]);
System.out.println(key[1]);
System.out.println(simScoreMap.containsKey(key));
Double simValue = simScoreMap.get(key);*/
double sim = sim_matrix[i][j];
w.set(i, j, sim);
w.set(j, i, sim);
}
// Partitions points
int[][] p = partition(w, alpha_star);
// Deploys results
numOfClusters = p.length;
cluster = new int[n];
for (int i = 0; i < p.length; i++) for (int j = 0; j < p[i].length; j++) cluster[p[i][j]] = i;
// System.out.println("Final partition:");
// UtilsJS.printMatrix(p);
// System.out.println("Cluster:\n");
// UtilsJS.printArray(cluster);
this.numOfClusters = cluster[Utils.maxIndex(cluster)] + 1;
// System.out.println("Num clusters:\t"+this.numOfClusters);
}
/**
* return a string describing this clusterer
*
* @return a description of the clusterer as a string
*/
public String toString() {
StringBuffer temp = new StringBuffer();
temp.append("\n FarthestFirst\n==============\n");
temp.append("\nCluster centroids:\n");
for (int i = 0; i < m_NumClusters; i++) {
temp.append("\nCluster " + i + "\n\t");
for (int j = 0; j < m_ClusterCentroids.numAttributes(); j++) {
if (m_ClusterCentroids.attribute(j).isNominal()) {
temp.append(
" "
+ m_ClusterCentroids
.attribute(j)
.value((int) m_ClusterCentroids.instance(i).value(j)));
} else {
temp.append(" " + m_ClusterCentroids.instance(i).value(j));
}
}
}
temp.append("\n\n");
return temp.toString();
}
/**
* Tests a certain range of attributes of the given data, whether it can be processed by the
* handler, given its capabilities. Classifiers implementing the <code>
* MultiInstanceCapabilitiesHandler</code> interface are checked automatically for their
* multi-instance Capabilities (if no bags, then only the bag-structure, otherwise only the first
* bag).
*
* @param data the data to test
* @param fromIndex the range of attributes - start (incl.)
* @param toIndex the range of attributes - end (incl.)
* @return true if all the tests succeeded
* @see MultiInstanceCapabilitiesHandler
* @see #m_InstancesTest
* @see #m_MissingValuesTest
* @see #m_MissingClassValuesTest
* @see #m_MinimumNumberInstancesTest
*/
public boolean test(Instances data, int fromIndex, int toIndex) {
int i;
int n;
int m;
Attribute att;
Instance inst;
boolean testClass;
Capabilities cap;
boolean missing;
Iterator iter;
// shall we test the data?
if (!m_InstancesTest) return true;
// no Capabilities? -> warning
if ((m_Capabilities.size() == 0)
|| ((m_Capabilities.size() == 1) && handles(Capability.NO_CLASS)))
System.err.println(createMessage("No capabilities set!"));
// any attributes?
if (toIndex - fromIndex < 0) {
m_FailReason = new WekaException(createMessage("No attributes!"));
return false;
}
// do wee need to test the class attribute, i.e., is the class attribute
// within the range of attributes?
testClass =
(data.classIndex() > -1)
&& (data.classIndex() >= fromIndex)
&& (data.classIndex() <= toIndex);
// attributes
for (i = fromIndex; i <= toIndex; i++) {
att = data.attribute(i);
// class is handled separately
if (i == data.classIndex()) continue;
// check attribute types
if (!test(att)) return false;
}
// class
if (!handles(Capability.NO_CLASS) && (data.classIndex() == -1)) {
m_FailReason = new UnassignedClassException(createMessage("Class attribute not set!"));
return false;
}
// special case: no class attribute can be handled
if (handles(Capability.NO_CLASS) && (data.classIndex() > -1)) {
cap = getClassCapabilities();
cap.disable(Capability.NO_CLASS);
iter = cap.capabilities();
if (!iter.hasNext()) {
m_FailReason = new WekaException(createMessage("Cannot handle any class attribute!"));
return false;
}
}
if (testClass && !handles(Capability.NO_CLASS)) {
att = data.classAttribute();
if (!test(att, true)) return false;
// special handling of RELATIONAL class
// TODO: store additional Capabilities for this case
// missing class labels
if (m_MissingClassValuesTest) {
if (!handles(Capability.MISSING_CLASS_VALUES)) {
for (i = 0; i < data.numInstances(); i++) {
if (data.instance(i).classIsMissing()) {
m_FailReason =
new WekaException(createMessage("Cannot handle missing class values!"));
return false;
}
}
} else {
if (m_MinimumNumberInstancesTest) {
int hasClass = 0;
for (i = 0; i < data.numInstances(); i++) {
if (!data.instance(i).classIsMissing()) hasClass++;
}
// not enough instances with class labels?
