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  • How can I find the common ancestor of two nodes in a binary tree?

    - by Siddhant
    The Binary Tree here is not a Binary Search Tree. Its just a Binary Tree. The structure could be taken as - struct node { int data; struct node *left; struct node *right; }; The maximum solution I could work out with a friend was something of this sort - Consider this binary tree (from http://lcm.csa.iisc.ernet.in/dsa/node87.html) : The inorder traversal yields - 8, 4, 9, 2, 5, 1, 6, 3, 7 And the postorder traversal yields - 8, 9, 4, 5, 2, 6, 7, 3, 1 So for instance, if we want to find the common ancestor of nodes 8 and 5, then we make a list of all the nodes which are between 8 and 5 in the inorder tree traversal, which in this case happens to be [4, 9, 2]. Then we check which node in this list appears last in the postorder traversal, which is 2. Hence the common ancestor for 8 and 5 is 2. The complexity for this algorithm, I believe is O(n) (O(n) for inorder/postorder traversals, the rest of the steps again being O(n) since they are nothing more than simple iterations in arrays). But there is a strong chance that this is wrong. :-) But this is a very crude approach, and I'm not sure if it breaks down for some case. Is there any other (possibly more optimal) solution to this problem?

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  • Tree iterator, can you optimize this any further?

    - by Ron
    As a follow up to my original question about a small piece of this code I decided to ask a follow up to see if you can do better then what we came up with so far. The code below iterates over a binary tree (left/right = child/next ). I do believe there is room for one less conditional in here (the down boolean). The fastest answer wins! The cnt statement can be multiple statements so lets make sure this appears only once The child() and next() member functions are about 30x as slow as the hasChild() and hasNext() operations. Keep it iterative <-- dropped this requirement as the recursive solution presented was faster. This is C++ code visit order of the nodes must stay as they are in the example below. ( hit parents first then the children then the 'next' nodes). BaseNodePtr is a boost::shared_ptr as thus assignments are slow, avoid any temporary BaseNodePtr variables. Currently this code takes 5897ms to visit 62200000 nodes in a test tree, calling this function 200,000 times. void processTree (BaseNodePtr current, unsigned int & cnt ) { bool down = true; while ( true ) { if ( down ) { while (true) { cnt++; // this can/will be multiple statesments if (!current->hasChild()) break; current = current->child(); } } if ( current->hasNext() ) { down = true; current = current->next(); } else { down = false; current = current->parent(); if (!current) return; // done. } } }

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  • Is there an algorithm for finding an item that matches certain properties, like a 20 questions game?

    - by lala
    A question about 20 questions games was asked here: However, if I'm understanding it correctly, the answers seem to assume that each question will go down a hierarchal branching tree. A binary tree should work if the game went like this: Is it an animal? Yes. Is it a mammal? Yes. Is it a feline? Yes. Because feline is an example of a mammal and mammal is an example of an animal. But what if the questions go like this? Is it a mammal? Yes. Is it a predator? Yes. Does it have a long nose? No. You can't branch down a tree with those kinds of questions, because there are plenty of predators that aren't mammals. So you can't have your program just narrow it down to mammal and have predators be a subset of mammals. So is there a way to use a binary search tree that I'm not understanding or is there a different algorithm for this problem?

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  • Finding the index of a given value in a pre-sorted array

    - by bobo
    Today, I went for an interview and the interviewer asked me how I would find the index of a given value (number) in a pre-sorted array like this: $preSortedArr=array(23,32,36,41,45,54); He also said that using recursion is not allowed. I think the function should look like this: function findIndexByValue($preSortedArray,$value){ //some codes here } What solution do you think he was expecting from me? EDIT: sorry, I forgot to add that, he originally asked me to write psuedo codes but I said I don't know. I tried to write in PHP, but I think he's expecting a language-independent solution.

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  • How do I add an object to a binary tree based on the value of a member variable?

    - by Max
    How can I get a specific value from an object? I'm trying to get a value of an instance for eg. ListOfPpl newListOfPpl = new ListOfPpl(id, name, age); Object item = newListOfPpl; How can I get a value of name from an Object item?? Even if it is easy or does not interest you can anyone help me?? Edited: I was trying to build a binary tree contains the node of ListOfPpl, and need to sort it in the lexicographic. Here's my code for insertion on the node. Any clue?? public void insert(Object item){ Node current = root; Node follow = null; if(!isEmpty()){ root = new Node(item, null, null); return; }boolean left = false, right = false; while(current != null){ follow = current; left = false; right = false; //I need to compare and sort it if(item.compareTo(current.getFighter()) < 0){ current = current.getLeft(); left = true; }else { current = current.getRight(); right = true; } }if(left) follow.setLeft(new Node(item, null, null)); else follow.setRight(new Node(item, null, null)); }

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  • How to reflect over T to build an expression tree for a query?

