def __init__(self, scenario_flag="Freeway_Free"):
"""
Totally five scenarios are supported here:
Freeway_Night, Freeway_Free, Freeway_Rush;
Urban_Peak, Urban_Nonpeak.
The PDFs of the vehicle speed and the inter-vehicle space are adapted
from existing references.
"""
if scenario_flag == "Freeway_Night":
self.headway_random = expon(0.0, 1.0 / 256.41)
meanSpeed = 30.93 #m/s
stdSpeed = 1.2 #m/s
elif scenario_flag == "Freeway_Free":
self.headway_random = lognorm(0.75, 0.0, np.exp(3.4))
meanSpeed = 29.15 #m/s
stdSpeed = 1.5 #m/s
elif scenario_flag == "Freeway_Rush":
self.headway_random = lognorm(0.5, 0.0, np.exp(2.5))
meanSpeed = 10.73 #m/s
stdSpeed = 2.0 #m/s
elif scenario_flag == "Urban_Peak":
scale = 1.096
c = 0.314
loc = 0.0
self.headway_random = fisk(c, loc, scale)
meanSpeed = 6.083 #m/s
stdSpeed = 1.2 #m/s
elif scenario_flag == "Urban_Nonpeak":
self.headway_random = lognorm(0.618, 0.0, np.exp(0.685))
meanSpeed = 12.86 #m/s
stdSpeed = 1.5 #m/s
else:
raise
self.speed_random = norm(meanSpeed, stdSpeed)
def __init__(self, scenario_flag = "Freeway_Free"):
"""
Totally five scenarios are supported here:
Freeway_Night, Freeway_Free, Freeway_Rush;
Urban_Peak, Urban_Nonpeak.
The PDFs of the vehicle speed and the inter-vehicle space are adapted
from existing references.
"""
if scenario_flag == "Freeway_Night":
self.headway_random = expon(0.0, 1.0/256.41)
meanSpeed = 30.93 #m/s
stdSpeed = 1.2 #m/s
elif scenario_flag == "Freeway_Free":
self.headway_random = lognorm(0.75, 0.0, np.exp(3.4))
meanSpeed = 29.15 #m/s
stdSpeed = 1.5 #m/s
elif scenario_flag == "Freeway_Rush":
self.headway_random = lognorm(0.5, 0.0, np.exp(2.5))
meanSpeed = 10.73 #m/s
stdSpeed = 2.0 #m/s
elif scenario_flag == "Urban_Peak":
scale = 1.096
c = 0.314
loc = 0.0
self.headway_random = fisk(c, loc, scale)
meanSpeed = 6.083 #m/s
stdSpeed = 1.2 #m/s
elif scenario_flag == "Urban_Nonpeak":
self.headway_random = lognorm(0.618, 0.0, np.exp(0.685))
meanSpeed = 12.86 #m/s
stdSpeed = 1.5 #m/s
else:
raise
self.speed_random = norm(meanSpeed, stdSpeed)
def _get_dist(latent, theta):
if latent == 'normal':
dist = st.norm(*theta)
elif latent == 'logistic':
dist = st.logistic(*theta)
elif latent == 'log-logistic':
dist = st.fisk(c=theta[1], scale=theta[0])
return dist
def __init__(self, alpha, beta):
"""
Parameters
----------
alpha : float, positive
Scale parameter
beta : float, positive
Shape parameter
"""
assert alpha > 0, "alpha must be positive"
assert beta > 0, "alpha must be positive"
# Parameters
self.alpha = alpha
self.beta = beta
# Scipy backend
self.sp = fisk(c=beta, scale=alpha)
super().__init__()
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 = fisk.stats(c, moments='mvsk')
# Display the probability density function (``pdf``):
x = np.linspace(fisk.ppf(0.01, c),
fisk.ppf(0.99, c), 100)
ax.plot(x, fisk.pdf(x, c),
'r-', lw=5, alpha=0.6, label='fisk 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 = fisk(c)
ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
# Check accuracy of ``cdf`` and ``ppf``:
vals = fisk.ppf([0.001, 0.5, 0.999], c)
np.allclose([0.001, 0.5, 0.999], fisk.cdf(vals, c))
# True
# Generate random numbers:
r = fisk.rvs(c, size=1000)
# And compare the histogram:
ax.hist(r, normed=True, histtype='stepfilled', alpha=0.2)
#shape, scale, loc = (2.99480920063372, 2.46709346372752, 0.0249999999999978) #(1.0, 1.321950408033661, 0.85874898071806682) #(0.7180, 0.9328, 1.2021)
#fitting = stats.lognorm(4.67861713792556, 6.91953608758707)
'''generalized extreme OK
lognormal OK
birnbaumsaunders OK
generalized pareto OK
inversegaussian OK
logistic NO
loglogistic/fisk NO '''
#v = numpy.random.gumbel(loc=1.20212649309532, scale=0.932804666751013, size=10000)
#fitting = stats.genextreme(-0.718044067607244, loc=1.20212649309532, scale=0.932804666751013)
#fitting = stats.lognorm(4.67861713792556, scale=6.91953608758707)
#fitting = stats.invgauss(5.3146e+003, scale=0.9277)
#fitting = stats.fatiguelife(18.1441, scale=359.6783)
fitting = stats.fisk(0.4190, shape=0.5201)
#mu=9.31524829249769 sigma=41.5219061720147
#fitting = stats.lognorm(0.8491, loc=-0.089)
#fitting = stats.logistic(64.9711, shape=69.3347)
#print stats.genpareto.fit(rvs)
test = open("test.dat", "w")
for i in range(10000):
print >> test, fitting.rvs()
#print stats.genpareto(2.9948, scale=2.4671, loc=0.0250).rvs()
#c=[0.7180, 0.9328, 1.2021]
#print stats.fisk.rvs(0.5201, 0.4190)
test.close()
'''STEREOTYPE_RECIPES_PATH = "D:/Documentos/Recerca/Publicaciones/2015/user_stereotypes/Stereotype_Analysis/"
to_fit = []
for line in open(STEREOTYPE_RECIPES_PATH + "/data/backup_GetContentResponse_GetContentResponse.dat", "r"):
to_fit.append(float(line.split(",")[1])/1000.0)