Recovered the code of the gesture library
diff --git a/tests/sketch/src/com/android/gesture/InstanceLearner.java b/tests/sketch/src/com/android/gesture/InstanceLearner.java
new file mode 100644
index 0000000..95241d4
--- /dev/null
+++ b/tests/sketch/src/com/android/gesture/InstanceLearner.java
@@ -0,0 +1,105 @@
+/*
+ * 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 com.android.gesture;
+
+import android.util.Config;
+import android.util.Log;
+
+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 {
+
+ private static final String LOGTAG = "InstanceLearner";
+
+ @Override
+ ArrayList<Prediction> classify(GestureLibrary lib, Instance instance) {
+ 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 != instance.vector.length) {
+ continue;
+ }
+ double distance;
+ if (lib.getGestureType() == GestureLibrary.SEQUENCE_SENSITIVE) {
+ distance = GestureUtils.cosineDistance(sample, instance);
+ } else {
+ distance = GestureUtils.squaredEuclideanDistance(sample.vector, instance.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;
+ Iterator<String> lableIterator = label2score.keySet().iterator();
+ while (lableIterator.hasNext()) {
+ String name = lableIterator.next();
+ double score = label2score.get(name);
+ sum += score;
+ predictions.add(new Prediction(name, score));
+ }
+
+ // normalize
+ Iterator<Prediction> predictionIterator = predictions.iterator();
+ while (predictionIterator.hasNext()) {
+ Prediction name = predictionIterator.next();
+ name.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;
+ }
+ }
+ });
+
+ if (Config.DEBUG) {
+ predictionIterator = predictions.iterator();
+ while (predictionIterator.hasNext()) {
+ Prediction name = predictionIterator.next();
+ Log.v(LOGTAG, "prediction [" + name.name + " = " + name.score + "]");
+ }
+ }
+
+ return predictions;
+ }
+}