def testExponentiallyModifiedGaussianLogPDF(self):
batch_size = 6
mu = tf.constant([3.0] * batch_size, dtype=self.dtype)
sigma = tf.constant([math.sqrt(10.0)] * batch_size, dtype=self.dtype)
rate = tf.constant([2.] * batch_size, dtype=self.dtype)
x = np.array([-2.5, 2.5, 4.0, 0.0, -1.0, 2.0], dtype=self.dtype)
exgaussian = tfd.ExponentiallyModifiedGaussian(
loc=mu, scale=sigma, rate=rate, validate_args=True)
log_pdf = exgaussian.log_prob(x)
self.assertAllEqual(
self.evaluate(exgaussian.batch_shape_tensor()), log_pdf.shape)
self.assertAllEqual(
self.evaluate(exgaussian.batch_shape_tensor()),
self.evaluate(log_pdf).shape)
self.assertAllEqual(exgaussian.batch_shape, log_pdf.shape)
self.assertAllEqual(exgaussian.batch_shape, self.evaluate(log_pdf).shape)
pdf = exgaussian.prob(x)
self.assertAllEqual(
self.evaluate(exgaussian.batch_shape_tensor()), pdf.shape)
self.assertAllEqual(
self.evaluate(exgaussian.batch_shape_tensor()),
self.evaluate(pdf).shape)
self.assertAllEqual(exgaussian.batch_shape, pdf.shape)
self.assertAllEqual(exgaussian.batch_shape, self.evaluate(pdf).shape)
expected_log_pdf = sp_stats.exponnorm(
1. / (self.evaluate(rate) * self.evaluate(sigma)),
loc=self.evaluate(mu),
scale=self.evaluate(sigma)).logpdf(x)
self.assertAllClose(
expected_log_pdf, self.evaluate(log_pdf), atol=1e-5, rtol=1e-5)
self.assertAllClose(
np.exp(expected_log_pdf), self.evaluate(pdf), atol=1e-5, rtol=1e-5)
def __init__(self,
nbElevator,
typeAlgo,
lamb=0.5,
expo=60,
typeIdle="noMoveIdle"):
self.elevators = []
self.users = {
'1': [],
'2': [],
'3': [],
'4': [],
'5': [],
'6': [],
'7': []
}
self.totalUsers = 0
self.totalTravels = 0
self.totalWaitingTime = 0
self.meanWaitingTime = 0
self.calls = []
self.expo = expo
self.exp = exponnorm(expo)
self.lamb = lamb
self.typeIdle = typeIdle
self.tpsAttendu = []
for i in range(nbElevator):
newElevator = Elevator(False, False, [], 1, typeAlgo,
self.typeIdle)
self.elevators.append(newElevator)
userT = userThread(self)
userT.start()
def testExponentiallyModifiedGaussianLogCDF(self):
batch_size = 50
mu = self._rng.randn(batch_size)
sigma = self._rng.rand(batch_size) + 1.0
rate = self._rng.rand(batch_size) + 1.0
x = np.linspace(-8.0, 8.0, batch_size).astype(self.dtype)
exgaussian = tfd.ExponentiallyModifiedGaussian(
loc=mu, scale=sigma, rate=rate, validate_args=True)
cdf = exgaussian.log_cdf(x)
self.assertAllEqual(
self.evaluate(exgaussian.batch_shape_tensor()), cdf.shape)
self.assertAllEqual(
self.evaluate(exgaussian.batch_shape_tensor()),
self.evaluate(cdf).shape)
self.assertAllEqual(exgaussian.batch_shape, cdf.shape)
self.assertAllEqual(exgaussian.batch_shape, self.evaluate(cdf).shape)
expected_cdf = sp_stats.exponnorm(
1. / (rate * sigma), loc=mu, scale=sigma).logcdf(x)
self.assertAllClose(expected_cdf, self.evaluate(cdf), atol=0)
def Fit_background_distribution(image, mask_deep):
# Check background, fit with gaussian and exp-gaussian distribution
from scipy import stats
plt.figure(figsize=(6, 4))
z_sky = image[~mask_deep]
if seaborn_plot:
sns.distplot(z_sky, label='Data', hist_kws={'alpha': 0.3})
else:
plt.hist(z_sky, label='Data', alpha=0.3)
mu_fit, std_fit = stats.norm.fit(z_sky)
print(mu_fit, std_fit)
d_mod = stats.norm(loc=mu_fit, scale=std_fit)
x = np.linspace(d_mod.ppf(0.001), d_mod.ppf(0.999), 100)
plt.plot(x, d_mod.pdf(x), 'g-', lw=2, alpha=0.6, label='Norm Fit')
K_fit, mu_fit, std_fit = stats.exponnorm.fit(z_sky)
print(K_fit, mu_fit, std_fit)