if (hasClass < getMinimumNumberInstances()) {
m_FailReason =
new WekaException(
createMessage(
"Not enough training instances with class labels (required: "
+ getMinimumNumberInstances()
+ ", provided: "
+ hasClass
+ ")!"));
return false;
}
}
}
}
}
// missing values
if (m_MissingValuesTest) {
if (!handles(Capability.MISSING_VALUES)) {
missing = false;
for (i = 0; i < data.numInstances(); i++) {
inst = data.instance(i);
if (inst instanceof SparseInstance) {
for (m = 0; m < inst.numValues(); m++) {
n = inst.index(m);
// out of scope?
if (n < fromIndex) continue;
if (n > toIndex) break;
// skip class
if (n == inst.classIndex()) continue;
if (inst.isMissing(n)) {
missing = true;
break;
}
}
} else {
for (n = fromIndex; n <= toIndex; n++) {
// skip class
if (n == inst.classIndex()) continue;
if (inst.isMissing(n)) {
missing = true;
break;
}
}
}
if (missing) {
m_FailReason =
new NoSupportForMissingValuesException(
createMessage("Cannot handle missing values!"));
return false;
}
}
}
}
// instances
if (m_MinimumNumberInstancesTest) {
if (data.numInstances() < getMinimumNumberInstances()) {
m_FailReason =
new WekaException(
createMessage(
"Not enough training instances (required: "
+ getMinimumNumberInstances()
+ ", provided: "
+ data.numInstances()
+ ")!"));
return false;
}
}
// Multi-Instance? -> check structure (regardless of attribute range!)
if (handles(Capability.ONLY_MULTIINSTANCE)) {
// number of attributes?
if (data.numAttributes() != 3) {
m_FailReason =
new WekaException(
createMessage("Incorrect Multi-Instance format, must be 'bag-id, bag, class'!"));
return false;
}
// type of attributes and position of class?
if (!data.attribute(0).isNominal()
|| !data.attribute(1).isRelationValued()
|| (data.classIndex() != data.numAttributes() - 1)) {
m_FailReason =
new WekaException(
createMessage(
"Incorrect Multi-Instance format, must be 'NOMINAL att, RELATIONAL att, CLASS att'!"));
return false;
}
// check data immediately
if (getOwner() instanceof MultiInstanceCapabilitiesHandler) {
MultiInstanceCapabilitiesHandler handler = (MultiInstanceCapabilitiesHandler) getOwner();
cap = handler.getMultiInstanceCapabilities();
boolean result;
if (data.numInstances() > 0) result = cap.test(data.attribute(1).relation(0));
else result = cap.test(data.attribute(1).relation());
if (!result) {
m_FailReason = cap.m_FailReason;
return false;
}
}
}
// passed all tests!
return true;
}
/**
* Tests the given data, whether it can be processed by the handler, given its capabilities.
* Classifiers implementing the <code>MultiInstanceCapabilitiesHandler</code> interface are
* checked automatically for their multi-instance Capabilities (if no bags, then only the
* bag-structure, otherwise only the first bag).
*
* @param data the data to test
* @return true if all the tests succeeded
* @see #test(Instances, int, int)
*/
public boolean test(Instances data) {
return test(data, 0, data.numAttributes() - 1);
}
/**
* returns a Capabilities object specific for this data. The minimum number of instances is not
* set, the check for multi-instance data is optional.
