/**
* Builds a regression model for the given data.
*
* @param data the training data to be used for generating the linear regression function
* @throws Exception if the classifier could not be built successfully
*/
public void buildClassifier(Instances data) throws Exception {
if (!m_checksTurnedOff) {
// can classifier handle the data?
getCapabilities().testWithFail(data);
// remove instances with missing class
data = new Instances(data);
data.deleteWithMissingClass();
}
// Preprocess instances
if (!m_checksTurnedOff) {
m_TransformFilter = new NominalToBinary();
m_TransformFilter.setInputFormat(data);
data = Filter.useFilter(data, m_TransformFilter);
m_MissingFilter = new ReplaceMissingValues();
m_MissingFilter.setInputFormat(data);
data = Filter.useFilter(data, m_MissingFilter);
data.deleteWithMissingClass();
} else {
m_TransformFilter = null;
m_MissingFilter = null;
}
m_ClassIndex = data.classIndex();
m_TransformedData = data;
// Turn all attributes on for a start
m_SelectedAttributes = new boolean[data.numAttributes()];
for (int i = 0; i < data.numAttributes(); i++) {
if (i != m_ClassIndex) {
m_SelectedAttributes[i] = true;
}
}
m_Coefficients = null;
// Compute means and standard deviations
m_Means = new double[data.numAttributes()];
m_StdDevs = new double[data.numAttributes()];
for (int j = 0; j < data.numAttributes(); j++) {
if (j != data.classIndex()) {
m_Means[j] = data.meanOrMode(j);
m_StdDevs[j] = Math.sqrt(data.variance(j));
if (m_StdDevs[j] == 0) {
m_SelectedAttributes[j] = false;
}
}
}
m_ClassStdDev = Math.sqrt(data.variance(m_TransformedData.classIndex()));
m_ClassMean = data.meanOrMode(m_TransformedData.classIndex());
// Perform the regression
findBestModel();
// Save memory
m_TransformedData = new Instances(data, 0);
}
/**
* 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.");
}
}
/**
* @param ex the given test exemplar
* @return the classification
* @throws Exception if the exemplar could not be classified successfully
*/
public double classifyInstance(Instance ex) throws Exception {
// Instance ex = new Exemplar(e);
Instances exi = ex.relationalValue(1);
double[] n = new double[m_Dimension];
double[] xBar = new double[m_Dimension];
for (int i = 0; i < exi.numAttributes(); i++) xBar[i] = exi.meanOrMode(i);
for (int w = 0, t = 0; w < m_Dimension; w++, t++) {
// if((t==m_ClassIndex) || (t==m_IdIndex))
// t++;
for (int u = 0; u < exi.numInstances(); u++)
if (!exi.instance(u).isMissing(t)) n[w] += exi.instance(u).weight();
}
double logOdds = likelihoodRatio(n, xBar);
return (logOdds > m_Cutoff) ? 1 : 0;
}
/**
* Calculate metric value
*
* @param mlData Multi-label dataset to which calculate the metric
* @return Value of the metric
*/
public double calculate(MultiLabelInstances mlData) {
Instances instances = mlData.getDataSet();
int nInstances = mlData.getNumInstances();
double avg;
double var2;
double var4;
double val;
int nNumeric = 0;
double mean = 0;
Set<Attribute> attributesSet = mlData.getFeatureAttributes();
for (Attribute att : attributesSet) {
if (att.isNumeric()) {
nNumeric++;
avg = instances.meanOrMode(att);
var2 = 0;
var4 = 0;
for (Instance inst : instances) {
val = inst.value(att);
var2 += Math.pow(val - avg, 2);
var4 += Math.pow(val - avg, 4);
}
double kurtosis = (nInstances * var4 / Math.pow(var2, 2)) - 3;
double sampleKurtosis =
(kurtosis * (nInstances + 1) + 6)
* (nInstances - 1)
/ ((nInstances - 2) * (nInstances - 3));
mean += sampleKurtosis;
}
}
if (nNumeric > 0) {
mean = mean / nNumeric;
} else {
mean = Double.NaN;
}
this.value = mean;
return value;
}
/**
* Signify that this batch of input to the filter is finished. If the filter requires all
* instances prior to filtering, output() may now be called to retrieve the filtered instances.
