Add the X10 language (http://x10-lang.org/).

samples/X10/KMeans.x10

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+/*
+ *  This file is part of the X10 project (http://x10-lang.org).
+ *
+ *  This file is licensed to You under the Eclipse Public License (EPL);
+ *  You may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *      http://www.opensource.org/licenses/eclipse-1.0.php
+ *
+ *  (C) Copyright IBM Corporation 2006-2014.
+ */
+
+import x10.io.Console;
+import x10.util.Random;
+
+/**
+ * A KMeans object o can compute K means of a given set of 
+ * points of dimension o.myDim.
+ * <p> 
+ * This class implements a sequential program, that is readily parallelizable.
+ *
+ * For a scalable, high-performance version of this benchmark see
+ * KMeans.x10 in the X10 Benchmarks (separate download from x10-lang.org)
+ */
+public class KMeans(myDim:Long) {
+
+    static val DIM=2;
+    static val K=4;
+    static val POINTS=2000;
+    static val ITERATIONS=50;
+    static val EPS=0.01F;
+    
+    static type ValVector(k:Long) = Rail[Float]{self.size==k};
+    static type ValVector = ValVector(DIM);
+    
+    static type Vector(k:Long) = Rail[Float]{self.size==k};
+    static type Vector = Vector(DIM);
+    
+    static type SumVector(d:Long) = V{self.dim==d};
+    static type SumVector = SumVector(DIM);
+
+    /**
+     * V represents the sum of 'count' number of vectors of dimension 'dim'.
+     */
+    static class V(dim:Long) implements (Long)=>Float {
+        var vec: Vector(dim);
+        var count:Int;
+        def this(dim:Long, init:(Long)=>Float): SumVector(dim) {
+           property(dim);
+           vec = new Rail[Float](this.dim, init);
+           count = 0n;
+        }
+        public operator this(i:Long) = vec(i);
+        def makeZero() {
+            for (i in 0..(dim-1))
+                vec(i) =0.0F;
+            count=0n;
+        }
+        def addIn(a:ValVector(dim)) {
+            for (i in 0..(dim-1))
+                vec(i) += a(i);
+            count++;
+        }
+        def div(f:Int) {
+            for (i in 0..(dim-1))
+                vec(i) /= f;
+        }
+        def dist(a:ValVector(dim)):Float {
+            var dist:Float=0.0F;
+            for (i in 0..(dim-1)) {
+                val tmp = vec(i)-a(i);
+                dist += tmp*tmp;
+            }
+            return dist;
+        }
+        def dist(a:SumVector(dim)):Float {
+            var dist:Float=0.0F;
+            for (i in 0..(dim-1)) {
+                val tmp = vec(i)-a(i);
+                dist += tmp*tmp;
+            }
+            return dist;
+        }
+        def print() {
+            Console.OUT.println();
+            for (i in 0..(dim-1)) {
+                Console.OUT.print((i>0? " " : "") + vec(i));
+            }
+        }
+        def normalize() { div(count);}
+        def count() = count;
+    }
+    
+    
+    def this(myDim:Long):KMeans{self.myDim==myDim} {
+        property(myDim);
+    }
+    static type KMeansData(myK:Long, myDim:Long)= Rail[SumVector(myDim)]{self.size==myK};
+
+    /**
+     * Compute myK means for the given set of points of dimension myDim.
+     */
+    def computeMeans(myK:Long, points:Rail[ValVector(myDim)]):KMeansData(myK, myDim) {
+        var redCluster : KMeansData(myK, myDim) =
+            new Rail[SumVector(myDim)](myK, (i:long)=> new V(myDim, (j:long)=>points(i)(j)));
+        var blackCluster: KMeansData(myK, myDim) =
+            new Rail[SumVector(myDim)](myK, (i:long)=> new V(myDim, (j:long)=>0.0F));
+        for (i in 1..ITERATIONS) {
+            val tmp = redCluster;
+            redCluster = blackCluster;
+            blackCluster=tmp;
+            for (p in 0..(POINTS-1)) { 
+                var closest:Long = -1;
+                var closestDist:Float = Float.MAX_VALUE;
+                val point = points(p);
+                for (k in 0..(myK-1)) { // compute closest mean in cluster.
+                    val dist = blackCluster(k).dist(point);
+                    if (dist < closestDist) {
+                        closestDist = dist;
+                        closest = k;
+                    }
+                }
+                redCluster(closest).addIn(point);
+            }
+            for (k in 0..(myK-1))
+                redCluster(k).normalize(); 
+            
+            var b:Boolean = true;
+                for (k in 0..(myK-1)) {
+                    if (redCluster(k).dist(blackCluster(k)) > EPS) {
+                        b=false;
+                        break;
+                    }
+                }
+            if (b) 
+                break;
+            for (k in 0..(myK-1))
+                blackCluster(k).makeZero(); 
+        }
+        return redCluster;  
+    }
+  
+    public static def main (Rail[String]) {
+        val rnd = new Random(0);
+        val points = new Rail[ValVector](POINTS, 
+                        (long)=>new Rail[Float](DIM, (long)=>rnd.nextFloat()));
+        val result = new KMeans(DIM).computeMeans(K, points);
+        for (k in 0..(K-1)) result(k).print();
+    }
+}
+
+// vim: shiftwidth=4:tabstop=4:expandtab