    - by Alex
    Hi all, I'm trying to build a generic class to work with entities from EF. This class talks to repositories, but it's this class that creates the expressions sent to the repositories. Anyway, I'm just trying to implement one virtual method that will act as a base for common querying. Specifically, it will accept a an int and it only needs to perform a query over the primary key of the entity in question. I've been screwing around with it and I've built a reflection which may or may not work. I say that because I get a NotSupportedException with a message of LINQ to Entities does not recognize the method 'System.Object GetValue(System.Object, System.Object[])' method, and this method cannot be translated into a store expression. So then I tried another approach and it produced the same exception but with the error of The LINQ expression node type 'ArrayIndex' is not supported in LINQ to Entities. I know it's because EF will not parse the expression the way L2S will. Anyway, I'm hopping someone with a bit more experience can point me into the right direction on this. I'm posting the entire class with both attempts I've made. public class Provider<T> where T : class { protected readonly Repository<T> Repository = null; private readonly string TEntityName = typeof(T).Name; [Inject] public Provider( Repository<T> Repository) { this.Repository = Repository; } public virtual void Add( T TEntity) { this.Repository.Insert(TEntity); } public virtual T Get( int PrimaryKey) { // The LINQ expression node type 'ArrayIndex' is not supported in // LINQ to Entities. return this.Repository.Select( t => (((int)(t as EntityObject).EntityKey.EntityKeyValues[0].Value) == PrimaryKey)).Single(); // LINQ to Entities does not recognize the method // 'System.Object GetValue(System.Object, System.Object[])' method, // and this method cannot be translated into a store expression. return this.Repository.Select( t => (((int)t.GetType().GetProperties().Single( p => (p.Name == (this.TEntityName + "Id"))).GetValue(t, null)) == PrimaryKey)).Single(); } public virtual IList<T> GetAll() { return this.Repository.Select().ToList(); } protected virtual void Save() { this.Repository.Update(); } }

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  • Best tree/heap data structure for fixed set of nodes with changing values + need top 20 values?

    - by user350139
    I'm writing something like a game in C++ where I have a database table containing the current score for each user. I want to read that table into memory at the start of the game, quickly change each user's score while the game is being played in response to what each user does, and then when the game ends write the current scores back to the database. I also want to be able to find the 20 or so users with the highest scores. No users will be added or deleted during the short period when the game is being played. I haven't tried it yet, but updating the database might take too much time during the period when the game is being played. Fixed set of users (might be 10,000 to 50,000 users) Will map user IDs to their score and other user-specific information. User IDs will be auto_increment values. If the structure has a high memory overhead that's probably not an issue. If the program crashes during gameplay it can just be re-started. Quickly get a user's current score. Quickly add to a user's current score (and return their current score) Quickly get 20 users with highest score. No deletes. No inserts except when the structure is first created, and how long that takes isn't critical. Getting the top 20 users will only happen every five or ten seconds, but getting/adding will happen much more frequently. If not for the last, I could just create a memory block equal to sizeof(user) * max(user id) and put each user at user id * sizeof(user) for fast access. Should I do that plus some other structure for the Top 20 feature, or is there one structure that will handle all of this together?

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  • LINQ Expression help with Func TEntity,TType

    - by Chris Conway
    I have a repository method that accepts an order by parameter in the form: public IEnumerable<TEntity> Get<TEntity>(Expression<Func<TEntity,string>> orderBy) Now that works fine when trying to sort by a property of type string, var entities = rep.Get(x => x.Name); but what if i want to sort by double or int or any other type. Doing something like var entities = rep.Get(x => x.Price); obviously throws a compile error saying I can't convert double to string. How can I make this more generic so I can sort by any property in my entity, or at least the properties where the type implements IComparable or something similar?

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  • Create an Action<T> to "set" a property, when I am provided with the LINQ Expression for the "get"

    - by Alex
    I'd like to be able to generate a compiled expression to set a property, given the lambda expression that provides the "get" method for a property. Here's what I'm looking for: public Action<int> CreateSetter<T>(Expression<Func<T, int>> getter) { // returns a compiled action using the details of the getter expression tree, or null // if the write property is not defined. } I'm still trying to understand the various types of Expression classes, so if you can point me in the right direction that would be great.

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  • Haskell Binary Tree Function (map)

    - by Bizarro
    How can i define a Haskell function which will apply a function to every value in a binary tree? So i know that it is similar to the map function - and that its type would be: mapT :: (a - b) - Tree a - Tree b but thats about it...

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  • What's the right way to do mutable data structures (e.g., skip lists, splay trees) in F#?

