def call_random(i,
x,
training_data,
testing_data,
fold,
goal="Max",
eval_time=3600,
lifes=5):
# test_data = testing_data.values
random_optimizer = Random(NP=10,
Goal=goal,
eval_time=eval_time,
num_lifes=lifes)
v, num_evals, tuning_time = random_optimizer.solve(
process, OrderedDict(param_grid[i]['learners_para_dic']),
param_grid[i]['learners_para_bounds'],
param_grid[i]['learners_para_categories'], param_grid[i]['model'], x,
training_data, fold)
params = v.ind.values()
start_time = time.time()
predicted_tune = param_grid[i]['model'](training_data.iloc[:, :-1],
training_data.iloc[:, -1],
testing_data.iloc[:, :-1], params)
predicted_default = param_grid[i]['model'](training_data.iloc[:, :-1],
training_data.iloc[:, -1],
testing_data.iloc[:, :-1], None)
val_tune = evaluation(x, predicted_tune, testing_data.iloc[:, -1],
testing_data.iloc[:, :-1])
val_predicted = evaluation(x, predicted_default, testing_data.iloc[:, -1],
testing_data.iloc[:, :-1])
print("For measure %s: default=%s, predicted=%s" %
(x, val_predicted, val_tune))
tuning_time += time.time() - start_time
# print(val_tune, params, num_evals, tuning_time)
return val_tune, params, num_evals, tuning_time
def test():
""" Testing stuff to see it works properly """
a = Random()
b = Sequential()
a.enter_name("Player Random")
b.enter_name("Player Sequential")
game = SingleGame(a, b)
game.perform_game()
game.show_result()
def calculer_next(previous, next): rand_nbr = Random() tab_to_return = [] for i in range(0, (2**16)+1): for j in range(0,2): t = 0 t = j t = t << 15 | previous t = t << 16 | i rand_nbr.srand(t) if (verif_next(rand_nbr.rand(), next)): tab_to_return.append(t) return tab_to_return
def compare_rate(req):
parameters = get_parameters(req)
params = {
"bank1": parameters['Australian_Banks'],
"bank2": parameters['Australian_Banks1'],
"mortgage": parameters['Mortgage_types'],
"year_fixed": parameters['year_fixed'],
"ownership_status": parameters['ownership_status']
}
# TODO: Check if bank1 and bank2 are the same. if the same bank,
# tell user that he/she put in the same name of bank.
best_rate_bank1 = get_best_rate(params["bank1"] or None, params["mortgage"]
or None, params["year_fixed"] or None,
params['ownership_status'] or None)
best_rate_bank2 = get_best_rate(
params["bank2"] or None, params["mortgage"]
or best_rate_bank1['repayment_type'], params["year_fixed"]
or best_rate_bank1['year_fixed'], params['ownership_status']
or best_rate_bank1['ownership_type'])
response = Random.compare_bank(best_rate_bank1, best_rate_bank2)
return response
def Cookie_Derp(Seedd, Ratee, Nmeass, Nexpp, OutputFileNamee):
# default seed
Seed = 5555
# default rate parameter for cookie disappearance (rate =1, means a cookies per day)
Rate = 1
# default number of time measurements (time to next missing cookie) - per experiment
Nmeas = 1
# default number of experiments
Nexp = 1
# output file defaults
doOutputFile = False
# class instance of our Random class using seed
Seed = int(Seedd)
Rate = float(Ratee)
Nmeas = int(Nmeass)
Nexp = int(Nexpp)
random = Random(Seed)
doOutputFile = True
if doOutputFile == True:
OutputFileName = OutputFileNamee
else:
OutputFileName = "Cookie.txt"
if doOutputFile:
outfile = open(OutputFileName, 'w+')
outfile.write(str(Rate)+"\n")
for e in range(0,Nexp):
for t in range(0,Nmeas):
outfile.write(str(random.Exponential(Rate))+" ")
outfile.write(" \n")
outfile.close()
else:
print(Rate)
for e in range(0,Nexp):
for t in range(0,Nmeas):
print(random.Exponential(Rate), end=' ')
print(" ")
def setResult(self, value):
"""
Schreibt das Ergebnis des Wurfs in die GUI. Dabei wird auch je nach Erfolgsqualität bei dem dargestellten Würfel eine andere Augenzahl gezeigt.
