def q_one_two(): c1 = c2 = 2.05 w = 0.5 swarm = [Particle(x_y_range, x_y_range) for i in range(100)] velocity_position_fn = inertia_velocity(x_y_range, x_y_range, w, c1, c2) avg_fit, best_fit, g_best = simulation( termination_condition=termination_condition(iterations), swarm=swarm, fitness_function=six_hump_camelback, velocity_position_update=velocity_position_fn) test = "Inertia Weight PSO" title = f"Fitness vs Iterations: {test}" data = [("Average Fitness", avg_fit)] data.append(("Best Fitness", best_fit)) graph(title, data) print(test) print(f"\t c1: {c1}") print(f"\t c2: {c2}") print(f"\t w: {w}") print(f"\t iterations: {iterations}") print(f"\t particles: {particle_count}") print(f"\t best fitness: {six_hump_camelback(g_best[0],g_best[1])}") print(f"\t best x,y: {g_best[0]} {g_best[1]}")
def main(filename, iterations, eva, count, processCt): print "Retrieving dataset..." data = getData(filename) intervals = [] intervals.append(1) for x in range(1,count+1): intervals.append(x*(eva/count)) processPool = mp.Pool(processes=processCt) print "Beginning simulations..." geneticJobs = [processPool.apply_async(simulate, args=(data,eva,intervals)) for x in range (iterations)] randomJobs = [processPool.apply_async(randomSearch, args=(data,eva,intervals)) for x in range (iterations)] res = [g.get() for g in geneticJobs] randRes = [r.get() for r in randomJobs] print "Simulation Complete" print "Best Equation Found: " print findBestEquation(res,randRes) graph(selectFirst(res),selectFirst(randRes),intervals)
def main(name,iterations,eva,count,processCt): data = getData(name) intervals = [] intervals.append(1) for x in range(1,count+1): intervals.append(x*(eva/count)) evaluations = eva travWorkers = mp.Pool(processes=processCt) genPmxJobs = [travWorkers.apply_async(genetic, args=(data,evaluations,intervals,False)) for x in range(iterations)] genHalfJobs = [travWorkers.apply_async(genetic, args=(data,evaluations,intervals,True)) for x in range(iterations)] stochJobs = [travWorkers.apply_async(stochClimb, args=(data,evaluations,intervals)) for x in range(iterations)] randJobs= [travWorkers.apply_async(randSearch, args=(data,evaluations,intervals)) for x in range(iterations)] # Close the pool and wait until all processes are finished travWorkers.close() travWorkers.join() # Retrieve process outputs genPmx = [t.get() for t in genPmxJobs] genHalf = [t.get() for t in genHalfJobs] stoch = [t.get() for t in stochJobs] rand = [t.get() for t in randJobs] # Display graph of simulation results graph(genPmx,genHalf,stoch,rand,intervals)
def __init__(self, **kwargs): super(mainScreenLayout, self).__init__(**kwargs) warnings.filterwarnings('ignore') self.tree = tr.TreeRunner() self.gender = gd.video() if self.gender == "man": self.speak = sp.SpeechTextProcessing(0) else: self.speak = sp.SpeechTextProcessing(1) # user details self.name = None self.age = None self.symptoms = None self.disease = None self.medicine = None self.language = None self.message = None self.data = list() self.index = 0 self.pdf = None self.medication = None self.DB = None self.confidence = 0 self.plt = pt.graph() self.questions = [ "self.start1()", "self.start2()", "self.start3()", "self.predictDisease()", "self.predictMedication()", "self.printPrescription()", "self.startExportingToDatabase()" ]
def main(): print("***************************************************") print("Anusha Kondapalli") print("Program 3 - Elliptical curve") print("***************************************************") parser = argparse.ArgumentParser() parser.add_argument("-a", dest="a", help="Part 'a' of elliptical curve: y^2 = x^3 + ax + b") parser.add_argument("-b", dest="b", help="Part 'b' of elliptical curve: y^2 = x^3 + ax + b") parser.add_argument("-x1", dest="x1", help="") parser.add_argument("-y1", dest="y1", help="") parser.add_argument("-x2", dest="x2", help="") parser.add_argument("-y2", dest="y2", help="") args = parser.parse_args() # changing the given input string to integers a = fractions.Fraction(args.a) b = fractions.Fraction(args.b) x1 = fractions.Fraction(args.x1) y1 = fractions.Fraction(args.y1) x2 = fractions.Fraction(args.x2) y2 = fractions.Fraction(args.y2) # checking whether the given points are correct or not # 1.Both points are on the curve or not if (pow(y1, 2) == pow(x1, 3) + a * x1 + b) and (pow(y2, 2) == pow(x2, 3) + a * x2 + b): # if y coordinates are different if y1 != y2: m = (y2 - y1) / (x2 - x1) # if y coordinates are same else: m = ((3 * (x1 ** 2)) + a) / (2 * y1) x3 = pow(m, 2) - x1 - x2 y3 = (m) * (x3 - x2) + y2 x = fractions.Fraction(x3).limit_denominator(1000) y = fractions.Fraction(y3).limit_denominator(1000) print("x3=", x, "y3=", y) plot.graph(a, b, x1, y1, x2, y2, x3, y3) else: print("Error Error") print("Given points should be on given curve ") print("***************************************************")