    - by dan
    What's a good way to implement mutable data structures in F#? The reason I’m asking is because I want to go back and implement the data structures I learned about in the algorithms class I took this semester (skip lists, splay trees, fusion trees, y-fast tries, van Emde Boas trees, etc.), which was a pure theory course with no coding whatsoever, and I figure I might as well try to learn F# while I’m doing it. I know that I “should” use finger trees to get splay tree functionality in a functional language, and that I should do something with laziness to get skip-list functionality, etc. , but I want to get the basics nailed down before I try playing with purely functional implementations. There are lots of examples of how to do functional data structures in F#, but there isn’t much on how to do mutable data structures, so I started by fixing up the doubly linked list here into something that allows inserts and deletes anywhere. My plan is to turn this into a skip list, and then use a similar structure (discriminated union of a record) for the tree structures I want to implement. Before I start on something more substantial, is there a better way to do mutable structures like this in F#? Should I just use records and not bother with the discriminated union? Should I use a class instead? Is this question "not even wrong"? Should I be doing the mutable structures in C#, and not dip into F# until I want to compare them to their purely functional counterparts? And, if a DU of records is what I want, could I have written the code below better or more idiomatically? It seems like there's a lot of redundancy here, but I'm not sure how to get rid of it. module DoublyLinkedList = type 'a ll = | None | Node of 'a ll_node and 'a ll_node = { mutable Prev: 'a ll; Element : 'a ; mutable Next: 'a ll; } let insert x l = match l with | None -> Node({ Prev=None; Element=x; Next=None }) | Node(node) -> match node.Prev with | None -> let new_node = { Prev=None; Element=x; Next=Node(node)} node.Prev <- Node(new_node) Node(new_node) | Node(prev_node) -> let new_node = { Prev=node.Prev; Element=x; Next=Node(node)} node.Prev <- Node(new_node) prev_node.Next <- Node(new_node) Node(prev_node) let rec nth n l = match n, l with | _,None -> None | _,Node(node) when n > 0 -> nth (n-1) node.Next | _,Node(node) when n < 0 -> nth (n+1) node.Prev | _,Node(node) -> Node(node) //hopefully only when n = 0 :-) let rec printLinkedList head = match head with | None -> () | Node(x) -> let prev = match x.Prev with | None -> "-" | Node(y) -> y.Element.ToString() let cur = x.Element.ToString() let next = match x.Next with | None -> "-" | Node(y) -> y.Element.ToString() printfn "%s, <- %s -> %s" prev cur next printLinkedList x.Next

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  • How often do you use data structures (ie Binary Trees, Linked Lists) in your jobs/side projects?

    - by Chris2021
    It seems to me that, for everyday use, more primitive data structures like arrays get the job done just as well as a binary tree would. My question is how common is to use these structures when writing code for projects at work or projects that you pursue in your free time? I understand the better insertion time/deletion time/sorting time for certain structures but would that really matter that much if you were working with a relatively small amount of data?

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  • Why would you use data structures (ie Binary Trees, Linked Lists) in your jobs/side projects? [closed]

    - by Chris2021
    It seems to me that, for everyday use, more primitive data structures like arrays get the job done just as well as a binary tree would. My question is how common is to use these structures when writing code for projects at work or projects that you pursue in your free time? I understand the better insertion time/deletion time/sorting time for certain structures but would that really matter that much if you were working with a relatively small amount of data?

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  • How to set up an Android source repo while hosting the git trees as private repositories on github?

    - by gby
    Hello there, I am trying to set up a private repository of Android source code while hosting the git trees on github as private repos. I have no problem changing the manifest.xml file to point to public git trees hosted on github in the same way that CynagonMod does, but when trying to point to private repos I get the following error when trying "repo sync": Initializing project username/android_external_webkit ... fatal: The remote end hung up unexpectedly error: Cannot fetch username/android_external_webkit Where username/android_external_webkit is of course a private github repo of the same name. I understand the error occurs since I did not specify my user name and credentials to github, but I fail to see how to do it in the manifest.xml with repo. Any ideas? Thanks! Gilad

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  • How to find largest common sub-tree in the given two binary search trees?

    - by Bhushan
    Two BSTs (Binary Search Trees) are given. How to find largest common sub-tree in the given two binary trees? EDIT 1: Here is what I have thought: Let, r1 = current node of 1st tree r2 = current node of 2nd tree There are some of the cases I think we need to consider: Case 1 : r1.data < r2.data 2 subproblems to solve: first, check r1 and r2.left second, check r1.right and r2 Case 2 : r1.data > r2.data 2 subproblems to solve: - first, check r1.left and r2 - second, check r1 and r2.right Case 3 : r1.data == r2.data Again, 2 cases to consider here: (a) current node is part of largest common BST compute common subtree size rooted at r1 and r2 (b)current node is NOT part of largest common BST 2 subproblems to solve: first, solve r1.left and r2.left second, solve r1.right and r2.right I can think of the cases we need to check, but I am not able to code it, as of now. And it is NOT a homework problem. Does it look like?

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  • More localized, efficient Lowest Common Ancestor algorithm given multiple binary trees?

    - by mstksg
    I have multiple binary trees stored as an array. In each slot is either nil (or null; pick your language) or a fixed tuple storing two numbers: the indices of the two "children". No node will have only one child -- it's either none or two. Think of each slot as a binary node that only stores pointers to its children, and no inherent value. Take this system of binary trees: 0 1 / \ / \ 2 3 4 5 / \ / \ 6 7 8 9 / \ 10 11 The associated array would be: 0 1 2 3 4 5 6 7 8 9 10 11 [ [2,3] , [4,5] , [6,7] , nil , nil , [8,9] , nil , [10,11] , nil , nil , nil , nil ] I've already written simple functions to find direct parents of nodes (simply by searching from the front until there is a node that contains the child) Furthermore, let us say that at relevant times, both all trees are anywhere between a few to a few thousand levels deep. I'd like to find a function P(m,n) to find the lowest common ancestor of m and n -- to put more formally, the LCA is defined as the "lowest", or deepest node in which have m and n as descendants (children, or children of children, etc.). If there is none, a nil would be a valid return. Some examples, given our given tree: P( 6,11) # => 2 P( 3,10) # => 0 P( 8, 6) # => nil P( 2,11) # => 2 The main method I've been able to find is one that uses an Euler trace, which turns the given tree, with a node A to be the invisible parent of 0 and 1 with a depth of -1, into: A-0-2-6-2-7-10-7-11-7-2-0-3-0-A-1-4-1-5-8-5-9-5-1-A And from that, simply find the node between your given m and n that has the lowest number; For example, to find P(6,11), look for a 6 and an 11 on the trace. The number between them that is the lowest is 2, and that's your answer. If A is in between them, return nil. -- Calculating P(6,11) -- A-0-2-6-2-7-10-7-11-7-2-0-3-0-A-1-4-1-5-8-5-9-5-1-A ^ ^ ^ | | | m lowest n Unfortunately, I do believe that finding the Euler trace of a tree that can be several thousands of levels deep is a bit machine-taxing...and because my tree is constantly being changed throughout the course of the programming, every time I wanted to find the LCA, I'd have to re-calculate the Euler trace and hold it in memory every time. Is there a more memory efficient way, given the framework I'm using? One that maybe iterates upwards? One way I could think of would be the "count" the generation/depth of both nodes, and climb the lowest node until it matched the depth of the highest, and increment both until they find someone similar. But that'd involve climbing up from level, say, 3025, back to 0, twice, to count the generation, and using a terribly inefficient climbing-up algorithm in the first place, and then re-climbing back up. Are there any other better ways?

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  • Recursive N-way merge/diff algorithm for directory trees?

    - by BobMcGee
    What algorithms or Java libraries are available to do N-way, recursive diff/merge of directories? I need to be able to generate a list of folder trees that have many identical files, and have subdirectories with many similar files. I want to be able to use 2-way merge operations to quickly remove as much redundancy as possible. Goals: Find pairs of directories that have many similar files between them. Generate short list of directory pairs that can be synchronized with 2-way merge to eliminate duplicates Should operate recursively (there may be nested duplicates of higher-level directories) Run time and storage should be O(n log n) in numbers of directories and files Should be able to use an embedded DB or page to disk for processing more files than fit in memory (100,000+). Optional: generate an ancestry and change-set between folders Optional: sort the merge operations by how many duplicates they can elliminate I know how to use hashes to find duplicate files in roughly O(n) space, but I'm at a loss for how to go from this to finding partially overlapping sets between folders and their children. EDIT: some clarification The tricky part is the difference between "exact same" contents (otherwise hashing file hashes would work) and "similar" (which will not). Basically, I want to feed this algorithm at a set of directories and have it return a set of 2-way merge operations I can perform in order to reduce duplicates as much as possible with as few conflicts possible. It's effectively constructing an ancestry tree showing which folders are derived from each other. The end goal is to let me incorporate a bunch of different folders into one common tree. For example, I may have a folder holding programming projects, and then copy some of its contents to another computer to work on it. Then I might back up and intermediate version to flash drive. Except I may have 8 or 10 different versions, with slightly different organizational structures or folder names. I need to be able to merge them one step at a time, so I can chose how to incorporate changes at each step of the way. This is actually more or less what I intend to do with my utility (bring together a bunch of scattered backups from different points in time). I figure if I can do it right I may as well release it as a small open source util. I think the same tricks might be useful for comparing XML trees though.

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  • Is there a simple library that will render JSON objects as trees?

    - by Robert Gould
    So, is there a VERY simple library that will render JSON objects as trees? I know that this can be done in many ways (such as YUI), but for debug purposes I'd like to simply be able to view a JSON objects I receive from a server as a tree, nothing fancy (but collapsable tree's would be a bonus). The kind of solution I'm looking for would be something like: <script source="something.js"/> <script> obj ={"hello":"world"} lib.renderJSON("someid",obj); </script> ... <div id="someid"/> Any ideas?

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  • Does a successful exit of rsync -acvvv s d guarantee identical directory trees?

    - by user259774
    I have two volumes, one xfs, and another ntfs - ntfs was empty, and xfs had 10 subitems. I needed to sync them. I initially copied a few of the subitems by dragging them over in a gui fm. Several of the direct descendants which i had dragged finished, apparently. One I stopped before it was done, and the rest I cancelled while it still appeared to be gathering information about the files. Then I ran rsync -acvvv xmp/ nmp/, where xmp and nmp are the volumes' respective mountpoints, which exited with a 0 status. find xmp -printf x | wc -c and find nmp -printf x | wc -c both return 372926. My question is: Am I guaranteed that the two drives' contents are identical?

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  • Is there a program that will show a tree of the differences in two file trees?

