def fit(self, X, y=None, sample_weight=None):
"""Fit the model with X.
Parameters
----------
X : Triangle-like
Set of LDFs to which the munich adjustment will be applied.
y : Ignored
sample_weight : Ignored
Returns
-------
self : object
Returns the instance itself.
"""
if (type(X.ddims) != np.ndarray):
raise ValueError(
'Triangle must be expressed with development lags')
tri_array = X.values.copy()
tri_array[tri_array == 0] = np.nan
if type(self.average) is str:
average = [self.average] * (tri_array.shape[-1] - 1)
else:
average = self.average
average = np.array(average)
self.average_ = average
weight_dict = {'regression': 0, 'volume': 1, 'simple': 2}
x, y = tri_array[..., :-1], tri_array[..., 1:]
val = np.array([weight_dict.get(item.lower(), 1) for item in average])
for i in [2, 1, 0]:
val = np.repeat(val[np.newaxis], tri_array.shape[i], axis=0)
val = np.nan_to_num(val * (y * 0 + 1))
link_ratio = np.divide(y, x, where=np.nan_to_num(x) != 0)
self.w_ = np.array(self._assign_n_periods_weight(X) *
self._drop_adjustment(X, link_ratio),
dtype='float16')
w = self.w_ / (x**(val))
params = WeightedRegression(axis=2, thru_orig=True).fit(x, y, w)
if self.n_periods != 1:
params = params.sigma_fill(self.sigma_interpolation)
else:
warnings.warn('Setting n_periods=1 does not allow enough degrees '
'of freedom to support calculation of all regression'
' statistics. Only LDFs have been calculated.')
params.std_err_ = np.nan_to_num(params.std_err_) + \
np.nan_to_num(
(1-np.nan_to_num(params.std_err_*0+1)) *
params.sigma_ /
np.swapaxes(np.sqrt(x**(2-val))[..., 0:1, :], -1, -2))
params = np.concatenate(
(params.slope_, params.sigma_, params.std_err_), 3)
params = np.swapaxes(params, 2, 3)
self.ldf_ = self._param_property(X, params, 0)
self.cdf_ = self._get_cdf(self)
self.sigma_ = self._param_property(X, params, 1)
self.std_err_ = self._param_property(X, params, 2)
return self
def fit(self, X, y=None, sample_weight=None):
"""Fit the model with X.
Parameters
----------
X : Triangle-like
Set of LDFs to which the munich adjustment will be applied.
y : Ignored
sample_weight : Ignored
Returns
-------
self : object
Returns the instance itself.
"""
tri_array = X.triangle.copy()
tri_array[tri_array == 0] = np.nan
if type(self.average) is str:
average = [self.average] * (tri_array.shape[-1] - 1)
else:
average = self.average
average = np.array(average)
self.average_ = average
weight_dict = {'regression': 2, 'volume': 1, 'simple': 0}
_x = tri_array[..., :-1]
_y = tri_array[..., 1:]
val = np.array([weight_dict.get(item.lower(), 2)
for item in average])
for i in [2, 1, 0]:
val = np.repeat(np.expand_dims(val, 0), tri_array.shape[i], axis=0)
val = np.nan_to_num(val * (_y * 0 + 1))
_w = self._assign_n_periods_weight(X) / (_x**(val))
self.w_ = self._assign_n_periods_weight(X)
params = WeightedRegression(_w, _x, _y, axis=2, thru_orig=True).fit()
if self.n_periods != 1:
params = params.sigma_fill(self.sigma_interpolation)
else:
warnings.warn('Setting n_periods=1 does not allow enough degrees of'
' freedom to support calculation of all regression '
'statistics. Only LDFs have been calculated.')
params.std_err_ = np.nan_to_num(params.std_err_) + \
np.nan_to_num((1-np.nan_to_num(params.std_err_*0+1)) *
params.sigma_/np.swapaxes(np.sqrt(_x**(2-val))[..., 0:1, :], -1, -2))
params = np.concatenate((params.slope_,
params.sigma_,
params.std_err_), 3)
params = np.swapaxes(params, 2, 3)
self.ldf_ = self._param_property(X, params, 0)
self.sigma_ = self._param_property(X, params, 1)
self.std_err_ = self._param_property(X, params, 2)
return self