Search Results

Search found 116 results on 5 pages for 'fitness'.

Page 1/5 | 1 2 3 4 5  | Next Page >

  • MATLAB: Best fitness vs mean fitness, initial range

    - by Sa Ta
    Based on the example of Rastrigin's function. At the plot function, if I chose 'best fitness', on the same graph 'mean fitness' will also be plotted. I understand well about 'best fitness' whereby it plots the best function value in each generation versus iteration number. It will reach value zero after some times. I don't understand about 'mean fitness'in the graph plotted. What do those 'mean fitness' values mean? How does the 'mean fitness' graph help to understand Rastrigin's function? What are the meaning of the term initial population, initial score and initial range? I wish to have a better understanding of these terms. The default value for initial range is [0,1]. Does it mean that 0 is the lower bound (lb) and 1 is the upper bound (ub)? Do these values interfere with the lb and ub values I set in the constraints? I try to better understand about lb and ub. If my lb is 0 and ub is 5, does it mean that my final point values will be within 0 and 5? If I know the lb and ub for my problem is between 0 and 5, do I just set the initial range as [0,5] at all times and may I assume that this is the best option for initial range, and I need not try it with any other values?

    Read the article

  • What Counts For a DBA: Fitness

    - by Louis Davidson
    If you know me, you can probably guess that physical exercise is not really my thing. There was a time in my past when it a larger part of my life, but even then never in the same sort of passionate way as a number of our SQL friends.  For me, I find that mental exercise satisfies what I believe to be the same inner need that drives people to run farther than I like to drive on most Saturday mornings, and it is certainly just as addictive. Mental fitness shares many common traits with physical fitness, especially the need to attain it through repetitive training. I only wish that mental training burned off a bacon cheeseburger in the same manner as does jogging around a dewy park on Saturday morning. In physical training, there are at least two goals, the first of which is to be physically able to do a task. The second is to train the brain to perform the task without thinking too hard about it. No matter how long it has been since you last rode a bike, you will be almost certainly be able to hop on and start riding without thinking about the process of pedaling or balancing. If you’ve never ridden a bike, you could be a physics professor /Olympic athlete and still crash the first few times you try, even though you are as strong as an ox and your knowledge of the physics of bicycle riding makes the concept child’s play. For programming tasks, the process is very similar. As a DBA, you will come to know intuitively how to backup, optimize, and secure database systems. As a data programmer, you will work to instinctively use the clauses of Transact-SQL DML so that, when you need to group data three ways (and not four), you will know to use the GROUP BY clause with GROUPING SETS without resorting to a search engine.  You have the skill. Making it naturally then requires repetition and experience is the primary requirement, not just simply learning about a topic. The hardest part of being really good at something is this difference between knowledge and skill. I have recently taken several informative training classes with Kimball University on data warehousing and ETL. Now I have a lot more knowledge about designing data warehouses than before. I have also done a good bit of data warehouse designing of late and have started to improve to some level of proficiency with the theory. Yet, for all of this head knowledge, it is still a struggle to take what I have learned and apply it to the designs I am working on.  Data warehousing is still a task that is not yet deeply ingrained in my brain muscle memory. On the other hand, relational database design is something that no matter how much or how little I may get to do it, I am comfortable doing it. I have done it as a profession now for well over a decade, I teach classes on it, and I also have done (and continue to do) a lot of mental training beyond the work day. Sometimes the training is just basic education, some reading blogs and attending sessions at PASS events.  My best training comes from spending time working on other people’s design issues in forums (though not nearly as much as I would like to lately). Working through other people’s problems is a great way to exercise your brain on problems with which you’re not immediately familiar. The final bit of exercise I find useful for cultivating mental fitness for a data professional is also probably the nerdiest thing that I will ever suggest you do.  Akin to running in place, the idea is to work through designs in your head. I have designed more than one database system that would revolutionize grocery store operations, sales at my local Target store, the ordering process at Amazon, and ways to improve Disney World operations to get me through a line faster (some of which they are starting to implement without any of my help.) Never are the designs truly fleshed out, but enough to work through structures and processes.  On “paper”, I have designed database systems to catalog things as trivial as my Lego creations, rental car companies and my audio and video collections. Once I get the database designed mentally, sometimes I will create the database, add some data (often using Red-Gate’s Data Generator), and write a few queries to see if a concept was realistic, but I will rarely fully flesh out the database since I have no desire to do any user interface programming anymore.  The mental training allows me to keep in practice for when the time comes to do the work I love the most for real…even if I have been spending most of my work time lately building data warehouses.  If you are really strong of mind and body, perhaps you can mix a mental run with a physical run; though don’t run off of a cliff while contemplating how you might design a database to catalog the trees on a mountain…that would be contradictory to the purpose of both types of exercise.

