# NeuralNetwork. Build a bayesian Self-Organizing Map. Example II from jhplot import * from java.awt import Color from java.util import Random c1 = HPlot("Canvas") c1.setGTitle("Bayesian Self-Organizing Map") c1.visible() c1.setAutoRange() h1 = H1D("Data", 20, -100.0, 300.0) r = Random() for i in range(2000): h1.fill(100 + r.nextGaussian() * 100) p1d = P1D(h1, 0, 0) p1d.setErrToZero(1) bs = HBsom() bs.setNPoints(30) bs.setData(p1d) bs.run() result = bs.getResult() result.setStyle("pl") result.setColor(Color.blue) c1.draw(p1d) c1.draw(result)
Commandline parameter(s): first parameter must be the ARFF file """ # check commandline parameters if (not (len(sys.argv) == 2)): print "Usage: supervised.py <ARFF-file>" sys.exit() # load data file print "Loading data..." datafile = FileReader(sys.argv[1] + ".arff") data = Instances(datafile) rand = Random() # seed from the system time data.randomize(rand) # randomize data with number generator # open output files bufsize=0 datafile = "data/plot/" + str(os.path.splitext(os.path.basename(__file__))[0]) + "_" + \ str(os.path.splitext(os.path.basename(sys.argv[1]))[0]) + "_rmse.csv" file=open(datafile, 'w', bufsize) # open a file for rmse data file.write("iterations,rmse\n") wallfile = "data/plot/" + str(os.path.splitext(os.path.basename(__file__))[0]) + "_" + \ str(os.path.splitext(os.path.basename(sys.argv[1]))[0]) + "_wall.csv" filewall=open(wallfile, 'w', bufsize) # open a file for wall clock time filewall.write("epochs,seconds\n")
from java.lang import Math from java.util import Random rnd = Random() # Active le mode verbeux debug = 1 ################################################################## class Own: def __init__(this): this._target = None this._base = 0 this._direction_ttl = 0 this._dest_x = None this._dest_y = None def toggleSens(this): this.sens *= -1 def getSens(this): return this.sens def getTeam(this): return self.getTeam() def setTarget(this, x, y): if this._target: this._target.seen(x, y) else:
import opt.RandomizedHillClimbing as RandomizedHillClimbing import opt.GenericHillClimbingProblem as GenericHillClimbingProblem import dist.DiscretePermutationDistribution as DiscretePermutationDistribution import dist.DiscreteUniformDistribution as DiscreteUniformDistribution import dist.DiscreteDependencyTree as DiscreteDependencyTree import java.util.Random as Random import time from array import array from itertools import product from time import clock # Adapted from https://github.com/JonathanTay/CS-7641-assignment-2/blob/master/tsp.py # set N value. This is the number of points N = 100 random = Random() maxIters = 3001 numTrials = 5 OUTPUT_DIRECTORY = './output' base.make_dirs(OUTPUT_DIRECTORY) points = [[0 for x in xrange(2)] for x in xrange(N)] for i in range(0, len(points)): points[i][0] = random.nextDouble() points[i][1] = random.nextDouble() outfile = OUTPUT_DIRECTORY + '/TSP/TSP_{}_{}_LOG.csv' ef = TravelingSalesmanRouteEvaluationFunction(points) odd = DiscretePermutationDistribution(N) nf = SwapNeighbor()
def knapsackfunc(NUM_ITEMS, iterations): rhcMult = 600 saMult = 600 gaMult = 4 mimicMult = 3 # Random number generator */ random = Random() # The number of items #NUM_ITEMS = 40 # The number of copies each COPIES_EACH = 4 # The maximum weight for a single element MAX_WEIGHT = 50 # The maximum volume for a single element MAX_VOLUME = 50 # The volume of the knapsack KNAPSACK_VOLUME = MAX_VOLUME * NUM_ITEMS * COPIES_EACH * .4 # create copies fill = [COPIES_EACH] * NUM_ITEMS copies = array('i', fill) # create weights and volumes fill = [0] * NUM_ITEMS weights = array('d', fill) volumes = array('d', fill) for i in range(0, NUM_ITEMS): weights[i] = random.nextDouble() * MAX_WEIGHT volumes[i] = random.nextDouble() * MAX_VOLUME # create range fill = [COPIES_EACH + 1] * NUM_ITEMS ranges = array('i', fill) ef = KnapsackEvaluationFunction(weights, volumes, KNAPSACK_VOLUME, copies) odd = DiscreteUniformDistribution(ranges) nf = DiscreteChangeOneNeighbor(ranges) mf = DiscreteChangeOneMutation(ranges) cf = UniformCrossOver() df = DiscreteDependencyTree(.1, ranges) hcp = GenericHillClimbingProblem(ef, odd, nf) gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf) pop = GenericProbabilisticOptimizationProblem(ef, odd, df) optimalOut = [] timeOut = [] evalsOut = [] for niter in iterations: iterOptimalOut = [NUM_ITEMS, niter] iterTimeOut = [NUM_ITEMS, niter] iterEvals = [NUM_ITEMS, niter] start = time.time() rhc = RandomizedHillClimbing(hcp) fit = FixedIterationTrainer(rhc, niter*rhcMult) fit.train() end = time.time() rhcOptimal = ef.value(rhc.getOptimal()) rhcTime = end-start print "RHC optimum: " + str(rhcOptimal) print "RHC time: " + str(rhcTime) iterOptimalOut.append(rhcOptimal) iterTimeOut.append(rhcTime) functionEvals = ef.getNumEvals() ef.zeroEvals() iterEvals.append(functionEvals) start = time.time() sa = SimulatedAnnealing(100, .95, hcp) fit = FixedIterationTrainer(sa, niter*saMult) fit.train() end = time.time() saOptimal = ef.value(sa.getOptimal()) saTime = end-start print "SA optimum: " + str(saOptimal) print "SA time: " + str(saTime) iterOptimalOut.append(saOptimal) iterTimeOut.append(saTime) functionEvals = ef.getNumEvals() ef.zeroEvals() iterEvals.append(functionEvals) start = time.time() ga = StandardGeneticAlgorithm(200, 150, 25, gap) fit = FixedIterationTrainer(ga, niter*gaMult) fit.train() end = time.time() gaOptimal = ef.value(ga.getOptimal()) gaTime = end - start print "GA optimum: " + str(gaOptimal) print "GA time: " + str(gaTime) iterOptimalOut.append(gaOptimal) iterTimeOut.append(gaTime) functionEvals = ef.getNumEvals() ef.zeroEvals() iterEvals.append(functionEvals) start = time.time() mimic = MIMIC(200, 100, pop) fit = FixedIterationTrainer(mimic, niter*mimicMult) fit.train() end = time.time() mimicOptimal = ef.value(mimic.getOptimal()) mimicTime = end - start print "MIMIC optimum: " + str(mimicOptimal) print "MIMIC time: " + str(mimicTime) iterOptimalOut.append(mimicOptimal) iterTimeOut.append(mimicTime) functionEvals = ef.getNumEvals() ef.zeroEvals() iterEvals.append(functionEvals) optimalOut.append(iterOptimalOut) timeOut.append(iterTimeOut) evalsOut.append(iterEvals) return [optimalOut, timeOut, evalsOut]
for t in self.obstacles.items(): if (t[0].getDistance() < n.getDistance()): n = t[0] def nearest_foe(): n = self.attackers.items()[0][0] for t in self.attackers.items(): if (t[0].getDistance() < n.getDistance()): n = t[0] # (1) Workaround pour un bug qui n'existe peut-être plus def _ennemy_count(self): return (len(self.attackers), len(self.explorers), len(self.homes)) rnd = Random() # Generateur aleatoire groupName = 'sAMetmAX-' + self.getTeam() #journaliser les messages : 0/1 log = 1 #println sur la console : 0/1 console = 1 #cette globale est initialisee a chaque tour a #une liste de percepts vus par l'agent percepts = PerceptBag(self.getTeam()) #celle ci contient ce qu'il faut pour calculer #le mouvement courant #methode qui filtre l'affichage def toterm(msg):
def _longRandomPrime(): BigInteger prime = BigInteger.probablePrime(31, new Random()) return prime.longValue()
fp.setColor(50) # membrane fp.setLineWidth(3) for roi in rois: fp.draw(roi) fp.setColor(90) # oblique membrane fp.setLineWidth(5) roi_oblique = OvalRoi(w / 2 + w / 8, h / 2 + h / 8, w / 4, h / 4) fp.draw(roi_oblique) # Add noise # 1. Vesicles fp.setLineWidth(1) random = Random(67779) for i in xrange(150): x = random.nextFloat() * (w - 1) y = random.nextFloat() * (h - 1) fp.draw(OvalRoi(x, y, 4, 4)) fp.setRoi(None) # 2. blur sigma = 1.0 GaussianBlur().blurFloat(fp, sigma, sigma, 0.02) # 3. shot noise fp.noise(25.0) fp.setMinAndMax(0, 255) imp.show()
// User has not entered a valid input. Prompt again. return getMove(); } public boolean playAgain() { System.out.print("Do you want to play again? "); String userInput = inputScanner.nextLine(); userInput = userInput.toUpperCase(); return userInput.charAt(0) == 'Y'; } } private class Computer { public Move getMove() { Move[] moves = Move.values(); Random random = new Random(); int index = random.nextInt(moves.length); return moves[index]; } } public RockPaperScissors() { user = new User(); computer = new Computer(); userScore = 0; computerScore = 0; numberOfGames = 0; } public void startGame() { System.out.println("ROCK, PAPER, SCISSORS!");