def fit_cov(self, X):
"""Fit the InequalityTimeGraphicalLasso model to covariances.
Parameters
----------
X : ndarray, shape = (n_dimensions, n_dimensions, n_samples, time_steps)
"""
n_dimensions, _, n_samples, time_steps = X.shape
self.emp_cov = []
self.emp_inv = []
self.emp_inv_score = []
self.sam_inv_score = []
self.C = []
for i in range(time_steps):
self.emp_cov.append(np.mean(X[:, :, :, i], 2))
self.emp_inv.append(np.linalg.inv(self.emp_cov[i]))
if self.loss == 'LL':
self.emp_inv_score.append(neg_logl(self.emp_cov[i], self.emp_inv[i]))
self.sam_inv_score.append(np.array([neg_logl(X[:, :, j, i], self.emp_inv[i]) for j in range(n_samples)]))
else:
self.emp_inv_score.append(dtrace(self.emp_cov[i], self.emp_inv[i]))
self.sam_inv_score.append(np.array([dtrace(X[:, :, j, i], self.emp_inv[i]) for j in range(n_samples)]))
self.C.append(np.quantile(self.sam_inv_score[i], 1 - self.c_level, 0))
self.emp_cov = np.array(self.emp_cov)
self.emp_inv = np.array(self.emp_inv)
self.emp_inv_score = np.array(self.emp_inv_score)
self.sam_inv_score = np.array(self.sam_inv_score)
return self._fit(self.emp_cov)
def eval_cov_pre(self):
"""
Evaluate the log likelihood of estimated precisions compared to the inverse sample covariance at each time step
"""
precisions = self.precision_
fit_score = []
for i in range(precisions.shape[0]):
precision = precisions[i]
if self.loss == 'LL':
fit_score.append(neg_logl(self.emp_cov[i], precision)[0])
else:
fit_score.append(dtrace(self.emp_cov[i], precision)[0])
return self.emp_inv_score, self.C, fit_score, precisions