    - by Huckle
    In windows I manually back up from time to time by formatting my external drive and copying the contents of my data partition over. Inevitably there is a difference in the number and size of the files copied because of system files, etc. Is there a program that would diff two directories recursively and compile the differences into a nice GUI tree that I could peruse (preferably filter) to ensure that everything I want made it over to the drive? It should only show files that are not in both directories. (Also, please ignore the inadequacy of my backup solution)

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  • JsTree v1.0 - How to manipulate effectively the data from the backend to render the trees and operate correctly?

    - by Jean Paul
    Backend info: PHP 5 / MySQL URL: http://github.com/downloads/vakata/jstree/jstree_pre1.0_fix_1.zip Table structure for table discussions_tree -- CREATE TABLE IF NOT EXISTS `discussions_tree` ( `id` int(11) NOT NULL AUTO_INCREMENT, `parent_id` int(11) NOT NULL DEFAULT '0', `user_id` int(11) NOT NULL DEFAULT '0', `label` varchar(16) DEFAULT NULL, `position` bigint(20) unsigned NOT NULL DEFAULT '0', `left` bigint(20) unsigned NOT NULL DEFAULT '0', `right` bigint(20) unsigned NOT NULL DEFAULT '0', `level` bigint(20) unsigned NOT NULL DEFAULT '0', `type` varchar(255) CHARACTER SET utf8 COLLATE utf8_unicode_ci DEFAULT NULL, `h_label` varchar(16) NOT NULL DEFAULT '', `fulllabel` varchar(255) DEFAULT NULL, UNIQUE KEY `uidx_3` (`id`), KEY `idx_1` (`user_id`), KEY `idx_2` (`parent_id`) ) ENGINE=MyISAM DEFAULT CHARSET=latin1 AUTO_INCREMENT=8 ; /*The first element should in my understanding not even be shown*/ INSERT INTO `discussions_tree` (`id`, `parent_id`, `user_id`, `label`, `position`, `left`, `right`, `level`, `type`, `h_label`, `fulllabel`) VALUES (0, 0, 0, 'Contacts', 0, 1, 1, 0, NULL, '', NULL); INSERT INTO `discussions_tree` (`id`, `parent_id`, `user_id`, `label`, `position`, `left`, `right`, `level`, `type`, `h_label`, `fulllabel`) VALUES (1, 0, 0, 'How to Tag', 1, 2, 2, 0, 'drive', '', NULL); Front End : I've simplified the logic, it has 6 trees actually inside of a panel and that works fine $array = array("Discussions"); $id_arr = array("d"); $nid = 0; foreach ($array as $k=> $value) { $nid++; ?> <li id="<?=$value?>" class="label"> <a href='#<?=$value?>'><span> <?=$value?> </span></a> <div class="sub-menu" style="height:auto; min-height:120px; background-color:#E5E5E5" > <div class="menu" id="menu_<?=$id_arr[$k]?>" style="position:relative; margin-left:56%"> <img src="./js/jsTree/create.png" alt="" id="create" title="Create" > <img src="./js/jsTree/rename.png" alt="" id="rename" title="Rename" > <img src="./js/jsTree/remove.png" alt="" id="remove" title="Delete"> <img src="./js/jsTree/cut.png" alt="" id="cut" title="Cut" > <img src="./js/jsTree/copy.png" alt="" id="copy" title="Copy"> <img src="./js/jsTree/paste.png" alt="" id="paste" title="Paste"> </div> <div id="<?=$id_arr[$k]?>" class="jstree_container"></div> </div> </li> <!-- JavaScript neccessary for this tree : <?=$value?> --> <script type="text/javascript" > jQuery(function ($) { $("#<?=$id_arr[$k]?>").jstree({ // List of active plugins used "plugins" : [ "themes", "json_data", "ui", "crrm" , "hotkeys" , "types" , "dnd", "contextmenu"], // "ui" :{ "initially_select" : ["#node_"+ $nid ] } , "crrm": { "move": { "always_copy": "multitree" }, "input_width_limit":128 }, "core":{ "strings":{ "new_node" : "New Tag" }}, "themes": {"theme": "classic"}, "json_data" : { "ajax" : { "url" : "./js/jsTree/server-<?=$id_arr[$k]?>.php", "data" : function (n) { // the result is fed to the AJAX request `data` option return { "operation" : "get_children", "id" : n.attr ? n.attr("id").replace("node_","") : 1, "state" : "", "user_id": <?=$uid?> }; } } } , "types" : { "max_depth" : -1, "max_children" : -1, "types" : { // The default type "default" : { "hover_node":true, "valid_children" : [ "default" ], }, // The `drive` nodes "drive" : { // can have files and folders inside, but NOT other `drive` nodes "valid_children" : [ "default", "folder" ], "hover_node":true, "icon" : { "image" : "./js/jsTree/root.png" }, // those prevent the functions with the same name to be used on `drive` nodes.. internally the `before` event is used "start_drag" : false, "move_node" : false, "remove_node" : false } } }, "contextmenu" : { "items" : customMenu , "select_node": true} }) //Hover function binded to jstree .bind("hover_node.jstree", function (e, data) { $('ul li[rel="drive"], ul li[rel="default"], ul li[rel=""]').each(function(i) { $(this).find("a").attr('href', $(this).attr("id")+".php" ); }) }) //Create function binded to jstree .bind("create.jstree", function (e, data) { $.post( "./js/jsTree/server-<?=$id_arr[$k]?>.php", { "operation" : "create_node", "id" : data.rslt.parent.attr("id").replace("node_",""), "position" : data.rslt.position, "label" : data.rslt.name, "href" : data.rslt.obj.attr("href"), "type" : data.rslt.obj.attr("rel"), "user_id": <?=$uid?> }, function (r) { if(r.status) { $(data.rslt.obj).attr("id", "node_" + r.id); } else { $.jstree.rollback(data.rlbk); } } ); }) //Remove operation .bind("remove.jstree", function (e, data) { data.rslt.obj.each(function () { $.ajax({ async : false, type: 'POST', url: "./js/jsTree/server-<?=$id_arr[$k]?>.php", data : { "operation" : "remove_node", "id" : this.id.replace("node_",""), "user_id": <?=$uid?