Search Results

Search found 97 results on 4 pages for 'dijkstra'.

Page 2/4 | < Previous Page | 1 2 3 4  | Next Page >

  • java and mysql geting shortest path from to two points

    - by shaharnakash
    hi i have a mysql database that hold id ,name , oneid , twoid , size 1 1 1 2 4 2 2 1 3 1 3 3 2 1 74 4 4 2 4 2 5 5 2 5 12 6 6 4 2 12 7 7 4 6 74 8 8 4 7 12 9 9 3 5 32 10 10 3 8 22 11 11 5 3 66 12 12 5 6 76 13 13 5 9 33 14 14 6 10 11 15 15 6 7 21 16 16 8 3 12 17 17 8 9 10 18 18 9 8 2 19 19 9 10 72 20 20 10 6 31 21 21 10 7 7 22 22 10 9 18 23 23 7 6 8 i want to do Dijkstra algorithm but i cant get the details right if i got the contents to class Conn id ,name , oneid , twoid , size how do i find the path from oneid 1 to twoid 7 and believe me i triad many Dijkstra algorithms so please dont give me only reference

    Read the article

  • Programming language shootout: code most like pseudocode for Dijkstra's Algorithm

    - by Casebash
    Okay, so this question here asked which language is most like executable pseudocode, so why not find out by actually writing some code! Here we have a competition where I will award a 100 point bounty (I know its not much, but I am poor after the recalc) to the code which most resembles this pseudocode. I've read through this a few times so I'm pretty sure that this pseudocode below is correct and about as unambiguous as pseudocode can be. Personally, I'm going to have a go in Python and probably Haskell as well, but I'm just learning the later so my attempt will probably be pretty poor. Note: Obviously to implement anything looking like this you'll have to define quite a few library functions. define DirectedGraph G with: Vertices as V, Edges as E define Vertex A, Z declare each e in E as having properties: Boolean fixed with: initial=false Real minSoFar with: initial=0 for A else infinity define PriorityQueue pq with: objects=V initial=A priority v=v.minSoFar create triggers for v in V: when v.minSoFar event reduced then pq.addOrUpdate v when v.fixed event becomesTrue then pq.remove v Repeat until Z.fixed==True: define Vertex U=pq.pop() U.fixed=True for Edge E adjacentTo U with other Vertex V: V.minSoFar=U.minSoFar+length(E) if reducesValue return Z.name, Z.minSoFar

    Read the article

  • Running time for Dijkstra's algorithm on a priority queue implemented by sorted list/array

    - by jay
    So I'm curious to know what the running time for the algorithm is on on priority queue implemented by a sorted list/array. I know for an unsorted list/array it is O((n^2+m)) where n is the number of vertices and m the number of edges. Thus that equates to O(n^2) time. But would it be faster if i used an sorted list/array...What would the running time be? I know extractmin would be constant time.

    Read the article

  • Neo4j Reading data / performing shortest path calculations on stored data

    - by paddydub
    I'm using the Batch_Insert example to insert Data into the database How can i read this data back from the database. I can't find any examples of how i do this. public static void CreateData() { // create the batch inserter BatchInserter inserter = new BatchInserterImpl( "var/graphdb", BatchInserterImpl.loadProperties( "var/neo4j.props" ) ); Map<String,Object> properties = new HashMap<String,Object>(); properties.put( "name", "Mr. Andersson" ); properties.put( "age", 29 ); long node1 = inserter.createNode( properties ); properties.put( "name", "Trinity" ); properties.remove( "age" ); long node2 = inserter.createNode( properties ); inserter.createRelationship( node1, node2, DynamicRelationshipType.withName( "KNOWS" ), null ); inserter.shutdown(); } I would like to store graph data in the database, graph.makeEdge( "s", "c", "cost", (double) 7 ); graph.makeEdge( "c", "e", "cost", (double) 7 ); graph.makeEdge( "s", "a", "cost", (double) 2 ); graph.makeEdge( "a", "b", "cost", (double) 7 ); graph.makeEdge( "b", "e", "cost", (double) 2 ); Dijkstra<Double> dijkstra = getDijkstra( graph, 0.0, "s", "e" ); What is the best method to store this kind data with 10000's of edges. Then run the Dijskra algorighm to find shortest path calculations using the stored graph data.