*
* @return true if there are instances pending output
* @throws IllegalStateException if no input structure has been defined
*/
public boolean batchFinished() {
if (getInputFormat() == null)
throw new IllegalStateException("No input instance format defined");
if (m_Means == null) {
Instances input = getInputFormat();
m_Means = new double[input.numAttributes()];
for (int i = 0; i < input.numAttributes(); i++) {
if (input.attribute(i).isNumeric() && (input.classIndex() != i)) {
m_Means[i] = input.meanOrMode(i);
}
}
// Convert pending input instances
for (int i = 0; i < input.numInstances(); i++) convertInstance(input.instance(i));
}
// Free memory
flushInput();
m_NewBatch = true;
return (numPendingOutput() != 0);
}
/**
* Computes the distribution for a given exemplar
*
* @param ex the exemplar for which distribution is computed
* @return the distribution
* @throws Exception if the distribution can't be computed successfully
*/
public double[] distributionForInstance(Instance ex) throws Exception {
double[] distribution = new double[2];
Instances exi = ex.relationalValue(1);
double[] n = new double[m_Dimension];
double[] xBar = new double[m_Dimension];
for (int i = 0; i < exi.numAttributes(); i++) xBar[i] = exi.meanOrMode(i);
for (int w = 0, t = 0; w < m_Dimension; w++, t++) {
for (int u = 0; u < exi.numInstances(); u++)
if (!exi.instance(u).isMissing(t)) n[w] += exi.instance(u).weight();
}
double logOdds = likelihoodRatio(n, xBar);
// returned logOdds value has been divided by m_Dimension to avoid
// Math.exp(logOdds) getting too large or too small,
// that may result in two fixed distribution value (1 or 0).
distribution[0] = 1 / (1 + Math.exp(logOdds)); // Prob. for class 0 (negative)
distribution[1] = 1 - distribution[0];
return distribution;
}
/**
* @param exs the training exemplars
* @throws Exception if the model cannot be built properly
*/
public void buildClassifier(Instances exs) throws Exception {
// can classifier handle the data?
getCapabilities().testWithFail(exs);
// remove instances with missing class
exs = new Instances(exs);
exs.deleteWithMissingClass();
int numegs = exs.numInstances();
m_Dimension = exs.attribute(1).relation().numAttributes();
Instances pos = new Instances(exs, 0), neg = new Instances(exs, 0);
for (int u = 0; u < numegs; u++) {
Instance example = exs.instance(u);
if (example.classValue() == 1) pos.add(example);
else neg.add(example);
}
int pnum = pos.numInstances(), nnum = neg.numInstances();
m_MeanP = new double[pnum][m_Dimension];
m_VarianceP = new double[pnum][m_Dimension];
m_SumP = new double[pnum][m_Dimension];
m_MeanN = new double[nnum][m_Dimension];
m_VarianceN = new double[nnum][m_Dimension];
m_SumN = new double[nnum][m_Dimension];
m_ParamsP = new double[4 * m_Dimension];
m_ParamsN = new double[4 * m_Dimension];
// Estimation of the parameters: as the start value for search
double[] pSumVal = new double[m_Dimension], // for m
nSumVal = new double[m_Dimension];
double[] maxVarsP = new double[m_Dimension], // for a
maxVarsN = new double[m_Dimension];
// Mean of sample variances: for b, b=a/E(\sigma^2)+2
double[] varMeanP = new double[m_Dimension], varMeanN = new double[m_Dimension];
// Variances of sample means: for w, w=E[var(\mu)]/E[\sigma^2]
double[] meanVarP = new double[m_Dimension], meanVarN = new double[m_Dimension];
// number of exemplars without all values missing
double[] numExsP = new double[m_Dimension], numExsN = new double[m_Dimension];
// Extract metadata fro both positive and negative bags
for (int v = 0; v < pnum; v++) {
/*Exemplar px = pos.exemplar(v);
m_MeanP[v] = px.meanOrMode();
m_VarianceP[v] = px.variance();
Instances pxi = px.getInstances();
*/