d_mod2 = stats.exponnorm(loc=mu_fit, scale=std_fit, K=K_fit)
x = np.linspace(d_mod2.ppf(0.001), d_mod2.ppf(0.9999), 100)
plt.plot(x, d_mod2.pdf(x), 'r-', lw=2, alpha=0.6, label='Exp-Norm Fit')
plt.legend(fontsize=12)
def generador_estadistica(base):
from scipy.stats import expon, exponnorm, gamma, logistic, lognorm, norm, uniform, triang
lista_distributions = []
lista_parameters = []
for z in range(0, len(base)):
if base["Parameters"][z] == "Action can not be performed":
lista_distributions.append("Action can not be performed")
lista_parameters.append("Action can not be performed")
else:
distri_prueba = [*base["Parameters"][z]]
lista_distributions.append(distri_prueba)
lista_parameters.append(base["Parameters"][z].get(
distri_prueba[0]))
lista_mean = []
lista_std = []
for xx in range(0, len(base)):
if lista_distributions[xx][0] == "expon":
seleccion = expon(lista_parameters[xx][0], lista_parameters[xx][1])
lista_mean.append(seleccion.mean())
lista_std.append(seleccion.std())
elif lista_distributions[xx][0] == "exponnorm":
seleccion = exponnorm(lista_parameters[xx][0],
lista_parameters[xx][1],
lista_parameters[xx][2])
lista_mean.append(seleccion.mean())
lista_std.append(seleccion.std())
elif lista_distributions[xx][0] == "gamma":
seleccion = gamma(lista_parameters[xx][0], lista_parameters[xx][1],
lista_parameters[xx][2])
lista_mean.append(seleccion.mean())
lista_std.append(seleccion.std())
elif lista_distributions[xx][0] == "logistic":
seleccion = logistic(lista_parameters[xx][0],
lista_parameters[xx][1])
lista_mean.append(seleccion.mean())
lista_std.append(seleccion.std())
elif lista_distributions[xx][0] == "lognorm":
seleccion = lognorm(lista_parameters[xx][0],
lista_parameters[xx][1],
lista_parameters[xx][2])
lista_mean.append(seleccion.mean())
lista_std.append(seleccion.std())
elif lista_distributions[xx][0] == "norm":
seleccion = norm(lista_parameters[xx][0], lista_parameters[xx][1])
lista_mean.append(seleccion.mean())
lista_std.append(seleccion.std())
elif lista_distributions[xx][0] == "uniform":
seleccion = uniform(lista_parameters[xx][0],
lista_parameters[xx][1])
lista_mean.append(seleccion.mean())
lista_std.append(seleccion.std())
elif lista_distributions[xx][0] == "triang":
seleccion = triang(lista_parameters[xx][0],
lista_parameters[xx][1],
lista_parameters[xx][2])
lista_mean.append(seleccion.mean())
lista_std.append(seleccion.std())
elif lista_distributions[xx] == "Action can not be performed":
lista_mean.append("No data for mean")
lista_std.append("No data for std")
base["mean"] = lista_mean
base["std"] = lista_std
return base
def generador_estadistica_2(base):
dis = next(iter(base))
lista_parameters = np.asarray(base[dis])
if dis == "expon":
seleccion = expon(lista_parameters[0], lista_parameters[1])
lista_mean = seleccion.mean()
lista_std = seleccion.std()
lista_generador = seleccion.rvs(1000)
elif dis == "exponnorm":
seleccion = exponnorm(lista_parameters[0], lista_parameters[1],
lista_parameters[2])
lista_mean = seleccion.mean()
lista_std = seleccion.std()
lista_generador = seleccion.rvs(1000)
elif dis == "gamma":
seleccion = gamma(lista_parameters[0], lista_parameters[1],
lista_parameters[2])
lista_mean = seleccion.mean()
lista_std = seleccion.std()
lista_generador = seleccion.rvs(1000)
elif dis == "logistic":
seleccion = logistic(lista_parameters[0], lista_parameters[1])
lista_mean = seleccion.mean()
lista_std = seleccion.std()
lista_generador = seleccion.rvs(1000)
elif dis == "lognorm":
