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  • algorithms that destruct and copy_construct

    - by FredOverflow
    I am currently building my own toy vector for fun, and I was wondering if there is something like the following in the current or next standard or in Boost? template<class T> void destruct(T* begin, T* end) { while (begin != end) { begin -> ~T(); ++begin; } } template<class T> T* copy_construct(T* begin, T* end, T* dst) { while (begin != end) { new(dst) T(*begin); ++begin; ++dst; } return dst; }

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  • Algorithms for finding the intersections of intervals

    - by tomwu
    I am wondering how I can find the number of intervals that intersect with the ones before it. for the intervals [2, 4], [1, 6], [5, 6], [0, 4], the output should be 2. from [2,4] [5,6] and [5,6] [0,4]. So now we have 1 set of intervals with size n all containing a point a, then we add another set of intervals size n as well, and all of the intervals are to the right of a. Can you do this in O(nlgn) and O(nlg^2n)?

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  • complexity of algorithms

    - by davit-datuashvili
    i have question what is complexity of this algorithm public class smax{ public static void main(String[]args){ int b[]=new int[11]; int a[]=new int[]{4,9,2,6,8,7,5}; for (int i=0;i int m=0; while (m int k=a[0]; for (int i=0;i k && b[a[i]]!=1){ b[a[i]]=1; } } m++; } for (int i=0;i for (int j=0;j //result=2 4 5 6 7 8 9 } } ?

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  • algorithms undirected graph twodegree[]

    - by notamathwiz
    For each node u in an undirected graph, let twodegree[u] be the sum of the degrees of u's neighbors. Show how to compute the entire array of twodegree[.] values in linear time, given a graph in adjacency list format. This is the solution for all u ? V : degree[u] = 0 for all (u; w) ? E: degree[u] = degree[u] + 1 for all u ? V : twodegree[u] = 0 for all (u; w) ? E: twodegree[u] = twodegree[u] + degree[w] can someone explain what degree[u] does in this case and how twodegree[u] = twodegree[u] + degree[w] is supposed to be the sum of the degrees of u's neighbors?

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  • Need help with fixing Genetic Algorithm that's not evolving correctly

