/**
* The key part of this class. This function takes in a set of tokens, runs the classifier on them
* and returns the classId to which they're classified. //TODO Fix a logic to be followed and
* describe it over here.
*
* @param tokens
* @return
*/
public int classifyDoc(ArrayList<String> tokens) {
int numOfMatchedFeatures = 0;
ArrayList<Double> datasetClassifierProbabilities = new ArrayList<Double>();
ArrayList<Double> userDataClassifierProbabilities = new ArrayList<Double>();
Double temp = 0.0;
for (int i = 0; i < numberOfClasses; i++) {
temp = (1.0 * classContents.get(i)) /* / totalNumberOfDocs */;
datasetClassifierProbabilities.add(temp);
temp = (1.0 * userDataClassContents.get(i)) /* / totalNumberOfDocs */;
userDataClassifierProbabilities.add(temp);
}
Map<String, Map<Integer, TermDistributionDao>> termDistributions =
databaseConnector.getAllTokensDistribution(tokens, false);
Map<String, Map<Integer, TermDistributionDao>> userDataTermDistributions =
databaseConnector.getAllTokensDistribution(tokens, true);
ArrayList<Double> productProbabilities = new ArrayList<Double>();
for (int i = 0; i < numberOfClasses; i++) {
productProbabilities.add(-1.0);
}
for (String token : tokens) {
numOfMatchedFeatures++;
for (int i = 0; i < numberOfClasses; i++) {
int termDistA = 0;
if ((termDistributions.containsKey(token))
&& (termDistributions.get(token).containsKey(i))) {
termDistA = termDistributions.get(token).get(i).getA();
}
temp = 1000 * ((1.0 * (1 + termDistA)) / (classContents.get(i) + numberOfClasses));
if (productProbabilities.get(i) < 0) {
productProbabilities.set(i, temp);
} else {
productProbabilities.set(i, temp * productProbabilities.get(i));
}
}
}
for (int i = 0; i < numberOfClasses; i++) {
temp = productProbabilities.get(i);
if (temp < 0) {
temp = 0.0;
}
datasetClassifierProbabilities.set(i, temp);
}
productProbabilities.clear();
for (int i = 0; i < numberOfClasses; i++) {
productProbabilities.add(-1.0);
}
for (String token : tokens) {
numOfMatchedFeatures++;
for (int i = 0; i < numberOfClasses; i++) {
int termDistA = 0;
if ((userDataTermDistributions.containsKey(token))
&& (userDataTermDistributions.get(token).containsKey(i))) {
termDistA = userDataTermDistributions.get(token).get(i).getA();
}
temp = 1000 * ((1.0 * (1 + termDistA)) / (userDataClassContents.get(i) + numberOfClasses));
if (productProbabilities.get(i) < 0) {
productProbabilities.set(i, temp);
} else {
productProbabilities.set(i, temp * productProbabilities.get(i));
}
}
}
for (int i = 0; i < numberOfClasses; i++) {
temp = productProbabilities.get(i);
if (temp < 0) {
temp = 0.0;
}
userDataClassifierProbabilities.set(i, temp);
}
if (numOfMatchedFeatures == 0) {
return -1;
}
ArrayList<Double> combinedProbabilities = new ArrayList<Double>();
for (int i = 0; i < numberOfClasses; i++) {
double alpha = 1.0, beta = 0.0;
double c1 = classContents.get(i);
double c2 = userDataClassContents.get(i);
if (c2 >= 5) {
if (c2 >= c1) {
alpha = 0.5;
beta = 0.5;
} else {
beta = c1 / (c1 + c2);
alpha = c2 / (c1 + c2);
}
}
Double combinedProbability =
(alpha * datasetClassifierProbabilities.get(i))
+ (beta * userDataClassifierProbabilities.get(i));
combinedProbabilities.add(combinedProbability);
}
int maxIndex = 0;
Double maximumValue = -1.0;
for (int i = 0; i < numberOfClasses; i++) {
if (combinedProbabilities.get(i) > maximumValue) {
maximumValue = combinedProbabilities.get(i);
maxIndex = i;
}
}
return maxIndex;
}
/**
* This is the initial classification method we used.
*
* @param tokens
* @return
*/
public int classifyDocClassic(ArrayList<String> tokens) {
int numOfMatchedFeatures = 0;
ArrayList<Double> probabilities = new ArrayList<Double>();
Double temp = 0.0;
for (int i = 0; i < numberOfClasses; i++) {
temp = (1.0 * classContents.get(i)) /* / totalNumberOfDocs */;
temp += (1.0 * userDataClassContents.get(i)) /* / totalNumberOfDocs */;
probabilities.add(temp);
}
Map<String, Map<Integer, TermDistributionDao>> termDistributions =
databaseConnector.getAllTokensDistribution(tokens, false);
Map<String, Map<Integer, TermDistributionDao>> userDataTermDistributions =
databaseConnector.getAllTokensDistribution(tokens, true);
ArrayList<Double> productProbabilities = new ArrayList<Double>();
for (int i = 0; i < numberOfClasses; i++) {
productProbabilities.add(-1.0);
}
for (String token : tokens) {
numOfMatchedFeatures++;
for (int i = 0; i < numberOfClasses; i++) {
int termDistA = 0;
if ((termDistributions.containsKey(token))
&& (termDistributions.get(token).containsKey(i))) {
termDistA = termDistributions.get(token).get(i).getA();
}
if ((userDataTermDistributions.containsKey(token))
&& (userDataTermDistributions.get(token).containsKey(i))) {
termDistA += userDataTermDistributions.get(token).get(i).getA();
}
temp =
1000
* ((1.0 * (1 + termDistA))
/ ((classContents.get(i) + userDataClassContents.get(i)) + numberOfClasses));
if (productProbabilities.get(i) < 0) {
productProbabilities.set(i, temp);
} else {
productProbabilities.set(i, temp * productProbabilities.get(i));
}
}
}
for (int i = 0; i < numberOfClasses; i++) {
temp = productProbabilities.get(i);
if (temp < 0) {
temp = 0.0;
}
probabilities.set(i, temp);
}
if (numOfMatchedFeatures == 0) {
return -1;
}
int maxIndex = 0;
Double maximumValue = -1.0;
for (int i = 0; i < numberOfClasses; i++) {
if (probabilities.get(i) > maximumValue) {
maximumValue = probabilities.get(i);
maxIndex = i;
}
}
return maxIndex;
}