*
* @param data the data to base the capabilities on
* @param multi if true then the structure is checked, too
* @return a data-specific capabilities object
* @throws Exception in case an error occurrs, e.g., an unknown attribute type
*/
public static Capabilities forInstances(Instances data, boolean multi) throws Exception {
Capabilities result;
Capabilities multiInstance;
int i;
int n;
int m;
Instance inst;
boolean missing;
result = new Capabilities(null);
// class
if (data.classIndex() == -1) {
result.enable(Capability.NO_CLASS);
} else {
switch (data.classAttribute().type()) {
case Attribute.NOMINAL:
if (data.classAttribute().numValues() == 1) result.enable(Capability.UNARY_CLASS);
else if (data.classAttribute().numValues() == 2) result.enable(Capability.BINARY_CLASS);
else result.enable(Capability.NOMINAL_CLASS);
break;
case Attribute.NUMERIC:
result.enable(Capability.NUMERIC_CLASS);
break;
case Attribute.STRING:
result.enable(Capability.STRING_CLASS);
break;
case Attribute.DATE:
result.enable(Capability.DATE_CLASS);
break;
case Attribute.RELATIONAL:
result.enable(Capability.RELATIONAL_CLASS);
break;
default:
throw new UnsupportedAttributeTypeException(
"Unknown class attribute type '" + data.classAttribute() + "'!");
}
// missing class values
for (i = 0; i < data.numInstances(); i++) {
if (data.instance(i).classIsMissing()) {
result.enable(Capability.MISSING_CLASS_VALUES);
break;
}
}
}
// attributes
for (i = 0; i < data.numAttributes(); i++) {
// skip class
if (i == data.classIndex()) continue;
switch (data.attribute(i).type()) {
case Attribute.NOMINAL:
result.enable(Capability.UNARY_ATTRIBUTES);
if (data.attribute(i).numValues() == 2) result.enable(Capability.BINARY_ATTRIBUTES);
else if (data.attribute(i).numValues() > 2) result.enable(Capability.NOMINAL_ATTRIBUTES);
break;
case Attribute.NUMERIC:
result.enable(Capability.NUMERIC_ATTRIBUTES);
break;
case Attribute.DATE:
result.enable(Capability.DATE_ATTRIBUTES);
break;
case Attribute.STRING:
result.enable(Capability.STRING_ATTRIBUTES);
break;
case Attribute.RELATIONAL:
result.enable(Capability.RELATIONAL_ATTRIBUTES);
break;
default:
throw new UnsupportedAttributeTypeException(
"Unknown attribute type '" + data.attribute(i).type() + "'!");
}
}
// missing values
missing = false;
for (i = 0; i < data.numInstances(); i++) {
inst = data.instance(i);
if (inst instanceof SparseInstance) {
for (m = 0; m < inst.numValues(); m++) {
n = inst.index(m);
// skip class
if (n == inst.classIndex()) continue;
if (inst.isMissing(n)) {
missing = true;
break;
}
}
} else {
for (n = 0; n < data.numAttributes(); n++) {
// skip class
if (n == inst.classIndex()) continue;
if (inst.isMissing(n)) {
missing = true;
break;
}
}
}
if (missing) {
result.enable(Capability.MISSING_VALUES);
break;
}
}
// multi-instance data?
if (multi) {
if ((data.numAttributes() == 3)
&& (data.attribute(0).isNominal()) // bag-id
&& (data.attribute(1).isRelationValued()) // bag
&& (data.classIndex() == data.numAttributes() - 1)) {
multiInstance = new Capabilities(null);
multiInstance.or(result.getClassCapabilities());
multiInstance.enable(Capability.NOMINAL_ATTRIBUTES);
multiInstance.enable(Capability.RELATIONAL_ATTRIBUTES);
multiInstance.enable(Capability.ONLY_MULTIINSTANCE);
result.assign(multiInstance);
}
}
return result;
}
/**
* ************************************************** Convert a table to a set of instances, with
* <b>columns</b> representing individual </b>instances</b> and <b>rows</b> representing
* <b>attributes</b> (e.g. as is common with microarray data)
*/
public Instances tableColsToInstances(Table t, String relationName) {
System.err.print("Converting table cols to instances...");
// Set up attributes, which for colInstances will be the rowNames...
FastVector atts = new FastVector();
ArrayList<Boolean> isNominal = new ArrayList<Boolean>();
ArrayList<FastVector> allAttVals = new ArrayList<FastVector>(); // Save values for later...
System.err.print("creating attributes...");
for (int r = 0; r < t.numRows; r++) {
if (rowIsNumeric(t, r)) {
isNominal.add(false);
atts.addElement(new Attribute(t.rowNames[r]));
allAttVals.add(null); // No enumeration of attribute values.