Instances pxi = pos.instance(v).relationalValue(1);
for (int k = 0; k < pxi.numAttributes(); k++) {
m_MeanP[v][k] = pxi.meanOrMode(k);
m_VarianceP[v][k] = pxi.variance(k);
}
for (int w = 0, t = 0; w < m_Dimension; w++, t++) {
// if((t==m_ClassIndex) || (t==m_IdIndex))
// t++;
if (!Double.isNaN(m_MeanP[v][w])) {
for (int u = 0; u < pxi.numInstances(); u++) {
Instance ins = pxi.instance(u);
if (!ins.isMissing(t)) m_SumP[v][w] += ins.weight();
}
numExsP[w]++;
pSumVal[w] += m_MeanP[v][w];
meanVarP[w] += m_MeanP[v][w] * m_MeanP[v][w];
if (maxVarsP[w] < m_VarianceP[v][w]) maxVarsP[w] = m_VarianceP[v][w];
varMeanP[w] += m_VarianceP[v][w];
m_VarianceP[v][w] *= (m_SumP[v][w] - 1.0);
if (m_VarianceP[v][w] < 0.0) m_VarianceP[v][w] = 0.0;
}
}
}
for (int v = 0; v < nnum; v++) {
/*Exemplar nx = neg.exemplar(v);
m_MeanN[v] = nx.meanOrMode();
m_VarianceN[v] = nx.variance();
Instances nxi = nx.getInstances();
*/
Instances nxi = neg.instance(v).relationalValue(1);
for (int k = 0; k < nxi.numAttributes(); k++) {
m_MeanN[v][k] = nxi.meanOrMode(k);
m_VarianceN[v][k] = nxi.variance(k);
}
for (int w = 0, t = 0; w < m_Dimension; w++, t++) {
// if((t==m_ClassIndex) || (t==m_IdIndex))
// t++;
if (!Double.isNaN(m_MeanN[v][w])) {
for (int u = 0; u < nxi.numInstances(); u++)
if (!nxi.instance(u).isMissing(t)) m_SumN[v][w] += nxi.instance(u).weight();
numExsN[w]++;
nSumVal[w] += m_MeanN[v][w];
meanVarN[w] += m_MeanN[v][w] * m_MeanN[v][w];
if (maxVarsN[w] < m_VarianceN[v][w]) maxVarsN[w] = m_VarianceN[v][w];
varMeanN[w] += m_VarianceN[v][w];
m_VarianceN[v][w] *= (m_SumN[v][w] - 1.0);
if (m_VarianceN[v][w] < 0.0) m_VarianceN[v][w] = 0.0;
}
}
}
for (int w = 0; w < m_Dimension; w++) {
pSumVal[w] /= numExsP[w];
nSumVal[w] /= numExsN[w];
if (numExsP[w] > 1)
meanVarP[w] =
meanVarP[w] / (numExsP[w] - 1.0) - pSumVal[w] * numExsP[w] / (numExsP[w] - 1.0);
if (numExsN[w] > 1)
meanVarN[w] =
meanVarN[w] / (numExsN[w] - 1.0) - nSumVal[w] * numExsN[w] / (numExsN[w] - 1.0);
varMeanP[w] /= numExsP[w];
varMeanN[w] /= numExsN[w];
}
// Bounds and parameter values for each run
double[][] bounds = new double[2][4];
double[] pThisParam = new double[4], nThisParam = new double[4];
// Initial values for parameters
double a, b, w, m;
// Optimize for one dimension
for (int x = 0; x < m_Dimension; x++) {
if (getDebug()) System.err.println("\n\n!!!!!!!!!!!!!!!!!!!!!!???Dimension #" + x);
// Positive examplars: first run
a = (maxVarsP[x] > ZERO) ? maxVarsP[x] : 1.0;
if (varMeanP[x] <= ZERO) varMeanP[x] = ZERO; // modified by LinDong (09/2005)
b = a / varMeanP[x] + 2.0; // a/(b-2) = E(\sigma^2)
w = meanVarP[x] / varMeanP[x]; // E[var(\mu)] = w*E[\sigma^2]
if (w <= ZERO) w = 1.0;
m = pSumVal[x];
pThisParam[0] = a; // a
pThisParam[1] = b; // b
pThisParam[2] = w; // w
pThisParam[3] = m; // m
// Negative examplars: first run
a = (maxVarsN[x] > ZERO) ? maxVarsN[x] : 1.0;
if (varMeanN[x] <= ZERO) varMeanN[x] = ZERO; // modified by LinDong (09/2005)
b = a / varMeanN[x] + 2.0; // a/(b-2) = E(\sigma^2)
w = meanVarN[x] / varMeanN[x]; // E[var(\mu)] = w*E[\sigma^2]
if (w <= ZERO) w = 1.0;
m = nSumVal[x];
nThisParam[0] = a; // a
nThisParam[1] = b; // b
nThisParam[2] = w; // w
nThisParam[3] = m; // m
// Bound constraints
bounds[0][0] = ZERO; // a > 0
bounds[0][1] = 2.0 + ZERO; // b > 2
bounds[0][2] = ZERO; // w > 0
bounds[0][3] = Double.NaN;
for (int t = 0; t < 4; t++) {
bounds[1][t] = Double.NaN;
m_ParamsP[4 * x + t] = pThisParam[t];
m_ParamsN[4 * x + t] = nThisParam[t];
}
double pminVal = Double.MAX_VALUE, nminVal = Double.MAX_VALUE;
Random whichEx = new Random(m_Seed);
TLD_Optm pOp = null, nOp = null;
boolean isRunValid = true;
double[] sumP = new double[pnum], meanP = new double[pnum], varP = new double[pnum];
