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  • PDF hyperlinks on iPhone/iPad

    - by RoLYroLLs
    Hi, I've been looking around Google and SO and haven't quite found an answer to my question, or at least a more recent answer. I have a PDF with hyperlinks/hotspots in it and would like to display the PDF file in my own iPhone/iPad app. When the user clicks on a hyperlink/hotspot I would like the user to be taken to the appropriate location of the link (whether another page on the PDF or a webpage outside of the app). I have found many questions like this on here, but most dated over 6 moths ago. While that might not be so long ago, it kind of is in-spite of newer technologies and the probability of someone comping up with new code/way to do it. I looked into the QuartzDemo sample app and edited the PDF to have a hotspot and it does not work. Maybe the ability is there, but not implemented? I have found one app that DOES work great! The GoodReader app displays my PDF and allows the clicking of hotspots in my PDF. However, I'd like this implemented in my own app. So, has anyone been playing around with this? Anyone find a solution? Can anyone point others in a direction? Thanks for your time.

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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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  • Sample uniformly at random from an n-dimensional unit simplex.

    - by dreeves
    Sampling uniformly at random from an n-dimensional unit simplex is the fancy way to say that you want n random numbers such that they are all non-negative, they sum to one, and every possible vector of n non-negative numbers that sum to one are equally likely. In the n=2 case you want to sample uniformly from the segment of the line x+y=1 (ie, y=1-x) that is in the positive quadrant. In the n=3 case you're sampling from the triangle-shaped part of the plane x+y+z=1 that is in the positive octant of R3: (Image from http://en.wikipedia.org/wiki/Simplex.) Note that picking n uniform random numbers and then normalizing them so they sum to one does not work. You end up with a bias towards less extreme numbers. Similarly, picking n-1 uniform random numbers and then taking the nth to be one minus the sum of them also introduces bias. Wikipedia gives two algorithms to do this correctly: http://en.wikipedia.org/wiki/Simplex#Random_sampling (Though the second one currently claims to only be correct in practice, not in theory. I'm hoping to clean that up or clarify it when I understand this better. I initially stuck in a "WARNING: such-and-such paper claims the following is wrong" on that Wikipedia page and someone else turned it into the "works only in practice" caveat.) Finally, the question: What do you consider the best implementation of simplex sampling in Mathematica (preferably with empirical confirmation that it's correct)? Related questions http://stackoverflow.com/questions/2171074/generating-a-probability-distribution http://stackoverflow.com/questions/3007975/java-random-percentages

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  • Web crawler update strategy

    - by superb
    I want to crawl useful resource (like background picture .. ) from certain websites. It is not a hard job, especially with the help of some wonderful projects like scrapy. The problem here is I not only just want crawl this site ONE TIME. I also want to keep my crawl long running and crawl the updated resource. So I want to know is there any good strategy for a web crawler to get updated pages? Here's a coarse algorithm I've thought of. I divided the crawl process into rounds. Each round URL repository will give crawler a certain number (like , 10000) of URLs to crawl. And then next round. The detailed steps are: crawler add start URLs to URL repository crawler ask URL repository for at most N URL to crawl crawler fetch the URLs, and update certain information in URL repository, like the page content, the fetch time and whether the content has been changed. just go back to step 2 To further specify that, I still need to solve following question: How to decide the "refresh-ness" of a web page, which indicates the probability that this web page has been updated ? Since that is an open question, hopefully it will brought some fruitful discussion here.

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  • Kruskal-Wallis test with details on pairwise comparisons

    - by dalloliogm
    The standard stats::kruskal.test module allows to calculate the kruskal-wallis test on a dataset: >>> data(diamonds) >>> kruskal.test.test(price~carat, data=diamonds) Kruskal-Wallis rank sum test data: price by carat by color Kruskal-Wallis chi-squared = 50570.15, df = 272, p-value < 2.2e-16 this is correct, it is giving me a probability that all the groups in the data have the same mean. However, I would like to have the details for each pair comparison, like if diamonds of colors D and E have the same mean price, as some other softwares do (SPSS) when you ask for a Kruskal test. I have found kruskalmc from the package pgirmess which allows me to do what I want to do: > kruskalmc(diamonds$price, diamonds$color) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference D-E 571.7459 747.4962 FALSE D-F 2237.4309 751.5684 TRUE D-G 2643.1778 726.9854 TRUE D-H 4539.4392 774.4809 TRUE D-I 6002.6286 862.0150 TRUE D-J 8077.2871 1061.7451 TRUE E-F 2809.1767 680.4144 TRUE E-G 3214.9237 653.1587 TRUE E-H 5111.1851 705.6410 TRUE E-I 6574.3744 800.7362 TRUE E-J 8649.0330 1012.6260 TRUE F-G 405.7470 657.8152 FALSE F-H 2302.0083 709.9533 TRUE F-I 3765.1977 804.5390 TRUE F-J 5839.8562 1015.6357 TRUE G-H 1896.2614 683.8760 TRUE G-I 3359.4507 781.6237 TRUE G-J 5434.1093 997.5813 TRUE H-I 1463.1894 825.9834 TRUE H-J 3537.8479 1032.7058 TRUE I-J 2074.6585 1099.8776 TRUE However, this package only allows for one categoric variable (e.g. I can't study the prices clustered by color and by carat, as I can do with kruskal.test), and I don't know anything about the pgirmess package, whether it is maintained or not, or if it is tested. Can you recommend me a package to execute the Kruskal-Wallis test which returns details for every comparison? How would you handle the problem?