    - by EnderMB
    I am working on a maze solving application that uses a Genetic Algorithm to evolve a set of genes (within Individuals) to evolve a Population of Individuals that power an Agent through a maze. The majority of the code used appears to be working fine but when the code runs it's not selecting the best Individual's to be in the new Population correctly. When I run the application it outputs the following: Total Fitness: 380.0 - Best Fitness: 11.0 Total Fitness: 406.0 - Best Fitness: 15.0 Total Fitness: 344.0 - Best Fitness: 12.0 Total Fitness: 373.0 - Best Fitness: 11.0 Total Fitness: 415.0 - Best Fitness: 12.0 Total Fitness: 359.0 - Best Fitness: 11.0 Total Fitness: 436.0 - Best Fitness: 13.0 Total Fitness: 390.0 - Best Fitness: 12.0 Total Fitness: 379.0 - Best Fitness: 15.0 Total Fitness: 370.0 - Best Fitness: 11.0 Total Fitness: 361.0 - Best Fitness: 11.0 Total Fitness: 413.0 - Best Fitness: 16.0 As you can clearly see the fitnesses are not improving and neither are the best fitnesses. The main code responsible for this problem is here, and I believe the problem to be within the main method, most likely where the selection methods are called: package GeneticAlgorithm; import GeneticAlgorithm.Individual.Action; import Robot.Robot.Direction; import Maze.Maze; import Robot.Robot; import java.util.ArrayList; import java.util.Random; public class RunGA { protected static ArrayList tmp1, tmp2 = new ArrayList(); // Implementation of Elitism protected static int ELITISM_K = 5; // Population size protected static int POPULATION_SIZE = 50 + ELITISM_K; // Max number of Iterations protected static int MAX_ITERATIONS = 200; // Probability of Mutation protected static double MUTATION_PROB = 0.05; // Probability of Crossover protected static double CROSSOVER_PROB = 0.7; // Instantiate Random object private static Random rand = new Random(); // Instantiate Population of Individuals private Individual[] startPopulation; // Total Fitness of Population private double totalFitness; Robot robot = new Robot(); Maze maze; public void setElitism(int result) { ELITISM_K = result; } public void setPopSize(int result) { POPULATION_SIZE = result + ELITISM_K; } public void setMaxIt(int result) { MAX_ITERATIONS = result; } public void setMutProb(double result) { MUTATION_PROB = result; } public void setCrossoverProb(double result) { CROSSOVER_PROB = result; } /** * Constructor for Population */ public RunGA(Maze maze) { // Create a population of population plus elitism startPopulation = new Individual[POPULATION_SIZE]; // For every individual in population fill with x genes from 0 to 1 for (int i = 0; i < POPULATION_SIZE; i++) { startPopulation[i] = new Individual(); startPopulation[i].randGenes(); } // Evaluate the current population's fitness this.evaluate(maze, startPopulation); } /** * Set Population * @param newPop */ public void setPopulation(Individual[] newPop) { System.arraycopy(newPop, 0, this.startPopulation, 0, POPULATION_SIZE); } /** * Get Population * @return */ public Individual[] getPopulation() { return this.startPopulation; } /** * Evaluate fitness * @return */ public double evaluate(Maze maze, Individual[] newPop) { this.totalFitness = 0.0; ArrayList<Double> fitnesses = new ArrayList<Double>(); for (int i = 0; i < POPULATION_SIZE; i++) { maze = new Maze(8, 8); maze.fillMaze(); fitnesses.add(startPopulation[i].evaluate(maze, newPop)); //this.totalFitness += startPopulation[i].evaluate(maze, newPop); } //totalFitness = (Math.round(totalFitness / POPULATION_SIZE)); StringBuilder sb = new StringBuilder(); for(Double tmp : fitnesses) { sb.append(tmp + ", "); totalFitness += tmp; } // Progress of each Individual //System.out.println(sb.toString()); return this.totalFitness; } /** * Roulette Wheel Selection * @return */ public Individual rouletteWheelSelection() { // Calculate sum of all chromosome fitnesses in population - sum S. double randNum = rand.nextDouble() * this.totalFitness; int i; for (i = 0; i < POPULATION_SIZE && randNum > 0; ++i) { randNum -= startPopulation[i].getFitnessValue(); } return startPopulation[i-1]; } /** * Tournament Selection * @return */ public Individual tournamentSelection() { double randNum = rand.nextDouble() * this.totalFitness; // Get random number of population (add 1 to stop nullpointerexception) int k = rand.nextInt(POPULATION_SIZE) + 1; int i; for (i = 1; i < POPULATION_SIZE && i < k && randNum > 0; ++i) { randNum -= startPopulation[i].getFitnessValue(); } return startPopulation[i-1]; } /** * Finds the best individual * @return */ public Individual findBestIndividual() { int idxMax = 0; double currentMax = 0.0; double currentMin = 1.0; double currentVal; for (int idx = 0; idx < POPULATION_SIZE; ++idx) { currentVal = startPopulation[idx].getFitnessValue(); if (currentMax < currentMin) { currentMax = currentMin = currentVal; idxMax = idx; } if (currentVal > currentMax) { currentMax = currentVal; idxMax = idx; } } // Double check to see if this has the right one //System.out.println(startPopulation[idxMax].getFitnessValue()); // Maximisation return startPopulation[idxMax]; } /** * One Point Crossover * @param firstPerson * @param secondPerson * @return */ public static Individual[] onePointCrossover(Individual firstPerson, Individual secondPerson) { Individual[] newPerson = new Individual[2]; newPerson[0] = new Individual(); newPerson[1] = new Individual(); int size = Individual.SIZE; int randPoint = rand.nextInt(size); int i; for (i = 0; i < randPoint; ++i) { newPerson[0].setGene(i, firstPerson.getGene(i)); newPerson[1].setGene(i, secondPerson.getGene(i)); } for (; i < Individual.SIZE; ++i) { newPerson[0].setGene(i, secondPerson.getGene(i)); newPerson[1].setGene(i, firstPerson.getGene(i)); } return newPerson; } /** * Uniform Crossover * @param firstPerson * @param secondPerson * @return */ public static Individual[] uniformCrossover(Individual firstPerson, Individual secondPerson) { Individual[] newPerson = new Individual[2]; newPerson[0] = new Individual(); newPerson[1] = new Individual(); for(int i = 0; i < Individual.SIZE; ++i) { double r = rand.nextDouble(); if (r > 0.5) { newPerson[0].setGene(i, firstPerson.getGene(i)); newPerson[1].setGene(i, secondPerson.getGene(i)); } else { newPerson[0].setGene(i, secondPerson.getGene(i)); newPerson[1].setGene(i, firstPerson.getGene(i)); } } return newPerson; } public double getTotalFitness() { return totalFitness; } public static void main(String[] args) { // Initialise Environment Maze maze = new Maze(8, 8); maze.fillMaze(); // Instantiate Population //Population pop = new Population(); RunGA pop = new RunGA(maze); // Instantiate Individuals for Population Individual[] newPop = new Individual[POPULATION_SIZE]; // Instantiate two individuals to use for selection Individual[] people = new Individual[2]; Action action = null; Direction direction = null; String result = ""; /*result += "Total Fitness: " + pop.getTotalFitness() + " - Best Fitness: " + pop.findBestIndividual().getFitnessValue();*/ // Print Current Population System.out.println("Total Fitness: " + pop.getTotalFitness() + " - Best Fitness: " + pop.findBestIndividual().getFitnessValue()); // Instantiate counter for selection int count; for (int i = 0; i < MAX_ITERATIONS; i++) { count = 0; // Elitism for (int j = 0; j < ELITISM_K; ++j) { // This one has the best fitness newPop[count] = pop.findBestIndividual(); count++; } // Build New Population (Population size = Steps (28)) while (count < POPULATION_SIZE) { // Roulette Wheel Selection people[0] = pop.rouletteWheelSelection(); people[1] = pop.rouletteWheelSelection(); // Tournament Selection //people[0] = pop.tournamentSelection(); //people[1] = pop.tournamentSelection(); // Crossover if (rand.nextDouble() < CROSSOVER_PROB) { // One Point Crossover //people = onePointCrossover(people[0], people[1]); // Uniform Crossover people = uniformCrossover(people[0], people[1]); } // Mutation if (rand.nextDouble() < MUTATION_PROB) { people[0].mutate(); } if (rand.nextDouble() < MUTATION_PROB) { people[1].mutate(); } // Add to New Population newPop[count] = people[0]; newPop[count+1] = people[1]; count += 2; } // Make new population the current population pop.setPopulation(newPop); // Re-evaluate the current population //pop.evaluate(); pop.evaluate(maze, newPop); // Print results to screen System.out.println("Total Fitness: " + pop.totalFitness + " - Best Fitness: " + pop.findBestIndividual().getFitnessValue()); //result += "\nTotal Fitness: " + pop.totalFitness + " - Best Fitness: " + pop.findBestIndividual().getFitnessValue(); } // Best Individual Individual bestIndiv = pop.findBestIndividual(); //return result; } } I have uploaded the full project to RapidShare if you require the extra files, although if needed I can add the code to them here. This problem has been depressing me for days now and if you guys can help me I will forever be in your debt.