double[] sumN = new double[nnum], meanN = new double[nnum], varN = new double[nnum];
// One dimension
for (int p = 0; p < pnum; p++) {
sumP[p] = m_SumP[p][x];
meanP[p] = m_MeanP[p][x];
varP[p] = m_VarianceP[p][x];
}
for (int q = 0; q < nnum; q++) {
sumN[q] = m_SumN[q][x];
meanN[q] = m_MeanN[q][x];
varN[q] = m_VarianceN[q][x];
}
for (int y = 0; y < m_Run; ) {
if (getDebug()) System.err.println("\n\n!!!!!!!!!!!!!!!!!!!!!!???Run #" + y);
double thisMin;
if (getDebug()) System.err.println("\nPositive exemplars");
pOp = new TLD_Optm();
pOp.setNum(sumP);
pOp.setSSquare(varP);
pOp.setXBar(meanP);
pThisParam = pOp.findArgmin(pThisParam, bounds);
while (pThisParam == null) {
pThisParam = pOp.getVarbValues();
if (getDebug()) System.err.println("!!! 200 iterations finished, not enough!");
pThisParam = pOp.findArgmin(pThisParam, bounds);
}
thisMin = pOp.getMinFunction();
if (!Double.isNaN(thisMin) && (thisMin < pminVal)) {
pminVal = thisMin;
for (int z = 0; z < 4; z++) m_ParamsP[4 * x + z] = pThisParam[z];
}
if (Double.isNaN(thisMin)) {
pThisParam = new double[4];
isRunValid = false;
}
if (getDebug()) System.err.println("\nNegative exemplars");
nOp = new TLD_Optm();
nOp.setNum(sumN);
nOp.setSSquare(varN);
nOp.setXBar(meanN);
nThisParam = nOp.findArgmin(nThisParam, bounds);
while (nThisParam == null) {
nThisParam = nOp.getVarbValues();
if (getDebug()) System.err.println("!!! 200 iterations finished, not enough!");
nThisParam = nOp.findArgmin(nThisParam, bounds);
}
thisMin = nOp.getMinFunction();
if (!Double.isNaN(thisMin) && (thisMin < nminVal)) {
nminVal = thisMin;
for (int z = 0; z < 4; z++) m_ParamsN[4 * x + z] = nThisParam[z];
}
if (Double.isNaN(thisMin)) {
nThisParam = new double[4];
isRunValid = false;
}
if (!isRunValid) {
y--;
isRunValid = true;
}
if (++y < m_Run) {
// Change the initial parameters and restart
int pone = whichEx.nextInt(pnum), // Randomly pick one pos. exmpl.
none = whichEx.nextInt(nnum);
// Positive exemplars: next run
while ((m_SumP[pone][x] <= 1.0) || Double.isNaN(m_MeanP[pone][x]))
pone = whichEx.nextInt(pnum);
a = m_VarianceP[pone][x] / (m_SumP[pone][x] - 1.0);
if (a <= ZERO) a = m_ParamsN[4 * x]; // Change to negative params
m = m_MeanP[pone][x];
double sq = (m - m_ParamsP[4 * x + 3]) * (m - m_ParamsP[4 * x + 3]);
b = a * m_ParamsP[4 * x + 2] / sq + 2.0; // b=a/Var+2, assuming Var=Sq/w'
if ((b <= ZERO) || Double.isNaN(b) || Double.isInfinite(b)) b = m_ParamsN[4 * x + 1];
w =
sq
* (m_ParamsP[4 * x + 1] - 2.0)
/ m_ParamsP[4 * x]; // w=Sq/Var, assuming Var=a'/(b'-2)
if ((w <= ZERO) || Double.isNaN(w) || Double.isInfinite(w)) w = m_ParamsN[4 * x + 2];
pThisParam[0] = a; // a
pThisParam[1] = b; // b
pThisParam[2] = w; // w
pThisParam[3] = m; // m
// Negative exemplars: next run
while ((m_SumN[none][x] <= 1.0) || Double.isNaN(m_MeanN[none][x]))
none = whichEx.nextInt(nnum);
a = m_VarianceN[none][x] / (m_SumN[none][x] - 1.0);
if (a <= ZERO) a = m_ParamsP[4 * x];
m = m_MeanN[none][x];
sq = (m - m_ParamsN[4 * x + 3]) * (m - m_ParamsN[4 * x + 3]);
b = a * m_ParamsN[4 * x + 2] / sq + 2.0; // b=a/Var+2, assuming Var=Sq/w'
if ((b <= ZERO) || Double.isNaN(b) || Double.isInfinite(b)) b = m_ParamsP[4 * x + 1];
w =
sq
* (m_ParamsN[4 * x + 1] - 2.0)
/ m_ParamsN[4 * x]; // w=Sq/Var, assuming Var=a'/(b'-2)
if ((w <= ZERO) || Double.isNaN(w) || Double.isInfinite(w)) w = m_ParamsP[4 * x + 2];
nThisParam[0] = a; // a
nThisParam[1] = b; // b
nThisParam[2] = w; // w
nThisParam[3] = m; // m
}
}
}
for (int x = 0, y = 0; x < m_Dimension; x++, y++) {
// if((x==exs.classIndex()) || (x==exs.idIndex()))
// y++;
a = m_ParamsP[4 * x];
b = m_ParamsP[4 * x + 1];
w = m_ParamsP[4 * x + 2];
m = m_ParamsP[4 * x + 3];
