def from_dict(cls, copula_dict):
"""Create a new instance from dictionary.
Args:
copula_dict: `dict` with the parameters to replicate the copula.
Like the output of `Multivariate.to_dict`
Returns:
Multivariate: Instance of the copula defined on the parameters.
"""
copula_class = import_object(copula_dict['type'])
return copula_class.from_dict(copula_dict)
def fit(self, X):
"""Compute the distribution for each variable and then its covariance matrix.
Args:
X: `numpy.ndarray` or `pandas.DataFrame`. Data to model.
Returns:
None
"""
LOGGER.debug('Fitting Gaussian Copula')
column_names = self.get_column_names(X)
distribution_class = import_object(self.distribution)
for column_name in column_names:
self.distribs[column_name] = distribution_class()
column = self.get_column(X, column_name)
self.distribs[column_name].fit(column)
self.covariance = self._get_covariance(X)
self.fitted = True
def fit(self, X):
"""Compute the distribution for each variable and then its covariance matrix.
Args:
X(numpy.ndarray or pandas.DataFrame): Data to model.
Returns:
None
"""
LOGGER.debug('Fitting Gaussian Copula')
distribution_class = import_object(self.distribution)
if not isinstance(X, pd.DataFrame):
X = pd.DataFrame(X)
for column_name, column in X.items():
self.distribs[column_name] = distribution_class()
self.distribs[column_name].fit(column)
self.covariance = self._get_covariance(X)
self.fitted = True
def from_dict(cls, param_dict):
"""Create new instance from dictionary."""
distribution_class = import_object(param_dict['type'])
return distribution_class.from_dict(param_dict)