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  • A detail question when applying genetic algorithm to traveling salesman

    - by burrough
    I read various stuff on this and understand the principle and concepts involved, however, none of paper mentions the details of how to calculate the fitness of a chromosome (which represents a route) involving adjacent cities (in the chromosome) that are not directly connected by an edge (in the graph). For example, given a chromosome 1|3|2|8|4|5|6|7, in which each gene represents the index of a city on the graph/map, how do we calculate its fitness (i.e. the total sum of distances traveled) if, say, there is no direct edge/link between city 2 and 8. Do we follow some sort of greedy algorithm to work out a route between 2 and 8, and add the distance of this route to the total? This problem seems pretty common when applying GA to TSP. Anyone who's done it before please share your experience. Thanks.

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  • How does heap compaction work quickly?

    - by Mason Wheeler
    They say that compacting garbage collectors are faster than traditional memory management because they only have to collect live objects, and by rearranging them in memory so everything's in one contiguous block, you end up with no heap fragmentation. But how can that be done quickly? It seems to me that that's equivalent to the bin-packing problem, which is NP-hard and can't be completed in a reasonable amount of time on a large dataset within the current limits of our knowledge about computation. What am I missing?

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  • Find unique vertices from a 'triangle-soup'

    - by sum1stolemyname
    I am building a CAD-file converter on top of two libraries (Opencascade and DWF Toolkit). However, my question is plattform agnostic: Given: I have generated a mesh as a list of triangular faces form a model constructed through my application. Each Triangle is defined through three vertexes, which consist of three floats (x, y & z coordinate). Since the triangles form a mesh, most of the vertices are shared by more then one triangle. Goal: I need to find the list of unique vertices, and to generate an array of faces consisting of tuples of three indices in this list. What i want to do is this: //step 1: build a list of unique vertices for each triangle for each vertex in triangle if not vertex in listOfVertices Add vertex to listOfVertices //step 2: build a list of faces for each triangle for each vertex in triangle Get Vertex Index From listOfvertices AddToMap(vertex Index, triangle) While I do have an implementation which does this, step1 (the generation of the list of unique vertices) is really slow in the order of O(n!), since each vertex is compared to all vertices already in the list. I thought "Hey, lets build a hashmap of my vertices' components using std::map, that ought to speed things up!", only to find that generating a unique key from three floating point values is not a trivial task. Here, the experts of stackoverflow come into play: I need some kind of hash-function which works on 3 floats, or any other function generating a unique value from a 3d-vertex position.

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  • C++ find method is not const?

    - by Rachel
    I've written a method that I'd like to declare as const, but the compiler complains. I traced through and found that this part of the method was causing the difficulty: bool ClassA::MethodA(int x) { bool y = false; if(find(myList.begin(), myList.end(), x) != myList.end()) { y = true; } return y; } There is more happening in the method than that, but with everything else stripped away, this was the part that didn't allow the method to be const. Why does the stl find algorithm prevent the method from being const? Does it change the list in any way?

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  • Algorithm on trajectory analysis.

    - by Arman
    Hello, I would like to analyse the trajectory data based on given templates. I need to stack the similar trajectories together. The data is a set of coordinates xy,xy,xy and the templates are again the lines defined by the set of control points. I don't know to what direction to go, maybe to Neural Networks or pattern recognition? Could you please advace me page, book or library to start with? kind regards Arman. PS. Is it the right place to ask the question?

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  • Initial Genetic Programming Parameters

    - by cmptrer
    I did a little GP (note:very little) work in college and have been playing around with it recently. My question is in regards to the intial run settings (population size, number of generations, min/max depth of trees, min/max depth of initial trees, percentages to use for different reproduction operations, etc.). What is the normal practice for setting these parameters? What papers/sites do people use as a good guide?

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  • little oh notation as the limit of n goes to infinity

    - by Tony
    Hi all, I'm just trying to understand how in little o notation this is true: f(n)/g(n) as n goes to infinity = infinity? Can someone explain that to me? I do get the idea that f(n) = o(g(n)) means that f(n) grows no faster then cg(n) for all constants c 0. I just don't get the bit in bold above.

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  • Why does adding Crossover to my Genetic Algorithm gives me worse results?

    - by MahlerFive
    I have implemented a Genetic Algorithm to solve the Traveling Salesman Problem (TSP). When I use only mutation, I find better solutions than when I add in crossover. I know that normal crossover methods do not work for TSP, so I implemented both the Ordered Crossover and the PMX Crossover methods, and both suffer from bad results. Here are the other parameters I'm using: Mutation: Single Swap Mutation or Inverted Subsequence Mutation (as described by Tiendil here) with mutation rates tested between 1% and 25%. Selection: Roulette Wheel Selection Fitness function: 1 / distance of tour Population size: Tested 100, 200, 500, I also run the GA 5 times so that I have a variety of starting populations. Stop Condition: 2500 generations With the same dataset of 26 points, I usually get results of about 500-600 distance using purely mutation with high mutation rates. When adding crossover my results are usually in the 800 distance range. The other confusing thing is that I have also implemented a very simple Hill-Climbing algorithm to solve the problem and when I run that 1000 times (faster than running the GA 5 times) I get results around 410-450 distance, and I would expect to get better results using a GA. Any ideas as to why my GA performing worse when I add crossover? And why is it performing much worse than a simple Hill-Climb algorithm which should get stuck on local maxima as it has no way of exploring once it finds a local max?

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  • Provable planarity of flowcharts

    - by Nikolaos Kavvadias
    Hi all I have a question: is there any reference (e.g. paper) with a proof of the planarity of flowchart layouts? Can anyone suggest an algorithm for generating flowchart (planar) layouts? I know that there are some code-to-flowchart tools out there, but i'm unaware of their internals. Thanks in advance -kavi

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  • What is the name of this geometrical function?