    Read the article

  • Iterative and Incremental Principle Series 3: The Implementation Plan (a.k.a The Fitness Plan)

    - by llowitz
    Welcome back to the Iterative and Incremental Blog series.  Yesterday, I demonstrated how shorter interval sets allowed me to focus on my fitness goals and achieve success.  Likewise, in a project setting, shorter milestones allow the project team to maintain focus and experience a sense of accomplishment throughout the project lifecycle.  Today, I will discuss project planning and how to effectively plan your iterations. Admittedly, there is more to applying the iterative and incremental principle than breaking long durations into multiple, shorter ones.  In order to effectively apply the iterative and incremental approach, one should start by creating an implementation plan.   In a project setting, the Implementation Plan is a high level plan that focuses on milestones, objectives, and the number of iterations.  It is the plan that is typically developed at the start of an engagement identifying the project phases and milestones.  When the iterative and incremental principle is applied, the Implementation Plan also identified the number of iterations planned for each phase.  The implementation plan does not include the detailed plan for the iterations, as this detail is determined prior to each iteration start during Iteration Planning.  An individual iteration plan is created for each project iteration. For my fitness regime, I also created an “Implementation Plan” for my weekly exercise.   My high level plan included exercising 6 days a week, and since I cross train, trying not to repeat the same exercise two days in a row.  Because running on the hills outside is the most difficult and consequently, the most effective exercise, my implementation plan includes running outside at least 2 times a week.   Regardless of the exercise selected, I always apply a series of 6-minute interval sets.  I never plan what I will do each day in advance because there are too many changing factors that need to be considered before that level of detail is determined.  If my Implementation Plan included details on the exercise I was to perform each day of the week, it is quite certain that I would be unable to follow my plan to that level.  It is unrealistic to plan each day of the week without considering the unique circumstances at that time.  For example, what is the weather?  Are there are conflicting schedule commitments?  Are there injuries that need to be considered?  Likewise, in a project setting, it is best to plan for the iteration details prior to its start. Join me for tomorrow’s blog where I will discuss when and how to plan the details of your iterations.

    Read the article

  • Steering evaluate fitness

    - by Vodemki
    I've made a simple game with a steering model that manage a crowd of agents. I use an genetic algorithm to find the best parameters to use in my system but I need to determine a fitness for each simulation. I know it's something like that: number of collisions * time to reach goal * effort But I don't know how to calculate the effort, is there a special way to do that ? Here is what I've done so far: // Evaluate the distance from agents to goal Real totalDistance(0.0); for (unsigned i=0; i<_agents.size(); i++) { totalDistance += _agents[i]->position().distance(_agents[i]->_goal->position()); } Real totalWallsCollision(0.0); for (unsigned i=0; i<_agents.size(); i++) { for (unsigned j=0; j<walls.size(); j++) { if ( walls[j]->inside(_agents[i]->position()) ) { totalCollision += 1.0; } } } return totalDistance + totalWallsCollision; Thanks for your help.

    Read the article

  • 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.

    Read the article

  • Tracking fitness in a genetic algorithm

    - by Chuck Vose
    I'm still hacking on my old ruby for the undead post (I know, I know, stop trying to bring the post back from the dead Chuck). But the code has gotten a little out of hand and now I'm working on a genetic algorithm to create the ultimate battle of living and dead with the fitness being how long the battle lasts. So, I've got the basics of it down; how to adjust attributes of the game and how to acquire the fitness of a solution, what I can't figure out is how to store the fitness so that I know when I've tried a combination before. I've not been able to find much genetic code to look at let alone code that I can read well enough to tell what's going on. Does anyone have an idea how this is normally done or just simply an algorithm that could help point me in the right direction?

    Read the article

  • Novel fitness measure for evolutionary image matching simulation

    - by Nick Johnson
    I'm sure many people have already seen demos of using genetic algorithms to generate an image that matches a sample image. You start off with noise, and gradually it comes to resemble the target image more and more closely, until you have a more-or-less exact duplicate. All of the examples I've seen, however, use a fairly straightforward pixel-by-pixel comparison, resulting in a fairly predictable 'fade in' of the final image. What I'm looking for is something more novel: A fitness measure that comes closer to what we see as 'similar' than the naive approach. I don't have a specific result in mind - I'm just looking for something more 'interesting' than the default. Suggestions?

    Read the article

  • How to exercise and feel well when programming?

    - by Filip Ekberg
    While I'm sitting here in my expensive chair which was told to me were gonna help me with my neck and shoulder pains; it didn't. So don't go spend $2,000 on a chair because it's not gonna help. I am trying everything to keep my body in shape, exercising to keep my pizza-body slim and just to feel well in general. What I'd like to do is take a couple of seconds, maybe when the code compiles, to reach up, do a couple of X and feel good. But, what is this X? When I sit there at work, what will everyone think when I stand up and start to hula hula because I want to exercise my basin? I know a lot of programmers out there do have pain so let's come up with a little list together to help us all keep our joints feeling good. Programming gives me joint pain, how do i avoid it without quitting programming?