if (getDebug())
System.err.println(
"\n\n???Positive: ( "
+ exs.attribute(1).relation().attribute(y)
+ "): a="
+ a
+ ", b="
+ b
+ ", w="
+ w
+ ", m="
+ m);
a = m_ParamsN[4 * x];
b = m_ParamsN[4 * x + 1];
w = m_ParamsN[4 * x + 2];
m = m_ParamsN[4 * x + 3];
if (getDebug())
System.err.println(
"???Negative: ("
+ exs.attribute(1).relation().attribute(y)
+ "): a="
+ a
+ ", b="
+ b
+ ", w="
+ w
+ ", m="
+ m);
}
if (m_UseEmpiricalCutOff) {
// Find the empirical cut-off
double[] pLogOdds = new double[pnum], nLogOdds = new double[nnum];
for (int p = 0; p < pnum; p++)
pLogOdds[p] = likelihoodRatio(m_SumP[p], m_MeanP[p], m_VarianceP[p]);
for (int q = 0; q < nnum; q++)
nLogOdds[q] = likelihoodRatio(m_SumN[q], m_MeanN[q], m_VarianceN[q]);
// Update m_Cutoff
findCutOff(pLogOdds, nLogOdds);
} else m_Cutoff = -Math.log((double) pnum / (double) nnum);
if (getDebug()) System.err.println("???Cut-off=" + m_Cutoff);
}
public void buildClassifier(Instances insts) throws Exception {
// Compute mean of target value
double yMean = insts.meanOrMode(insts.classIndex());
// Choose best attribute
double minMsq = Double.MAX_VALUE;
m_attribute = null;
int chosen = -1;
double chosenSlope = Double.NaN;
double chosenIntercept = Double.NaN;
for (int i = 0; i < insts.numAttributes(); i++) {
if (i != insts.classIndex()) {
if (!insts.attribute(i).isNumeric()) {
throw new Exception("UnivariateLinearRegression: Only numeric attributes!");
}
m_attribute = insts.attribute(i);
// Compute slope and intercept
double xMean = insts.meanOrMode(i);
double sumWeightedXDiffSquared = 0;
double sumWeightedYDiffSquared = 0;
m_slope = 0;
for (int j = 0; j < insts.numInstances(); j++) {
Instance inst = insts.instance(j);
if (!inst.isMissing(i) && !inst.classIsMissing()) {
double xDiff = inst.value(i) - xMean;
double yDiff = inst.classValue() - yMean;
double weightedXDiff = inst.weight() * xDiff;
double weightedYDiff = inst.weight() * yDiff;
m_slope += weightedXDiff * yDiff;
sumWeightedXDiffSquared += weightedXDiff * xDiff;
sumWeightedYDiffSquared += weightedYDiff * yDiff;
}
}
// Skip attribute if not useful
if (sumWeightedXDiffSquared == 0) {
continue;
}
double numerator = m_slope;
m_slope /= sumWeightedXDiffSquared;
m_intercept = yMean - m_slope * xMean;
// Compute sum of squared errors
double msq = sumWeightedYDiffSquared - m_slope * numerator;
// Check whether this is the best attribute
if (msq < minMsq) {
minMsq = msq;
chosen = i;
chosenSlope = m_slope;
chosenIntercept = m_intercept;
}
}
}
// Set parameters
if (chosen == -1) {
System.err.println("----- no useful attribute found");
m_attribute = null;
m_slope = 0;
m_intercept = yMean;
} else {
m_attribute = insts.attribute(chosen);
m_slope = chosenSlope;
m_intercept = chosenIntercept;
}
}
/**
* 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;
}
@Override
public void buildClusterer(Instances data) throws Exception {
reset();
meanInstance = new DenseInstance(data.numAttributes());
for (int i = 0; i < data.numAttributes(); i++) meanInstance.setValue(i, data.meanOrMode(i));
numInstances = data.numInstances();
kMeans.setDistanceFunction(distanceFunction);
kMeans.setMaxIterations(maxIterations);
// kMeans.setInitializeUsingKMeansPlusPlusMethod(initializeWithKMeansPlusPlus);
if (initializeWithKMeansPlusPlus) {
kMeans.setInitializationMethod(
new weka.core.SelectedTag(SimpleKMeans.KMEANS_PLUS_PLUS, SimpleKMeans.TAGS_SELECTION));
}
/** step 1: iterate over all restarts and possible k values, record CH-scores */
Random r = new Random(m_Seed);
double meanCHs[] = new double[maxNumClusters + 1 - minNumClusters];
double maxCHs[] = new double[maxNumClusters + 1 - minNumClusters];
int maxSeed[] = new int[maxNumClusters + 1 - minNumClusters];