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  • Axis2 webservice (aar archive) properties file

    - by XpiritO
    Hi there, guys. I'm currently developing a set of SOAP webservices over Axis2, deployed over a clustered WebLogic 10.3.2 environment. My webservices use some user settings that I want to be editable without the need for recompiling and regenerating the AAR archive. With this in mind, I chose to put them into a properties file that is loaded and consumed in runtime. Unfortunately, I'm having some questions about this: As far as I know, to achieve what I want, the only option is to put the properties file into the ../axis2/WEB-INF/classes directory of each one of the deployments (on each WebLogic instance) I currently have on my clustered configuration, and then load the file, as follows (or equivalent, this has not been verified for optimization): InputStreamReader fMainProp = new InputStreamReader(this.getClass().getResourceAsStream("myfile.properties")); Properties mainProp = new Properties(); mainProp.load(fMainProp); This is not as practical as I wanted it to be, because each time I want to alter some setting on the properties file, I have to edit each one of the files (deployed over different WebLogic instances) and there is a high probability of modifying one of these files without modifying the others. What I would like to know is if there is any (better) alternative to accomplish what I want, minimizing the potential conflict of configuration that is created by distributing and replicating the properties file through multiple WebLogic instances.

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  • Implementing Naïve Bayes algorithm in Java - Need some guidance

    - by techventure
    hello stackflow people As a School assignment i'm required to implement Naïve Bayes algorithm which i am intending to do in Java. In trying to understand how its done, i've read the book "Data Mining - Practical Machine Learning Tools and Techniques" which has a section on this topic but am still unsure on some primary points that are blocking my progress. Since i'm seeking guidance not solution in here, i'll tell you guys what i thinking in my head, what i think is the correct approach and in return ask for correction/guidance which will very much be appreciated. please note that i am an absolute beginner on Naïve Bayes algorithm, Data mining and in general programming so you might see stupid comments/calculations below: The training data set i'm given has 4 attributes/features that are numeric and normalized(in range[0 1]) using Weka (no missing values)and one nominal class(yes/no) 1) The data coming from a csv file is numeric HENCE * Given the attributes are numeric i use PDF (probability density function) formula. + To calculate the PDF in java i first separate the attributes based on whether they're in class yes or class no and hold them into different array (array class yes and array class no) + Then calculate the mean(sum of the values in row / number of values in that row) and standard divination for each of the 4 attributes (columns) of each class + Now to find PDF of a given value(n) i do (n-mean)^2/(2*SD^2), + Then to find P( yes | E) and P( no | E) i multiply the PDF value of all 4 given attributes and compare which is larger, which indicates the class it belongs to In temrs of Java, i'm using ArrayList of ArrayList and Double to store the attribute values. lastly i'm unsure how to to get new data? Should i ask for input file (like csv) or command prompt and ask for 4 values? I'll stop here for now (do have more questions) but I'm worried this won't get any responses given how long its got. I will really appreciate for those that give their time reading my problems and comment.

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  • Porting a select loop application to Android with NDK. Design question.

    - by plaisthos
    Hi, I have an network application which uses a select loop like this: bool shutdown=false; while (!shutdown) { [do something] select(...,timeout); } THe main loop cannot work like this in an Android application anymore since the application needs to receive Intents, need to handle GUI, etc. I think I have basically three possibilities: Move the main loop to the java part of the application. Let the loop run in its own thread and somehow communicate from/to java. Screw Android <= 2.3 and use a native activity and use AInputQueue/ALooper instead of select. The first possibility is not easy since java has no select which works on fds. Simply using the select and return after each loop to java is not an elegant possibility either since that requires setting the timeout to something like 20ms to have a good response time in the java part of the program. The second probability sound nicer but I have do some communication between java and the c++/c part of the program. Things that cold work: Using a socket, kind of ugly. using native calls in the "java gui thread" and callback from native in the "c thread". Both threads need to have thread safe implementations but this is managable. I have not explored the third possibility but I think that it is not the way to go. I think I can hack something together which will work but I asking what is the best path to chose.

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  • Calculating Nearest Match to Mean/Stddev Pair With LibSVM

    - by Chris S
    I'm new to SVMs, and I'm trying to use the Python interface to libsvm to classify a sample containing a mean and stddev. However, I'm getting nonsensical results. Is this task inappropriate for SVMs or is there an error in my use of libsvm? Below is the simple Python script I'm using to test: #!/usr/bin/env python # Simple classifier test. # Adapted from the svm_test.py file included in the standard libsvm distribution. from collections import defaultdict from svm import * # Define our sparse data formatted training and testing sets. labels = [1,2,3,4] train = [ # key: 0=mean, 1=stddev {0:2.5,1:3.5}, {0:5,1:1.2}, {0:7,1:3.3}, {0:10.3,1:0.3}, ] problem = svm_problem(labels, train) test = [ ({0:3, 1:3.11},1), ({0:7.3,1:3.1},3), ({0:7,1:3.3},3), ({0:9.8,1:0.5},4), ] # Test classifiers. kernels = [LINEAR, POLY, RBF] kname = ['linear','polynomial','rbf'] correct = defaultdict(int) for kn,kt in zip(kname,kernels): print kt param = svm_parameter(kernel_type = kt, C=10, probability = 1) model = svm_model(problem, param) for test_sample,correct_label in test: pred_label, pred_probability = model.predict_probability(test_sample) correct[kn] += pred_label == correct_label # Show results. print '-'*80 print 'Accuracy:' for kn,correct_count in correct.iteritems(): print '\t',kn, '%.6f (%i of %i)' % (correct_count/float(len(test)), correct_count, len(test)) The domain seems fairly simple. I'd expect that if it's trained to know a mean of 2.5 means label 1, then when it sees a mean of 2.4, it should return label 1 as the most likely classification. However, each kernel has an accuracy of 0%. Why is this? On a side note, is there a way to hide all the verbose training output dumped by libsvm in the terminal? I've searched libsvm's docs and code, but I can't find any way to turn this off.