seleccion = lognorm(lista_parameters[0], lista_parameters[1],
lista_parameters[2])
lista_mean = seleccion.mean()
lista_std = seleccion.std()
lista_generador = seleccion.rvs(1000)
elif dis == "norm":
seleccion = norm(lista_parameters[0], lista_parameters[1])
lista_mean = seleccion.mean()
lista_std = seleccion.std()
lista_generador = seleccion.rvs(1000)
elif dis == "uniform":
seleccion = uniform(lista_parameters[0], lista_parameters[1])
lista_mean = seleccion.mean()
lista_std = seleccion.std()
lista_generador = seleccion.rvs(1000)
elif dis == "triang":
seleccion = triang(lista_parameters[0], lista_parameters[1],
lista_parameters[2])
lista_mean = seleccion.mean()
lista_std = seleccion.std()
lista_generador = seleccion.rvs(1000)
elif dis == "Action can not be performed":
lista_mean = "No data for mean"
lista_std = "No data for std"
lista_generador = seleccion.rvs(1000)
base["mean"] = lista_mean
base["std"] = lista_std
base["generador"] = lista_generador
return base
def all_dists():
# dists param were taken from scipy.stats official
# documentaion examples
# Total - 89
return {
"alpha":
stats.alpha(a=3.57, loc=0.0, scale=1.0),
"anglit":
stats.anglit(loc=0.0, scale=1.0),
"arcsine":
stats.arcsine(loc=0.0, scale=1.0),
"beta":
stats.beta(a=2.31, b=0.627, loc=0.0, scale=1.0),
"betaprime":
stats.betaprime(a=5, b=6, loc=0.0, scale=1.0),
"bradford":
stats.bradford(c=0.299, loc=0.0, scale=1.0),
"burr":
stats.burr(c=10.5, d=4.3, loc=0.0, scale=1.0),
"cauchy":
stats.cauchy(loc=0.0, scale=1.0),
"chi":
stats.chi(df=78, loc=0.0, scale=1.0),
"chi2":
stats.chi2(df=55, loc=0.0, scale=1.0),
"cosine":
stats.cosine(loc=0.0, scale=1.0),
"dgamma":
stats.dgamma(a=1.1, loc=0.0, scale=1.0),
"dweibull":
stats.dweibull(c=2.07, loc=0.0, scale=1.0),
"erlang":
stats.erlang(a=2, loc=0.0, scale=1.0),
"expon":
stats.expon(loc=0.0, scale=1.0),
"exponnorm":
stats.exponnorm(K=1.5, loc=0.0, scale=1.0),
"exponweib":
stats.exponweib(a=2.89, c=1.95, loc=0.0, scale=1.0),
"exponpow":
stats.exponpow(b=2.7, loc=0.0, scale=1.0),
"f":
stats.f(dfn=29, dfd=18, loc=0.0, scale=1.0),
"fatiguelife":
stats.fatiguelife(c=29, loc=0.0, scale=1.0),
"fisk":
stats.fisk(c=3.09, loc=0.0, scale=1.0),
"foldcauchy":
stats.foldcauchy(c=4.72, loc=0.0, scale=1.0),
"foldnorm":
stats.foldnorm(c=1.95, loc=0.0, scale=1.0),
# "frechet_r": stats.frechet_r(c=1.89, loc=0.0, scale=1.0),
# "frechet_l": stats.frechet_l(c=3.63, loc=0.0, scale=1.0),
"genlogistic":
stats.genlogistic(c=0.412, loc=0.0, scale=1.0),
"genpareto":
stats.genpareto(c=0.1, loc=0.0, scale=1.0),
"gennorm":
stats.gennorm(beta=1.3, loc=0.0, scale=1.0),
"genexpon":
stats.genexpon(a=9.13, b=16.2, c=3.28, loc=0.0, scale=1.0),
"genextreme":
stats.genextreme(c=-0.1, loc=0.0, scale=1.0),
"gausshyper":
stats.gausshyper(a=13.8, b=3.12, c=2.51, z=5.18, loc=0.0, scale=1.0),
"gamma":
stats.gamma(a=1.99, loc=0.0, scale=1.0),
"gengamma":
stats.gengamma(a=4.42, c=-3.12, loc=0.0, scale=1.0),
"genhalflogistic":
stats.genhalflogistic(c=0.773, loc=0.0, scale=1.0),
"gilbrat":
stats.gilbrat(loc=0.0, scale=1.0),
"gompertz":
stats.gompertz(c=0.947, loc=0.0, scale=1.0),
"gumbel_r":
stats.gumbel_r(loc=0.0, scale=1.0),
"gumbel_l":
stats.gumbel_l(loc=0.0, scale=1.0),
"halfcauchy":
stats.halfcauchy(loc=0.0, scale=1.0),
"halflogistic":
stats.halflogistic(loc=0.0, scale=1.0),
"halfnorm":
stats.halfnorm(loc=0.0, scale=1.0),
"halfgennorm":
stats.halfgennorm(beta=0.675, loc=0.0, scale=1.0),
"hypsecant":
stats.hypsecant(loc=0.0, scale=1.0),
"invgamma":