for (int i = 0; i < restarts; i++) {
if (printDebug) System.out.println("cascade> restarts: " + (i + 1) + " / " + restarts);
for (int k = minNumClusters; k <= maxNumClusters; k++) {
if (printDebug) System.out.print("cascade> k:" + k + " ");
int seed = r.nextInt();
kMeans.setSeed(seed);
kMeans.setNumClusters(k);
kMeans.buildClusterer(data);
double ch = getCalinskiHarabasz();
int index = k - minNumClusters;
meanCHs[index] = (meanCHs[index] * i + ch) / (double) (i + 1);
if (i == 0 || ch > maxCHs[index]) {
maxCHs[index] = ch;
maxSeed[index] = seed;
}
if (printDebug)
System.out.println(
" CH:"
+ df.format(ch)
+ " W:"
+ df.format(
kMeans.getSquaredError() / (double) (numInstances - kMeans.getNumClusters()))
+ " (unweighted:"
+ df.format(kMeans.getSquaredError())
+ ") B:"
+ df.format(
getSquaredErrorBetweenClusters() / (double) (kMeans.getNumClusters() - 1))
+ " (unweighted:"
+ df.format(getSquaredErrorBetweenClusters())
+ ") ");
}
}
if (printDebug) {
String s = "cascade> max CH: [ ";
for (int i = 0; i < maxSeed.length; i++) s += df.format(maxCHs[i]) + " ";
System.out.println(s + "]");
}
String s = "cascade> mean CH: [ ";
for (int i = 0; i < maxSeed.length; i++) s += df.format(meanCHs[i]) + " ";
finalMeanCH = s + "]";
// System.out.println(s + "]");
/** step 2: select k with best mean CH-score; select seed for max CH score for this k */
int bestK = -1;
double maxCH = -1;
for (int k = minNumClusters; k <= maxNumClusters; k++) {
int index = k - minNumClusters;
if (bestK == -1 || meanCHs[index] > maxCH) {
maxCH = meanCHs[index];
bestK = k;
}
}
if (manuallySelectNumClusters) {
int selectedK = selectKManually(meanCHs, bestK);
if (selectedK != -1) bestK = selectedK;
}
int bestSeed = maxSeed[bestK - minNumClusters];
finalBestK = bestK;
finalBestSeed = bestSeed;
// System.out.println("cascade> k (yields highest mean CH): " + bestK);
// System.out.println("cascade> seed (highest CH for k=" + bestK + ") : " + bestSeed);
kMeans.setSeed(bestSeed);
kMeans.setNumClusters(bestK);
kMeans.buildClusterer(data);
}
/**
* @param exs the training exemplars
* @throws Exception if the model cannot be built properly
*/
public void buildClassifier(Instances exs) throws Exception {
// can classifier handle the data?
getCapabilities().testWithFail(exs);
// remove instances with missing class
exs = new Instances(exs);
exs.deleteWithMissingClass();
int numegs = exs.numInstances();
m_Dimension = exs.attribute(1).relation().numAttributes();
m_Attribute = exs.attribute(1).relation().stringFreeStructure();
Instances pos = new Instances(exs, 0), neg = new Instances(exs, 0);
// Divide into two groups
for (int u = 0; u < numegs; u++) {
Instance example = exs.instance(u);
if (example.classValue() == 1) pos.add(example);
else neg.add(example);
}
int pnum = pos.numInstances(), nnum = neg.numInstances();
// xBar, n
m_MeanP = new double[pnum][m_Dimension];
m_SumP = new double[pnum][m_Dimension];
m_MeanN = new double[nnum][m_Dimension];
m_SumN = new double[nnum][m_Dimension];
// w, m
m_ParamsP = new double[2 * m_Dimension];
m_ParamsN = new double[2 * m_Dimension];
// \sigma^2
m_SgmSqP = new double[m_Dimension];
m_SgmSqN = new double[m_Dimension];
// S^2
double[][] varP = new double[pnum][m_Dimension], varN = new double[nnum][m_Dimension];
// numOfEx 'e' without all missing
double[] effNumExP = new double[m_Dimension], effNumExN = new double[m_Dimension];
// For the starting values
double[] pMM = new double[m_Dimension],
nMM = new double[m_Dimension],
pVM = new double[m_Dimension],
nVM = new double[m_Dimension];
// # of exemplars with only one instance
double[] numOneInsExsP = new double[m_Dimension], numOneInsExsN = new double[m_Dimension];
