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Diffstat (limited to 'core/java/android/gesture/InstanceLearner.java')
-rw-r--r-- | core/java/android/gesture/InstanceLearner.java | 91 |
1 files changed, 91 insertions, 0 deletions
diff --git a/core/java/android/gesture/InstanceLearner.java b/core/java/android/gesture/InstanceLearner.java new file mode 100644 index 0000000..1739cdc --- /dev/null +++ b/core/java/android/gesture/InstanceLearner.java @@ -0,0 +1,91 @@ +/* + * 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 android.util.Config; +import android.util.Log; +import static android.gesture.GestureConstants.*; + +import java.util.ArrayList; +import java.util.Collections; +import java.util.Comparator; +import java.util.Iterator; +import java.util.TreeMap; + +/** + * An implementation of an instance-based learner + */ + +class InstanceLearner extends Learner { + @Override + ArrayList<Prediction> classify(int gestureType, 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 (gestureType == GestureLibrary.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, 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; + } +} |