} else {
// It's nominal... determine the range of values and create a nominal attribute...
isNominal.add(true);
FastVector attVals = getRowValues(t, r);
atts.addElement(new Attribute(t.rowNames[r], attVals));
// Save it for later
allAttVals.add(attVals);
}
}
System.err.print("creating instances...");
// Create Instances object..
Instances data = new Instances(relationName, atts, 0);
data.setRelationName(relationName);
/** ***** CREATE INSTANCES ************* */
// Fill the instances with data...
// For each instance...
for (int c = 0; c < t.numCols; c++) {
double[] vals =
new double[data.numAttributes()]; // Even nominal values are stored as double pointers.
// For each attribute fill in the numeric or attributeValue index...
for (int r = 0; r < t.numRows; r++) {
String val = (String) t.matrix.getQuick(r, c);
if (val == "?") vals[r] = Instance.missingValue();
else if (isNominal.get(r)) {
vals[r] = allAttVals.get(r).indexOf(val);
} else {
vals[r] = Double.parseDouble((String) val);
}
}
// Add the a newly minted instance with those attribute values...
data.add(new Instance(1.0, vals));
}
System.err.print("add feature names...");
/** ***** ADD FEATURE NAMES ************* */
// takes basically zero time... all time is in previous 2 chunks.
if (addInstanceNamesAsFeatures) {
Instances newData = new Instances(data);
newData.insertAttributeAt(new Attribute("ID", (FastVector) null), 0);
int attrIdx = newData.attribute("ID").index(); // Paranoid... should be 0
// We save the instanceNames in a list because it's handy later on...
instanceNames = new ArrayList<String>();
for (int c = 0; c < t.colNames.length; c++) {
instanceNames.add(t.colNames[c]);
newData.instance(c).setValue(attrIdx, t.colNames[c]);
}
data = newData;
}
System.err.println("done.");
return (data);
}
/**
* ************************************************** Convert a table to a set of instances, with
* <b>rows</b> representing individual </b>instances</b> and <b>columns</b> representing
* <b>attributes</b>
*/
public Instances tableRowsToNominalInstances(Table t, String relationName) {
System.err.print("Converting table rows to instances...");
// Set up attributes, which for rowInstances will be the colNames...
FastVector atts = new FastVector();
ArrayList<Boolean> isNominal = new ArrayList<Boolean>();
ArrayList<FastVector> allAttVals = new ArrayList<FastVector>(); // Save values for later...
System.err.print("creating attributes...");
for (int c = 0; c < t.numCols; c++) {
// It's nominal... determine the range of values
isNominal.add(true);
FastVector attVals = getColValues(t, c);
atts.addElement(new Attribute(t.colNames[c], attVals));
// Save it for later
allAttVals.add(attVals);
}
System.err.print("creating instances...");
// Create Instances object..
Instances data = new Instances(relationName, atts, 0);
data.setRelationName(relationName);
// Fill the instances with data...
// For each instance...
for (int r = 0; r < t.numRows; r++) {
double[] vals = new double[data.numAttributes()];
// for each attribute
for (int c = 0; c < t.numCols; c++) {
String val = (String) t.matrix.getQuick(r, c);
if (val == "?") vals[c] = Instance.missingValue();
else if (isNominal.get(c)) {
vals[c] = allAttVals.get(c).indexOf(val);
} else {
vals[c] = Double.parseDouble((String) val);
}
}
// Add the a newly minted instance with those attribute values...
data.add(new Instance(1.0, vals));
}
System.err.print("add feature names...");
if (addInstanceNamesAsFeatures) {
Instances newData = new Instances(data);
newData.insertAttributeAt(new Attribute("ID", (FastVector) null), 0);
int attrIdx = newData.attribute("ID").index(); // Paranoid... should be 0
// We save the instanceNames in a list because it's handy later on...
instanceNames = new ArrayList<String>();
for (int r = 0; r < t.rowNames.length; r++) {
instanceNames.add(t.rowNames[r]);
newData.instance(r).setValue(attrIdx, t.rowNames[r]);
}
data = newData;
}
System.err.println("done.");
return (data);
}
/**
* If we know in advance that the table is numeric, can optimize a lot... For example, on 9803 x
* 294 table, TableFileLoader.readNumeric takes 6s compared to 12s for WekaMine readFromTable.