    Read the article

  • Dijkstras Algorithm exaplination java

    - by alchemey89
    Hi, I have found an implementation for dijkstras algorithm on the internet and was wondering if someone could help me understand how the code works. Many thanks private int nr_points=0; private int[][]Cost; private int []mask; private void dijkstraTSP() { if(nr_points==0)return; //algorithm=new String("Dijkstra"); nod1=new Vector(); nod2=new Vector(); weight=new Vector(); mask=new int[nr_points]; //initialise mask with zeros (mask[x]=1 means the vertex is marked as used) for(int i=0;i<nr_points;i++)mask[i]=0; //Dijkstra: int []dd=new int[nr_points]; int []pre=new int[nr_points]; int []path=new int[nr_points+1]; int init_vert=0,pos_in_path=0,new_vert=0; //initialise the vectors for(int i=0;i<nr_points;i++) { dd[i]=Cost[init_vert][i]; pre[i]=init_vert; path[i]=-1; } pre[init_vert]=0; path[0]=init_vert; pos_in_path++; mask[init_vert]=1; for(int k=0;k<nr_points-1;k++) { //find min. cost in dd for(int j=0;j<nr_points;j++) if(dd[j]!=0 && mask[j]==0){new_vert=j; break;} for(int j=0;j<nr_points;j++) if(dd[j]<dd[new_vert] && mask[j]==0 && dd[j]!=0)new_vert=j; mask[new_vert]=1; path[pos_in_path]=new_vert; pos_in_path++; for(int j=0;j<nr_points;j++) { if(mask[j]==0) { if(dd[j]>dd[new_vert]+Cost[new_vert][j]) { dd[j]=dd[new_vert]+Cost[new_vert][j]; } } } } //Close the cycle path[nr_points]=init_vert; //Save the solution in 3 vectors (for graphical purposes) for(int i=0;i<nr_points;i++) { nod1.addElement(path[i]); nod2.addElement(path[i+1]); weight.addElement(Cost[path[i]][path[i+1]]); } }

    Read the article

  • Suggestions of the easiest algorithms for some Graph operations

    - by Nazgulled
    Hi, The deadline for this project is closing in very quickly and I don't have much time to deal with what it's left. So, instead of looking for the best (and probably more complicated/time consuming) algorithms, I'm looking for the easiest algorithms to implement a few operations on a Graph structure. The operations I'll need to do is as follows: List all users in the graph network given a distance X List all users in the graph network given a distance X and the type of relation Calculate the shortest path between 2 users on the graph network given a type of relation Calculate the maximum distance between 2 users on the graph network Calculate the most distant connected users on the graph network A few notes about my Graph implementation: The edge node has 2 properties, one is of type char and another int. They represent the type of relation and weight, respectively. The Graph is implemented with linked lists, for both the vertices and edges. I mean, each vertex points to the next one and each vertex also points to the head of a different linked list, the edges for that specific vertex. What I know about what I need to do: I don't know if this is the easiest as I said above, but for the shortest path between 2 users, I believe the Dijkstra algorithm is what people seem to recommend pretty often so I think I'm going with that. I've been searching and searching and I'm finding it hard to implement this algorithm, does anyone know of any tutorial or something easy to understand so I can implement this algorithm myself? If possible, with C source code examples, it would help a lot. I see many examples with math notations but that just confuses me even more. Do you think it would help if I "converted" the graph to an adjacency matrix to represent the links weight and relation type? Would it be easier to perform the algorithm on that instead of the linked lists? I could easily implement a function to do that conversion when needed. I'm saying this because I got the feeling it would be easier after reading a couple of pages about the subject, but I could be wrong. I don't have any ideas about the other 4 operations, suggestions?

    Read the article

  • Efficiently finding the shortest path in large graphs

    - by Björn Lindqvist
    I'm looking to find a way to in real-time find the shortest path between nodes in a huge graph. It has hundreds of thousands of vertices and millions of edges. I know this question has been asked before and I guess the answer is to use a breadth-first search, but I'm more interested in to know what software you can use to implement it. For example, it would be totally perfect if it already exist a library (with python bindings!) for performing bfs in undirected graphs.

    Read the article

  • Correct formulation of the A* algorithm

    - by Eli Bendersky
    Hello, I'm looking at definitions of the A* path-finding algorithm, and it seems to be defined somewhat differently in different places. The difference is in the action performed when going through the successors of a node, and finding that a successor is on the closed list. One approach (suggested by Wikipedia, and this article) says: if the successor is on the closed list, just ignore it Another approach (suggested here and here, for example) says: if the successor is on the closed list, examine its cost. If it's higher than the currently computed score, remove the item from the closed list for future examination. I'm confused - which method is correct ? Intuitively, the first makes more sense to me, but I wonder about the difference in definition. Is one of the definitions wrong, or are they somehow isomorphic ?