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  • Boost Mersenne Twister: how to seed with more than one value?

    - by Eamon Nerbonne
    I'm using the boost mt19937 implementation for a simulation. The simulation needs to be reproducible, and that means storing and potentially reusing the RNG seeds later. I'm using the windows crypto api to generate the seed values because I need an external source for the seeds and not because of any particular guarantees of randomness. The output of any simulation run will have a note including the RNG seed - so the seed needs to be reasonably short. On the other hand, as part of the analysis of the simulation, I'll be comparing several runs - but to be sure that these runs are actually different, I'll need to use different seeds - so the seed needs to be long enough to avoid accidental collisions. I've determined that 64-bits of seeding should suffice; the chance of a collision will reach 50% after about 2^32 runs - that probability is low enough that the average error caused by it is negligible to me. Using just 32-bits of seed is tricky; the chance of a collision reaches 50% already after 2^16 runs; and that's a little too likely for my tastes. Unfortunately, the boost implementation either seeds with a full state vector - which is far, far too long - or a single 32-bit unsigned long - which isn't ideal. How can I seed the generator with more than 32-bits but less than a full state vector? I tried just padding the vector or repeating the seeds to fill the state vector, but even a cursory glance at the results shows that that generates poor results.

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  • Random encounter not so random

    - by DoomStone
    Hello i am having some problems generating random numbers with C# Now i have this function. public Color getRandomColor() { Color1 = new Random().Next(new Random().Next(0, 100), new Random().Next(200, 255)); Color2 = new Random().Next(new Random().Next(0, 100), new Random().Next(200, 255)); Color3 = new Random().Next(new Random().Next(0, 100), new Random().Next(200, 255)); Color color = Color.FromArgb(Color1, Color2, Color3); Console.WriteLine("R: " + Color1 + " G: " + Color2 + " B: " + Color3 + " = " + color.Name); return color; } Now you might notice that there are ALOT of new Random() there, that is because i want to weed out the probability that it could be a same instance error. I now run this function 8 times, a couple of times. Now here are the out puts. R: 65 G: 65 B: 65 = ff414141 R: 242 G: 242 B: 242 = fff2f2f2 R: 205 G: 205 B: 205 = ffcdcdcd R: 40 G: 40 B: 40 = ff282828 R: 249 G: 249 B: 249 = fff9f9f9 R: 249 G: 249 B: 249 = fff9f9f9 R: 94 G: 94 B: 94 = ff5e5e5e R: 186 G: 186 B: 186 = ffbababa R: 142 G: 142 B: 142 = ff8e8e8e R: 190 G: 190 B: 190 = ffbebebe R: 19 G: 19 B: 19 = ff131313 R: 119 G: 119 B: 119 = ff777777 R: 119 G: 119 B: 119 = ff777777 R: 75 G: 75 B: 75 = ff4b4b4b R: 169 G: 169 B: 169 = ffa9a9a9 R: 127 G: 127 B: 127 = ff7f7f7f R: 73 G: 73 B: 73 = ff494949 R: 27 G: 27 B: 27 = ff1b1b1b R: 125 G: 125 B: 125 = ff7d7d7d R: 212 G: 212 B: 212 = ffd4d4d4 R: 174 G: 174 B: 174 = ffaeaeae R: 0 G: 0 B: 0 = ff000000 R: 0 G: 0 B: 0 = ff000000 R: 220 G: 220 B: 220 = ffdcdcdc As you can see this is not so random again, but why dose this happens? and how can i counter it?

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  • ai: Determining what tests to run to get most useful data

    - by Sai Emrys
    This is for http://cssfingerprint.com I have a system (see about page on site for details) where: I need to output a ranked list, with confidences, of categories that match a particular feature vector the binary feature vectors are a list of site IDs & whether this session detected a hit feature vectors are, for a given categorization, somewhat noisy (sites will decay out of history, and people will visit sites they don't normally visit) categories are a large, non-closed set (user IDs) my total feature space is approximately 50 million items (URLs) for any given test, I can only query approx. 0.2% of that space I can only make the decision of what to query, based on results so far, ~10-30 times, and must do so in <~100ms (though I can take much longer to do post-processing, relevant aggregation, etc) getting the AI's probability ranking of categories based on results so far is mildly expensive; ideally the decision will depend mostly on a few cheap sql queries I have training data that can say authoritatively that any two feature vectors are the same category but not that they are different (people sometimes forget their codes and use new ones, thereby making a new user id) I need an algorithm to determine what features (sites) are most likely to have a high ROI to query (i.e. to better discriminate between plausible-so-far categories [users], and to increase certainty that it's any given one). This needs to take into balance exploitation (test based on prior test data) and exploration (test stuff that's not been tested enough to find out how it performs). There's another question that deals with a priori ranking; this one is specifically about a posteriori ranking based on results gathered so far. Right now, I have little enough data that I can just always test everything that anyone else has ever gotten a hit for, but eventually that won't be the case, at which point this problem will need to be solved. I imagine that this is a fairly standard problem in AI - having a cheap heuristic for what expensive queries to make - but it wasn't covered in my AI class, so I don't actually know whether there's a standard answer. So, relevant reading that's not too math-heavy would be helpful, as well as suggestions for particular algorithms. What's a good way to approach this problem?