    - by Spike
    In a two dimentional integer space, you have two points, A and B. This function returns an enumeration of the points in the quadrilateral subset bounded by A and B. A = {1,1} B = {2,3} Fn(A,B) = {{1,1},{1,2},{1,3},{2,1},{2,2},{2,3}} I can implement it in a few lines of LINQ. private void UnknownFunction(Point to, Point from, List<Point> list) { var vectorX = Enumerable.Range(Math.Min(to.X, from.X), Math.Abs(to.X - from.Y) + 1); var vectorY = Enumerable.Range(Math.Min(to.Y, from.Y), Math.Abs(to.Y - from.Y) + 1); foreach (var x in vectorX) foreach (var y in vectorY) list.Add(new Point(x, y)); } I'm fairly sure that this is a standard mathematical operation, but I can't think what it is. Feel free to tell me that it's one line of code in your language of choice. Or to give me a cunning implementation with lambdas or some such. But mostly I just want to know what it's called. It's driving me nuts. It feels a little like a convolution, but it's been too long since I was at school for me to be sure.

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  • binary search and trio

    - by user121196
    given a large list of alphabetically sorted words in a file,I need to write a program that, given a word x, determines if x is in the list. priorties: 1. speed. 2. memory I already know I can use (n is number of words, m is average length of the words) 1. a tri, time is O(log(n)), space(best case) is O(log(n*m)), space(worst case) is O(n*m). 2. load the complete list into memory, then binary search, time is O(log(n)), space is O(n*m) I'm not sure about the complexity on tri, please correct me if they are wrong. Also are there other good approaches?

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  • Merge overlapping triangles into a polygon

    - by nornagon
    I've got a bunch of overlapping triangles from a 3D model projected into a 2D plane. I need to merge each island of touching triangles into a closed, non-convex polygon. The resultant polygons shouldn't have any holes in them (since the source data doesn't). Many of the source triangles share (floating point identical) edges with other triangles in the source data. What's the easiest way to do this? Performance isn't particularly important, since this will be done at design time.

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  • Combinatorial optimisation of a distance metric

    - by Jose
    I have a set of trajectories, made up of points along the trajectory, and with the coordinates associated with each point. I store these in a 3d array ( trajectory, point, param). I want to find the set of r trajectories that have the maximum accumulated distance between the possible pairwise combinations of these trajectories. My first attempt, which I think is working looks like this: max_dist = 0 for h in itertools.combinations ( xrange(num_traj), r): for (m,l) in itertools.combinations (h, 2): accum = 0. for ( i, j ) in itertools.izip ( range(k), range(k) ): A = [ (my_mat[m, i, z] - my_mat[l, j, z])**2 \ for z in xrange(k) ] A = numpy.array( numpy.sqrt (A) ).sum() accum += A if max_dist < accum: selected_trajectories = h This takes forever, as num_traj can be around 500-1000, and r can be around 5-20. k is arbitrary, but can typically be up to 50. Trying to be super-clever, I have put everything into two nested list comprehensions, making heavy use of itertools: chunk = [[ numpy.sqrt((my_mat[m, i, :] - my_mat[l, j, :])**2).sum() \ for ((m,l),i,j) in \ itertools.product ( itertools.combinations(h,2), range(k), range(k)) ]\ for h in itertools.combinations(range(num_traj), r) ] Apart from being quite unreadable (!!!), it is also taking a long time. Can anyone suggest any ways to improve on this?

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  • delete item from array

    - by davit-datuashvili
    hello can anybody tell me what is wrong here? i want delete item from array but it shows me error ArrayIndexOutBound exception public class delete{ public static void main(String[]args){ int i; //delete item from array int k[]=new int[]{77,99,44,11,00,55,66,33,10}; //delete 55 int searchkey=55; int nums=k.length; for ( i=0;i<nums;i++) if (k[i]==searchkey) break; for (int t=i;t<nums;t++) k[t]=k[t+1]; nums--; for (int m=0;m<nums;m++){ System.out.println(k[m]); } } }

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  • An Efficient data structure for Sorted List

    - by holydiver
    I want to save my objects according to a key in the attributes of my object in a sorted fashion. Later on I'll access these objects sequentially from max key to min key. I'll do some search tasks as well. I consider to use either AVL tree or RB Tree. As far as i know they are nearly equivalent in theory(Both have O(logn)). But in practice which one might be better in performance in my situation. And is there a better alternative than those, considering that I'll mostly do insert and sequentially access to the ds.

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