    Read the article

  • Single CAS web application in a cluster

    - by Dolf Dijkstra
    Recently a customer wanted to set up a cluster of CAS nodes to be used together with WebCenter Sites. In the process of setting this up they realized that they needed to create a web application per managed server. They did not want to have this management burden but would like to have one web application deployed to multiple nodes. The reason that there is a need for a unique application per node is that the web-application contains information that needs to be unique per node, the postfix for the ticket id.  My customer would like to externalize the node specific configuration to either a specific classpath per managed server or to system properties set at startup.It turns out that the postfix for ticket ids is managed through a property host.name and that this property can be externalized.The host.name property is used in: /webapps/cas/WEB-INF/spring-configuration/uniqueIdGenerators.xmlIt is set in /webapps/cas/WEB-INF/spring-configuration/propertyFileConfigurer.xmlin a PropertyPlaceholderConfigurer.The documentation for PropertyPlaceholderConfigurer:http://static.springsource.org/spring/docs/2.0.x/api/org/springframework/beans/factory/config/PropertyPlaceholderConfigurer.htmlThis indicates that the properties defined through the PropertyPlaceHolderConfigurer can be externalized.To enable this externalization you would need to change host.properties so it is generic for all the managed servers and thus can be reused for all the managed servers: host.name=${cluster.node.id}Next step is to change the startup scripts for the managed servers and add a system property for -Dcluster.node.id=<something unique and stable>.Viola, the postfix is externalized and the web application can be shared amongst the cluster nodes.

    Read the article

  • GOTO still considered harmful?

    - by Kyle Cronin
    Everyone is aware of Dijkstra's Letters to the editor: go to statement considered harmful (also here .html transcript and here .pdf) and there has been a formidable push since that time to eschew the goto statement whenever possible. While it's possible to use goto to produce unmaintainable, sprawling code, it nevertheless remains in modern programming languages. Even the advanced continuation control structure in Scheme can be described as a sophisticated goto. What circumstances warrant the use of goto? When is it best to avoid? As a followup question: C provides a pair of functions, setjmp and longjmp, that provide the ability to goto not just within the current stack frame but within any of the calling frames. Should these be considered as dangerous as goto? More dangerous? Dijkstra himself regretted that title, of which he was not responsible for. At the end of EWD1308 (also here .pdf) he wrote: Finally a short story for the record. In 1968, the Communications of the ACM published a text of mine under the title "The goto statement considered harmful", which in later years would be most frequently referenced, regrettably, however, often by authors who had seen no more of it than its title, which became a cornerstone of my fame by becoming a template: we would see all sorts of articles under the title "X considered harmful" for almost any X, including one titled "Dijkstra considered harmful". But what had happened? I had submitted a paper under the title "A case against the goto statement", which, in order to speed up its publication, the editor had changed into a "letter to the Editor", and in the process he had given it a new title of his own invention! The editor was Niklaus Wirth. A well thought out classic paper about this topic, to be matched to that of Dijkstra, is Structured Programming with go to Statements (also here .pdf), by Donald E. Knuth. Reading both helps to reestablish context and a non-dogmatic understanding of the subject. In this paper, Dijkstra's opinion on this case is reported and is even more strong: Donald E. Knuth: I believe that by presenting such a view I am not in fact disagreeing sharply with Dijkstra's ideas, since he recently wrote the following: "Please don't fall into the trap of believing that I am terribly dogmatical about [the go to statement]. I have the uncomfortable feeling that others are making a religion out of it, as if the conceptual problems of programming could be solved by a single trick, by a simple form of coding discipline!"

    Read the article

  • Why won't C++ allow this default value

    - by nieldw
    Why won't GCC allow a default parameter here? template<class edgeDecor, class vertexDecor, bool dir> Graph<edgeDecor,int,dir> Graph<edgeDecor,vertexDecor,dir>::Dijkstra(vertex s, bool print = false) const { This is the output I get: graph.h:82: error: default argument given for parameter 2 of ‘Graph<edgeDecor, int, dir> Graph<edgeDecor, vertexDecor, dir>::Dijkstra(Vertex<edgeDecor, vertexDecor, dir>, bool)’ graph.h:36: error: after previous specification in ‘Graph<edgeDecor, int, dir> Graph<edgeDecor, vertexDecor, dir>::Dijkstra(Vertex<edgeDecor, vertexDecor, dir>, bool)’ Can anyone see why I'm getting this?