> }, success : function (r) { if(!r.status) { data.inst.refresh(); } } }); }); }) //Rename operation .bind("rename.jstree", function (e, data) { data.rslt.obj.each(function () { $.ajax({ async : true, type: 'POST', url: "./js/jsTree/server-<?=$id_arr[$k]?>.php", data : { "operation" : "rename_node", "id" : this.id.replace("node_",""), "label" : data.rslt.new_name, "user_id": <?=$uid?> }, success : function (r) { if(!r.status) { data.inst.refresh(); } } }); }); }) //Move operation .bind("move_node.jstree", function (e, data) { data.rslt.o.each(function (i) { $.ajax({ async : false, type: 'POST', url: "./js/jsTree/server-<?=$id_arr[$k]?>.php", data : { "operation" : "move_node", "id" : $(this).attr("id").replace("node_",""), "ref" : data.rslt.cr === -1 ? 1 : data.rslt.np.attr("id").replace("node_",""), "position" : data.rslt.cp + i, "label" : data.rslt.name, "copy" : data.rslt.cy ? 1 : 0, "user_id": <?=$uid?> }, success : function (r) { if(!r.status) { $.jstree.rollback(data.rlbk); } else { $(data.rslt.oc).attr("id", "node_" + r.id); if(data.rslt.cy && $(data.rslt.oc).children("UL").length) { data.inst.refresh(data.inst._get_parent(data.rslt.oc)); } } } }); }); }); // This is for the context menu to bind with operations on the right clicked node function customMenu(node) { // The default set of all items var control; var items = { createItem: { label: "Create", action: function (node) { return {createItem: this.create(node) }; } }, renameItem: { label: "Rename", action: function (node) { return {renameItem: this.rename(node) }; } }, deleteItem: { label: "Delete", action: function (node) { return {deleteItem: this.remove(node) }; }, "separator_after": true }, copyItem: { label: "Copy", action: function (node) { $(node).addClass("copy"); return {copyItem: this.copy(node) }; } }, cutItem: { label: "Cut", action: function (node) { $(node).addClass("cut"); return {cutItem: this.cut(node) }; } }, pasteItem: { label: "Paste", action: function (node) { $(node).addClass("paste"); return {pasteItem: this.paste(node) }; } } }; // We go over all the selected items as the context menu only takes action on the one that is right clicked $.jstree._reference("#<?=$id_arr[$k]?>").get_selected(false, true).each(function(index,element) { if ( $(element).attr("id") != $(node).attr("id") ) { // Let's deselect all nodes that are unrelated to the context menu -- selected but are not the one right clicked $("#<?=$id_arr[$k]?>").jstree("deselect_node", '#'+$(element).attr("id") ); } }); //if any previous click has the class for copy or cut $("#<?=$id_arr[$k]?>").find("li").each(function(index,element) { if ($(element) != $(node) ) { if( $(element).hasClass("copy") || $(element).hasClass("cut") ) control=1; } else if( $(node).hasClass("cut") || $(node).hasClass("copy")) { control=0; } }); //only remove the class for cut or copy if the current operation is to paste if($(node).hasClass("paste") ) { control=0; // Let's loop through all elements and try to find if the paste operation was done already $("#<?=$id_arr[$k]?>").find("li").each(function(index,element) { if( $(element).hasClass("copy") ) $(this).removeClass("copy"); if ( $(element).hasClass("cut") ) $(this).removeClass("cut"); if ( $(element).hasClass("paste") ) $(this).removeClass("paste"); }); } switch (control) { //Remove the paste item from the context menu case 0: switch ($(node).attr("rel")) { case "drive": delete items.renameItem; delete items.deleteItem; delete items.cutItem; delete items.copyItem; delete items.pasteItem; break; case "default": delete items.pasteItem; break; } break; //Remove the paste item from the context menu only on the node that has either copy or cut added class case 1: if( $(node).hasClass("cut") || $(node).hasClass("copy") ) { switch ($(node).attr("rel")) { case "drive": delete items.renameItem; delete items.deleteItem; delete items.cutItem; delete items.copyItem; delete items.pasteItem; break; case "default": delete items.pasteItem; break; } } else //Re-enable it on the clicked node that does not have the cut or copy class { switch ($(node).attr("rel")) { case "drive": delete items.renameItem; delete items.deleteItem; delete items.cutItem; delete items.copyItem; break; } } break; //initial state don't show the paste option on any node default: switch ($(node).attr("rel")) { case "drive": delete items.renameItem; delete items.deleteItem; delete items.cutItem; delete items.copyItem; delete items.pasteItem; break; case "default": delete items.pasteItem; break; } break; } return items; } $("#menu_<?=$id_arr[$k]?> img").hover( function () { $(this).css({'cursor':'pointer','outline':'1px double teal'}) }, function () { $(this).css({'cursor':'none','outline':'1px groove transparent'}) } ); $("#menu_<?=$id_arr[$k]?> img").click(function () { switch(this.id) { //Create only the first element case "create": if ( $.jstree._reference("#<?=$id_arr[$k]?>").get_selected(false, true).length ) { $.jstree._reference("#<?=$id_arr[$k]?>").get_selected(false, true).each(function(index,element){ switch(index) { case 0: $("#<?=$id_arr[$k]?>").jstree("create", '#'+$(element).attr("id"), null, /*{attr : {href: '#' }}*/null ,null, false); break; default: $("#<?=$id_arr[$k]?>").jstree("deselect_node", '#'+$(element).attr("id") ); break; } }); } else { $.facebox('<p class=\'p_inner error bold\'>A selection needs to be made to work with this operation'); setTimeout(function(){ $.facebox.close(); }, 2000); } break; //REMOVE case "remove": if ( $.jstree._reference("#<?=$id_arr[$k]?>").get_selected(false, true).length ) { $.jstree._reference("#<?=$id_arr[$k]?