// sum_i(1/n_i)
double[] pInvN = new double[m_Dimension], nInvN = new double[m_Dimension];
// Extract metadata from both positive and negative bags
for (int v = 0; v < pnum; v++) {
// Instance px = pos.instance(v);
Instances pxi = pos.instance(v).relationalValue(1);
for (int k = 0; k < pxi.numAttributes(); k++) {
m_MeanP[v][k] = pxi.meanOrMode(k);
varP[v][k] = pxi.variance(k);
}
for (int w = 0, t = 0; w < m_Dimension; w++, t++) {
// if((t==m_ClassIndex) || (t==m_IdIndex))
// t++;
if (varP[v][w] <= 0.0) varP[v][w] = 0.0;
if (!Double.isNaN(m_MeanP[v][w])) {
for (int u = 0; u < pxi.numInstances(); u++)
if (!pxi.instance(u).isMissing(t)) m_SumP[v][w] += pxi.instance(u).weight();
pMM[w] += m_MeanP[v][w];
pVM[w] += m_MeanP[v][w] * m_MeanP[v][w];
if ((m_SumP[v][w] > 1) && (varP[v][w] > ZERO)) {
m_SgmSqP[w] += varP[v][w] * (m_SumP[v][w] - 1.0) / m_SumP[v][w];
// m_SgmSqP[w] += varP[v][w]*(m_SumP[v][w]-1.0);
effNumExP[w]++; // Not count exemplars with 1 instance
pInvN[w] += 1.0 / m_SumP[v][w];
// pInvN[w] += m_SumP[v][w];
} else numOneInsExsP[w]++;
}
}
}
for (int v = 0; v < nnum; v++) {
// Instance nx = neg.instance(v);
Instances nxi = neg.instance(v).relationalValue(1);
for (int k = 0; k < nxi.numAttributes(); k++) {
m_MeanN[v][k] = nxi.meanOrMode(k);
varN[v][k] = nxi.variance(k);
}
// Instances nxi = nx.getInstances();
for (int w = 0, t = 0; w < m_Dimension; w++, t++) {
// if((t==m_ClassIndex) || (t==m_IdIndex))
// t++;
if (varN[v][w] <= 0.0) varN[v][w] = 0.0;
if (!Double.isNaN(m_MeanN[v][w])) {
for (int u = 0; u < nxi.numInstances(); u++)
if (!nxi.instance(u).isMissing(t)) m_SumN[v][w] += nxi.instance(u).weight();
nMM[w] += m_MeanN[v][w];
nVM[w] += m_MeanN[v][w] * m_MeanN[v][w];
if ((m_SumN[v][w] > 1) && (varN[v][w] > ZERO)) {
m_SgmSqN[w] += varN[v][w] * (m_SumN[v][w] - 1.0) / m_SumN[v][w];
// m_SgmSqN[w] += varN[v][w]*(m_SumN[v][w]-1.0);
effNumExN[w]++; // Not count exemplars with 1 instance
nInvN[w] += 1.0 / m_SumN[v][w];
// nInvN[w] += m_SumN[v][w];
} else numOneInsExsN[w]++;
}
}
}
// Expected \sigma^2
/* if m_SgmSqP[u] or m_SgmSqN[u] is 0, assign 0 to sigma^2.
* Otherwise, may cause k m_SgmSqP / m_SgmSqN to be NaN.
* Modified by Lin Dong (Sep. 2005)
*/
for (int u = 0; u < m_Dimension; u++) {
// For exemplars with only one instance, use avg(\sigma^2) of other exemplars
if (m_SgmSqP[u] != 0) m_SgmSqP[u] /= (effNumExP[u] - pInvN[u]);
else m_SgmSqP[u] = 0;
if (m_SgmSqN[u] != 0) m_SgmSqN[u] /= (effNumExN[u] - nInvN[u]);
else m_SgmSqN[u] = 0;
// m_SgmSqP[u] /= (pInvN[u]-effNumExP[u]);
// m_SgmSqN[u] /= (nInvN[u]-effNumExN[u]);
effNumExP[u] += numOneInsExsP[u];
effNumExN[u] += numOneInsExsN[u];
pMM[u] /= effNumExP[u];
nMM[u] /= effNumExN[u];
pVM[u] =
pVM[u] / (effNumExP[u] - 1.0) - pMM[u] * pMM[u] * effNumExP[u] / (effNumExP[u] - 1.0);
nVM[u] =
nVM[u] / (effNumExN[u] - 1.0) - nMM[u] * nMM[u] * effNumExN[u] / (effNumExN[u] - 1.0);
}
// Bounds and parameter values for each run
double[][] bounds = new double[2][2];
double[] pThisParam = new double[2], nThisParam = new double[2];
// Initial values for parameters
double w, m;
Random whichEx = new Random(m_Seed);
// Optimize for one dimension
for (int x = 0; x < m_Dimension; x++) {
// System.out.println("\n\n!!!!!!!!!!!!!!!!!!!!!!???Dimension #"+x);
// Positive examplars: first run
pThisParam[0] = pVM[x]; // w
if (pThisParam[0] <= ZERO) pThisParam[0] = 1.0;
pThisParam[1] = pMM[x]; // m
// Negative examplars: first run
nThisParam[0] = nVM[x]; // w
if (nThisParam[0] <= ZERO) nThisParam[0] = 1.0;
nThisParam[1] = nMM[x]; // m
// Bound constraints
bounds[0][0] = ZERO; // w > 0
bounds[0][1] = Double.NaN;
bounds[1][0] = Double.NaN;
bounds[1][1] = Double.NaN;
