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* Various algorithms and data structures, licensed under the MIT-license.
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* (c) 2010 by Johann Philipp Strathausen <strathausen@gmail.com>
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* http://strathausen.eu
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Path-finding algorithm, finds the shortest paths from one node to all nodes.
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O( |E| · |V| ), where E = edges and V = vertices (nodes)
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Can run on graphs with negative edge weights as long as they do not have
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any negative weight cycles.
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function bellman_ford(g, source) {
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/* STEP 1: initialisation */
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g.nodes[n].distance = Infinity;
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/* predecessors are implicitly null */
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step("Initially, all distances are infinite and all predecessors are null.");
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/* STEP 2: relax each edge (this is at the heart of Bellman-Ford) */
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/* repeat this for the number of nodes minus one */
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for(var i = 1; i < g.nodes.length; i++)
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for(var e in g.edges) {
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var edge = g.edges[e];
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if(edge.source.distance + edge.weight < edge.target.distance) {
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step("Relax edge between " + edge.source.id + " and " + edge.target.id + ".");
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edge.target.distance = edge.source.distance + edge.weight;
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edge.target.predecessor = edge.source;
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//Added by Jake Stothard (Needs to be tested)
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if(!edge.style.directed) {
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if(edge.target.distance + edge.weight < edge.source.distance) {
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g.snapShot("Relax edge between "+edge.target.id+" and "+edge.source.id+".");
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edge.source.distance = edge.target.distance + edge.weight;
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edge.source.predecessor = edge.target;
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/* STEP 3: TODO Check for negative cycles */
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/* For now we assume here that the graph does not contain any negative
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weights cycles. (this is left as an excercise to the reader[tm]) */
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Path-finding algorithm Dijkstra
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- worst-case running time is O((|E| + |V|) · log |V| ) thus better than
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Bellman-Ford for sparse graphs (with less edges), but cannot handle
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function dijkstra(g, source) {
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/* initially, all distances are infinite and all predecessors are null */
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g.nodes[n].distance = Infinity;
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/* predecessors are implicitly null */
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g.snapShot("Initially, all distances are infinite and all predecessors are null.");
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/* set of unoptimized nodes, sorted by their distance (but a Fibonacci heap
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var q = new BinaryMinHeap(g.nodes, "distance");
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/* pointer to the node in focus */
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/* get the node with the smallest distance
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as long as we have unoptimized nodes. q.min() can have O(log n). */
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while(q.min() != undefined) {
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/* remove the latest */
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node = q.extractMin();
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node.optimized = true;
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/* no nodes accessible from this one, should not happen */
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if(node.distance == Infinity)
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throw "Orphaned node!";
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/* for each neighbour of node */
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for(e in node.edges) {
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var other = (node == node.edges[e].target) ? node.edges[e].source : node.edges[e].target;
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/* look for an alternative route */
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var alt = node.distance + node.edges[e].weight;
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/* update distance and route if a better one has been found */
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if (alt < other.distance) {
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/* update distance of neighbour */
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other.distance = alt;
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/* update priority queue */
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other.predecessor = node;
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g.snapShot("Enhancing node.")
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/* All-Pairs-Shortest-Paths */
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/* Runs at worst in O(|V|³) and at best in Omega(|V|³) :-)
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complexity Sigma(|V|²) */
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/* This implementation is not yet ready for general use, but works with the
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Dracula graph library. */
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function floyd_warshall(g, source) {
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/* Step 1: initialising empty path matrix (second dimension is implicit) */
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var n = g.nodes.length;
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/* construct path matrix, initialize with Infinity */
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path[j][i] = j == i ? 0 : Infinity;
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/* initialize path with edge weights */
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path[g.edges[e].source.id][g.edges[e].target.id] = g.edges[e].weight;
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/* Note: Usually, the initialisation is done by getting the edge weights
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from a node matrix representation of the graph, not by iterating through
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a list of edges as done here. */
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/* Step 2: find best distances (the heart of Floyd-Warshall) */
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if(path[i][j] > path[i][k] + path[k][j]) {
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path[i][j] = path[i][k] + path[k][j];
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/* Step 2.b: remember the path */
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/* Step 3: Path reconstruction, get shortest path */
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function getPath(i, j) {
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if(path[i][j] == Infinity)
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throw "There is no path.";
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var intermediate = next[i][j];
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if(intermediate == undefined)
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return getPath(i, intermediate)
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.concat([intermediate])
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.concat(getPath(intermediate, j));
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/* TODO use the knowledge, e.g. mark path in graph */
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Max-Flow-Min-Cut Algorithm finding the maximum flow through a directed
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graph from source to sink.
