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/*
* Copyright (C) 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 com.android.gesture;
import android.content.Context;
import android.content.res.Resources;
import android.util.Log;
import java.io.IOException;
import java.io.DataInputStream;
import java.io.BufferedInputStream;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Comparator;
public class LetterRecognizer {
private static final String LOG_TAG = "LetterRecognizer";
public final static int LATIN_LOWERCASE = 0;
private SigmoidUnit[] mHiddenLayer;
private SigmoidUnit[] mOutputLayer;
private final String[] mClasses;
private final int mInputCount;
private static class SigmoidUnit {
final float[] mWeights;
SigmoidUnit(float[] weights) {
mWeights = weights;
}
private float compute(float[] inputs) {
float sum = 0;
final int count = inputs.length;
final float[] weights = mWeights;
for (int i = 0; i < count; i++) {
sum += inputs[i] * weights[i];
}
sum += weights[weights.length - 1];
return 1.0f / (float) (1 + Math.exp(-sum));
}
}
private LetterRecognizer(int numOfInput, int numOfHidden, String[] classes) {
mInputCount = (int)Math.sqrt(numOfInput);
mHiddenLayer = new SigmoidUnit[numOfHidden];
mClasses = classes;
mOutputLayer = new SigmoidUnit[classes.length];
}
public static LetterRecognizer getLetterRecognizer(Context context, int type) {
switch (type) {
case LATIN_LOWERCASE: {
return createFromResource(context, com.android.internal.R.raw.latin_lowercase);
}
}
return null;
}
public ArrayList<Prediction> recognize(Gesture gesture) {
return classify(GestureUtilities.spatialSampling(gesture, mInputCount));
}
private ArrayList<Prediction> classify(float[] vector) {
final float[] intermediateOutput = compute(mHiddenLayer, vector);
final float[] output = compute(mOutputLayer, intermediateOutput);
final ArrayList<Prediction> predictions = new ArrayList<Prediction>();
double sum = 0;
final String[] classes = mClasses;
final int count = classes.length;
for (int i = 0; i < count; i++) {
double score = output[i];
sum += score;
predictions.add(new Prediction(classes[i], score));
}
for (int i = 0; i < count; i++) {
predictions.get(i).score /= sum;
}
Collections.sort(predictions, 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;
}
}
});
return predictions;
}
private float[] compute(SigmoidUnit[] layer, float[] input) {
final float[] output = new float[layer.length];
final int count = layer.length;
for (int i = 0; i < count; i++) {
output[i] = layer[i].compute(input);
}
return output;
}
private static LetterRecognizer createFromResource(Context context, int resourceID) {
final Resources resources = context.getResources();
DataInputStream in = null;
LetterRecognizer classifier = null;
try {
in = new DataInputStream(new BufferedInputStream(resources.openRawResource(resourceID)));
final int iCount = in.readInt();
final int hCount = in.readInt();
final int oCount = in.readInt();
final String[] classes = new String[oCount];
for (int i = 0; i < classes.length; i++) {
classes[i] = in.readUTF();
}
classifier = new LetterRecognizer(iCount, hCount, classes);
SigmoidUnit[] hiddenLayer = new SigmoidUnit[hCount];
SigmoidUnit[] outputLayer = new SigmoidUnit[oCount];
for (int i = 0; i < hCount; i++) {
float[] weights = new float[iCount];
for (int j = 0; j < iCount; j++) {
weights[j] = in.readFloat();
}
hiddenLayer[i] = new SigmoidUnit(weights);
}
for (int i = 0; i < oCount; i++) {
float[] weights = new float[hCount];
for (int j = 0; j < hCount; j++) {
weights[j] = in.readFloat();
}
outputLayer[i] = new SigmoidUnit(weights);
}
classifier.mHiddenLayer = hiddenLayer;
classifier.mOutputLayer = outputLayer;
} catch (IOException e) {
Log.d(LOG_TAG, "Failed to load gestures:", e);
} finally {
GestureUtilities.closeStream(in);
}
return classifier;
}
}
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