/**
* Test using Fayyad and Irani's MDL criterion.
*
* @param priorCounts
* @param bestCounts
* @param numInstances
* @param numCutPoints
* @return true if the splits is acceptable
*/
private boolean FayyadAndIranisMDL(
double[] priorCounts, double[][] bestCounts, double numInstances, int numCutPoints) {
double priorEntropy, entropy, gain;
double entropyLeft, entropyRight, delta;
int numClassesTotal, numClassesRight, numClassesLeft;
// Compute entropy before split.
priorEntropy = ContingencyTables.entropy(priorCounts);
// Compute entropy after split.
entropy = ContingencyTables.entropyConditionedOnRows(bestCounts);
// Compute information gain.
gain = priorEntropy - entropy;
// Number of classes occuring in the set
numClassesTotal = 0;
for (double priorCount : priorCounts) {
if (priorCount > 0) {
numClassesTotal++;
}
}
// Number of classes occuring in the left subset
numClassesLeft = 0;
for (int i = 0; i < bestCounts[0].length; i++) {
if (bestCounts[0][i] > 0) {
numClassesLeft++;
}
}
// Number of classes occuring in the right subset
numClassesRight = 0;
for (int i = 0; i < bestCounts[1].length; i++) {
if (bestCounts[1][i] > 0) {
numClassesRight++;
}
}
// Entropy of the left and the right subsets
entropyLeft = ContingencyTables.entropy(bestCounts[0]);
entropyRight = ContingencyTables.entropy(bestCounts[1]);
// Compute terms for MDL formula
delta =
Utils.log2(Math.pow(3, numClassesTotal) - 2)
- ((numClassesTotal * priorEntropy)
- (numClassesRight * entropyRight)
- (numClassesLeft * entropyLeft));
// Check if split is to be accepted
return (gain > (Utils.log2(numCutPoints) + delta) / numInstances);
}
/**
* Selects cutpoints for sorted subset.
*
* @param instances
* @param attIndex
* @param first
* @param lastPlusOne
* @return
*/
private double[] cutPointsForSubset(
Instances instances, int attIndex, int first, int lastPlusOne) {
double[][] counts, bestCounts;
double[] priorCounts, left, right, cutPoints;
double currentCutPoint = -Double.MAX_VALUE,
bestCutPoint = -1,
currentEntropy,
bestEntropy,
priorEntropy,
gain;
int bestIndex = -1, numCutPoints = 0;
double numInstances = 0;
// Compute number of instances in set
if ((lastPlusOne - first) < 2) {
return null;
}
// Compute class counts.
counts = new double[2][instances.numClasses()];
for (int i = first; i < lastPlusOne; i++) {
numInstances += instances.instance(i).weight();
counts[1][(int) instances.instance(i).classValue()] += instances.instance(i).weight();
}
// Save prior counts
priorCounts = new double[instances.numClasses()];
System.arraycopy(counts[1], 0, priorCounts, 0, instances.numClasses());
// Entropy of the full set
priorEntropy = ContingencyTables.entropy(priorCounts);
bestEntropy = priorEntropy;
// Find best entropy.
bestCounts = new double[2][instances.numClasses()];
for (int i = first; i < (lastPlusOne - 1); i++) {
counts[0][(int) instances.instance(i).classValue()] += instances.instance(i).weight();
counts[1][(int) instances.instance(i).classValue()] -= instances.instance(i).weight();
if (instances.instance(i).value(attIndex) < instances.instance(i + 1).value(attIndex)) {
currentCutPoint =
(instances.instance(i).value(attIndex) + instances.instance(i + 1).value(attIndex))
/ 2.0;
currentEntropy = ContingencyTables.entropyConditionedOnRows(counts);
if (currentEntropy < bestEntropy) {
bestCutPoint = currentCutPoint;
bestEntropy = currentEntropy;
bestIndex = i;
System.arraycopy(counts[0], 0, bestCounts[0], 0, instances.numClasses());
System.arraycopy(counts[1], 0, bestCounts[1], 0, instances.numClasses());
}
numCutPoints++;
}
}
// Use worse encoding?
if (!m_UseBetterEncoding) {
numCutPoints = (lastPlusOne - first) - 1;
}
// Checks if gain is zero
gain = priorEntropy - bestEntropy;
if (gain <= 0) {
return null;
}
// Check if split is to be accepted
if ((m_UseKononenko && KononenkosMDL(priorCounts, bestCounts, numInstances, numCutPoints))
|| (!m_UseKononenko
&& FayyadAndIranisMDL(priorCounts, bestCounts, numInstances, numCutPoints))) {
// Select split points for the left and right subsets
left = cutPointsForSubset(instances, attIndex, first, bestIndex + 1);
right = cutPointsForSubset(instances, attIndex, bestIndex + 1, lastPlusOne);
// Merge cutpoints and return them
if ((left == null) && (right) == null) {
cutPoints = new double[1];
cutPoints[0] = bestCutPoint;
} else if (right == null) {
cutPoints = new double[left.length + 1];
System.arraycopy(left, 0, cutPoints, 0, left.length);
cutPoints[left.length] = bestCutPoint;
} else if (left == null) {
cutPoints = new double[1 + right.length];
cutPoints[0] = bestCutPoint;
System.arraycopy(right, 0, cutPoints, 1, right.length);
} else {
cutPoints = new double[left.length + right.length + 1];
System.arraycopy(left, 0, cutPoints, 0, left.length);
cutPoints[left.length] = bestCutPoint;
System.arraycopy(right, 0, cutPoints, left.length + 1, right.length);
}
return cutPoints;
} else {
return null;
}
}
/**
* Classifies the given test instance.
*
* @param instance the instance to be classified
* @return the predicted class for the instance
* @throws Exception if the instance can't be classified
*/
public double[] distributionForInstance(Instance instance) throws Exception {
double[] dist = new double[m_NumClasses];
double[] temp = new double[m_NumClasses];
double weight = 1.0;
for (int i = 0; i < instance.numAttributes(); i++) {
if (i != m_ClassIndex && !instance.isMissing(i)) {
double val = instance.value(i);
boolean ok = false;
if (instance.attribute(i).isNumeric()) {
int k;
for (k = m_intervalBounds[i].length - 1; k >= 0; k--) {
if (val > m_intervalBounds[i][k]) {
for (int j = 0; j < m_NumClasses; j++) {
if (m_globalCounts[j] > 0) {
temp[j] = ((m_counts[i][k][j] + TINY) / (m_globalCounts[j] + TINY));
}
}
ok = true;
break;
} else if (val == m_intervalBounds[i][k]) {
for (int j = 0; j < m_NumClasses; j++) {
if (m_globalCounts[j] > 0) {
temp[j] = ((m_counts[i][k][j] + m_counts[i][k - 1][j]) / 2.0) + TINY;
temp[j] /= (m_globalCounts[j] + TINY);
}
}
ok = true;
break;
}
}
if (!ok) {
throw new Exception("This shouldn't happen");
}
} else { // nominal attribute
ok = true;
for (int j = 0; j < m_NumClasses; j++) {
if (m_globalCounts[j] > 0) {
temp[j] = ((m_counts[i][(int) val][j] + TINY) / (m_globalCounts[j] + TINY));
}
}
}
double sum = Utils.sum(temp);
if (sum <= 0) {
for (int j = 0; j < temp.length; j++) {
temp[j] = 1.0 / (double) temp.length;
}
} else {
Utils.normalize(temp, sum);
}
if (m_weightByConfidence) {
weight = weka.core.ContingencyTables.entropy(temp);
weight = Math.pow(weight, m_bias);
if (weight < 1.0) {
weight = 1.0;
}
}
for (int j = 0; j < m_NumClasses; j++) {
dist[j] += (temp[j] * weight);
}
}
}
double sum = Utils.sum(dist);
if (sum <= 0) {
for (int j = 0; j < dist.length; j++) {
dist[j] = 1.0 / (double) dist.length;
}
return dist;
} else {
Utils.normalize(dist, sum);
return dist;
}
}
/**
* evaluates an individual attribute by measuring the gain ratio of the class given the attribute.