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O(E * max(f)), max(f) being the maximum flow
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As long as there is an open path through the residual graph, send the
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minimum of the residual capacities on the path.
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The algorithm works only if all weights are integers. Otherwise it is
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possible that the Ford–Fulkerson algorithm will not converge to the maximum
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Maximum flow from Source s to Target t
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Max-Flow-Min-Cut Algorithm finding the maximum flow through a directed
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graph from source to sink. An implementation of the Ford-Fulkerson
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g - Graph object (with node and edge lists, capacity is a property of edge)
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function edmonds_karp(g, s, t) {
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A simple binary min-heap serving as a priority queue
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- takes an array as the input, with elements having a key property
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- elements will look like this:
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key: "... key property ...",
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value: "... element content ..."
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- provides insert(), min(), extractMin() and heapify()
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- example usage (e.g. via the Firebug or Chromium console):
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var x = {foo: 20, hui: "bla"};
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var a = new BinaryMinHeap([x,{foo:3},{foo:10},{foo:20},{foo:30},{foo:6},{foo:1},{foo:3}],"foo");
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console.log(a.extractMin());
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console.log(a.extractMin());
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x.foo = 0; // update key
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a.heapify(); // call this always after having a key updated
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console.log(a.extractMin());
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console.log(a.extractMin());
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- can also be used on a simple array, like [9,7,8,5]
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function BinaryMinHeap(array, key) {
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/* Binary tree stored in an array, no need for a complicated data structure */
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var key = key || 'key';
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/* Calculate the index of the parent or a child */
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var parent = function(index) { return Math.floor((index - 1)/2); };
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var right = function(index) { return 2 * index + 2; };
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var left = function(index) { return 2 * index + 1; };
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/* Helper function to swap elements with their parent
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as long as the parent is bigger */
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function bubble_up(i) {
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while((p >= 0) && (tree[i][key] < tree[p][key])) {
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/* swap with parent */
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tree[i] = tree.splice(p, 1, tree[i])[0];
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/* go up one level */
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/* Helper function to swap elements with the smaller of their children
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as long as there is one */
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function bubble_down(i) {
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/* as long as there are smaller children */
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while(tree[l] && (tree[i][key] > tree[l][key]) || tree[r] && (tree[i][key] > tree[r][key])) {
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/* find smaller child */
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var child = tree[l] ? tree[r] ? tree[l][key] > tree[r][key] ? r : l : l : l;
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/* swap with smaller child with current element */
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tree[i] = tree.splice(child, 1, tree[i])[0];
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/* go up one level */
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/* Insert a new element with respect to the heap property
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1. Insert the element at the end
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2. Bubble it up until it is smaller than its parent */
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this.insert = function(element) {
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/* make sure there's a key property */
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(element[key] == undefined) && (element = {key:element});
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/* insert element at the end */
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/* bubble up the element */
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bubble_up(tree.length - 1);
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/* Only show us the minimum */
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this.min = function() {
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return tree.length == 1 ? undefined : tree[0];
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/* Return and remove the minimum
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1. Take the root as the minimum that we are looking for
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2. Move the last element to the root (thereby deleting the root)
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3. Compare the new root with both of its children, swap it with the
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smaller child and then check again from there (bubble down)
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this.extractMin = function() {
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var result = this.min();
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/* move the last element to the root or empty the tree completely */
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/* bubble down the new root if necessary */
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(tree.length == 1) && (tree = []) || (tree[0] = tree.pop()) && bubble_down(0);
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/* currently unused, TODO implement */
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this.changeKey = function(index, key) {
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throw "function not implemented";
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this.heapify = function() {
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for(var start = Math.floor((tree.length - 2) / 2); start >= 0; start--) {
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/* insert the input elements one by one only when we don't have a key property (TODO can be done more elegant) */
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for(i in (array || []))
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this.insert(array[i]);
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1. Select some random value from the array, the median.
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2. Divide the array in three smaller arrays according to the elements
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being less, equal or greater than the median.
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3. Recursively sort the array containg the elements less than the
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median and the one containing elements greater than the median.
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4. Concatenate the three arrays (less, equal and greater).
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5. One or no element is always sorted.
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TODO: This could be implemented more efficiently by using only one array object and several pointers.
