def main():
file = 'example/nand.pm' # Selection du fichier nand.pmdans le dossier example
if len(argv) > 1:
file = argv[1]
pmc = myparse(file)
num_of_run = 10
length_of_run = 100
estimated_reward, estimated_variance = simu(
length_of_run, num_of_run,
pmc) # ,{pmc.param[0]:0.02,pmc.param[1]:0.9})
print("\nsimu OK\n")
print("random valuation:")
random_valuation = {}
for param in pmc.param:
random_valuation[param] = 0.5 - random() * 0.01
print(param)
print("=" + str(random_valuation[param]))
print(estimated_reward)
print(mysub(estimated_reward, random_valuation))
print(3.92 / sqrt(num_of_run) *
mysub(estimated_variance, random_valuation))
def toym():
file = 'example/toymul.pm' # Selection du fichier toymul.pl dans le dossier example
time1 = time.time()
pmc = myparse(file)
time2 = time.time()
print('parsing of %s took %0.3f ms' % (file, (time2 - time1) * 1000.0))
num_of_run = 10000
length_of_run = 100
time1 = time.time()
estimated_reward, estimated_variance = simu(length_of_run, num_of_run, pmc)
time2 = time.time()
print('the %d simulations took %0.3f ms' % (num_of_run,
(time2 - time1) * 1000.0))
def random_val1(q, e):
random_valuation = {}
random_valuation[pmc.param[0]] = q
for i in range(1, len(pmc.param)):
param = pmc.param[i]
random_valuation[param] = random() * (1 - q)
return e.subs(random_valuation)
random_val = np.vectorize(random_val1)
x = np.arange(0.05, 0.95, 0.05)
y = random_val(x, estimated_reward)
e = random_val(x, estimated_variance * 1.96 / sqrt(num_of_run))
fig = plt.figure()
ax = fig.add_subplot(111)
ax.errorbar(x, y, yerr=e, fmt='o')
ax.set_xlabel('q')
ax.set_ylabel('probability for random valuation')
plt.show()
def zeroconf():
file = 'example/zeroconf.pm' # Selection du fichier zeroconf.pm dans le dossier example
time1 = time.time()
pmc = myparse(file)
time2 = time.time()
print('parsing of %s took %0.3f ms' % (file, (time2 - time1) * 1000.0))
num_of_run = 10000
length_of_run = 500
time1 = time.time()
estimated_reward, estimated_variance = simu(
length_of_run, num_of_run,
pmc) # ,{pmc.param[0]:0.3,pmc.param[1]:0.3})
time2 = time.time()
print('the %d simulations took %0.3f ms' % (num_of_run,
(time2 - time1) * 1000.0))
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
f = sympy.lambdify(pmc.param, estimated_reward)
fv = np.vectorize(f)
v = sympy.lambdify(pmc.param, estimated_variance)
vv = np.vectorize(v)
x = np.arange(0.15, 0.55, 0.005)
y = np.arange(0.15, 0.55, 0.005)
X, Y = np.meshgrid(x, y)
Z = fv(X, Y)
C = 2 * 1.96 * vv(X, Y) / sqrt(num_of_run)
plot = ax.scatter(X, Y, Z, c=C.ravel())
ax.set_xlabel(str(pmc.param[0]))
ax.set_ylabel(str(pmc.param[1]))
ax.set_zlabel('Expected value')
cb = plt.colorbar(plot)
cb.set_label("CI width")
plt.savefig('zeroconf_%d.png' % num_of_run)
plt.show()
def toy():
file = 'example/toy.pm'
time1 = time.time()
pmc = myparse(file)
time2 = time.time()
print('parsing of %s took %0.3f ms' % (file, (time2 - time1) * 1000.0))
num_of_run = 10000
length_of_run = 100
time1 = time.time()
estimated_reward, estimated_variance = simu(length_of_run, num_of_run, pmc)
time2 = time.time()
print('the %d simulations took %0.3f ms' % (num_of_run,
(time2 - time1) * 1000.0))
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
f = sympy.lambdify(pmc.param, estimated_reward)
fv = np.vectorize(f)
v = sympy.lambdify(pmc.param, estimated_variance)
vv = np.vectorize(v)
x = np.arange(0.05, 0.95, 0.01)
