def transform(self, X):
""" If X and self are of different shapes, align self to X, else
return self.
Parameters
----------
X : Triangle
The triangle to be transformed
Returns
-------
X_new : New triangle with transformed attributes.
"""
X_new = copy.copy(X)
if 'ldf_' not in X_new:
X_new = Development().fit_transform(X_new)
X_new.p_to_i_X_ = self._get_p_to_i_object(X_new)
X_new.p_to_i_ldf_ = self._get_p_to_i_object(X_new.ldf_)
X_new.p_to_i_sigma_ = self._get_p_to_i_object(X_new.sigma_)
X_new.q_f_, X_new.rho_sigma_ = self._get_MCL_model(X_new)
X_new.munich_full_triangle_ = self._get_munich_full_triangle_(
X_new.p_to_i_X_, X_new.p_to_i_ldf_, X_new.p_to_i_sigma_,
self.lambda_coef_, X_new.rho_sigma_, X_new.q_f_)
X_new.ldf_ = self._set_ldf(X_new, self._get_mcl_cdf(X_new, X_new.munich_full_triangle_))
triangles = ['rho_', 'lambda_', 'lambda_coef_']
for item in triangles:
setattr(X_new, item, getattr(self, item))
X_new._set_slicers()
return X_new
def transform(self, X):
""" If X and self are of different shapes, align self to X, else
return self.
Parameters
----------
X : Triangle
The triangle to be transformed
Returns
-------
X_new : New triangle with transformed attributes.
"""
backend = X.array_backend
if backend == "sparse":
X_new = X.set_backend("numpy")
else:
X_new = X.copy()
xp = X_new.get_array_module()
self.xp = xp
if "ldf_" not in X_new:
X_new = Development().fit_transform(X_new)
self.xp = X_new.get_array_module()
X_new.p_to_i_X_ = self._get_p_to_i_object(X_new)
X_new.p_to_i_ldf_ = self._get_p_to_i_object(X_new.ldf_)
X_new.p_to_i_sigma_ = self._get_p_to_i_object(X_new.sigma_)
X_new.q_f_, X_new.rho_sigma_ = self._get_MCL_model(X_new)
X_new.munich_full_triangle_ = self._get_munich_full_triangle_(
X_new.p_to_i_X_,
X_new.p_to_i_ldf_,
X_new.p_to_i_sigma_,
self.lambda_coef_,
X_new.rho_sigma_,
X_new.q_f_,
)
X_new.ldf_ = self._set_ldf(
X_new, self._get_mcl_cdf(X_new, X_new.munich_full_triangle_)
)
del self.xp
triangles = ["rho_", "lambda_", "lambda_coef_"]
for item in triangles:
setattr(X_new, item, getattr(self, item))
X_new._set_slicers()
X_new.sigma_ = X_new.std_err_ = X_new.ldf_ * 0 + 1
return X_new
