@Override
public String classify(User user, Sample sample) {
Instances trainingSet =
new TrainingSetBuilder()
.setAttributes(user.getBssids())
.setClassAttribute(
"Location",
user.getLocations().stream().map(Location::getName).collect(Collectors.toList()))
.build("TrainingSet", 1);
// Create instance
Map<String, Integer> BSSIDLevelMap = getBSSIDLevelMap(sample);
Instance instance = new Instance(trainingSet.numAttributes());
for (Enumeration e = trainingSet.enumerateAttributes(); e.hasMoreElements(); ) {
Attribute attribute = (Attribute) e.nextElement();
String bssid = attribute.name();
int level = (BSSIDLevelMap.containsKey(bssid)) ? BSSIDLevelMap.get(bssid) : 0;
instance.setValue(attribute, level);
}
if (sample.getLocation() != null)
instance.setValue(trainingSet.classAttribute(), sample.getLocation());
instance.setDataset(trainingSet);
trainingSet.add(instance);
int predictedClass = classify(fromBase64(user.getClassifiers()), instance);
return trainingSet.classAttribute().value(predictedClass);
}
@Override
public List<Classifier> buildClassifiers(User user, List<Sample> validSamples) {
Instances trainingSet =
new TrainingSetBuilder()
.setAttributes(user.getBssids())
.setClassAttribute(
"Location",
user.getLocations().stream().map(Location::getName).collect(Collectors.toList()))
.build("TrainingSet", validSamples.size());
// Create instances
validSamples.forEach(
sample -> {
Map<String, Integer> BSSIDLevelMap = getBSSIDLevelMap(sample);
Instance instance = new Instance(trainingSet.numAttributes());
for (Enumeration e = trainingSet.enumerateAttributes(); e.hasMoreElements(); ) {
Attribute attribute = (Attribute) e.nextElement();
String bssid = attribute.name();
int level = (BSSIDLevelMap.containsKey(bssid)) ? BSSIDLevelMap.get(bssid) : 0;
instance.setValue(attribute, level);
}
instance.setValue(trainingSet.classAttribute(), sample.getLocation());
instance.setDataset(trainingSet);
trainingSet.add(instance);
});
// Build classifiers
List<Classifier> classifiers = buildClassifiers(trainingSet);
return classifiers;
}