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/*
* Copyright (C) 2012 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.bordeaux.services;
import android.bordeaux.learning.MulticlassPA;
import android.os.IBinder;
import java.util.List;
import java.util.ArrayList;
public class Learning_MulticlassPA extends ILearning_MulticlassPA.Stub
implements IBordeauxLearner {
private MulticlassPA mMulticlassPA_learner;
private ModelChangeCallback modelChangeCallback = null;
class IntFloatArray {
int[] indexArray;
float[] floatArray;
};
private IntFloatArray splitIntFloatArray(List<IntFloat> sample) {
IntFloatArray splited = new IntFloatArray();
ArrayList<IntFloat> s = (ArrayList<IntFloat>)sample;
splited.indexArray = new int[s.size()];
splited.floatArray = new float[s.size()];
for (int i = 0; i < s.size(); i++) {
splited.indexArray[i] = s.get(i).index;
splited.floatArray[i] = s.get(i).value;
}
return splited;
}
public Learning_MulticlassPA() {
mMulticlassPA_learner = new MulticlassPA(2, 2, 0.001f);
}
// Beginning of the IBordeauxLearner Interface implementation
public byte [] getModel() {
return null;
}
public boolean setModel(final byte [] modelData) {
return false;
}
public IBinder getBinder() {
return this;
}
public void setModelChangeCallback(ModelChangeCallback callback) {
modelChangeCallback = callback;
}
// End of IBordeauxLearner Interface implemenation
// This implementation, combines training and prediction in one step.
// The return value is the prediction value for the supplied sample. It
// also update the model with the current sample.
public void TrainOneSample(List<IntFloat> sample, int target) {
IntFloatArray splited = splitIntFloatArray(sample);
mMulticlassPA_learner.sparseTrainOneExample(splited.indexArray,
splited.floatArray,
target);
if (modelChangeCallback != null) {
modelChangeCallback.modelChanged(this);
}
}
public int Classify(List<IntFloat> sample) {
IntFloatArray splited = splitIntFloatArray(sample);
int prediction = mMulticlassPA_learner.sparseGetClass(splited.indexArray,
splited.floatArray);
return prediction;
}
}
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