    Read the article

  • Returning a list from a function in Python

    - by Jasper
    Hi, I'm creating a game for my sister, and I want a function to return a list variable, so I can pass it to another variable. The relevant code is as follows: def startNewGame(): while 1: #Introduction: print print """Hello, You will now be guided through the setup process. There are 7 steps to this. You can cancel setup at any time by typing 'cancelSetup' Thankyou""" #Step 1 (Name): print print """Step 1 of 7: Type in a name for your PotatoHead: """ inputPHName = raw_input('|Enter Name:|') if inputPHName == 'cancelSetup': sys.exit() #Step 2 (Gender): print print """Step 2 of 7: Choose the gender of your PotatoHead: input either 'm' or 'f' """ inputPHGender = raw_input('|Enter Gender:|') if inputPHGender == 'cancelSetup': sys.exit() #Step 3 (Colour): print print """Step 3 of 7: Choose the colour your PotatoHead will be: Only Red, Blue, Green and Yellow are currently supported """ inputPHColour = raw_input('|Enter Colour:|') if inputPHColour == 'cancelSetup': sys.exit() #Step 4 (Favourite Thing): print print """Step 4 of 7: Type your PotatoHead's favourite thing: """ inputPHFavThing = raw_input('|Enter Favourite Thing:|') if inputPHFavThing == 'cancelSetup': sys.exit() # Step 5 (First Toy): print print """Step 5 of 7: Choose a first toy for your PotatoHead: """ inputPHFirstToy = raw_input('|Enter First Toy:|') if inputPHFirstToy == 'cancelSetup': sys.exit() #Step 6 (Check stats): while 1: print print """Step 6 of 7: Check the following details to make sure that they are correct: """ print print """Name:\t\t\t""" + inputPHName + """ Gender:\t\t\t""" + inputPHGender + """ Colour:\t\t\t""" + inputPHColour + """ Favourite Thing:\t""" + inputPHFavThing + """ First Toy:\t\t""" + inputPHFirstToy + """ """ print print "Enter 'y' or 'n'" inputMCheckStats = raw_input('|Is this information correct?|') if inputMCheckStats == 'cancelSetup': sys.exit() elif inputMCheckStats == 'y': break elif inputMCheckStats == 'n': print "Re-enter info: ..." print break else: "The value you entered was incorrect, please re-enter your choice" if inputMCheckStats == 'y': break #Step 7 (Define variables for the creation of the PotatoHead): MFCreatePH = [] print print """Step 7 of 7: Your PotatoHead will now be created... Creating variables... """ MFCreatePH = [inputPHName, inputPHGender, inputPHColour, inputPHFavThing, inputPHFirstToy] time.sleep(1) print "inputPHName" print time.sleep(1) print "inputPHFirstToy" print return MFCreatePH print "Your PotatoHead varibles have been successfully created!" Then it is passed to another function that was imported from another module from potatohead import * ... welcomeMessage() MCreatePH = startGame() myPotatoHead = PotatoHead(MCreatePH) the code for the PotatoHead object is in the potatohead.py module which was imported above, and is as follows: class PotatoHead: #Initialise the PotatoHead object: def __init__(self, data): self.data = data #Takes the data from the start new game function - see main.py #Defines the PotatoHead starting attributes: self.name = data[0] self.gender = data[1] self.colour = data[2] self.favouriteThing = data[3] self.firstToy = data[4] self.age = '0.0' self.education = [self.eduScience, self.eduEnglish, self.eduMaths] = '0.0', '0.0', '0.0' self.fitness = '0.0' self.happiness = '10.0' self.health = '10.0' self.hunger = '0.0' self.tiredness = 'Not in this version' self.toys = [] self.toys.append(self.firstToy) self.time = '0' #Sets data lists for saving, loading and general use: self.phData = (self.name, self.gender, self.colour, self.favouriteThing, self.firstToy) self.phAdvData = (self.name, self.gender, self.colour, self.favouriteThing, self.firstToy, self.age, self.education, self.fitness, self.happiness, self.health, self.hunger, self.tiredness, self.toys) However, when I run the program this error appears: Traceback (most recent call last): File "/Users/Jasper/Documents/Programming/Potato Head Game/Current/main.py", line 158, in <module> myPotatoHead = PotatoHead(MCreatePH) File "/Users/Jasper/Documents/Programming/Potato Head Game/Current/potatohead.py", line 15, in __init__ self.name = data[0] TypeError: 'NoneType' object is unsubscriptable What am i doing wrong? -----EDIT----- The program finishes as so: Step 7 of 7: Your PotatoHead will now be created... Creating variables... inputPHName inputPHFirstToy Then it goes to the Tracback -----EDIT2----- This is the EXACT code I'm running in its entirety: #+--------------------------------------+# #| main.py |# #| A main module for the Potato Head |# #| Game to pull the other modules |# #| together and control through user |# #| input |# #| Author: |# #| Date Created / Modified: |# #| 3/2/10 | 20/2/10 |# #+--------------------------------------+# Tested: No #Import the required modules: import time import random import sys from potatohead import * from toy import * #Start the Game: def welcomeMessage(): print "----- START NEW GAME -----------------------" print "==Print Welcome Message==" print "loading... \t loading... \t loading..." time.sleep(1) print "loading..." time.sleep(1) print "LOADED..." print; print; print; print """Hello, Welcome to the Potato Head Game. In this game you can create a Potato Head, and look after it, like a Virtual Pet. This game is constantly being updated and expanded. Please look out for updates. """ #Choose whether to start a new game or load a previously saved game: def startGame(): while 1: print "--------------------" print """ Choose an option: New_Game or Load_Game """ startGameInput = raw_input('>>> >') if startGameInput == 'New_Game': startNewGame() break elif startGameInput == 'Load_Game': print "This function is not yet supported" print "Try Again" print else: print "You must have mistyped the command: Type either 'New_Game' or 'Load_Game'" print #Set the new game up: def startNewGame(): while 1: #Introduction: print print """Hello, You will now be guided through the setup process. There are 7 steps to this. You can cancel setup at any time by typing 'cancelSetup' Thankyou""" #Step 1 (Name): print print """Step 1 of 7: Type in a name for your PotatoHead: """ inputPHName = raw_input('|Enter Name:|') if inputPHName == 'cancelSetup': sys.exit() #Step 2 (Gender): print print """Step 2 of 7: Choose the gender of your PotatoHead: input either 'm' or 'f' """ inputPHGender = raw_input('|Enter Gender:|') if inputPHGender == 'cancelSetup': sys.exit() #Step 3 (Colour): print print """Step 3 of 7: Choose the colour your PotatoHead will be: Only Red, Blue, Green and Yellow are currently supported """ inputPHColour = raw_input('|Enter Colour:|') if inputPHColour == 'cancelSetup': sys.exit() #Step 4 (Favourite Thing): print print """Step 4 of 7: Type your PotatoHead's favourite thing: """ inputPHFavThing = raw_input('|Enter Favourite Thing:|') if inputPHFavThing == 'cancelSetup': sys.exit() # Step 5 (First Toy): print print """Step 5 of 7: Choose a first toy for your PotatoHead: """ inputPHFirstToy = raw_input('|Enter First Toy:|') if inputPHFirstToy == 'cancelSetup': sys.exit() #Step 6 (Check stats): while 1: print print """Step 6 of 7: Check the following details to make sure that they are correct: """ print print """Name:\t\t\t""" + inputPHName + """ Gender:\t\t\t""" + inputPHGender + """ Colour:\t\t\t""" + inputPHColour + """ Favourite Thing:\t""" + inputPHFavThing + """ First Toy:\t\t""" + inputPHFirstToy + """ """ print print "Enter 'y' or 'n'" inputMCheckStats = raw_input('|Is this information correct?