"""
self.ui.statusBar.showMessage(self.tr("Result of diceroll is displayed."))
if (value == DieResult.dramaticFailure):
self.ui.label_resultText.setText(self.tr("Dramatic Failure"));
self.ui.label_result.setPixmap(QPixmap(":/icons/actions/cnrdelete-all1.png"));
self.displayDice(1)
elif (value == DieResult.failure):
self.ui.label_resultText.setText(self.tr("Failure"));
self.ui.label_result.setPixmap(QPixmap(":/icons/actions/fileclose.png"));
self.displayDice(Random.random(2, 7))
elif (value == DieResult.success):
self.ui.label_resultText.setText(self.tr("Success"));
self.ui.label_result.setPixmap(QPixmap(":/icons/actions/ok.png"));
self.displayDice(Random.random(8, 9))
else:
self.ui.label_resultText.setText(self.tr("Exceptional Success"));
self.ui.label_result.setPixmap(QPixmap(":/icons/actions/bookmark.png"));
self.displayDice(0)
def rate_followup(req):
parameters = get_parameters(req)
params = {
"bank": parameters['Australian_Banks'],
"mortgage": parameters['Mortgage_types'],
"fixed_year": parameters['year_fixed'],
"ownership_status": parameters['ownership_status']
}
best_rate = get_best_rate(params['bank'] or None, params['mortgage']
or None, params['fixed_year'] or None,
params['ownership_status'] or None)
response = Random.best_bank(params, best_rate)
return response
def displayDice(self, value=None): """ @todo Der Würfel kann mehrmals in Folge das selbe Ergebnis anzeigen, was dazu führt, daß der Bildablauf zu stocken scheint. """ if (value == None): dieValue = Random.random(10)-1 else: dieValue = value for item in self.scene.items(): self.scene.removeItem(item) self.scene.addItem(self.dice[dieValue]) self.view.setSceneRect(self.scene.itemsBoundingRect()) self.view.fitInView(self.dice[dieValue])
def compare_followup(req):
parameters = get_parameters(req)
params = {
"bank": parameters['Australian_Banks'],
"mortgage": parameters['Mortgage_types'],
"fixed_year": parameters['year_fixed'],
"ownership_status": parameters['ownership_status']
}
context = get_contexts(req, pos=1)
old_rate = context['parameters']['rate']
new_best_rate = get_best_rate(params['bank'] or None, params['mortgage']
or None, params['fixed_year'] or None,
params['ownership_status'] or None)
response = Random.best_rate_compare_followup(old_rate, new_best_rate)
return response
def best_rate(req):
parameters = get_parameters(req)
params = {
"bank": parameters['Australian_Banks'],
"mortgage": parameters['Mortgage_types'],
"fixed_year": parameters['year_fixed'],
"ownership_status": parameters['ownership_status']
}
best_rate = get_best_rate(params['bank'] or None, params['mortgage']
or None, params['fixed_year'] or None,
params['ownership_status'] or None)
response = Random.best_bank(params, best_rate)
# TODO: Static Output Contexts, there should be a better way of doing it.
output_contexts = [{
"name":
"projects/ron-anpelr/agent/sessions/e1dc138a-9f22-7941-80de-8998ede6221b/contexts/showrate-followup",
"lifespanCount": 5,
"parameters": {
"fixed_year": best_rate['year_fixed'],
"Australian_Banks": best_rate['bank_name'],
"repayment_type": best_rate['repayment_type'],
"ownership_status": best_rate['ownership_type'],
"rate": best_rate['interest_rate']
}
}, {
"name":
"projects/ron-anpelr/agent/sessions/e1dc138a-9f22-7941-80de-8998ede6221b/contexts/bestrate-followup",
"lifespanCount": 5,
"parameters": {
"rate": best_rate['interest_rate']
}
}]
return response, output_contexts
def reproduceAndMutate(parentA, parentB, AMD):
# // Now go through Parents parmaters and exchange gentic info
# // Also mutate select gene within the same loop
# // no need having a seperte mutate function that loops through paramter matrices again
#
# // Loops can use child dimensions as all networks have fixed same topologies in this
child = Agent(initEmpty=True,
xPos=AMD[2],
yPos=AMD[3],
xPos_range=AMD[4],
yPos_range=AMD[5],
vertical_fuel_depletion_rate=AMD[0],
horizontal_fuel_depletion_rate=AMD[1],
color=parentA.color)
child.functional_system.name = parentA.functional_system.name
for i in range(len(child.functional_system.layers)):
rowsW = child.functional_system.layers[i]['weights'].shape[0]
colsW = child.functional_system.layers[i]['weights'].shape[1]
for j in range(rowsW):
for k in range(colsW):
if np.random.rand() < GenticAlgorithm.crossOverRate:
# Use Parent A gene
child.functional_system.layers[i]['weights'][j][
k] = parentA.functional_system.layers[i][
'weights'][j][k]
