Initial opendaylight infrastructure commit!!

[controller.git] / third-party / net.sf.jung2 / src / main / java / edu / uci / ics / jung / algorithms / generators / random / MixedRandomGraphGenerator.java

diff --git a/third-party/net.sf.jung2/src/main/java/edu/uci/ics/jung/algorithms/generators/random/MixedRandomGraphGenerator.java b/third-party/net.sf.jung2/src/main/java/edu/uci/ics/jung/algorithms/generators/random/MixedRandomGraphGenerator.java
new file mode 100644 (file)
index 0000000..a39a640
--- /dev/null
+++ b/third-party/net.sf.jung2/src/main/java/edu/uci/ics/jung/algorithms/generators/random/MixedRandomGraphGenerator.java
@@ -0,0 +1,88 @@
+/*
+ * Copyright (c) 2003, the JUNG Project and the Regents of the University of
+ * California All rights reserved.
+ * 
+ * This software is open-source under the BSD license; see either "license.txt"
+ * or http://jung.sourceforge.net/license.txt for a description.
+ */
+/*
+ * Created on Jul 2, 2003
+ *  
+ */
+package edu.uci.ics.jung.algorithms.generators.random;
+
+import java.util.Map;
+import java.util.Set;
+
+import org.apache.commons.collections15.Factory;
+
+import edu.uci.ics.jung.graph.Graph;
+import edu.uci.ics.jung.graph.util.EdgeType;
+
+/**
+ * 
+ * Generates a mixed-mode random graph based on the output of <code>BarabasiAlbertGenerator</code>.
+ * Primarily intended for providing a heterogeneous sample graph for visualization testing, etc.
+ *  
+ */
+public class MixedRandomGraphGenerator {
+
+       /**
+        * Equivalent to <code>generateMixedRandomGraph(edge_weight, num_vertices, true)</code>.
+        */
+       public static <V,E> Graph<V, E> generateMixedRandomGraph(
+                       Factory<Graph<V,E>> graphFactory,
+                       Factory<V> vertexFactory,
+               Factory<E> edgeFactory,
+               Map<E,Number> edge_weight, 
+                       int num_vertices, Set<V> seedVertices)
+       {
+               return generateMixedRandomGraph(graphFactory, vertexFactory, edgeFactory, 
+                               edge_weight, num_vertices, true, seedVertices);
+       }
+
+    /**
+     * Returns a random mixed-mode graph.  Starts with a randomly generated 
+     * Barabasi-Albert (preferential attachment) generator 
+     * (4 initial vertices, 3 edges added at each step, and num_vertices - 4 evolution steps).
+     * Then takes the resultant graph, replaces random undirected edges with directed
+     * edges, and assigns random weights to each edge.
+     */
+    public static <V,E> Graph<V,E> generateMixedRandomGraph(
+               Factory<Graph<V,E>> graphFactory,
+               Factory<V> vertexFactory,
+               Factory<E> edgeFactory,
+               Map<E,Number> edge_weights, 
+            int num_vertices, boolean parallel, Set<V> seedVertices)
+    {
+        int seed = (int)(Math.random() * 10000);
+        BarabasiAlbertGenerator<V,E> bag = 
+            new BarabasiAlbertGenerator<V,E>(graphFactory, vertexFactory, edgeFactory,
+                       4, 3, //false, parallel, 
+                       seed, seedVertices);
+        bag.evolveGraph(num_vertices - 4);
+        Graph<V, E> ug = bag.create();
+
+        // create a SparseMultigraph version of g
+        Graph<V, E> g = graphFactory.create();
+               //new SparseMultigraph<V, E>();
+        for(V v : ug.getVertices()) {
+               g.addVertex(v);
+        }
+        
+        // randomly replace some of the edges by directed edges to 
+        // get a mixed-mode graph, add random weights
+        
+        for(E e : ug.getEdges()) {
+            V v1 = ug.getEndpoints(e).getFirst();
+            V v2 = ug.getEndpoints(e).getSecond();
+
+            E me = edgeFactory.create();
+            g.addEdge(me, v1, v2, Math.random() < .5 ? EdgeType.DIRECTED : EdgeType.UNDIRECTED);
+            edge_weights.put(me, Math.random());
+        }
+        
+        return g;
+    }
+    
+}