|') if inputMCheckStats == 'cancelSetup': sys.exit() elif inputMCheckStats == 'y': break elif inputMCheckStats == 'n': print "Re-enter info: ..." print break else: "The value you entered was incorrect, please re-enter your choice" if inputMCheckStats == 'y': break #Step 7 (Define variables for the creation of the PotatoHead): MFCreatePH = [] print print """Step 7 of 7: Your PotatoHead will now be created... Creating variables... """ MFCreatePH = [inputPHName, inputPHGender, inputPHColour, inputPHFavThing, inputPHFirstToy] time.sleep(1) print "inputPHName" print time.sleep(1) print "inputPHFirstToy" print return MFCreatePH print "Your PotatoHead varibles have been successfully created!" #Run Program: welcomeMessage() MCreatePH = startGame() myPotatoHead = PotatoHead(MCreatePH) The potatohead.py module is as follows: #+--------------------------------------+# #| potatohead.py |# #| A module for the Potato Head Game |# #| Author: |# #| Date Created / Modified: |# #| 24/1/10 | 24/1/10 |# #+--------------------------------------+# Tested: Yes (24/1/10) #Create the PotatoHead class: class PotatoHead: #Initialise the PotatoHead object: def __init__(self, data): self.data = data #Takes the data from the start new game function - see main.py #Defines the PotatoHead starting attributes: self.name = data[0] self.gender = data[1] self.colour = data[2] self.favouriteThing = data[3] self.firstToy = data[4] self.age = '0.0' self.education = [self.eduScience, self.eduEnglish, self.eduMaths] = '0.0', '0.0', '0.0' self.fitness = '0.0' self.happiness = '10.0' self.health = '10.0' self.hunger = '0.0' self.tiredness = 'Not in this version' self.toys = [] self.toys.append(self.firstToy) self.time = '0' #Sets data lists for saving, loading and general use: self.phData = (self.name, self.gender, self.colour, self.favouriteThing, self.firstToy) self.phAdvData = (self.name, self.gender, self.colour, self.favouriteThing, self.firstToy, self.age, self.education, self.fitness, self.happiness, self.health, self.hunger, self.tiredness, self.toys) #Define the phStats variable, enabling easy display of PotatoHead attributes: def phDefStats(self): self.phStats = """Your Potato Head's Stats are as follows: ---------------------------------------- Name: \t\t""" + self.name + """ Gender: \t\t""" + self.gender + """ Colour: \t\t""" + self.colour + """ Favourite Thing: \t""" + self.favouriteThing + """ First Toy: \t""" + self.firstToy + """ Age: \t\t""" + self.age + """ Education: \t""" + str(float(self.eduScience) + float(self.eduEnglish) + float(self.eduMaths)) + """ -> Science: \t""" + self.eduScience + """ -> English: \t""" + self.eduEnglish + """ -> Maths: \t""" + self.eduMaths + """ Fitness: \t""" + self.fitness + """ Happiness: \t""" + self.happiness + """ Health: \t""" + self.health + """ Hunger: \t""" + self.hunger + """ Tiredness: \t""" + self.tiredness + """ Toys: \t\t""" + str(self.toys) + """ Time: \t\t""" + self.time + """ """ #Change the PotatoHead's favourite thing: def phChangeFavouriteThing(self, newFavouriteThing): self.favouriteThing = newFavouriteThing phChangeFavouriteThingMsg = "Your Potato Head's favourite thing is " + self.favouriteThing + "." #"Feed" the Potato Head i.e. Reduce the 'self.hunger' attribute's value: def phFeed(self): if float(self.hunger) >=3.0: self.hunger = str(float(self.hunger) - 3.0) elif float(self.hunger) < 3.0: self.hunger = '0.0' self.time = str(int(self.time) + 1) #Pass time #"Exercise" the Potato Head if between the ages of 5 and 25: def phExercise(self): if float(self.age) < 5.1 or float(self.age) > 25.1: print "This Potato Head is either too young or too old for this activity!" else: if float(self.fitness) <= 8.0: self.fitness = str(float(self.fitness) + 2.0) elif float(self.fitness) > 8.0: self.fitness = '10.0' self.time = str(int(self.time) + 1) #Pass time #"Teach" the Potato Head: def phTeach(self, subject): if subject == 'Science': if float(self.eduScience) <= 9.0: self.eduScience = str(float(self.eduScience) + 1.0) elif float(self.eduScience) > 9.0 and float(self.eduScience) < 10.0: self.eduScience = '10.0' elif float(self.eduScience) == 10.0: print "Your Potato Head has gained the highest level of qualifications in this subject! It cannot learn any more!" elif subject == 'English': if float(self.eduEnglish) <= 9.0: self.eduEnglish = str(float(self.eduEnglish) + 1.0) elif float(self.eduEnglish) > 9.0 and float(self.eduEnglish) < 10.0: self.eduEnglish = '10.0' elif float(self.eduEnglish) == 10.0: print "Your Potato Head has gained the highest level of qualifications in this subject! It cannot learn any more!" elif subject == 'Maths': if float(self.eduMaths) <= 9.0: self.eduMaths = str(float(self.eduMaths) + 1.0) elif float(self.eduMaths) > 9.0 and float(self.eduMaths) < 10.0: self.eduMaths = '10.0' elif float(self.eduMaths) == 10.0: print "Your Potato Head has gained the highest level of qualifications in this subject! It cannot learn any more!" else: print "That subject is not an option..." print "Please choose either Science, English or Maths" self.time = str(int(self.time) + 1) #Pass time #Increase Health: def phGoToDoctor(self): self.health = '10.0' self.time = str(int(self.time) + 1) #Pass time #Sleep: Age, change stats: #(Time Passes) def phSleep(self): self.time = '0' #Resets time for next 'day' (can do more things next day) #Increase hunger: if float(self.hunger) <= 5.0: self.hunger = str(float(self.hunger) + 5.0) elif float(self.hunger) > 5.0: self.hunger = '10.0' #Lower Fitness: if float(self.fitness) >= 0.5: self.fitness = str(float(self.fitness) - 0.5) elif float(self.fitness) < 0.5: self.fitness = '0.0' #Lower Health: if float(self.health) >= 0.5: self.health = str(float(self.health) - 0.5) elif float(self.health) < 0.5: self.health = '0.0' #Lower Happiness: if float(self.happiness) >= 2.0: self.happiness = str(float(self.happiness) - 2.0) elif float(self.happiness) < 2.0: self.happiness = '0.0' #Increase the Potato Head's age: self.age = str(float(self.age) + 0.1) The game is still under development - There may be parts of modules that aren't complete, but I don't think they're causing the problem

