T Distribution Weirdness

[angelgeek]

T Distribution Weirdness

[erdogant]

Maybe I missed this one but I store all the parameters returned after the distribution fitting.

As an example:

from distfit import distfit
X = np.random.normal(0, 2, 1000)
y = [-8, -6, 0, 1, 2, 3, 4, 5, 6]
dist = distfit(stats='ks', distr=['expon', 't', 'gamma', 'lognorm'])
results = dist.fit_transform(X)

print(dist.model)
{'distr': <scipy.stats._continuous_distns.t_gen at 0x2d4882810f0>,
 'stats': 'ks',
 'params': (3518324.248643998, -0.08180702912809554, 2.0838347069246876),
 'name': 't',
 'model': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x2d49debda80>,
 'score': 0.40237077133797083,
 'loc': -0.08180702912809554,
 'scale': 2.0838347069246876,
 'arg': (3518324.248643998,),
 'CII_min_alpha': -3.5094110072794593,
 'CII_max_alpha': 3.345796949023267}

When I now do the fit manually for only the t-distribution, the following parameters are returned:

import scipy.stats as st
# fit dist to data
params = st.t.fit(X)
print(params)
(3518324.248643998, -0.08180702912809554, 2.0838347069246876)

# Separate parts of parameters
arg = params[:-2]
loc = params[-2]
scale = params[-1]

If I now compare the returned parameters and the stored ones in distfit, it is exactly the same:

params==dist.model['params']
True

[erdogant]

I am closing this issue. Reopen if required.