*
* @param attribute the index of the attribute to be evaluated
* @return the gain ratio
* @throws Exception if the attribute could not be evaluated
*/
public double evaluateAttribute(int attribute) throws Exception {
int i, j, ii, jj;
int ni, nj;
double sum = 0.0;
ni = m_trainInstances.attribute(attribute).numValues() + 1;
nj = m_numClasses + 1;
double[] sumi, sumj;
Instance inst;
double temp = 0.0;
sumi = new double[ni];
sumj = new double[nj];
double[][] counts = new double[ni][nj];
sumi = new double[ni];
sumj = new double[nj];
for (i = 0; i < ni; i++) {
sumi[i] = 0.0;
for (j = 0; j < nj; j++) {
sumj[j] = 0.0;
counts[i][j] = 0.0;
}
}
// Fill the contingency table
for (i = 0; i < m_numInstances; i++) {
inst = m_trainInstances.instance(i);
if (inst.isMissing(attribute)) {
ii = ni - 1;
} else {
ii = (int) inst.value(attribute);
}
if (inst.isMissing(m_classIndex)) {
jj = nj - 1;
} else {
jj = (int) inst.value(m_classIndex);
}
counts[ii][jj]++;
}
// get the row totals
for (i = 0; i < ni; i++) {
sumi[i] = 0.0;
for (j = 0; j < nj; j++) {
sumi[i] += counts[i][j];
sum += counts[i][j];
}
}
// get the column totals
for (j = 0; j < nj; j++) {
sumj[j] = 0.0;
for (i = 0; i < ni; i++) {
sumj[j] += counts[i][j];
}
}
// distribute missing counts
if (m_missing_merge && (sumi[ni - 1] < m_numInstances) && (sumj[nj - 1] < m_numInstances)) {
double[] i_copy = new double[sumi.length];
double[] j_copy = new double[sumj.length];
double[][] counts_copy = new double[sumi.length][sumj.length];
for (i = 0; i < ni; i++) {
System.arraycopy(counts[i], 0, counts_copy[i], 0, sumj.length);
}
System.arraycopy(sumi, 0, i_copy, 0, sumi.length);
System.arraycopy(sumj, 0, j_copy, 0, sumj.length);
double total_missing = (sumi[ni - 1] + sumj[nj - 1] - counts[ni - 1][nj - 1]);
// do the missing i's
if (sumi[ni - 1] > 0.0) {
for (j = 0; j < nj - 1; j++) {
if (counts[ni - 1][j] > 0.0) {
for (i = 0; i < ni - 1; i++) {
temp = ((i_copy[i] / (sum - i_copy[ni - 1])) * counts[ni - 1][j]);
counts[i][j] += temp;
sumi[i] += temp;
}
counts[ni - 1][j] = 0.0;
}
}
}
sumi[ni - 1] = 0.0;
// do the missing j's
if (sumj[nj - 1] > 0.0) {
for (i = 0; i < ni - 1; i++) {
if (counts[i][nj - 1] > 0.0) {
for (j = 0; j < nj - 1; j++) {
temp = ((j_copy[j] / (sum - j_copy[nj - 1])) * counts[i][nj - 1]);
counts[i][j] += temp;
sumj[j] += temp;
}
counts[i][nj - 1] = 0.0;
}
}
}
sumj[nj - 1] = 0.0;
// do the both missing
if (counts[ni - 1][nj - 1] > 0.0 && total_missing != sum) {
for (i = 0; i < ni - 1; i++) {
for (j = 0; j < nj - 1; j++) {
temp = (counts_copy[i][j] / (sum - total_missing)) * counts_copy[ni - 1][nj - 1];
counts[i][j] += temp;
sumi[i] += temp;
sumj[j] += temp;
}
}
counts[ni - 1][nj - 1] = 0.0;
}
}
return ContingencyTables.gainRatio(counts);
}