    Read the article

  • R selecting duplicate rows

    - by Matt
    Okay, I'm fairly new to R and I've tried to search the documentation for what I need to do but here is the problem. I have a data.frame called heeds.data in the following form (some columns omitted for simplicity) eval.num, eval.count, ... fitness, fitness.mean, green.h.0, green.v.0, offset.0, green.h.1, green.v.1,...green.h.7, green.v.7, offset.7... And I have selected a row meeting the following criteria: best.fitness <- min(heeds.data$fitness.mean[heeds.data$eval.count = 10]) best.row <- heeds.data[heeds.data$fitness.mean == best.fitness] Now, what I want are all of the other rows with that have columns green.h.0 to offset.7 (a contiguous section of columns) equal to the best.row Basically I'm looking for rows that have some of the conditions the same as the "best" row. I thought I could just do this, heeds.best <- heeds.data$fitness[ heeds.data$green.h.0 == best.row$green.h.0 & ... ] But with 24 columns it seems like a stupid method. Looking for something a bit simpler with less manual typing. Thanks!

    Read the article

  • Using GA in GUI

    - by AlexT
    Sorry if this isn't clear as I'm writing this on a mobile device and I'm trying to make it quick. I've written a basic Genetic Algorithm with a binary encoding (genes) that builds a fitness value and evolves through several iterations using tournament selection, mutation and crossover. As a basic command-line example it seems to work. The problem I've got is with applying a genetic algorithm within a GUI as I am writing a maze-solving program that uses the GA to find a method through a maze. How do I turn my random binary encoded genes and fitness function (add all the binary values together) into a method to control a bot around a maze? I have built a basic GUI in Java consisting of a maze of labels (like a grid) with the available routes being in blue and the walls being in black. To reiterate my GA performs well and contains what any typical GA would (fitness method, get and set population, selection, crossover, etc) but now I need to plug it into a GUI to get my maze running. What needs to go where in order to get a bot that can move in different directions depending on what the GA says? Rough pseudocode would be great if possible As requested, an Individual is built using a separate class (Indiv), with all the main work being done in a Pop class. When a new individual is instantiated an array of ints represent the genes of said individual, with the genes being picked at random from a number between 0 and 1. The fitness function merely adds together the value of these genes and in the Pop class handles selection, mutation and crossover of two selected individuals. There's not much else to it, the command line program just shows evolution over n generations with the total fitness improving over each iteration. EDIT: It's starting to make a bit more sense now, although there are a few things that are bugging me... As Adamski has suggested I want to create an "Agent" with the options shown below. The problem I have is where the random bit string comes into play here. The agent knows where the walls are and has it laid out in a 4 bit string (i.e. 0111), but how does this affect the random 32 bit string? (i.e. 10001011011001001010011011010101) If I have the following maze (x is the start place, 2 is the goal, 1 is the wall): x 1 1 1 1 0 0 1 0 0 1 0 0 0 2 If I turn left I'm facing the wrong way and the agent will move completely off the maze if it moves forward. I assume that the first generation of the string will be completely random and it will evolve as the fitness grows but I don't get how the string will work within a maze. So, to get this straight... The fitness is the result of when the agent is able to move and is by a wall. The genes are a string of 32 bits, split into 16 sets of 2 bits to show the available actions and for the robot to move the two bits need to be passed with four bits from the agent showings its position near the walls. If the move is to go past a wall the move isn't made and it is deemed invalid and if the move is made and if a new wall is found then the fitness goes up. Is that right?