double pminVal = Double.MAX_VALUE, nminVal = Double.MAX_VALUE;
TLDSimple_Optm pOp = null, nOp = null;
boolean isRunValid = true;
double[] sumP = new double[pnum], meanP = new double[pnum];
double[] sumN = new double[nnum], meanN = new double[nnum];
// One dimension
for (int p = 0; p < pnum; p++) {
sumP[p] = m_SumP[p][x];
meanP[p] = m_MeanP[p][x];
}
for (int q = 0; q < nnum; q++) {
sumN[q] = m_SumN[q][x];
meanN[q] = m_MeanN[q][x];
}
for (int y = 0; y < m_Run; y++) {
// System.out.println("\n\n!!!!!!!!!Positive exemplars: Run #"+y);
double thisMin;
pOp = new TLDSimple_Optm();
pOp.setNum(sumP);
pOp.setSgmSq(m_SgmSqP[x]);
if (getDebug()) System.out.println("m_SgmSqP[" + x + "]= " + m_SgmSqP[x]);
pOp.setXBar(meanP);
// pOp.setDebug(true);
pThisParam = pOp.findArgmin(pThisParam, bounds);
while (pThisParam == null) {
pThisParam = pOp.getVarbValues();
if (getDebug()) System.out.println("!!! 200 iterations finished, not enough!");
pThisParam = pOp.findArgmin(pThisParam, bounds);
}
thisMin = pOp.getMinFunction();
if (!Double.isNaN(thisMin) && (thisMin < pminVal)) {
pminVal = thisMin;
for (int z = 0; z < 2; z++) m_ParamsP[2 * x + z] = pThisParam[z];
}
if (Double.isNaN(thisMin)) {
pThisParam = new double[2];
isRunValid = false;
}
if (!isRunValid) {
y--;
isRunValid = true;
}
// Change the initial parameters and restart
int pone = whichEx.nextInt(pnum);
// Positive exemplars: next run
while (Double.isNaN(m_MeanP[pone][x])) pone = whichEx.nextInt(pnum);
m = m_MeanP[pone][x];
w = (m - pThisParam[1]) * (m - pThisParam[1]);
pThisParam[0] = w; // w
pThisParam[1] = m; // m
}
for (int y = 0; y < m_Run; y++) {
// System.out.println("\n\n!!!!!!!!!Negative exemplars: Run #"+y);
double thisMin;
nOp = new TLDSimple_Optm();
nOp.setNum(sumN);
nOp.setSgmSq(m_SgmSqN[x]);
if (getDebug()) System.out.println(m_SgmSqN[x]);
nOp.setXBar(meanN);
// nOp.setDebug(true);
nThisParam = nOp.findArgmin(nThisParam, bounds);
while (nThisParam == null) {
nThisParam = nOp.getVarbValues();
if (getDebug()) System.out.println("!!! 200 iterations finished, not enough!");
nThisParam = nOp.findArgmin(nThisParam, bounds);
}
thisMin = nOp.getMinFunction();
if (!Double.isNaN(thisMin) && (thisMin < nminVal)) {
nminVal = thisMin;
for (int z = 0; z < 2; z++) m_ParamsN[2 * x + z] = nThisParam[z];
}
if (Double.isNaN(thisMin)) {
nThisParam = new double[2];
isRunValid = false;
}
if (!isRunValid) {
y--;
isRunValid = true;
}
// Change the initial parameters and restart
int none = whichEx.nextInt(nnum); // Randomly pick one pos. exmpl.
// Negative exemplars: next run
while (Double.isNaN(m_MeanN[none][x])) none = whichEx.nextInt(nnum);
m = m_MeanN[none][x];
w = (m - nThisParam[1]) * (m - nThisParam[1]);
nThisParam[0] = w; // w
nThisParam[1] = m; // m
}
}
m_LkRatio = new double[m_Dimension];
if (m_UseEmpiricalCutOff) {
// Find the empirical cut-off
double[] pLogOdds = new double[pnum], nLogOdds = new double[nnum];
for (int p = 0; p < pnum; p++) pLogOdds[p] = likelihoodRatio(m_SumP[p], m_MeanP[p]);
for (int q = 0; q < nnum; q++) nLogOdds[q] = likelihoodRatio(m_SumN[q], m_MeanN[q]);
// Update m_Cutoff
findCutOff(pLogOdds, nLogOdds);
} else m_Cutoff = -Math.log((double) pnum / (double) nnum);
/*
for(int x=0, y=0; x<m_Dimension; x++, y++){
if((x==exs.classIndex()) || (x==exs.idIndex()))
y++;
w=m_ParamsP[2*x]; m=m_ParamsP[2*x+1];
System.err.println("\n\n???Positive: ( "+exs.attribute(y)+
"): w="+w+", m="+m+", sgmSq="+m_SgmSqP[x]);
w=m_ParamsN[2*x]; m=m_ParamsN[2*x+1];
System.err.println("???Negative: ("+exs.attribute(y)+
"): w="+w+", m="+m+", sgmSq="+m_SgmSqN[x]+
"\nAvg. log-likelihood ratio in training data="
+(m_LkRatio[x]/(pnum+nnum)));
}
*/
if (getDebug()) System.err.println("\n\n???Cut-off=" + m_Cutoff);
}
