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()