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  • .NET Membership with Repository Pattern

    - by Zac
    My team is in the process of designing a domain model which will hide various different data sources behind a unified repository abstraction. One of the main drivers for this approach is the very high probability that these data sources will undergo significant change in the near future and we don't want to be re-writing business logic when this happens. One data source will be our membership database which was originally implemented using the default ASP.Net Membership Provider. The membership provider is tied to the System.Web.Security namespace but we have a design guideline requiring that our domain model layer is not dependent upon System.Web (or any other implementation/environment dependency) as it will be consumed in different environments - nor do we want our websites directly communicating with databases. I am considering what would be a good approach to reconciling the MembershipProvider approach with our abstracted n-tier architecture. My initial feeling is that we could create a "DomainMembershipProvider" which interacts with the domain model and then implement objects in the model which deal with the repository and handle validation/business logic. The repository would then implement data access using our (as-yet undecided) ORM/data access tool. Are there are any glaring holes in this approach - I haven't worked closely with the MembershipProvider class so may well be missing something. Alternatively, is there an approach that you think will better serve the requirements I described above? Thanks in advance for your thoughts and advice. Regards, Zac

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  • Designing bayesian networks

    - by devoured elysium
    I have a basic question about Bayesian networks. Let's assume we have an engine, that with 1/3 probability can stop working. I'll call this variable ENGINE. If it stops working, then your car doesn't work. If the engine is working, then your car will work 99% of the time. I'll call this one CAR. Now, if your car is old(OLD), instead of not working 1/3 of the time, your engine will stop working 1/2 of the time. I'm being asked to first design the network and then assign all the conditional probabilities associated with the table. I'd say the diagram of this network would be something like OLD -> ENGINE -> CAR Now, for the conditional probabilities tables I did the following: OLD |ENGINE ------------ True | 0.50 False | 0.33 and ENGINE|CAR ------------ True | 0.99 False | 0.00 Now, I am having trouble about how to define the probabilities of OLD. In my point of view, old is not something that has a CAUSE relationship with ENGINE, I'd say it is more a characteristic of it. Maybe there is a different way to express this in the diagram? If the diagram is indeed correct, how would I go to make the tables? Thanks

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  • average case running time of linear search algorithm

    - by Brahadeesh
    Hi all. I am trying to derive the average case running time for deterministic linear search algorithm. The algorithm searches an element x in an unsorted array A in the order A[1], A[2], A[3]...A[n]. It stops when it finds the element x or proceeds until it reaches the end of the array. I searched on wikipedia and the answer given was (n+1)/(k+1) where k is the number of times x is present in the array. I approached in another way and am getting a different answer. Can anyone please give me the correct proof and also let me know whats wrong with my method? E(T)= 1*P(1) + 2*P(2) + 3*P(3) ....+ n*P(n) where P(i) is the probability that the algorithm runs for 'i' time (i.e. compares 'i' elements). P(i)= (n-i)C(k-1) * (n-k)! / n! Here, (n-i)C(k-1) is (n-i) Choose (k-1). As the algorithm has reached the ith step, the rest of k-1 x's must be in the last n-i elements. Hence (n-i)C(k-i). (n-k)! is the total number of ways of arranging the rest non x numbers, and n! is the total number of ways of arranging the n elements in the array. I am not getting (n+1)/(k+1) on simplifying.

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  • Mathematics for Computer Science Students

    - by Ender
    To cut a long story short, I am a CS student that has received no formal Post-16 Maths education for years. Right now even my Algebra is extremely rusty and I have a couple of months to shape up my skills. I've got a couple of video lectures in my bookmarks, consisting of: Pre-Calculus Algebra Calculus Probability Introduction to Statistics Differential Equations Linear Algebra My aim as of today is to be able to read the CLRS book Introduction to Algorithms and be able to follow the Mathematical notation in that, as well as being able to confidently read and back-up any arguments written in Mathematical notation. Aside from these video lectures, can anyone recommend any good books to help teach someone wishing to go from a low-foundation level to a more advanced level of Mathematics? Just as a note, I've taken a first-year module in Analytical Modelling, so I understand some of the basic concepts of Discrete Mathematics. EDIT: Just a note to those that are looking to learn Linear Algebra using the Video Lectures I have posted up. Peteris Krumins' Blog contains a run-through of these lecture notes as well as his own commentary and lecture notes, an invaluable resource for those looking to follow the lectures too.