>").get_selected(false, true).each(function(index,element){ //only execute if the current node is not the first one (drive) if( $(element).attr("id") != $("div.jstree > ul > li").first().attr("id") ) { $("#<?=$id_arr[$k]?>").jstree("remove",'#'+$(element).attr("id")); } else $("#<?=$id_arr[$k]?>").jstree("deselect_node", '#'+$(element).attr("id") ); }); } else { $.facebox('<p class=\'p_inner error bold\'>A selection needs to be made to work with this operation'); setTimeout(function(){ $.facebox.close(); }, 2000); } break; //RENAME NODE only one selection case "rename": if ( $.jstree._reference("#<?=$id_arr[$k]?>").get_selected(false, true).length ) { $.jstree._reference("#<?=$id_arr[$k]?>").get_selected(false, true).each(function(index,element){ if( $(element).attr("id") != $("div.jstree > ul > li").first().attr("id") ) { switch(index) { case 0: $("#<?=$id_arr[$k]?>").jstree("rename", '#'+$(element).attr("id") ); break; default: $("#<?=$id_arr[$k]?>").jstree("deselect_node", '#'+$(element).attr("id") ); break; } } else $("#<?=$id_arr[$k]?>").jstree("deselect_node", '#'+$(element).attr("id") ); }); } else { $.facebox('<p class=\'p_inner error bold\'>A selection needs to be made to work with this operation'); setTimeout(function(){ $.facebox.close(); }, 2000); } break; //Cut case "cut": if ( $.jstree._reference("#<?=$id_arr[$k]?>").get_selected(false, true).length ) { $.jstree._reference("#<?=$id_arr[$k]?>").get_selected(false, true).each(function(index,element){ switch(index) { case 0: $("#<?=$id_arr[$k]?>").jstree("cut", '#'+$(element).attr("id")); $.facebox('<p class=\'p_inner teal\'>Operation "Cut" successfully done.<p class=\'p_inner teal bold\'>Where to place it?'); setTimeout(function(){ $.facebox.close(); $("#<?=$id_arr[$k]?>").jstree("deselect_node", '#'+$(element).attr("id")); }, 2000); break; default: $("#<?=$id_arr[$k]?>").jstree("deselect_node", '#'+$(element).attr("id") ); break; } }); } else { $.facebox('<p class=\'p_inner error bold\'>A selection needs to be made to work with this operation'); setTimeout(function(){ $.facebox.close(); }, 2000); } break; //Copy case "copy": if ( $.jstree._reference("#<?=$id_arr[$k]?>").get_selected(false, true).length ) { $.jstree._reference("#<?=$id_arr[$k]?>").get_selected(false, true).each(function(index,element){ switch(index) { case 0: $("#<?=$id_arr[$k]?>").jstree("copy", '#'+$(element).attr("id")); $.facebox('<p class=\'p_inner teal\'>Operation "Copy": Successfully done.<p class=\'p_inner teal bold\'>Where to place it?'); setTimeout(function(){ $.facebox.close(); $("#<?=$id_arr[$k]?>").jstree("deselect_node", '#'+$(element).attr("id") ); }, 2000); break; default: $("#<?=$id_arr[$k]?>").jstree("deselect_node", '#'+$(element).attr("id") ); break; } }); } else { $.facebox('<p class=\'p_inner error bold\'>A selection needs to be made to work with this operation'); setTimeout(function(){ $.facebox.close(); }, 2000); } break; case "paste": if ( $.jstree._reference("#<?=$id_arr[$k]?>").get_selected(false, true).length ) { $.jstree._reference("#<?=$id_arr[$k]?>").get_selected(false, true).each(function(index,element){ switch(index) { case 0: $("#<?=$id_arr[$k]?>").jstree("paste", '#'+$(element).attr("id")); break; default: $("#<?=$id_arr[$k]?>").jstree("deselect_node", '#'+$(element).attr("id") ); break; } }); } else { $.facebox('<p class=\'p_inner error bold\'>A selection needs to be made to work with this operation'); setTimeout(function(){ $.facebox.close(); }, 2000); } break; } }); <? } ?> server.php $path='../../../..'; require_once "$path/phpfoo/dbif.class"; require_once "$path/global.inc"; // Database config & class $db_config = array( "servername"=> $dbHost, "username" => $dbUser, "password" => $dbPW, "database" => $dbName ); if(extension_loaded("mysqli")) require_once("_inc/class._database_i.php"); else require_once("_inc/class._database.php"); //Tree class require_once("_inc/class.ctree.php"); $dbLink = new dbif(); $dbErr = $dbLink->connect($dbName,$dbUser,$dbPW,$dbHost); $jstree = new json_tree(); if(isset($_GET["reconstruct"])) { $jstree->_reconstruct(); die(); } if(isset($_GET["analyze"])) { echo $jstree->_analyze(); die(); } $table = '`discussions_tree`'; if($_REQUEST["operation"] && strpos($_REQUEST["operation"], "_") !== 0 && method_exists($jstree, $_REQUEST["operation"])) { foreach($_REQUEST as $k => $v) { switch($k) { case 'user_id': //We are passing the user_id from the $_SESSION on each request and trying to pick up the min and max value from the table that matches the 'user_id' $sql = "SELECT max(`right`) , min(`left`) FROM $table WHERE `user_id`=$v"; //If the select does not return any value then just let it be :P if (!list($right, $left)=$dbLink->getRow($sql)) { $sql = $dbLink->dbSubmit("UPDATE $table SET `user_id`=$v WHERE `id` = 1 AND `parent_id` = 0"); $sql = $dbLink->dbSubmit("UPDATE $table SET `user_id`=$v WHERE `parent_id` = 1 AND `label`='How to Tag' "); } else { $sql = $dbLink->dbSubmit("UPDATE $table SET `user_id`=$v, `right`=$right+2 WHERE `id` = 1 AND `parent_id` = 0"); $sql = $dbLink->dbSubmit("UPDATE $table SET `user_id`=$v, `left`=$left+1, `right`=$right+1 WHERE `parent_id` = 1 AND `label`='How to Tag' "); } break; } } header("HTTP/1.0 200 OK"); header('Content-type: application/json; charset=utf-8'); header("Cache-Control: no-cache, must-revalidate"); header("Expires: Mon, 26 Jul 1997 05:00:00 GMT"); header("Pragma: no-cache"); echo $jstree->{$_REQUEST["operation"]}($_REQUEST); die(); } header("HTTP/1.0 404 Not Found"); ?> The problem: DND *(Drag and Drop) works, Delete works, Create works, Rename works, but Copy, Cut and Paste don't work