*/
public static Instances readNumeric(String fileName, String relationName, String delimiter)
throws Exception {
int numAttributes = FileUtils.fastCountLines(fileName) - 1; // -1 exclude heading.
String[] attrNames = new String[numAttributes];
// Read the col headings and figure out the number of columns in the table..
BufferedReader reader = new BufferedReader(new FileReader(fileName), 4194304);
String line = reader.readLine();
String[] instanceNames = parseColNames(line, delimiter);
int numInstances = instanceNames.length;
System.err.print("reading " + numAttributes + " x " + numInstances + " table..");
// Create an array to hold the data as we read it in...
double dataArray[][] = new double[numAttributes][numInstances];
// Populate the matrix with values...
String valToken = "";
try {
int rowIdx = 0;
while ((line = reader.readLine()) != null) {
String[] tokens = line.split(delimiter, -1);
attrNames[rowIdx] = tokens[0].trim();
for (int colIdx = 0; colIdx < (tokens.length - 1); colIdx++) {
valToken = tokens[colIdx + 1];
double value;
if (valToken.equals("null")) {
value = Instance.missingValue();
} else if (valToken.equals("?")) {
value = Instance.missingValue();
} else if (valToken.equals("NA")) {
value = Instance.missingValue();
} else if (valToken.equals("")) {
value = Instance.missingValue();
// }else value = DoubleParser.lightningParse(valToken); // faster double parser with
// MANY assumptions
} else value = Double.parseDouble(valToken);
dataArray[rowIdx][colIdx] = value;
}
rowIdx++;
}
} catch (NumberFormatException e) {
System.err.println(e.toString());
System.err.println("Parsing line: " + line);
System.err.println("Parsing token: " + valToken);
}
// Set up attributes, which for colInstances will be the rowNames...
FastVector atts = new FastVector();
for (int a = 0; a < numAttributes; a++) {
atts.addElement(new Attribute(attrNames[a]));
}
// Create Instances object..
Instances data = new Instances(relationName, atts, 0);
data.setRelationName(relationName);
System.err.print("creating instances..");
// System.err.println("DEBUG: numAttributes "+numAttributes);
/** ***** CREATE INSTANCES ************* */
// Fill the instances with data...
// For each instance...
for (int c = 0; c < numInstances; c++) {
double[] vals =
new double[data.numAttributes()]; // Even nominal values are stored as double pointers.
for (int r = 0; r < numAttributes; r++) {
double val = dataArray[r][c];
vals[r] = val;
}
// Add the a newly minted instance with those attribute values...
data.add(new Instance(1.0, vals));
}
// System.err.println("DEBUG: data.numInstances: "+data.numInstances());
// System.err.println("DEBUG: data.numAttributes: "+data.numAttributes());
// System.err.println("DEBUG: data.relationNAme"+data.relationName());
System.err.print("add feature names..");
/** ***** ADD FEATURE NAMES ************* */
// takes basically zero time... all time is in previous 2 chunks.
Instances newData = new Instances(data);
newData.insertAttributeAt(new Attribute("ID", (FastVector) null), 0);
int attrIdx = newData.attribute("ID").index(); // Paranoid... should be 0
for (int c = 0; c < numInstances; c++) {
newData.instance(c).setValue(attrIdx, instanceNames[c]);
}
data = newData;
// System.err.println("DEBUG: data.numInstances: "+data.numInstances());
// System.err.println("DEBUG: data.numAttributes: "+data.numAttributes());
return (data);
}
// tri-training学习过程；
public void training() {
int length_L = 0;
int up_int = 0;
double temp = 0.0;
// 直到没有分类器发生更新时，跳出循环；
while (update1 || update2 || update3) {
update1 = false;
update2 = false;
update3 = false;
for (int i = 0; i < 3; i++) {
ins_Array[i] = new Instances(testIns, 0);
ins_Array[i].setClassIndex(testIns.numAttributes() - 1);
switch (i) {
case 0:
j = 1;
k = 2;
break;
case 1:
j = 0;
k = 2;
break;
case 2:
j = 0;
k = 1;
break;
}
// 获得用于加强第i个分类器的其它两个分类器j,k的分类错误率；
err_Array[i] = measureBothError(class_Array[j], class_Array[k], this.unlabeledIns);
// 如果这个两个分类器j,k的分类错误率小于前一次的时候，运用这两个分类器为第i个分类器标记样本；
if (err_Array[i] < error[i]) {
// 获得两个分类器j,k分类做出相同决策得到的样本集合ins_Array[i]
this.updateL(class_Array[j], class_Array[k], ins_Array[i], this.unlabeledIns);
length_L = ins_Array[i].numInstances();
if (length[i] == 0) {
// System.out.println("err_array[i] =" + err_Array[i] + " err=" + error );
length[i] = this.getDownInt(err_Array[i], error[i]);
// System.out.println("length[i] =" + length[i]);
}
if (length[i] < length_L) {
if (err_Array[i] * length_L < error[i] * length[i]) {
this.update[i] = true;
} else if (length[i] > (err_Array[i] / (error[i] - err_Array[i]))) {
up_int = this.getUpInt(err_Array[i], error[i], length[i]);
// System.out.println("err_array[i] =" + err_Array[i] + " err=" + error + "length:=" +
// length[i]);
// System.out.println("up_int=" + up_int );
this.SubSample(this.ins_Array[i], up_int);
this.update[i] = true;
}
}
}
}
// 更新分类器
for (int i = 0; i < 3; i++) {
// 如果第i个分类器的update为true,更新该分类器；
if (this.update[i]) {
try {
this.class_Array[i].buildClassifier(Util.add(this.instan_Array[i], this.ins_Array[i]));
temp = Util.errorRate(this.class_Array[i], this.testIns);
// 如果分类器更新以后的分类错误率比以前高，则恢复分类器到未更新时的状态。 这一点与论文中的算法有一点点不同。
// 论文中没有这一步的判断。
if (temp > err_Classifier[i]) {
this.update[i] = false;
this.class_Array[i].buildClassifier(this.instan_Array[i]);
} else {
// 如果分类器的分类错误率下降了，则更新length[i]以及error[i];
length[i] = this.ins_Array[i].numInstances();
error[i] = err_Array[i];
}
} catch (Exception e) {
System.out.println(e);
}
}
}
}
}
/**
* Returns a description of the classifier.
*
* @return a description of the classifier as a string.
*/
public String toString() {
if (m_entries == null) {
return "Decision Table: No model built yet.";
} else {
StringBuffer text = new StringBuffer();
text.append(
"Decision Table:"
+ "\n\nNumber of training instances: "
+ m_numInstances
+ "\nNumber of Rules : "
+ m_entries.size()
+ "\n");
if (m_useIBk) {
text.append("Non matches covered by IB1.\n");
} else {
text.append("Non matches covered by Majority class.\n");
}
text.append(m_search.toString());
/*text.append("Best first search for feature set,\nterminated after "+
m_maxStale+" non improving subsets.\n"); */
text.append("Evaluation (for feature selection): CV ");
if (m_CVFolds > 1) {
text.append("(" + m_CVFolds + " fold) ");
} else {
text.append("(leave one out) ");
}
text.append("\nFeature set: " + printFeatures());
if (m_displayRules) {
// find out the max column width
int maxColWidth = 0;
for (int i = 0; i < m_dtInstances.numAttributes(); i++) {
if (m_dtInstances.attribute(i).name().length() > maxColWidth) {
maxColWidth = m_dtInstances.attribute(i).name().length();
}
if (m_classIsNominal || (i != m_dtInstances.classIndex())) {
Enumeration e = m_dtInstances.attribute(i).enumerateValues();
while (e.hasMoreElements()) {
String ss = (String) e.nextElement();
if (ss.length() > maxColWidth) {
maxColWidth = ss.length();
}
}
}
}
text.append("\n\nRules:\n");
StringBuffer tm = new StringBuffer();
for (int i = 0; i < m_dtInstances.numAttributes(); i++) {
if (m_dtInstances.classIndex() != i) {
int d = maxColWidth - m_dtInstances.attribute(i).name().length();
tm.append(m_dtInstances.attribute(i).name());
for (int j = 0; j < d + 1; j++) {
tm.append(" ");
}
}
}
tm.append(m_dtInstances.attribute(m_dtInstances.classIndex()).name() + " ");
for (int i = 0; i < tm.length() + 10; i++) {
text.append("=");
}
text.append("\n");
text.append(tm);
text.append("\n");
for (int i = 0; i < tm.length() + 10; i++) {
text.append("=");
}
text.append("\n");
Enumeration e = m_entries.keys();
while (e.hasMoreElements()) {
DecisionTableHashKey tt = (DecisionTableHashKey) e.nextElement();
text.append(tt.toString(m_dtInstances, maxColWidth));
double[] ClassDist = (double[]) m_entries.get(tt);
if (m_classIsNominal) {
int m = Utils.maxIndex(ClassDist);
try {
text.append(m_dtInstances.classAttribute().value(m) + "\n");
} catch (Exception ee) {
System.out.println(ee.getMessage());
}
} else {
text.append((ClassDist[0] / ClassDist[1]) + "\n");
}
}
for (int i = 0; i < tm.length() + 10; i++) {
text.append("=");
}
text.append("\n");
text.append("\n");
}
return text.toString();
}
}
/**
* Generates the classifier.
*
* @param data set of instances serving as training data
* @throws Exception if the classifier has not been generated successfully
*/
public void buildClassifier(Instances data) throws Exception {
// can classifier handle the data?
getCapabilities().testWithFail(data);
// remove instances with missing class
m_theInstances = new Instances(data);
m_theInstances.deleteWithMissingClass();
m_rr = new Random(1);
if (m_theInstances.classAttribute().isNominal()) { // Set up class priors
m_classPriorCounts = new double[data.classAttribute().numValues()];
Arrays.fill(m_classPriorCounts, 1.0);
for (int i = 0; i < data.numInstances(); i++) {
Instance curr = data.instance(i);
m_classPriorCounts[(int) curr.classValue()] += curr.weight();
}
m_classPriors = m_classPriorCounts.clone();
Utils.normalize(m_classPriors);
}
setUpEvaluator();
if (m_theInstances.classAttribute().isNumeric()) {
m_disTransform = new weka.filters.unsupervised.attribute.Discretize();
m_classIsNominal = false;
// use binned discretisation if the class is numeric
((weka.filters.unsupervised.attribute.Discretize) m_disTransform).setBins(10);
((weka.filters.unsupervised.attribute.Discretize) m_disTransform).setInvertSelection(true);
// Discretize all attributes EXCEPT the class
String rangeList = "";
rangeList += (m_theInstances.classIndex() + 1);
// System.out.println("The class col: "+m_theInstances.classIndex());
((weka.filters.unsupervised.attribute.Discretize) m_disTransform)
.setAttributeIndices(rangeList);
} else {
m_disTransform = new weka.filters.supervised.attribute.Discretize();
((weka.filters.supervised.attribute.Discretize) m_disTransform).setUseBetterEncoding(true);
m_classIsNominal = true;
}
m_disTransform.setInputFormat(m_theInstances);
m_theInstances = Filter.useFilter(m_theInstances, m_disTransform);
m_numAttributes = m_theInstances.numAttributes();
m_numInstances = m_theInstances.numInstances();
m_majority = m_theInstances.meanOrMode(m_theInstances.classAttribute());
// Perform the search
int[] selected = m_search.search(m_evaluator, m_theInstances);
m_decisionFeatures = new int[selected.length + 1];
System.arraycopy(selected, 0, m_decisionFeatures, 0, selected.length);
m_decisionFeatures[m_decisionFeatures.length - 1] = m_theInstances.classIndex();
// reduce instances to selected features
m_delTransform = new Remove();
m_delTransform.setInvertSelection(true);
// set features to keep
m_delTransform.setAttributeIndicesArray(m_decisionFeatures);
m_delTransform.setInputFormat(m_theInstances);
m_dtInstances = Filter.useFilter(m_theInstances, m_delTransform);
// reset the number of attributes
m_numAttributes = m_dtInstances.numAttributes();
// create hash table
m_entries = new Hashtable((int) (m_dtInstances.numInstances() * 1.5));
// insert instances into the hash table
for (int i = 0; i < m_numInstances; i++) {
Instance inst = m_dtInstances.instance(i);
insertIntoTable(inst, null);
}
// Replace the global table majority with nearest neighbour?
if (m_useIBk) {
m_ibk = new IBk();
m_ibk.buildClassifier(m_theInstances);
}
// Save memory
if (m_saveMemory) {
m_theInstances = new Instances(m_theInstances, 0);
m_dtInstances = new Instances(m_dtInstances, 0);
}
m_evaluation = null;
}