    Read the article

  • Dijstra shortest path algorithm with edge cost.

    - by Svisstack
    Hello, I have a directed, positive weighted graph. Each edge have a cost of use. I have only A money, i want to calculate shortest paths with dijkstra algorithm, but sum of edges costs on route must be less or equal to A. I want to do this with most smallest Dijstra modification (if I can do it with small modification of Dijkstra). Anyone can help me with this?

    Read the article

  • A C# collection, which behaves like C++ set or priority_queue?

    - by Wojciech
    Hello, I have written the Dijkstra's algorithm many times in C++ - I need there set or priotity_queue, both give me possibility to add an element and find the least one (using specified comparator). Now, I've got a problem when trying to write Dijkstra in C# - is there any structure which could be useful for me? I need adding and finding or erasing the least element. Using Visual Studio '08

    Read the article

  • Finding the heaviest length-constrained path in a weighted Binary Tree

    - by Hristo
    UPDATE I worked out an algorithm that I think runs in O(n*k) running time. Below is the pseudo-code: routine heaviestKPath( T, k ) // create 2D matrix with n rows and k columns with each element = -8 // we make it size k+1 because the 0th column must be all 0s for a later // function to work properly and simplicity in our algorithm matrix = new array[ T.getVertexCount() ][ k + 1 ] (-8); // set all elements in the first column of this matrix = 0 matrix[ n ][ 0 ] = 0; // fill our matrix by traversing the tree traverseToFillMatrix( T.root, k ); // consider a path that would arc over a node globalMaxWeight = -8; findArcs( T.root, k ); return globalMaxWeight end routine // node = the current node; k = the path length; node.lc = node’s left child; // node.rc = node’s right child; node.idx = node’s index (row) in the matrix; // node.lc.wt/node.rc.wt = weight of the edge to left/right child; routine traverseToFillMatrix( node, k ) if (node == null) return; traverseToFillMatrix(node.lc, k ); // recurse left traverseToFillMatrix(node.rc, k ); // recurse right // in the case that a left/right child doesn’t exist, or both, // let’s assume the code is smart enough to handle these cases matrix[ node.idx ][ 1 ] = max( node.lc.wt, node.rc.wt ); for i = 2 to k { // max returns the heavier of the 2 paths matrix[node.idx][i] = max( matrix[node.lc.idx][i-1] + node.lc.wt, matrix[node.rc.idx][i-1] + node.rc.wt); } end routine // node = the current node, k = the path length routine findArcs( node, k ) if (node == null) return; nodeMax = matrix[node.idx][k]; longPath = path[node.idx][k]; i = 1; j = k-1; while ( i+j == k AND i < k ) { left = node.lc.wt + matrix[node.lc.idx][i-1]; right = node.rc.wt + matrix[node.rc.idx][j-1]; if ( left + right > nodeMax ) { nodeMax = left + right; } i++; j--; } // if this node’s max weight is larger than the global max weight, update if ( globalMaxWeight < nodeMax ) { globalMaxWeight = nodeMax; } findArcs( node.lc, k ); // recurse left findArcs( node.rc, k ); // recurse right end routine Let me know what you think. Feedback is welcome. I think have come up with two naive algorithms that find the heaviest length-constrained path in a weighted Binary Tree. Firstly, the description of the algorithm is as follows: given an n-vertex Binary Tree with weighted edges and some value k, find the heaviest path of length k. For both algorithms, I'll need a reference to all vertices so I'll just do a simple traversal of the Tree to have a reference to all vertices, with each vertex having a reference to its left, right, and parent nodes in the tree. Algorithm 1 For this algorithm, I'm basically planning on running DFS from each node in the Tree, with consideration to the fixed path length. In addition, since the path I'm looking for has the potential of going from left subtree to root to right subtree, I will have to consider 3 choices at each node. But this will result in a O(n*3^k) algorithm and I don't like that. Algorithm 2 I'm essentially thinking about using a modified version of Dijkstra's Algorithm in order to consider a fixed path length. Since I'm looking for heaviest and Dijkstra's Algorithm finds the lightest, I'm planning on negating all edge weights before starting the traversal. Actually... this doesn't make sense since I'd have to run Dijkstra's on each node and that doesn't seem very efficient much better than the above algorithm. So I guess my main questions are several. Firstly, do the algorithms I've described above solve the problem at hand? I'm not totally certain the Dijkstra's version will work as Dijkstra's is meant for positive edge values. Now, I am sure there exist more clever/efficient algorithms for this... what is a better algorithm? I've read about "Using spine decompositions to efficiently solve the length-constrained heaviest path problem for trees" but that is really complicated and I don't understand it at all. Are there other algorithms that tackle this problem, maybe not as efficiently as spine decomposition but easier to understand? Thanks.

