"""TruncatedGaussian module."""
import warnings
import numpy as np
from scipy.optimize import fmin_slsqp
from scipy.stats import truncnorm
from copulas import EPSILON, store_args, validate_random_state
from copulas.univariate.base import BoundedType, ParametricType, ScipyModel
[docs]class TruncatedGaussian(ScipyModel):
"""Wrapper around scipy.stats.truncnorm.
Documentation: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.truncnorm.html
"""
PARAMETRIC = ParametricType.PARAMETRIC
BOUNDED = BoundedType.BOUNDED
MODEL_CLASS = truncnorm
@store_args
def __init__(self, minimum=None, maximum=None, random_state=None):
self.random_state = validate_random_state(random_state)
self.min = minimum
self.max = maximum
def _fit_constant(self, X):
constant = np.unique(X)[0]
self._params = {
'a': constant,
'b': constant,
'loc': constant,
'scale': 0.0
}
def _fit(self, X):
if self.min is None:
self.min = X.min() - EPSILON
if self.max is None:
self.max = X.max() + EPSILON
def nnlf(params):
loc, scale = params
a = (self.min - loc) / scale
b = (self.max - loc) / scale
return truncnorm.nnlf((a, b, loc, scale), X)
initial_params = X.mean(), X.std()
with warnings.catch_warnings():
warnings.simplefilter('ignore', category=RuntimeWarning)
optimal = fmin_slsqp(nnlf, initial_params, iprint=False, bounds=[
(self.min, self.max),
(0.0, (self.max - self.min)**2)
])
loc, scale = optimal
a = (self.min - loc) / scale
b = (self.max - loc) / scale
self._params = {
'a': a,
'b': b,
'loc': loc,
'scale': scale
}
def _is_constant(self):
return self._params['a'] == self._params['b']
def _extract_constant(self):
return self._params['loc']