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  • a package for kruskal-wallis that shows pairwise comparison details

    - by dalloliogm
    The standard stats::kruskal.test module allows to calculate the kruskal-wallis test on a dataset: >>> data(diamonds) >>> kruskal.test.test(price~carat, data=diamonds) Kruskal-Wallis rank sum test data: price by carat by color Kruskal-Wallis chi-squared = 50570.15, df = 272, p-value < 2.2e-16 this is fine, it is giving me the probability that all the groups in the data have the same mean. However, I would like to have the details per each pair comparison, like if diamonds of colors D and E have the same mean price, as some other softwares (SPSS) do when you ask for a Kruskal test. I have found kruskalmc from the package pgirmess which allows me to do what I want to do: > kruskalmc(diamonds$price, diamonds$color) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference D-E 571.7459 747.4962 FALSE D-F 2237.4309 751.5684 TRUE D-G 2643.1778 726.9854 TRUE D-H 4539.4392 774.4809 TRUE D-I 6002.6286 862.0150 TRUE D-J 8077.2871 1061.7451 TRUE E-F 2809.1767 680.4144 TRUE E-G 3214.9237 653.1587 TRUE E-H 5111.1851 705.6410 TRUE E-I 6574.3744 800.7362 TRUE E-J 8649.0330 1012.6260 TRUE F-G 405.7470 657.8152 FALSE F-H 2302.0083 709.9533 TRUE F-I 3765.1977 804.5390 TRUE F-J 5839.8562 1015.6357 TRUE G-H 1896.2614 683.8760 TRUE G-I 3359.4507 781.6237 TRUE G-J 5434.1093 997.5813 TRUE H-I 1463.1894 825.9834 TRUE H-J 3537.8479 1032.7058 TRUE I-J 2074.6585 1099.8776 TRUE However, this package only allows for one categoric variable (e.g. I can't study the prices clustered by color and by carat, as I can do with kruskal.test), and I don't know anything about the pgirmess package, whether it is maintained or not, or if it is tested.

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  • Neural Network with softmax activation

    - by Cambium
    This is more or less a research project for a course, and my understanding of NN is very/fairly limited, so please be patient :) ============== I am currently in the process of building a neural network that attempts to examine an input dataset and output the probability/likelihood of each classification (there are 5 different classifications). Naturally, the sum of all output nodes should add up to 1. Currently, I have two layers, and I set the hidden layer to contain 10 nodes. I came up with two different types of implementations 1) Logistic sigmoid for hidden layer activation, softmax for output activation 2) Softmax for both hidden layer and output activation I am using gradient descent to find local maximums in order to adjust the hidden nodes' weights and the output nodes' weights. I am certain in that I have this correct for sigmoid. I am less certain with softmax (or whether I can use gradient descent at all), after a bit of researching, I couldn't find the answer and decided to compute the derivative myself and obtained softmax'(x) = softmax(x) - softmax(x)^2 (this returns an column vector of size n). I have also looked into the MATLAB NN toolkit, the derivative of softmax provided by the toolkit returned a square matrix of size nxn, where the diagonal coincides with the softmax'(x) that I calculated by hand; and I am not sure how to interpret the output matrix. I ran each implementation with a learning rate of 0.001 and 1000 iterations of back propagation. However, my NN returns 0.2 (an even distribution) for all five output nodes, for any subset of the input dataset. My conclusions: o I am fairly certain that my gradient of descent is incorrectly done, but I have no idea how to fix this. o Perhaps I am not using enough hidden nodes o Perhaps I should increase the number of layers Any help would be greatly appreciated! The dataset I am working with can be found here (processed Cleveland): http://archive.ics.uci.edu/ml/datasets/Heart+Disease

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  • Valgrind says "stack allocation," I say "heap allocation"