stats.invgamma(a=4.07, loc=0.0, scale=1.0),
"invgauss":
stats.invgauss(mu=0.145, loc=0.0, scale=1.0),
"invweibull":
stats.invweibull(c=10.6, loc=0.0, scale=1.0),
"johnsonsb":
stats.johnsonsb(a=4.32, b=3.18, loc=0.0, scale=1.0),
"johnsonsu":
stats.johnsonsu(a=2.55, b=2.25, loc=0.0, scale=1.0),
"ksone":
stats.ksone(n=1e03, loc=0.0, scale=1.0),
"kstwobign":
stats.kstwobign(loc=0.0, scale=1.0),
"laplace":
stats.laplace(loc=0.0, scale=1.0),
"levy":
stats.levy(loc=0.0, scale=1.0),
"levy_l":
stats.levy_l(loc=0.0, scale=1.0),
"levy_stable":
stats.levy_stable(alpha=0.357, beta=-0.675, loc=0.0, scale=1.0),
"logistic":
stats.logistic(loc=0.0, scale=1.0),
"loggamma":
stats.loggamma(c=0.414, loc=0.0, scale=1.0),
"loglaplace":
stats.loglaplace(c=3.25, loc=0.0, scale=1.0),
"lognorm":
stats.lognorm(s=0.954, loc=0.0, scale=1.0),
"lomax":
stats.lomax(c=1.88, loc=0.0, scale=1.0),
"maxwell":
stats.maxwell(loc=0.0, scale=1.0),
"mielke":
stats.mielke(k=10.4, s=3.6, loc=0.0, scale=1.0),
"nakagami":
stats.nakagami(nu=4.97, loc=0.0, scale=1.0),
"ncx2":
stats.ncx2(df=21, nc=1.06, loc=0.0, scale=1.0),
"ncf":
stats.ncf(dfn=27, dfd=27, nc=0.416, loc=0.0, scale=1.0),
"nct":
stats.nct(df=14, nc=0.24, loc=0.0, scale=1.0),
"norm":
stats.norm(loc=0.0, scale=1.0),
"pareto":
stats.pareto(b=2.62, loc=0.0, scale=1.0),
"pearson3":
stats.pearson3(skew=0.1, loc=0.0, scale=1.0),
"powerlaw":
stats.powerlaw(a=1.66, loc=0.0, scale=1.0),
"powerlognorm":
stats.powerlognorm(c=2.14, s=0.446, loc=0.0, scale=1.0),
"powernorm":
stats.powernorm(c=4.45, loc=0.0, scale=1.0),
"rdist":
stats.rdist(c=0.9, loc=0.0, scale=1.0),
"reciprocal":
stats.reciprocal(a=0.00623, b=1.01, loc=0.0, scale=1.0),
"rayleigh":
stats.rayleigh(loc=0.0, scale=1.0),
"rice":
stats.rice(b=0.775, loc=0.0, scale=1.0),
"recipinvgauss":
stats.recipinvgauss(mu=0.63, loc=0.0, scale=1.0),
"semicircular":
stats.semicircular(loc=0.0, scale=1.0),
"t":
stats.t(df=2.74, loc=0.0, scale=1.0),
"triang":
stats.triang(c=0.158, loc=0.0, scale=1.0),
"truncexpon":
stats.truncexpon(b=4.69, loc=0.0, scale=1.0),
"truncnorm":
stats.truncnorm(a=0.1, b=2, loc=0.0, scale=1.0),
"tukeylambda":
stats.tukeylambda(lam=3.13, loc=0.0, scale=1.0),
"uniform":
stats.uniform(loc=0.0, scale=1.0),
"vonmises":
stats.vonmises(kappa=3.99, loc=0.0, scale=1.0),
"vonmises_line":
stats.vonmises_line(kappa=3.99, loc=0.0, scale=1.0),
"wald":
stats.wald(loc=0.0, scale=1.0),
"weibull_min":
stats.weibull_min(c=1.79, loc=0.0, scale=1.0),
"weibull_max":
stats.weibull_max(c=2.87, loc=0.0, scale=1.0),
"wrapcauchy":
stats.wrapcauchy(c=0.0311, loc=0.0, scale=1.0),
}
mean, var, skew, kurt = exponnorm.stats(K, moments='mvsk')
# Display the probability density function (``pdf``):
x = np.linspace(exponnorm.ppf(0.01, K),
exponnorm.ppf(0.99, K), 100)
ax.plot(x, exponnorm.pdf(x, K),
'r-', lw=5, alpha=0.6, label='exponnorm pdf')
# Alternatively, the distribution object can be called (as a function)
# to fix the shape, location and scale parameters. This returns a "frozen"
# RV object holding the given parameters fixed.
# Freeze the distribution and display the frozen ``pdf``:
rv = exponnorm(K)
ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
# Check accuracy of ``cdf`` and ``ppf``:
vals = exponnorm.ppf([0.001, 0.5, 0.999], K)
np.allclose([0.001, 0.5, 0.999], exponnorm.cdf(vals, K))
# True
# Generate random numbers:
r = exponnorm.rvs(K, size=1000)
# And compare the histogram:
ax.hist(r, density=True, histtype='stepfilled', alpha=0.2)