def test_fit(self):
data = stats.alpha(5).rvs(size=37)
params = self.dist.fit(data)
check_params(
(params.alpha, 4.8356445312500096),
(params.loc, 0),
(params.scale, 1),
)
def test_fit(self):
data = stats.alpha(5).rvs(size=37)
params = self.dist.fit(data)
check_params(
(params.alpha, 4.8356445312500096),
(params.loc, 0),
(params.scale, 1),
)
#------------------------------------------------------------------------------- import numpy as np from scipy.stats import alpha import matplotlib.pyplot as plt fig, ax = plt.subplots(1, 1) a = 3.57 mean, var, skew, kurt = alpha.stats(a, moments='mvsk') #print(mean, var, skew, kurt) x = np.linspace(alpha.ppf(0.01, a), alpha.ppf(0.99, a), 100) #Display the probability density function (pdf) ax.plot(x, alpha.pdf(x, a), 'r-', lw=5, alpha=0.6, label='alpha pdf') #Freeze the distribution and display the frozen pdf rv = alpha(a) ax.plot(x, rv.pdf(x), 'k-', lw=1, label='frozen pdf') #Check accuracy of cdf and ppf vals = alpha.ppf([0.001, 0.5, 0.999], a) np.allclose([0.001, 0.5, 0.999], alpha.cdf(vals, a)) #Generate random numbers r = alpha.rvs(a, size=1000) #And compare the histogram ax.hist(r, density=True, histtype='stepfilled', alpha=0.2) ax.legend(loc='best', frameon=False) plt.show()
x2=rv2.rvs(size=1000)
isOutlier=[ True if norm.pdf(data,scale=3,loc=-5)<0.01 else False for data in x ]
data=[[xi,isOutlieri] for xi,isOutlieri in zip (x,isOutlier)]
isOutlier2=[ True if norm.pdf(data,loc=10,scale=2)<0.01 else False for data in x2 ]
data=data+[[xi,isOutlieri] for xi,isOutlieri in zip (x2,isOutlier2)]
fig, ax = plt.subplots(1, 1)
ax.hist([i[0] for i in data], density=True, histtype='stepfilled', alpha=0.2)
plt.savefig("distribution.eps",format="eps")
plt.show()
activitiesTimes.append(data)
#2 alpha distributions
a1,a2=3.2,3.2
scale1,scale2=10,10
loc1,loc2=0,15
rv = alpha(a1,loc=loc1,scale=scale1)
rv2=alpha(a2,loc=loc2,scale=scale2)
x = rv.rvs(size=1000)
x2 = rv2.rvs(size=1000)
isOutlier=[ True if alpha.pdf(data,a1,loc=loc1,scale=scale1)<0.01 else False for data in x ]
data=[[xi,isOutlieri] for xi,isOutlieri in zip (x,isOutlier)]
isOutlier2=[ True if alpha.pdf(data,a2,loc=loc2,scale=scale2)<0.01 else False for data in x2 ]
data=data+[[xi,isOutlieri] for xi,isOutlieri in zip (x2,isOutlier2)]
fig, ax = plt.subplots(1, 1)
ax.hist([i[0] for i in data], density=True, histtype='stepfilled', alpha=0.2)
plt.show()
activitiesTimes.append(data)
#2 exponentials
scale1,scale2=2,5
loc1,loc2=0,15
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),
}
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
distr = stats.alpha(a=3.57, loc=0.0, scale=1.0)
start = distr.ppf(0.01)
end = distr.ppf(0.99)
size = 10000
x = np.linspace(start, end, size)
y = distr.pdf(x)
plt.xkcd()
sad = plt.text(1500, 2, ':(')
sad.set_rotation(-90)
sad.set_fontsize(40)
sad.set_horizontalalignment('center')
plt.text(3500, 3, '/')
plt.text(4000, 3.5, 'I just want to be normal...')
plt.xticks([])
plt.yticks([])
plt.plot(y)
plt.show()