    Read the article

  • Do you think that exposure to BASIC can mutilate your mind? [closed]

    - by bigown
    It is practically impossible to teach good programming to students that have had a prior exposure to BASIC: as potential programmers they are mentally mutilated beyond hope of regeneration -- Edsger W. Dijkstra I have deep respect to Dijkstra but I don't agree with everything he said/wrote. I disagree specially with this quote on linked paper wrote 35 years ago about the Dartmouth BASIC implementation. Many of my coworkers or friends programmers started with BASIC, questions below have answers that indicate many programmers had their first experience on programming at BASIC. AFAIK many good programmers started at BASIC programming. I'm not talking about Visual Basic or other "modern" dialects of BASIC running on machines full of resources. I'm talking about old times BASIC running on "toy" computer, that the programmer had to worry about saving small numbers that need not be calculated as a string to save a measly byte because the computer had only a few hundreds of them, or have to use computed goto for lack of a more powerful feature, and many other things which require the programmer to think much before doing something and forcing the programmer to be creative. If you had experience with old time BASIC on a machine with limited resources (have in mind that a simple micro-controller today has much more resources than a computer in 1975, do you think that BASIC help your mind to find better solutions, to think like an engineer or BASIC drag you to dark side of programming and mutilated you mentally? Is good to learn a programming language running on a computer full of resources where the novice programmer can do all wrong and the program runs without big problems? Or is it better to learn where the programmer can't go wrong? What can you say about the BASIC have helped you to be a better/worse programmer? Would you teach old BASIC running on a 2KB (virtual) machine to a coming programmer? Sure, only exposure to BASIC is bad. Maybe you share my opinion that modern BASIC doesn't help too much because modern BASIC, as long other programming languages, gives facilities which allow the programmer doesn't think deeper. Additional information: Why BASIC?

    Read the article

  • Which reference provides your definition of "elegant" or "beautiful" code?

    - by Donnied
    This question is phrased in a very specific way - it asks for references. There was a similar question posted which was closed because it was considered a duplicate to a good code question. The Programmers FAQ points out that answers should have references - or its just an unproductive sharing of (seemingly) baseless opinions. There is a difference between shortest code and most elegant code. This becomes clear in several seminal texts: Dijkstra, E. W. (1972). The humble programmer. Communications of the ACM, 15(10), 859–866. Kernighan, B. W., & Plauger, P. J. (1974). Programming style: Examples and counterexamples. ACM Comput. Surv., 6(4), 303–319. Knuth, D. E. (1984). Literate programming. The Computer Journal, 27(2), 97–111. doi:10.1093/comjnl/27.2.97 They all note the importance of clarity over brevity. Kernighan & Plauger (1974) provide descriptions of "good" code, but "good code" is certainly not synonymous with "elegant". Knuth (1984) describes the impo rtance of exposition and "excellence of style" to elegant programs. He cites Hoare - who describes that code should be self documenting. Dijkstra (1972) indicates that beautiful programs optimize efficiency but are not opaque. This sort of conversation is qulaitatively different than a random sharing of opinions. Therefore, the question - Which reference provides your definition of "elegant" or "beautiful" code? "Which *reference*" is not subjective - anything else will most likely shut the thread down, so please supply *references* not opinions.

    Read the article

  • What are algorithmic paradigms?

    - by Vaibhav Agarwal
    We generally talk about paradigms of programming as functional, procedural, object oriented, imperative etc but what should I reply when I am asked the paradigms of algorithms? For example are Travelling Salesman Problem, Dijkstra Shortest Path Algorithm, Euclid GCD Algorithm, Binary search, Kruskal's Minimum Spanning Tree, Tower of Hanoi paradigms of algorithms? Should I answer the data structures I would use to design these algorithms?