else:
child.functional_system.layers[i]['weights'][j][
k] = parentB.functional_system.layers[i][
'weights'][j][k]
if np.random.rand() < GenticAlgorithm.mutateRate:
child.functional_system.layers[i]['weights'][j][
k] += Random.gaussian_distribution(mean=0,
sigma=0,
samples=1)
# Reproduce and Mutate Baiases
rowsB = child.functional_system.layers[i]['biases'].shape[0]
colsB = child.functional_system.layers[i]['biases'].shape[1]
for j in range(rowsB):
for k in range(colsB):
if np.random.rand() < GenticAlgorithm.crossOverRate:
# Use Parent A gene
child.functional_system.layers[i]['biases'][j][
k] = parentA.functional_system.layers[i]['biases'][
j][k]
else:
child.functional_system.layers[i]['biases'][j][
k] = parentB.functional_system.layers[i]['biases'][
j][k]
if np.random.rand() < GenticAlgorithm.mutateRate:
child.functional_system.layers[i]['biases'][j][
k] += Random.gaussian_distribution(mean=0,
sigma=0,
samples=1)
return child
p = sys.argv.index('-Ntoss')
Nt = int(sys.argv[p + 1])
if Nt > 0:
Ntoss = Nt
if '-Nexp' in sys.argv:
p = sys.argv.index('-Nexp')
Ne = int(sys.argv[p + 1])
if Ne > 0:
Nexp = Ne
if '-output' in sys.argv:
p = sys.argv.index('-output')
OutputFileName = sys.argv[p + 1]
doOutputFile = True
# class instance of our Random class using seed
random = Random(seed)
if doOutputFile:
outfile = open(OutputFileName, 'w')
for e in range(0, Nexp):
for t in range(0, Ntoss):
outfile.write(
str(random.random_triangular(prob, 0.6, 0.5)) + " ")
outfile.write(" \n")
outfile.close()
else:
for e in range(0, Nexp):
for t in range(0, Ntoss):
print(random.random_triangular(prob, 0.6, 0.5), end=' ')
print(" ")
#! /usr/bin/env python
from Random import Random
import numpy as np
import matplotlib.pyplot as plt
random_number = Random(77777777)
myx = []
for x in range(1, 10000):
faces = random_number.Category6()
myx.append(faces)
# create histogram of our data
plt.figure()
plt.hist(myx,
6,
density=True,
facecolor='green',
histtype="barstacked",
alpha=0.75)
# plot formating options
plt.xlabel('Dice faces')
plt.ylabel('Probability', fontweight="bold", fontsize="17")
plt.title('Categorical Distribution', fontweight="bold", fontsize="17")
plt.grid(True, color='r')
# save and show figure
plt.savefig("Dice.png")
#plt.show()
p = sys.argv.index('-seed')
seed = sys.argv[p+1]
if '-trial_b' in sys.argv:
p = sys.argv.index('-trial_b')
trial_b = sys.argv[p+1]
trial_b = int(sys.argv[p+1])
if '-trial_n' in sys.argv:
p = sys.argv.index('-trial_n')
trial_n = sys.argv[p+1]
trial_n = int(sys.argv[p+1])
#class instance for random class
random_number = Random(seed)
myx = []
for x in range(0,trial_b):
faces = random_number.Category6()
myx.append(int(faces))
# writing array in the file
f = open("biased_dice.txt", "w")
np.savetxt(f,myx)
f.close()
# making a dict and setting counter for each number as 0 and looping and adding # count
counter = {1:0,2:0,3:0,4:0,5:0,6:0}
p = sys.argv.index('-outputH1')
OutputFileNameH1 = sys.argv[p + 1]
doOutputFileH1 = True
#create p6 using the probability inputs, if H0
p6 = 1 - (p1 + p2 + p3 + p4 + p5)
#entering probabilities into list format
prob_list = [p1, p2, p3, p4, p5, p6]
prob1a_list = [p1a_1, p1a_2, p1a_3, p1a_4, p1a_5, p1a_6]
prob1b_list = [p1b_1, p1b_2, p1b_3, p1b_4, p1b_5, p1b_6]
print('probabilities of unfair die one: ', prob1a_list)
print('probabilities of unfair die two: ', prob1b_list)
# class instance of our Random class using seed
random = Random(seed)
a = 7
b = 3
#plot beta distribution
# x = np.arange (0.01, 1, 0.01)
# y = scipybeta.pdf(x,a,b)
# plt.figure()
# plt.plot(x,y,label = r'$\alpha =$' + str(a) +', '+ r'$\beta =$' + str(b))
# plt.axvline(0.5,color='r',linestyle='--')
# plt.text(0.2,1.5,'P1',fontsize=20)
# plt.text(0.7,1.5,'P2',fontsize=20)
# plt.xlabel('x',fontsize=16)
# plt.ylabel(r'Beta($\alpha, \beta$)',fontsize=16)
if __name__ == "__main__":
"""
Main du programme rand
"""
login = sys.argv[1]
if "-debug" in sys.argv:
debug_mode = True
else:
debug_mode = False
server = Server("http://pac.bouillaguet.info/TP3/rand/")
rand_nbr = Random()
inverse_modulaire = mod_inv(1103515245, 2**32)
IV = server.query('/challenge/' + login)
if debug_mode:
print("IV[0] : {0}".format(IV["IV"][0]))
print("IV[1] : {0}".format(IV["IV"][1]))
#1er envoi
k3_possibles = calculer_next(IV["IV"][0], IV["IV"][1])
if debug_mode:
print("Liste des k3 possibles: ", end = "")
print("{0}".format(k3_possibles))