    - by Joel J. Adamson
    Dear Friends, I am trying to trace a segfault with valgrind. I get the following message from valgrind: ==3683== Conditional jump or move depends on uninitialised value(s) ==3683== at 0x4C277C5: sparse_mat_mat_kron (sparse.c:165) ==3683== by 0x4C2706E: rec_mating (rec.c:176) ==3683== by 0x401C1C: age_dep_iterate (age_dep.c:287) ==3683== by 0x4014CB: main (age_dep.c:92) ==3683== Uninitialised value was created by a stack allocation ==3683== at 0x401848: age_dep_init_params (age_dep.c:131) ==3683== ==3683== Conditional jump or move depends on uninitialised value(s) ==3683== at 0x4C277C7: sparse_mat_mat_kron (sparse.c:165) ==3683== by 0x4C2706E: rec_mating (rec.c:176) ==3683== by 0x401C1C: age_dep_iterate (age_dep.c:287) ==3683== by 0x4014CB: main (age_dep.c:92) ==3683== Uninitialised value was created by a stack allocation ==3683== at 0x401848: age_dep_init_params (age_dep.c:131) However, here's the offending line: /* allocate mating table */ age_dep_data->mtable = malloc (age_dep_data->geno * sizeof (double *)); if (age_dep_data->mtable == NULL) error (ENOMEM, ENOMEM, nullmsg, __LINE__); for (int j = 0; j < age_dep_data->geno; j++) { 131=> age_dep_data->mtable[j] = calloc (age_dep_data->geno, sizeof (double)); if (age_dep_data->mtable[j] == NULL) error (ENOMEM, ENOMEM, nullmsg, __LINE__); } What gives? I thought any call to malloc or calloc allocated heap space; there is no other variable allocated here, right? Is it possible there's another allocation going on (the offending stack allocation) that I'm not seeing? You asked to see the code, here goes: /* Copyright 2010 Joel J. Adamson <[email protected]> $Id: age_dep.c 1010 2010-04-21 19:19:16Z joel $ age_dep.c:main file Joel J. Adamson -- http://www.unc.edu/~adamsonj Servedio Lab University of North Carolina at Chapel Hill CB #3280, Coker Hall Chapel Hill, NC 27599-3280 This file is part of an investigation of age-dependent sexual selection. This code is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with haploid. If not, see <http://www.gnu.org/licenses/>. */ #include "age_dep.h" /* global variables */ extern struct argp age_dep_argp; /* global error message variables */ char * nullmsg = "Null pointer: %i"; /* error message for conversions: */ char * errmsg = "Representation error: %s"; /* precision for formatted output: */ const char prec[] = "%-#9.8f "; const size_t age_max = AGEMAX; /* maximum age of males */ static int keep_going_p = 1; int main (int argc, char ** argv) { /* often used counters: */ int i, j; /* read the command line */ struct age_dep_args age_dep_args = { NULL, NULL, NULL }; argp_parse (&age_dep_argp, argc, argv, 0, 0, &age_dep_args); /* set the parameters here: */ /* initialize an age_dep_params structure, set the members */ age_dep_params_t * params = malloc (sizeof (age_dep_params_t)); if (params == NULL) error (ENOMEM, ENOMEM, nullmsg, __LINE__); age_dep_init_params (params, &age_dep_args); /* initialize frequencies: this initializes a list of pointers to initial frqeuencies, terminated by a NULL pointer*/ params->freqs = age_dep_init (&age_dep_args); params->by = 0.0; /* what range of parameters do we want, and with what stepsize? */ /* we should go from 0 to half-of-theta with a step size of about 0.01 */ double from = 0.0; double to = params->theta / 2.0; double stepsz = 0.01; /* did you think I would spell the whole word? */ unsigned int numparts = floor(to / stepsz); do { #pragma omp parallel for private(i) firstprivate(params) \ shared(stepsz, numparts) for (i = 0; i < numparts; i++) { params->by = i * stepsz; int tries = 0; while (keep_going_p) { /* each time through, modify mfreqs and mating table, then go again */ keep_going_p = age_dep_iterate (params, ++tries); if (keep_going_p == ERANGE) error (ERANGE, ERANGE, "Failure to converge\n"); } fprintf (stdout, "%i iterations\n", tries); } /* for i < numparts */ params->freqs = params->freqs->next; } while (params->freqs->next != NULL); return 0; } inline double age_dep_pmate (double age_dep_t, unsigned int genot, double bp, double ba) { /* the probability of mating between these phenotypes */ /* the female preference depends on whether the female has the preference allele, the strength of preference (parameter bp) and the male phenotype (age_dep_t); if the female lacks the preference allele, then this will return 0, which is not quite accurate; it should return 1 */ return bits_isset (genot, CLOCI)? 1.0 - exp (-bp * age_dep_t) + ba: 1.0; } inline double age_dep_trait (int age, unsigned int genot, double by) { /* return the male trait, a function of the trait locus, age, the age-dependent scaling parameter (bx) and the males condition genotype */ double C; double T; /* get the male's condition genotype */ C = (double) bits_popcount (bits_extract (0, CLOCI, genot)); /* get his trait genotype */ T = bits_isset (genot, CLOCI + 1)? 1.0: 0.0; /* return the trait value */ return T * by * exp (age * C); } int age_dep_iterate (age_dep_params_t * data, unsigned int tries) { /* main driver routine */ /* number of bytes for female frequencies */ size_t geno = data->age_dep_data->geno; size_t genosize = geno * sizeof (double); /* female frequencies are equal to male frequencies at birth (before selection) */ double ffreqs[geno]; if (ffreqs == NULL) error (ENOMEM, ENOMEM, nullmsg, __LINE__); /* do not set! Use memcpy (we need to alter male frequencies (selection) without altering female frequencies) */ memmove (ffreqs, data->freqs->freqs[0], genosize); /* for (int i = 0; i < geno; i++) */ /* ffreqs[i] = data->freqs->freqs[0][i]; */ #ifdef PRMTABLE age_dep_pr_mfreqs (data); #endif /* PRMTABLE */ /* natural selection: */ age_dep_ns (data); /* normalized mating table with new frequencies */ age_dep_norm_mtable (ffreqs, data); #ifdef PRMTABLE age_dep_pr_mtable (data); #endif /* PRMTABLE */ double * newfreqs; /* mutate here */ /* i.e. get the new frequency of 0-year-olds using recombination; */ newfreqs = rec_mating (data->age_dep_data); /* return block */ { if (sim_stop_ck (data->freqs->freqs[0], newfreqs, GENO, TOL) == 0) { /* if we have converged, stop the iterations and handle the data */ age_dep_sim_out (data, stdout); return 0; } else if (tries > MAXTRIES) return ERANGE; else { /* advance generations */ for (int j = age_max - 1; j < 0; j--) memmove (data->freqs->freqs[j], data->freqs->freqs[j-1], genosize); /* advance the first age-class */ memmove (data->freqs->freqs[0], newfreqs, genosize); return 1; } } } void age_dep_ns (age_dep_params_t * data) { /* calculate the new frequency of genotypes given additive fitness and selection coefficient s */ size_t geno = data->age_dep_data->geno; double w[geno]; double wbar, dtheta, ttheta, dcond, tcond; double t, cond; /* fitness parameters */ double mu, nu; mu = data->wparams[0]; nu = data->wparams[1]; /* calculate fitness */ for (int j = 0; j < age_max; j++) { int i; for (i = 0; i < geno; i++) { /* calculate male trait: */ t = age_dep_trait(j, i, data->by); /* calculate condition: */ cond = (double) bits_popcount (bits_extract(0, CLOCI, i)); /* trait-based fitness term */ dtheta = data->theta - t; ttheta = (dtheta * dtheta) / (2.0 * nu * nu); /* condition-based fitness term */ dcond = CLOCI - cond; tcond = (dcond * dcond) / (2.0 * mu * mu); /* calculate male fitness */ w[i] = 1 + exp(-tcond) - exp(-ttheta); } /* calculate mean fitness */ /* as long as we calculate wbar before altering any values of freqs[], we're safe */ wbar = gen_mean (data->freqs->freqs[j], w, geno); for (i = 0; i < geno; i++) data->freqs->freqs[j][i] = (data->freqs->freqs[j][i] * w[i]) / wbar; } } void age_dep_norm_mtable (double * ffreqs, age_dep_params_t * params) { /* this function produces a single mating table that forms the input for recombination () */ /* i is female genotype; j is male genotype; k is male age */ int i,j,k; double norm_denom; double trait; size_t geno = params->age_dep_data->geno; for (i = 0; i < geno; i++) { double norm_mtable[geno]; /* initialize the denominator: */ norm_denom = 0.0; /* find the probability of mating and add it to the denominator */ for (j = 0; j < geno; j++) { /* initialize entry: */ norm_mtable[j] = 0.0; for (k = 0; k < age_max; k++) { trait = age_dep_trait (k, j, params->by); norm_mtable[j] += age_dep_pmate (trait, i, params->bp, params->ba) * (params->freqs->freqs)[k][j]; } norm_denom += norm_mtable[j]; } /* now calculate entry (i,j) */ for (j = 0; j < geno; j++) params->age_dep_data->mtable[i][j] = (ffreqs[i] * norm_mtable[j]) / norm_denom; } } My current suspicion is the array newfreqs: I can't memmove, memcpy or assign a stack variable then hope it will persist, can I? rec_mating() returns double *.

