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diff --git a/core/java/android/gesture/InstanceLearner.java b/core/java/android/gesture/InstanceLearner.java
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+/*
+ * 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;
+    }
+}