    Read the article

  • Introduction aux algorithmes et aux structures de données, un cours par Thibaut Cuvelier

    En 1976, le livre Algorithms + Data Structures = Programs paraît : le postulat posé par ce titre est bien qu'un algorithme n'est rien s'il n'a pas de structure de données appropriée pour stocker ses données. On étudiera, dans cette introduction, tant les algorithmes principaux (tri, graphes %u2013 le bien connu Dijkstra mais aussi Bellman-Ford pour la recherche de plus court chemin) que des structures de données très fréquentes sur lesquelles viennent se construire des solutions élaborées à des problèmes complexes (pile, file, dictionnaire, etc.).

    Read the article

  • Pathfinding in Warcraft 1

    - by Valmond
    Dijkstra and A* are all nice and popular but what kind of algorithm was used in Warcraft 1 for pathfinding? I remember that the enemy could get trapped in bowl-like caverns which means there were (most probably) no full-path calculations from "start to end". If I recall correctly, the algorithm could be something like this: A) Move towards enemy until success or hitting a wall B) If blocked by a wall, follow the wall until you can move towards the enemy without being blocked and then do A) But I'd like to know, if someone knows :-)

    Read the article

  • Evaluating code for a graph [migrated]

    - by mazen.r.f
    This is relatively long code. Please take a look at this code if you are still willing to do so. I will appreciate your feedback. I have spent two days trying to come up with code to represent a graph, calculating the shortest path using Dijkstra's algorithm. But I am not able to get the right result, even though the code runs without errors. The result is not correct and I am always getting 0. I have three classes: Vertex, Edge, and Graph. The Vertex class represents the nodes in the graph and it has id and carried (which carry the weight of the links connected to it while using Dijkstra's algorithm) and a vector of the ids belong to other nodes the path will go through before arriving to the node itself. This vector is named previous_nodes. The Edge class represents the edges in the graph and has two vertices (one in each side) and a width (the distance between the two vertices). The Graph class represents the graph. It has two vectors, where one is the vertices included in this graph, and the other is the edges included in the graph. Inside the class Graph, there is a method named shortest() that takes the sources node id and the destination and calculates the shortest path using Dijkstra's algorithm. I think that it is the most important part of the code. My theory about the code is that I will create two vectors, one for the vertices in the graph named vertices, and another vector named ver_out (it will include the vertices out of calculation in the graph). I will also have two vectors of type Edge, where one is named edges (for all the edges in the graph), and the other is named track (to temporarily contain the edges linked to the temporary source node in every round). After the calculation of every round, the vector track will be cleared. In main(), I've created five vertices and 10 edges to simulate a graph. The result of the shortest path supposedly is 4, but I am always getting 0. That means I have something wrong in my code. If you are interesting in helping me find my mistake and making the code work, please take a look. The way shortest work is as follow: at the beginning, all the edges will be included in the vector edges. We select the edges related to the source and put them in the vector track, then we iterate through track and add the width of every edge to the vertex (node) related to it (not the source vertex). After that, we clear track and remove the source vertex from the vector vertices and select a new source. Then we start over again and select the edges related to the new source, put them in track, iterate over edges in track, adding the weights to the corresponding vertices, then remove this vertex from the vector vertices. Then clear track, and select a new source, and so on. #include<iostream> #include<vector> #include <stdlib.h> // for rand() using namespace std; class Vertex { private: unsigned int id; // the name of the vertex unsigned int carried; // the weight a vertex may carry when calculating shortest path vector<unsigned int> previous_nodes; public: unsigned int get_id(){return id;}; unsigned int get_carried(){return carried;}; void set_id(unsigned int value) {id = value;}; void set_carried(unsigned int value) {carried = value;}; void previous_nodes_update(unsigned int val){previous_nodes.push_back(val);}; void previous_nodes_erase(unsigned int val){previous_nodes.erase(previous_nodes.begin() + val);}; Vertex(unsigned int init_val = 0, unsigned int init_carried = 0) :id (init_val), carried(init_carried) // constructor { } ~Vertex() {}; // destructor }; class Edge { private: Vertex first_vertex; // a vertex on