y = np.arange(0.05, 0.95, 0.01)
X1, Y1 = np.meshgrid(x, y)
X = X1[X1 + Y1 < 0.95]
Y = Y1[X1 + Y1 < 0.95]
Z = fv(X, Y)
C = 2 * 1.96 * vv(X, Y) / sqrt(num_of_run)
plot = ax.scatter(X, Y, Z, c=C)
ax.set_xlabel('p')
ax.set_ylabel('q')
ax.set_zlabel('probability')
cb = plt.colorbar(plot)
cb.set_label("CI width")
plt.savefig('toy_%d.png' % num_of_run)
def nand():
file = 'example/nand.pm'
time1 = time.time()
pmc = myparse(file)
time2 = time.time()
print('parsing of %s took %0.3f ms' % (file, (time2 - time1) * 1000.0))
num_of_run = 2
length_of_run = 1000000
time1 = time.time()
estimated_reward, estimated_variance = simu(length_of_run, num_of_run, pmc)
time2 = time.time()
print('the %d simulations took %0.3f ms' % (num_of_run,
(time2 - time1) * 1000.0))
def toy():
file = 'example/toy.pm' # Selection du fichier toy.pm dans le dossier example
time1 = time.time() # Recuperation de time1
pmc = myparse(file)
time2 = time.time() # Reuperation de time2
print('parsing of %s took %0.3f ms' % (file, (time2 - time1) * 1000.0))
num_of_run = 10000 # Nombre de tours
length_of_run = 100 # Longueur des tours
time1 = time.time() # Recuperation de time1
estimated_reward, estimated_variance = simu(length_of_run, num_of_run, pmc)
time2 = time.time() # Recuperation de time2
print('the %d simulations took %0.3f ms' % (num_of_run,
(time2 - time1) * 1000.0))
fig = plt.figure() # Plot de la figure
ax = fig.add_subplot(
111, projection='3d') # Ajout d'un affichage dans la figure
f = sympy.lambdify(pmc.param, estimated_reward)
fv = np.vectorize(f)
v = sympy.lambdify(pmc.param, estimated_variance)
vv = np.vectorize(v)
x = np.arange(0.05, 0.95, 0.01)
y = np.arange(0.05, 0.95, 0.01)
X1, Y1 = np.meshgrid(x, y)
X = X1[X1 + Y1 < 0.95]
Y = Y1[X1 + Y1 < 0.95]
Z = fv(X, Y)
C = 2 * 1.96 * vv(X, Y) / sqrt(num_of_run)
plot = ax.scatter(X, Y, Z, c=C)
ax.set_xlabel('p')
ax.set_ylabel('q')
ax.set_zlabel('probability')
cb = plt.colorbar(plot)
cb.set_label("CI width")
plt.savefig('toy_%d.png' % num_of_run)
def crowd():
file = 'example/crowds.pm' # Selection du fichier crowd.pm dans le dossier example
time1 = time.time()
pmc = myparse(file)
time2 = time.time()
print('parsing of %s took %0.3f ms' % (file, (time2 - time1) * 1000.0))
num_of_run = 10000
length_of_run = 1000000
time1 = time.time()
estimated_reward, estimated_variance = simu(
length_of_run, num_of_run,
pmc) # ,{pmc.param[0]:0.8,pmc.param[1]:1/6})
time2 = time.time()
print('the %d simulations took %0.3f ms' % (num_of_run,
(time2 - time1) * 1000.0))
print(
'the estimated probability for %s=0.8 and %s=1/6 is %0.3f with CI length %0.3f'
% (str(pmc.param[0]), str(pmc.param[1]),
estimated_reward.subs({
pmc.param[0]: 0.8,
pmc.param[1]: 1 / 6
}), 2 * 1.96 * estimated_variance.subs({
pmc.param[0]: 0.8,
pmc.param[1]: 1 / 6
}) / sqrt(num_of_run)))
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
f = sympy.lambdify(pmc.param, estimated_reward)
fv = np.vectorize(f)
v = sympy.lambdify(pmc.param, estimated_variance)
vv = np.vectorize(v)
x = np.arange(0.05, 0.95, 0.01)
y = np.arange(0.05, 0.95, 0.01)
X, Y = np.meshgrid(x, y)
Z = fv(X, Y)
C = 2 * 1.96 * vv(X, Y) / sqrt(num_of_run)
plot = ax.scatter(X, Y, Z, c=C.ravel())
ax.set_xlabel(str(pmc.param[0]))
ax.set_ylabel(str(pmc.param[1]))
ax.set_zlabel('probability')
cb = plt.colorbar(plot)
cb.set_label("CI width")
plt.savefig('crowds_%d.png' % num_of_run)