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  • What is the best way to add and order to Doctrine Nested Set Trees?

    - by murze
    What is the best way to add a sense of order in Doctrine Nested Sets? The documention contains several examples of how to get al the childeren of a specific node $category->getNode()->getSiblings() But how can I for example: change the position of the fourth sibling to the second position get only the second sibling add a sibling between the second and third child etc... Do I have to manually add and ordercolumn to the model to do these operations?

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  • Are there any radix/patricia/critbit trees for Python?

    - by Andrew Dalke
    I have about 10,000 words used as a set of inverted indices to about 500,000 documents. Both are normalized so the index is a mapping of integers (word id) to a set of integers (ids of documents which contain the word). My prototype uses Python's set as the obvious data type. When I do a search for a document I find the list of N search words and their corresponding N sets. I want to return the set of documents in the intersection of those N sets. Python's "intersect" method is implemented as a pairwise reduction. I think I can do better with a parallel search of sorted sets, so long as the library offers a fast way to get the next entry after i. I've been looking for something like that for some time. Years ago I wrote PyJudy but I no longer maintain it and I know how much work it would take to get it to a stage where I'm comfortable with it again. I would rather use someone else's well-tested code, and I would like one which supports fast serialization/deserialization. I can't find any, or at least not any with Python bindings. There is avltree which does what I want, but since even the pair-wise set merge take longer than I want, I suspect I want to have all my operations done in C/C++. Do you know of any radix/patricia/critbit tree libraries written as C/C++ extensions for Python? Failing that, what is the most appropriate library which I should wrap? The Judy Array site hasn't been updated in 6 years, with 1.0.5 released in May 2007. (Although it does build cleanly so perhaps It Just Works.)

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