    Read the article

  • CakePHP: Interaction between different files/classes

    - by Alexx Hardt
    Hey, I'm cloning a commercial student management system. Students use the frontend to apply for lectures, uni staff can modify events (time, room, etc). The core of the app will be the algortihm which distributes the seats to students. I already asked about it here: How to implement a seat distribution algorithm for uni lectures Now, I found a class for that algorithm here: http://www.phpclasses.org/browse/file/10779.html I put the 'class GA' into app/vendors. I need to write a 'class Solution', which represents one object (a child, and later a parent for the evolutionary process). I'll also have to write functions mutate(), crossover() and fitness(). fitness calculates a score of a solution, based on if there are overbooked courses etc; crossover() is the crazy monkey sex function which produces a child from two parents, and mutate() modifies a child after crossover. Now, the fitness()-function needs to access a few related models, and their find()-functions. It evaluates a solution's fitness by checking e.g. if there are overbooked courses, or unfulfilled wishes, and penalizes that. Where would I put the ga.php, solution.php and the three functions? ga.php has to access the functions, but the functions have to access the models. I also don't want to call any App::import()'s from within the fitness()-function, because it gets called many thousand times when the algorithm runs. Hope someone can help me. Thanks in advance =)

    Read the article

  • Licenses that i can use for my works, web apps, desktop apps, wordpress themes etc

    - by jiewmeng
    I originally thought of creative commons when while reading a book about wordpress (professional wordpress), I learned that I should also specify that the product is provided ... WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE and they recommend GNU GPL. How do I write a license or select 1? btw, what does 'MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE' mean actually? Isn't without warranty enough?

    Read the article

  • 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 *.

    Read the article

  • I need to speed up a function. Should I use cython, ctypes, or something else?

    - by Peter Stewart
    I'm having a lot of fun learning Python by writing a genetic programming type of application. I've had some great advice from Torsten Marek, Paul Hankin and Alex Martelli on this site. The program has 4 main functions: generate (randomly) an expression tree. evaluate the fitness of the tree crossbreed mutate As all of generate, crossbreed and mutate call 'evaluate the fitness'. it is the busiest function and is the primary bottleneck speedwise. As is the nature of genetic algorithms, it has to search an immense solution space so the faster the better. I want to speed up each of these functions. I'll start with the fitness evaluator. My question is what is the best way to do this. I've been looking into cython, ctypes and 'linking and embedding'. They are all new to me and quite beyond me at the moment but I look forward to learning one and eventually all of them. The 'fitness function' needs to compare the value of the expression tree to the value of the target expression. So it will consist of a postfix evaluator which will read the tree in a postfix order. I have all the code in python. I need advice on which I should learn and use now: cython, ctypes or linking and embedding. Thank you.

    Read the article

  • why would i get a different views when called from different controller actions in asp.net-mvc

    - by ooo
    I have 2 different controller actions. As seen below, one calls the same view as the other one. The fitness version has a bunch of jquery ui tabs. public ActionResult FitnessByTab(string tab, DateTime entryDate) { return View("Fitness", GetFitnessVM(DateTime.Today.Date)); } public ActionResult Fitness() { return View(GetFitnessVM(DateTime.Today.Date)); } private FitnessVM GetFitnessVM(DateTime dt) { FitnessVM vm = new FitnessVM(); vm.Date = dt; // a bunch of other date that comes from a database return vm; } the issue is that on the action FitnessByTab() the tabs dont load correctly but on the Fitness() it loads fine. How could this be as my understanding is that they would be going through the same code path at that point. As you can see i am hard coded both to the same date to make sure its not a different date causing the issue. EDIT Issues has been solved. It was the relative referencing of all my links. I didn't get any issues until i used firebug that highlighted some missing references due to "../../" instead of Url.Content("

    Read the article

  • Heading in the Right Direction: Garmin Exadata adoption

    - by Javier Puerta
    A pioneer in global positioning system (GPS) navigation, Garmin International Inc. has been adopting Exadata to support the infrastructure that powered the company’s Oracle Advanced Supply Chain Planning, but also the company’s fitness segment, which provides customers with an online platform to store, retrieve, and interact with data captured using Garmin fitness products. The environment, which is built on an Oracle Database, processes approximately 40 million queries per week. Prior to using Oracle Exadata Database Machine, as the online offering grew in popularity, it began to face reliability issues that had negatively impacted the customer experience. We included the video testimonial in a previous post. Now you can find the a complete set of materials about this customer story Garmin Customer Reference Garmin video testimonial:  Garmin Consolidates on Exadata for 50% Performance Boost Profit Magazine article:  Heading in the Right Direction