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function quickSort(arr) {
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/* recursion anchor: one element is always sorted */
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if(arr.length <= 1) return arr;
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/* randomly selecting some value */
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var median = arr[Math.floor(Math.random() * arr.length)];
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var arr1 = [], arr2 = [], arr3 = [];
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arr[i] < median && arr1.push(arr[i]);
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arr[i] == median && arr2.push(arr[i]);
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arr[i] > median && arr3.push(arr[i]);
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/* recursive sorting and assembling final result */
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return quickSort(arr1).concat(arr2).concat(quickSort(arr3));
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1. Select the minimum and remove it from the array
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2. Sort the rest recursively
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3. Return the minimum plus the sorted rest
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4. An array with only one element is already sorted
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function selectionSort(arr) {
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/* recursion anchor: one element is always sorted */
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if(arr.length == 1) return arr;
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var minimum = Infinity;
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if(arr[i] < minimum) {
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index = i; /* remember the minimum index for later removal */
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/* remove the minimum */
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arr.splice(index, 1);
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/* assemble result and sort recursively (could be easily done iteratively as well)*/
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return [minimum].concat(selectionSort(arr));
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1. Cut the array in half
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2. Sort each of them recursively
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3. Merge the two sorted arrays
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4. An array with only one element is considered sorted
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function mergeSort(arr) {
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/* merges two sorted arrays into one sorted array */
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function merge(a, b) {
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/* as long as there are elements in the arrays to be merged */
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while(a.length > 0 || b.length > 0){
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/* are there elements to be merged, if yes, compare them and merge */
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var n = a.length > 0 && b.length > 0 ? a[0] < b[0] ? a.shift() : b.shift() : b.length > 0 ? b.shift() : a.length > 0 ? a.shift() : null;
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/* always push the smaller one onto the result set */
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n != null && c.push(n);
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/* this mergeSort implementation cuts the array in half, wich should be fine with randomized arrays, but introduces the risk of a worst-case scenario */
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median = Math.floor(arr.length / 2);
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var part1 = arr.slice(0, median); /* for some reason it doesn't work if inserted directly in the return statement (tried so with firefox) */
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var part2 = arr.slice(median - arr.length);
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return arr.length <= 1 ? arr : merge(
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mergeSort(part1), /* first half */
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mergeSort(part2) /* second half */
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/* Balanced Red-Black-Tree */
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function RedBlackTree(arr) {
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function BTree(arr) {
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function NaryTree(n, arr) {
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* Knuth-Morris-Pratt string matching algorithm - finds a pattern in a text.
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* FIXME: Doesn't work correctly yet.
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* PREFIX, OVERLAP or FALIURE function for KMP. Computes how many iterations
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* the algorithm can skip after a mismatch.
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* @input p - pattern (string)
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* @result array of skippable iterations
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/* pi contains the computed skip marks */
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for(q = 1; q < p.length; q++) {
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while(k > 0 && (p.charAt(k) != p.charAt(q)))
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(p.charAt(k) == p.charAt(q)) && k++;
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/* The actual KMP algorithm starts here. */
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var pi = prefix(p), q = 0, result = [];
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for(var i = 0; i < t.length; i++) {
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/* jump forward as long as the character doesn't match */
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while((q > 0) && (p.charAt(q) != t.charAt(i)))
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(p.charAt(q) == t.charAt(i)) && q++;
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(q == p.length) && result.push(i - p.length) && (q = pi[q]);
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/* step for algorithm visualisation */
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function step(comment, funct) {
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//display comment (before or after waiting)
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/* execute callback function */
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* Curry - Function currying
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* Copyright (c) 2008 Ariel Flesler - aflesler(at)gmail(dot)com | http://flesler.blogspot.com
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* Licensed under BSD (http://www.opensource.org/licenses/bsd-license.php)
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* @author Ariel Flesler
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function curry( fn ){
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var args = curry.args(arguments),
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master = arguments.callee,
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return args.length >= fn.length ? fn.apply(self,args) : function(){
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return master.apply( self, args.concat(curry.args(arguments)) );
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curry.args = function( args ){
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return Array.prototype.slice.call(args);
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Function.prototype.curry = function(){
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* Sort a directed graph based on incoming edges
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* Coded by Jake Stothard
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function topological_sort(g) {
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//Mark nodes as "deleted" instead of actually deleting them
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//That way we don't have to copy g
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g.nodes[i].deleted = false;
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var ret = topological_sort_helper(g);
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//Cleanup: Remove the deleted property
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delete g.nodes[i].deleted
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function topological_sort_helper(g) {
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//Find node with no incoming edges
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if(g.nodes[i].deleted)
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continue; //Bad style, meh
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var incoming = false;
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for(j in g.nodes[i].edges) {
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if(g.nodes[i].edges[j].target == g.nodes[i]
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&& g.nodes[i].edges[j].source.deleted == false) {
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// Either unsortable or done. Either way, GTFO
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if(node == undefined)
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//"Delete" node from g
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var tail = topological_sort_helper(g);
added
removed
addons/web_process/static/lib/dracula/dracula_algorithms.js