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  • Why should the "prime-based" hashcode implmentation be used instead of the "naive" one?

    - by Wilhelm
    I have seen that a prime number implmentation of the GetHashCode function is being recommend, for example here. However using the following code (in VB, sorry), it seems as if that implementation gives the same hash density as a "naive" xor implementation. If the density is the same, I would suppose there is the same probability of cllision in both implementations. Am I missing anything on why is the prime approach preferred? I am supossing that if the hash code is a byte I do not lose generality for the integer case. Sub Main() Dim XorHashes(255) As Integer Dim PrimeHashes(255) As Integer For i = 0 To 255 For j = 0 To 255 For k = 0 To 255 XorHashes(GetXorHash(i, j, k)) += 1 PrimeHashes(GetPrimeHash(i, j, k)) += 1 Next Next Next For i = 0 To 255 Console.WriteLine("{0}: {1}, {2}", i, XorHashes(i), PrimeHashes(i)) Next Console.ReadKey() End Sub Public Function GetXorHash(ByVal valueOne As Integer, ByVal valueTwo As Integer, ByVal valueThree As Integer) As Byte Return CByte((valueOne Xor valueTwo Xor valueThree) Mod 256) End Function Public Function GetPrimeHash(ByVal valueOne As Integer, ByVal valueTwo As Integer, ByVal valueThree As Integer) As Byte Dim TempHash = 17 TempHash = 31 * TempHash + valueOne TempHash = 31 * TempHash + valueTwo TempHash = 31 * TempHash + valueThree Return CByte(TempHash Mod 256) End Function

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  • Using Hidden Markov Model for designing AI mp3 player

    - by Casper Slynge
    Hey guys. Im working on an assignment, where I want to design an AI for a mp3 player. The AI must be trained and designed with the use of a HMM method. The mp3 player shall have the functionality of adapting to its user, by analyzing incoming biological sensor data, and from this data the mp3 player will choose a genre for the next song. Given in the assignment is 14 samples of data: One sample consist of Heart Rate, Respiration, Skin Conductivity, Activity and finally the output genre. Below is the 14 samples of data, just for you to get an impression of what im talking about. Sample HR RSP SC Activity Genre S1 Medium Low High Low Rock S2 High Low Medium High Rock S3 High High Medium Low Classic S4 High Medium Low Medium Classic S5 Medium Medium Low Low Classic S6 Medium Low High High Rock S7 Medium High Medium Low Classic S8 High Medium High Low Rock S9 High High Low Low Classic S10 Medium Medium Medium Low Classic S11 Medium Medium High High Rock S12 Low Medium Medium High Classic S13 Medium High Low Low Classic S14 High Low Medium High Rock My time of work regarding HMM is quite low, so my question to you is if I got the right angle on the assignment. I have three different states for each sensor: Low, Medium, High. Two observations/output symbols: Rock, Classic In my own opinion I see my start probabilities as the weightened factors for either a Low, Medium or High state in the Heart Rate. So the ideal solution for the AI is that it will learn these 14 sets of samples. And when a users sensor input is received, the AI will compare the combination of states for all four sensors, with the already memorized samples. If there exist a matching combination, the AI will choose the genre, and if not it will choose a genre according to the weightened transition probabilities, while simultaniously updating the transition probabilities with the new data. Is this a right approach to take, or am I missing something ? Is there another way to determine the output probability (read about Maximum likelihood estimation by EM, but dont understand the concept)? Best regards, Casper

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  • How to generate random numbers of lognormal distribution within specific range in Matlab