    Read the article

  • How do I organize a GUI application for passing around events and for setting up reads from a shared resource

    - by Savanni D'Gerinel
    My tools involved here are GTK and Haskell. My questions are probably pretty trivial for anyone who has done significant GUI work, but I've been off in the equivalent of CGI applications for my whole career. I'm building an application that displays tabular data, displays the same data in a graph form, and has an edit field for both entering new data and for editing existing data. After asking about sharing resources, I decided that all of the data involved will be stored in an MVar so that every component can just read the current state from the MVar. All of that works, but now it is time for me to rearrange the application so that it can be interactive. With that in mind, I have three widgets: a TextView (for editing), a TreeView (for displaying the data), and a DrawingArea (for displaying the data as a graph). I THINK I need to do two things, and the core of my question is, are these the right things, or is there a better way. Thing the first: All event handlers, those functions that will be called any time a redisplay is needed, need to be written at a high level and then passed into the function that actually constructs the widget to begin with. For instance: drawStatData :: DrawingArea -> MVar Core.ST -> (Core.ST -> SetRepWorkout.WorkoutStore) -> IO () createStatView :: (DrawingArea -> IO ()) -> IO VBox createUI :: MVar Core.ST -> (Core.ST -> SetRepWorkout.WorkoutStore) -> IO HBox createUI storeMVar field = do graphs <- createStatView (\area -> drawStatData area storeMVar field) hbox <- hBoxNew False 10 boxPackStart hbox graphs PackNatural 0 return hbox In this case, createStatView builds up a VBox that contains a DrawingArea to graph the data and potentially other widgets. It attaches drawStatData to the realize and exposeEvent events for the DrawingArea. I would do something similar for the TreeView, but I am not completely sure what since I have not yet done it and what I am thinking of would involve replacing the TreeModel every time the TreeView needs to be updated. My alternative to the above would be... drawStatData :: DrawingArea -> MVar Core.ST -> (Core.ST -> SetRepWorkout.WorkoutStore) -> IO () createStatView :: IO (VBox, DrawingArea) ... but in this case, I would arrange createUI like so: createUI :: MVar Core.ST -> (Core.ST -> SetRepWorkout.WorkoutStore) -> IO HBox createUI storeMVar field = do (graphbox, graph) <- createStatView (\area -> drawStatData area storeMVar field) hbox <- hBoxNew False 10 boxPackStart hbox graphs PackNatural 0 on graph realize (drawStatData graph storeMVar field) on graph exposeEvent (do liftIO $ drawStatData graph storeMVar field return ()) return hbox I'm not sure which is better, but that does lead me to... Thing the second: it will be necessary for me to rig up an event system so that various events can send signals all the way to my widgets. I'm going to need a mediator of some kind to pass events around and to translate application-semantic events to the actual events that my widgets respond to. Is it better for me to pass my addressable widgets up the call stack to the level where the mediator lives, or to pass the mediator down the call stack and have the widgets register directly with it? So, in summary, my two questions: 1) pass widgets up the call stack to a global mediator, or pass the global mediator down and have the widgets register themselves to it? 2) pass my redraw functions to the builders and have the builders attach the redraw functions to the constructed widgets, or pass the constructed widgets back and have a higher level attach the redraw functions (and potentially link some widgets together)? Okay, and... 3) Books or wikis about GUI application architecture, preferably coherent architectures where people aren't arguing about minute details? The application in its current form (displays data but does not write data or allow for much interaction) is available at https://bitbucket.org/savannidgerinel/fitness . You can run the application by going to the root directory and typing runhaskell -isrc src/Main.hs data/ or... cabal build dist/build/fitness/fitness data/ You may need to install libraries, but cabal should tell you which ones.

    Read the article

  • 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.

    Read the article

  • R counting the occurance of similar rows of data frame

    - by Matt
    I have data in the following format called DF (this is just a made up simplified sample): eval.num, eval.count, fitness, fitness.mean, green.h.0, green.v.0, offset.0 random 1 1 1500 1500 100 120 40 232342 2 2 1000 1250 100 120 40 11843 3 3 1250 1250 100 120 40 981340234 4 4 1000 1187.5 100 120 40 4363453 5 1 2000 2000 200 100 40 345902 6 1 3000 3000 150 90 10 943 7 1 2000 2000 90 90 100 9304358 8 2 1800 1900 90 90 100 284333 However, the eval.count column is incorrect and I need to fix it. It should report the number of rows with the same values for (green.h.0, green.v.0, and offset.0) by only looking at the previous rows. The example above uses the expected values, but assume they are incorrect. How can I add a new column (say "count") which will count all previous rows which have the same values of the specified variables? I have gotten help on a similar problem of just selecting all rows with the same values for specified columns, so I supposed I could just write a loop around that, but it seems inefficient to me.

