NoAI is an API (a framework) to build your own AIs in. See: http://wiki.openttd.org/wiki/index.php/AI:Main_Page With many thanks to: - glx and Rubidium for their syncing, feedback and hard work - Yexo for his feedback, patches, and AIs which tested the system very deep - Morloth for his feedback and patches - TJIP for hosting a challenge which kept NoAI on track - All AI authors for testing our AI API, and all other people who helped in one way or another -Remove: all old AIs and their cheats/hacks
		
			
				
	
	
		
			205 lines
		
	
	
		
			4.6 KiB
		
	
	
	
		
			Plaintext
		
	
	
	
	
	
			
		
		
	
	
			205 lines
		
	
	
		
			4.6 KiB
		
	
	
	
		
			Plaintext
		
	
	
	
	
	
/* $Id$ */
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/**
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 * Fibonacci heap.
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 *  This heap is heavily optimized for the Insert and Pop functions.
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 *  Peek and Pop always return the current lowest value in the list.
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 *  Insert is implemented as a lazy insert, as it will simply add the new
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 *  node to the root list. Sort is done on every Pop operation.
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 */
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class FibonacciHeap {
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	_min = null;
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	_min_index = 0;
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	_min_priority = 0;
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	_count = 0;
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	_root_list = null;
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	/**
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	 * Create a new fibonacci heap.
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	 * http://en.wikipedia.org/wiki/Fibonacci_heap
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	 */
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	constructor() {
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		_count = 0;
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		_min = Node();
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		_min.priority = 0x7FFFFFFF;
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		_min_index = 0;
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		_min_priority = 0x7FFFFFFF;
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		_root_list = [];
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	}
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	/**
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	 * Insert a new entry in the heap.
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	 *  The complexity of this operation is O(1).
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	 * @param item The item to add to the list.
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	 * @param priority The priority this item has.
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	 */
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	function Insert(item, priority);
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	/**
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	 * Pop the first entry of the list.
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	 *  This is always the item with the lowest priority.
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	 *  The complexity of this operation is O(ln n).
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	 * @return The item of the entry with the lowest priority.
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	 */
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	function Pop();
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	/**
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	 * Peek the first entry of the list.
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	 *  This is always the item with the lowest priority.
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	 *  The complexity of this operation is O(1).
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	 * @return The item of the entry with the lowest priority.
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	 */
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	function Peek();
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	/**
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	 * Get the amount of current items in the list.
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	 *  The complexity of this operation is O(1).
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	 * @return The amount of items currently in the list.
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	 */
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	function Count();
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	/**
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	 * Check if an item exists in the list.
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	 *  The complexity of this operation is O(n).
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	 * @param item The item to check for.
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	 * @return True if the item is already in the list.
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	 */
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	function Exists(item);
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};
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function FibonacciHeap::Insert(item, priority) {
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	/* Create a new node instance to add to the heap. */
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	local node = Node();
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	/* Changing params is faster than using constructor values */
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	node.item = item;
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	node.priority = priority;
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	/* Update the reference to the minimum node if this node has a
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	 * smaller priority. */
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	if (_min_priority > priority) {
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		_min = node;
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		_min_index = _root_list.len();
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		_min_priority = priority;
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	}
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	_root_list.append(node);
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	_count++;
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}
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function FibonacciHeap::Pop() {
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	if (_count == 0) return null;
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	/* Bring variables from the class scope to this scope explicitly to
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	 * optimize variable lookups by Squirrel. */
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	local z = _min;
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	local tmp_root_list = _root_list;
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	/* If there are any children, bring them all to the root level. */
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	tmp_root_list.extend(z.child);
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	/* Remove the minimum node from the rootList. */
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	tmp_root_list.remove(_min_index);
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	local root_cache = {};
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	/* Now we decrease the number of nodes on the root level by
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	 * merging nodes which have the same degree. The node with
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	 * the lowest priority value will become the parent. */
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	foreach(x in tmp_root_list) {
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		local y;
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		/* See if we encountered a node with the same degree already. */
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		while (y = root_cache.rawdelete(x.degree)) {
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			/* Check the priorities. */
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			if (x.priority > y.priority) {
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				local tmp = x;
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				x = y;
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				y = tmp;
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			}
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			/* Make y a child of x. */
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			x.child.append(y);
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			x.degree++;
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		}
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		root_cache[x.degree] <- x;
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	}
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	/* The root_cache contains all the nodes which will form the
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	 *  new rootList. We reset the priority to the maximum number
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	 *  for a 32 signed integer to find a new minumum. */
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	tmp_root_list.resize(root_cache.len());
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	local i = 0;
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	local tmp_min_priority = 0x7FFFFFFF;
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	/* Now we need to find the new minimum among the root nodes. */
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	foreach (val in root_cache) {
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		if (val.priority < tmp_min_priority) {
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			_min = val;
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			_min_index = i;
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			tmp_min_priority = val.priority;
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		}
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		tmp_root_list[i++] = val;
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	}
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	/* Update global variables. */
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	_min_priority = tmp_min_priority;
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	_count--;
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	return z.item;
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}
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function FibonacciHeap::Peek() {
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	if (_count == 0) return null;
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	return _min.item;
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}
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function FibonacciHeap::Count() {
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	return _count;
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}
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function FibonacciHeap::Exists(item) {
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	return ExistsIn(_root_list, item);
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}
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/**
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 * Auxilary function to search through the whole heap.
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 * @param list The list of nodes to look through.
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 * @param item The item to search for.
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 * @return True if the item is found, false otherwise.
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 */
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function FibonacciHeap::ExistsIn(list, item) {
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	foreach (val in list) {
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		if (val.item == item) {
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			return true;
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		}
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		foreach (c in val.child) {
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			if (ExistsIn(c, item)) {
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				return true;
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			}
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		}
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	}
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	/* No luck, item doesn't exists in the tree rooted under list. */
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	return false;
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}
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/**
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 * Basic class the fibonacci heap is composed of.
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 */
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class FibonacciHeap.Node {
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	degree = null;
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	child = null;
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	item = null;
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	priority = null;
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	constructor() {
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		child = [];
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		degree = 0;
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	}
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};
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