    - by Harpreet
    My grain sizes are defined as D=[1.19,1.00,0.84,0.71,0.59,0.50,0.42]. The problem is described below in steps. Grain sizes should follow lognormal distribution. The mean of the grain sizes is fixed as 0.84 and the standard deviation should be as low as possible but not zero. 90% of the grains (by weight %) fall in the size range of 1.19 to 0.59, and the rest 10% fall in size range of 0.50 to 0.42. Now I want to find the probabilities (weight percentage) of the grains falling in each grain size. It is allowable to split this grain size distribution into further small sizes but it must always be in the range of 1.19 and 0.42, i.e. 'D' can be continuous but 0.42 < D < 1.19. I need it fast. I tried on my own but I am not able to get the correct result. I am getting negative probabilities (weight percentages). Thanks to anyone who helps. I didn't incorporate the point 3 as I came to know about that condition later. Here are simple steps I tried: %% D=[1.19,1.00,0.84,0.71,0.59,0.50,0.42]; s=0.30; % std dev of the lognormal distribution m=0.84; % mean of the lognormal distribution mu=log(m^2/sqrt(s^2+m^2)); % mean of the associated normal dist. sigma=sqrt(log((s^2/m^2)+1)); % std dev of the associated normal dist. [r,c]=size(D); for i=1:c D(i)=mu+(sigma.*randn(1)); w(i)=(log(D(i))-mu)/sigma; % the probability or the wt. percentage of the grain sizes end grain_size=exp(D); %%

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  • Calculate posterior distribution of unknown mis-classification with PRTools in MATLAB

    - by Samuel Lampa
    I'm using the PRTools MATLAB library to train some classifiers, generating test data and testing the classifiers. I have the following details: N: Total # of test examples k: # of mis-classification for each classifier and class I want to do: Calculate and plot Bayesian posterior distributions of the unknown probabilities of mis-classification (denoted q), that is, as probability density functions over q itself (so, P(q) will be plotted over q, from 0 to 1). I have that (math formulae, not matlab code!): P(q|k,N) = Posterior * Prior / Normalization constant = P(k|q,N) * P(q|N) / P(k|N) The prior is set to 1, so I only need to calculate the posterior and normalization constant. I know that the posterior can be expressed as (where B(N,k) is the binomial coefficient): P(k|q,N) = B(N,k) * q^k * (1-q)^(N-k) ... so the Normalization constant is simply an integral of the posterior above, from 0 to 1: P(k|N) = B(N,k) * integralFromZeroToOne( q^k * (1-q)^(N-k) ) (The Binomial coefficient ( B(N,k) ) can be omitted thoughappears in both the posterior and normalization constant, so it can be omitted.) Now, I've heard that the integral for the normalization constant should be able to be calculated as a series ... something like: k!(N-k)! / (N+1)! Is that correct? (I have some lecture notes from with this series, but can't figure out if it is for the normalization constant integral, or for the posterior distribution of mis-classification (q)) Also, hints are welcome as how to practically calculate this? (factorials are easily creating truncation errors right?) ... AND, how to practically calculate the final plot (the posterior distribution over q, from 0 to 1).

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  • can i know the Thread runnable class attributes in java?

    - by dori naji
    probability this question have been asked before but i cant find anything in my searching mechanism. I am trying to create a multiple threads, in an array list but i want to retrieve them from an arraylist and filter them by the attribute of w1 i used in my code. any ideas ? w1 = new FirstWorker(ProductsList, OrdersList, s); FirstWorkerThread = new Thread(w1); ThreadArrayList.add(FirstWorkerThread); //I know i cant do the code below but i want to do that how ? for(Thread x : ThreadArrayList){ x.ProductsList } this is FirstWorker class import java.lang.String; import java.util.HashMap; /* * To change this template, choose Tools | Templates and open the template in * the editor. */ /** * * @author Dimitris */ public class FirstWorker extends Thread implements Runnable { private OrderList orderlist; private ProductList productlist; private String Worker; boolean Stop; private int speed = 1000; public FirstWorker(ProductList productlist, OrderList orderlist, String Worker) { this.productlist = productlist; this.orderlist = orderlist; this.Worker = Worker; this.Stop = true; } public void run() { if (Stop == true) { try { Thread.sleep(100); } catch (InterruptedException e) { } while (orderlist.returnLengthofOrder() != 0) { if (Thread.interrupted()) { System.out.println("I am in the thread inturrupt"); // We've been interrupted: no more crunching. return; } if (orderlist.getDone() == true) { } else if (orderlist.getDone() == false) { orderlist.setDoneTrue(); orderlist.Purchased(Worker); orderlist.setDoneFalse(); try { Thread.sleep(this.speed); } catch (InterruptedException e) { return; } } } } } public void setWork() { Stop = false; } public void setSpeed(int speed) { this.speed = speed; } }

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  • Kohana Sessions data does not persist across pages in chrome and ir browsers

    - by user1062637
    Kohana Session data does not persist across pages opened in Chrome and IE browsers the same works fine in a Firefox browser Kohana version used is 2.3 session config files hold $config['driver'] = 'native'; /** * Session storage parameter, used by drivers. */ $config['storage'] = ''; /** * Session name. * It must contain only alphanumeric characters and underscores. At least one letter must be present. */ $config['name'] = 'NITWSESSID'; /** * Session parameters to validate: user_agent, ip_address, expiration. */ $config['validate'] = array(); /** * Enable or disable session encryption. * Note: this has no effect on the native session driver. * Note: the cookie driver always encrypts session data. Set to TRUE for stronger encryption. */ $config['encryption'] = FALSE; /** * Session lifetime. Number of seconds that each session will last. * A value of 0 will keep the session active until the browser is closed (with a limit of 24h). */ $config['expiration'] = 2700; /** * Number of page loads before the session id is regenerated. * A value of 0 will disable automatic session id regeneration. */ $config['regenerate'] = 0; /** * Percentage probability that the gc (garbage collection) routine is started. */ $config['gc_probability'] = 2; Help needed urgently

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