generation_count = 1024 * 2 survivor_count = 50 p_m = 0.00 k = 7 def init_population(num_individuals, max_depth): def f(): return [ Individual(max_depth=1 + (i % max_depth)) for i in range(num_individuals) ] return f best_per_gen, best_individual = simulation( generation_count=generation_count, init_population=init_population(num_individuals=num_individuals, max_depth=max_depth), parent_selection=parent_selection(k=k), variation=variation(p_m=p_m), survivor_selection=survivor_selection(population_size=num_individuals, survival_count=survivor_count), best_of_generation=best_of_generation, debug=True) best_individual.to_diagram() graph(title="Best Fitness vs Generation eleven multiplexer", data=[("Eleven Multiplexer", best_per_gen)])
from plot import graph ddqnr1 = [] varTitle = "DQN T3: Var100 vs Episode" medTitle = "DQN T3: Med100 vs Episode" rewardTitle = "DQN T3: Rewards vs Episode" fileName = './data/dqn/dqnr3.csv' algo = "DQN" with open(fileName, 'r') as csvfile: reader = csv.reader(csvfile, delimiter=',', quotechar='|') for row in reader: ddqnr1.append(np.array(row).astype(np.float)) y = [np.sum(row) for row in ddqnr1] x = list(range(len(y))) graph(rewardTitle, [(algo, y)]) y2 = [np.var(y[i - 100:i]) for i in range(100, len(ddqnr1))] x2 = list(range(100, len(y2) + 100)) graph(varTitle, [(algo, y2)]) y3 = [np.median(y[i - 100:i]) for i in range(100, len(ddqnr1))] x3 = list(range(100, len(y3) + 100)) graph(medTitle, [(algo, y3)]) # dqnr1 = [] # with open('./data/dqn/dqnr1.csv','r') as csvfile: # reader = csv.reader(csvfile, delimiter=',', quotechar='|') # for row in reader: # dqnr1.append(np.array(row).astype(np.float)) # y = [np.sum(row) for row in dqnr1]
import plot as pl pl.graph(['x**2', '2*x'], 0, 30, 1)
for r in range(num_runs): print("1000 episode run : ", r) rlglue.rl_init() for e in range(num_episodes): rlglue.rl_episode(max_eps_steps) steps[r, e] = rlglue.num_ep_steps() # get the list of value functions [X,Y,Z] represents position, velocity, state-value Return = rlglue.rl_agent_message(1) return Return if __name__ == "__main__": # first graph method question_1() # generate 2d graph using provided plot file graph() #initialize a list of all state values Z = [0] * 2500 X = [0] * 2500 Y = [0] * 2500 #generate data for the 1000run 3d graph a = question_3() #seperate data for i in range(2500): Z[i] = -a[i][2] X[i] = a[i][0] Y[i] = a[i][1] # reshapte data so it fits in a 3d form Z = np.array(Z).reshape((50, 50))
def main(args, config): originalPath= os.getcwd() fasta_file = os.path.join(os.getcwd(),args.fasta_file) profilePath = os.path.join(os.getcwd(),'profiles') wDir = os.path.join(os.getcwd(), datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')) os.mkdir(wDir) os.chdir(wDir) #add a check module fileWork = os.path.join(os.getcwd(),'final_sequences.fasta') validFile = Check(fasta_file, args, config) validFile.lenFasta() validFile.reverseFile() validFile.makeLog() if int(args.mode) == 1: basicMode(config, fasta_file, profilePath) if int(args.mode) == 2: newConfig = config.copy() dfCompare = os.path.join(originalPath, args.compare) xList = [] yList = [] countResults = OrderedDict() for i,j in config.items(): if type(j) is list: label = i valStart= j[0] valStop= j[1] valStep= j[2] k = valStart while k <= float(valStop): newConfig[i] = k folder = i + str(k) subDir = os.path.join(wDir, folder) os.mkdir(subDir) os.chdir(subDir) if i == 'win_length': newConfig['win_step'] = k configFile(newConfig) basicMode(newConfig, fasta_file, profilePath) result, trueEvents, times, baseEvents, seqDict = compareResults(dfCompare) xList.append(k) yList.append(result) countResults[k] = times baseReport(countResults, label, baseEvents, config['win_length'], seqDict) k = k + valStep os.chdir('..') graph(xList, yList, label, trueEvents) #print xList #print yList #print countResults baseReport(countResults, label, baseEvents, config['win_length'], seqDict)