def tst_lomax():
t = Lomax.samples_(1.1, 50, size=10000)
start = time.time()
params = Lomax.est_params(t)
end = time.time()
print("Estimating parameters of Lomax took: " + str(end - start))
return abs(params[0] - 1.1) < 1e-1
def compare_loglogistic_fitting_approaches():
"""
This experiment convinced me to abandon the Lomax
and Weibull based LogLogistic estimation.
"""
ti, xi = mixed_loglogistic_model()
wbl = Weibull.est_params(ti)
lmx = Lomax.est_params(ti)
#Now estimate Lomax and Weibull params and construct feature vector.
x_features = cnstrct_feature(ti)
beta = sum(x_features * LogLogistic.lin_betas)
alpha = sum(x_features * LogLogistic.lin_alphas)