one side of the edge Vertex second_vertex; // a vertex on the other side of the edge unsigned int weight; // the value of the edge ( or its weight ) public: unsigned int get_weight() {return weight;}; void set_weight(unsigned int value) {weight = value;}; Vertex get_ver_1(){return first_vertex;}; Vertex get_ver_2(){return second_vertex;}; void set_first_vertex(Vertex v1) {first_vertex = v1;}; void set_second_vertex(Vertex v2) {second_vertex = v2;}; Edge(const Vertex& vertex_1 = 0, const Vertex& vertex_2 = 0, unsigned int init_weight = 0) : first_vertex(vertex_1), second_vertex(vertex_2), weight(init_weight) { } ~Edge() {} ; // destructor }; class Graph { private: std::vector<Vertex> vertices; std::vector<Edge> edges; public: Graph(vector<Vertex> ver_vector, vector<Edge> edg_vector) : vertices(ver_vector), edges(edg_vector) { } ~Graph() {}; vector<Vertex> get_vertices(){return vertices;}; vector<Edge> get_edges(){return edges;}; void set_vertices(vector<Vertex> vector_value) {vertices = vector_value;}; void set_edges(vector<Edge> vector_ed_value) {edges = vector_ed_value;}; unsigned int shortest(unsigned int src, unsigned int dis) { vector<Vertex> ver_out; vector<Edge> track; for(unsigned int i = 0; i < edges.size(); ++i) { if((edges[i].get_ver_1().get_id() == vertices[src].get_id()) || (edges[i].get_ver_2().get_id() == vertices[src].get_id())) { track.push_back (edges[i]); edges.erase(edges.begin()+i); } }; for(unsigned int i = 0; i < track.size(); ++i) { if(track[i].get_ver_1().get_id() != vertices[src].get_id()) { track[i].get_ver_1().set_carried((track[i].get_weight()) + track[i].get_ver_2().get_carried()); track[i].get_ver_1().previous_nodes_update(vertices[src].get_id()); } else { track[i].get_ver_2().set_carried((track[i].get_weight()) + track[i].get_ver_1().get_carried()); track[i].get_ver_2().previous_nodes_update(vertices[src].get_id()); } } for(unsigned int i = 0; i < vertices.size(); ++i) if(vertices[i].get_id() == src) vertices.erase(vertices.begin() + i); // removing the sources vertex from the vertices vector ver_out.push_back (vertices[src]); track.clear(); if(vertices[0].get_id() != dis) {src = vertices[0].get_id();} else {src = vertices[1].get_id();} for(unsigned int i = 0; i < vertices.size(); ++i) if((vertices[i].get_carried() < vertices[src].get_carried()) && (vertices[i].get_id() != dis)) src = vertices[i].get_id(); //while(!edges.empty()) for(unsigned int round = 0; round < vertices.size(); ++round) { for(unsigned int k = 0; k < edges.size(); ++k) { if((edges[k].get_ver_1().get_id() == vertices[src].get_id()) || (edges[k].get_ver_2().get_id() == vertices[src].get_id())) { track.push_back (edges[k]); edges.erase(edges.begin()+k); } }; for(unsigned int n = 0; n < track.size(); ++n) if((track[n].get_ver_1().get_id() != vertices[src].get_id()) && (track[n].get_ver_1().get_carried() > (track[n].get_ver_2().get_carried() + track[n].get_weight()))) { track[n].get_ver_1().set_carried((track[n].get_weight()) + track[n].get_ver_2().get_carried()); track[n].get_ver_1().previous_nodes_update(vertices[src].get_id()); } else if(track[n].get_ver_2().get_carried() > (track[n].get_ver_1().get_carried() + track[n].get_weight())) { track[n].get_ver_2().set_carried((track[n].get_weight()) + track[n].get_ver_1().get_carried()); track[n].get_ver_2().previous_nodes_update(vertices[src].get_id()); } for(unsigned int t = 0; t < vertices.size(); ++t) if(vertices[t].get_id() == src) vertices.erase(vertices.begin() + t); track.clear(); if(vertices[0].get_id() != dis) {src = vertices[0].get_id();} else {src = vertices[1].get_id();} for(unsigned int tt = 0; tt < edges.size(); ++tt) { if(vertices[tt].get_carried() < vertices[src].get_carried()) { src = vertices[tt].get_id(); } } } return vertices[dis].get_carried(); } }; int main() { cout<< "Hello, This is a graph"<< endl; vector<Vertex> vers(5); vers[0].set_id(0); vers[1].set_id(1); vers[2].set_id(2); vers[3].set_id(3); vers[4].set_id(4); vector<Edge> eds(10); eds[0].set_first_vertex(vers[0]); eds[0].set_second_vertex(vers[1]); eds[0].set_weight(5); eds[1].set_first_vertex(vers[0]); eds[1].set_second_vertex(vers[2]); eds[1].set_weight(9); eds[2].set_first_vertex(vers[0]); eds[2].set_second_vertex(vers[3]); eds[2].set_weight(4); eds[3].set_first_vertex(vers[0]); eds[3].set_second_vertex(vers[4]); eds[3].set_weight(6); eds[4].set_first_vertex(vers[1]); eds[4].set_second_vertex(vers[2]); eds[4].set_weight(2); eds[5].set_first_vertex(vers[1]); eds[5].set_second_vertex(vers[3]); eds[5].set_weight(5); eds[6].set_first_vertex(vers[1]); eds[6].set_second_vertex(vers[4]); eds[6].set_weight(7); eds[7].set_first_vertex(vers[2]); eds[7].set_second_vertex(vers[3]); eds[7].set_weight(1); eds[8].set_first_vertex(vers[2]); eds[8].set_second_vertex(vers[4]); eds[8].set_weight(8); eds[9].set_first_vertex(vers[3]); eds[9].set_second_vertex(vers[4]); eds[9].set_weight(3); unsigned int path; Graph graf(vers, eds); path = graf.shortest(2, 4); cout<< path << endl; return 0; }