    Read the article

  • R counting the occurrences of similar rows of data frame

    - by Matt
    I have data in the following format called DF (this is just a made up simplified sample): eval.num, eval.count, fitness, fitness.mean, green.h.0, green.v.0, offset.0 random 1 1 1500 1500 100 120 40 232342 2 2 1000 1250 100 120 40 11843 3 3 1250 1250 100 120 40 981340234 4 4 1000 1187.5 100 120 40 4363453 5 1 2000 2000 200 100 40 345902 6 1 3000 3000 150 90 10 943 7 1 2000 2000 90 90 100 9304358 8 2 1800 1900 90 90 100 284333 However, the eval.count column is incorrect and I need to fix it. It should report the number of rows with the same values for (green.h.0, green.v.0, and offset.0) by only looking at the previous rows. The example above uses the expected values, but assume they are incorrect. How can I add a new column (say "count") which will count all previous rows which have the same values of the specified variables? I have gotten help on a similar problem of just selecting all rows with the same values for specified columns, so I supposed I could just write a loop around that, but it seems inefficient to me.

    Read the article

  • GA written in Java

    - by EnderMB
    I am attempting to write a Genetic Algorithm based on techniques I had picked up from the book "AI Techniques for Game Programmers" that uses a binary encoding and fitness proportionate selection (also known as roulette wheel selection) on the genes of the population that are randomly generated within the program in a two-dimensional array. I recently came across a piece of pseudocode and have tried to implement it, but have come across some problems with the specifics of what I need to be doing. I've checked a number of books and some open-source code and am still struggling to progress. I understand that I have to get the sum of the total fitness of the population, pick a random number between the sum and zero, then if the number is greater than the parents to overwrite it, but I am struggling with the implementation of these ideas. Any help in the implementation of these ideas would be very much appreciated as my Java is rusty.

    Read the article

  • Measuring the performance of classification algorithm

    - by Silver Dragon
    I've got a classification problem in my hand, which I'd like to address with a machine learning algorithm ( Bayes, or Markovian probably, the question is independent on the classifier to be used). Given a number of training instances, I'm looking for a way to measure the performance of an implemented classificator, with taking data overfitting problem into account. That is: given N[1..100] training samples, if I run the training algorithm on every one of the samples, and use this very same samples to measure fitness, it might stuck into a data overfitting problem -the classifier will know the exact answers for the training instances, without having much predictive power, rendering the fitness results useless. An obvious solution would be seperating the hand-tagged samples into training, and test samples; and I'd like to learn about methods selecting the statistically significant samples for training. White papers, book pointers, and PDFs much appreciated!

    Read the article

  • Java SE Embedded-Enabled Raspberry Pi Ice Bucket Challenge

    - by hinkmond
    Help fight ALS at: http://www.alsa.org/fight-als/ See: Java SE Embedded-Enabled Raspberry Pi Ice Bucket Challenge My Java SE Enabled Raspberry Pi accepts the nomination for the ALS Ice Bucket Challenge and I hereby nominate the Nest thermostat, the Fitbit fitness tracker, and Apple TV. Take the Ice Bucket Challenge. Help find the cure for ALS: http://www.alsa.org/fight-als/ice-bucket-challenge.html Hinkmond

    Read the article

  • java ioexception error=24 too many files open

    - by MattS
    I'm writing a genetic algorithm that needs to read/write lots of files. The fitness test for the GA is invoking a program called gradif, which takes a file as input and produces a file as output. Everything is working except when I make the population size and/or the total number of generations of the genetic algorithm too large. Then, after so many generations, I start getting this: java.io.FileNotFoundException: testfiles/GradifOut29 (Too many open files). (I get it repeatedly for many different files, the index 29 was just the one that came up first last time I ran it). It's strange because I'm not getting the error after the first or second generation, but after a significant amount of generations, which would suggest that each generation opens up more files that it doesn't close. But as far as I can tell I'm closing all of the files. The way the code is set up is the main() function is in the Population class, and the Population class contains an array of Individuals. Here's my code: Initial creation of input files (they're random access so that I could reuse the same file across multiple generations) files = new RandomAccessFile[popSize]; for(int i=0; i<popSize; i++){ files[i] = new RandomAccessFile("testfiles/GradifIn"+i, "rw"); } At the end of the entire program: for(int i=0; i<individuals.length; i++){ files[i].close(); } Inside the Individual's fitness test: FileInputStream fin = new FileInputStream("testfiles/GradifIn"+index); FileOutputStream fout = new FileOutputStream("testfiles/GradifOut"+index); Process process = Runtime.getRuntime().exec ("./gradif"); OutputStream stdin = process.getOutputStream(); InputStream stdout = process.getInputStream(); Then, later.... try{ fin.close(); fout.close(); stdin.close(); stdout.close(); process.getErrorStream().close(); }catch (IOException ioe){ ioe.printStackTrace(); } Then, afterwards, I append an 'END' to the files to make parsing them easier. FileWriter writer = new FileWriter("testfiles/GradifOut"+index, true); writer.write("END"); try{ writer.close(); }catch(IOException ioe){ ioe.printStackTrace(); } My redirection of stdin and stdout for gradif are from this answer. I tried using the try{close()}catch{} syntax to see if there was a problem with closing any of the files (there wasn't), and I got that from this answer. It should also be noted that the Individuals' fitness tests run concurrently. UPDATE: I've actually been able to narrow it down to the exec() call. In my most recent run, I first ran in to trouble at generation 733 (with a population size of 100). Why are the earlier generations fine? I don't understand why, if there's no leaking, the algorithm should be able to pass earlier generations but fail on later generations. And if there is leaking, then where is it coming from? UPDATE2: In trying to figure out what's going on here, I would like to be able to see (preferably in real-time) how many files the JVM has open at any given point. Is there an easy way to do that?

    Read the article

1 2 3 4 5  | Next Page >