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/*
* Copyright (C) 2008-2009 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package android.gesture;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Comparator;
import java.util.TreeMap;
/**
* An implementation of an instance-based learner
*/
class InstanceLearner extends Learner {
private static final Comparator<Prediction> sComparator = new Comparator<Prediction>() {
public int compare(Prediction object1, Prediction object2) {
double score1 = object1.score;
double score2 = object2.score;
if (score1 > score2) {
return -1;
} else if (score1 < score2) {
return 1;
} else {
return 0;
}
}
};
@Override
ArrayList<Prediction> classify(int sequenceType, float[] vector) {
ArrayList<Prediction> predictions = new ArrayList<Prediction>();
ArrayList<Instance> instances = getInstances();
int count = instances.size();
TreeMap<String, Double> label2score = new TreeMap<String, Double>();
for (int i = 0; i < count; i++) {
Instance sample = instances.get(i);
if (sample.vector.length != vector.length) {
continue;
}
double distance;
if (sequenceType == GestureStore.SEQUENCE_SENSITIVE) {
distance = GestureUtilities.cosineDistance(sample.vector, vector);
} else {
distance = GestureUtilities.squaredEuclideanDistance(sample.vector, vector);
}
double weight;
if (distance == 0) {
weight = Double.MAX_VALUE;
} else {
weight = 1 / distance;
}
Double score = label2score.get(sample.label);
if (score == null || weight > score) {
label2score.put(sample.label, weight);
}
}
// double sum = 0;
for (String name : label2score.keySet()) {
double score = label2score.get(name);
// sum += score;
predictions.add(new Prediction(name, score));
}
// normalize
// for (Prediction prediction : predictions) {
// prediction.score /= sum;
// }
Collections.sort(predictions, sComparator);
return predictions;
}
}
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