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)
