def _check(self):
# super(GaussianHMM, self)._check()
self.means_ = np.asarray(self.means_)
self.n_features = self.means_.shape[1]
if self.covariance_type not in COVARIANCE_TYPES:
raise ValueError(
'covariance_type must be one of {0}'.format(COVARIANCE_TYPES))
_validate_covars(self._covars_, self.covariance_type,
self.n_components)
def _check(self):
super(GaussianHMM, self)._check()
self.means_ = np.asarray(self.means_)
self.n_features = self.means_.shape[1]
if self.covariance_type not in COVARIANCE_TYPES:
raise ValueError('covariance_type must be one of {0}'
.format(COVARIANCE_TYPES))
_validate_covars(self._covars_, self.covariance_type,
self.n_components)
def _set_covars(self, covars):
covars = np.asarray(covars)
_validate_covars(covars, self._covariance_type, self.n_components)
self._covars_ = covars.copy()
def _set_covars(self, covars):
covars = np.asarray(covars)
_validate_covars(covars, self._covariance_type, self.n_components)
self._covars_ = covars.copy()