    Read the article

  • What makes this "declarator invalid"? C++

    - by nieldw
    I have Vertex template in vertex.h. From my graph.h: 20 template<class edgeDecor, class vertexDecor, bool dir> 21 class Vertex; which I use in my Graph template. I've used the Vertex template successfully throughout my Graph, return pointers to Vertices, etc. Now for the first time I am trying to declare and instantiate a Vertex object, and gcc is telling me that my 'declarator' is 'invalid'. How can this be? 81 template<class edgeDecor, class vertexDecor, bool dir> 82 Graph<edgeDecor,int,dir> Graph<edgeDecor,vertexDecor,dir>::Dijkstra(vertex s, bool print = false) const 83 { 84 /* Construct new Graph with apropriate decorators */ 85 Graph<edgeDecor,int,dir> span = new Graph<edgeDecor,int,dir>(); 86 span.E.reserve(this->E.size()); 87 88 typename Vertex<edgeDecor,int,dir> v = new Vertex(INT_MAX); 89 span.V = new vector<Vertex<edgeDecor,int,dir> >(this->V.size,v); 90 }; And gcc is saying: graph.h: In member function ‘Graph<edgeDecor, int, dir> Graph<edgeDecor, vertexDecor, dir>::Dijkstra(Vertex<edgeDecor, vertexDecor, dir>, bool) const’: graph.h:88: error: invalid declarator before ‘v’ graph.h:89: error: ‘v’ was not declared in this scope I know this is probably another noob question, but I'll appreciate any help.

    Read the article

  • ArchBeat Link-o-Rama for 2012-03-30

    - by Bob Rhubart
    The One Skill All Leaders Should Work On | Scott Edinger blogs.hbr.org Assertiveness, according to HBR blogger Scott Edinger, has the "power to magnify so many other leadership strengths." When Your Influence Is Ineffective | Chris Musselwhite and Tammie Plouffe blogs.hbr.org "Influence becomes ineffective when individuals become so focused on the desired outcome that they fail to fully consider the situation," say Chris Musselwhite and Tammie Plouffe. BPM in Retail Industry | Sanjeev Sharma blogs.oracle.com Sanjeev Sharma shares links to a pair of blog posts that address common BPM use-cases in the Retail industry. Oracle VM: What if you have just 1 HDD system | Yury Velikanov www.pythian.com "To start playing with Oracle VM v3 you need to configure some storage to be used for new VM hosts," says Yury Velikanov. He shows you how in this post. Thought for the Day "Elegance is not a dispensable luxury but a factor that decides between success and failure." — Edsger Dijkstra

    Read the article

  • How often do CPUs make calculation errors?

    - by veryfoolish
    In Dijkstra's Notes on Structured Programming he talks a lot about the provability of computer programs as abstract entities. As a corollary, he remarks how testing isn't enough. E.g., he points out the fact that it would be impossible to test a multiplication function f(x,y) = x*y for any large values of x and y across the entire ranges of x and y. My question concerns his misc. remarks on "lousy hardware". I know the essay was written in the 1970s when computer hardware was less reliable, but computers still aren't perfect, so they must make calculation mistakes sometimes. Does anybody know how often this happens or if there are any statistics on this?

    Read the article

< Previous Page | 1 2 3 4  | Next Page >