if "psis" not in self.covars_prior:
self.covars_prior["psis"] = np.asarray(0.0)
if "nus" not in self.covars_prior:
self.covars_prior["nus"] = np.asarray(
-(self.n_mix + self.n_features + 1.0)
)
elif self.covariance_type == "diag":
if "alphas" not in self.covars_prior:
self.covars_prior["alphas"] = np.asarray(-1.5)
if "betas" not in self.covars_prior:
self.covars_prior["betas"] = np.asarray(0.0)
elif self.covariance_type == "spherical":
if "alphas" not in self.covars_prior:
self.covars_prior["alphas"] = np.asarray(
-(self.n_mix + 2.0) / 2.0
After Change
elif self.covariance_type == "diag":
if self.covars_prior is None:
self.covars_prior = -1.5
if self.covars_weight is None:
self.covars_weight = 0.0
elif self.covariance_type == "spherical":
if self.covars_prior is None:
self.covars_prior = -(self.n_mix + 2.0) / 2.0
if self.covars_weight is None:
