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