How to calculate tied covariance matrix

[lumliolum]

As per the formulae given in the paper

which is equalivalent to calculate the covaraince matrix for each class and then take the weighted average to get the tied covariance matrix. But in the code,

deep_Mahalanobis_detector/lib_generation.py

Lines 107 to 120 in 90c2105

	precision = []
	for k in range(num_output):
	X = 0
	for i in range(num_classes):
	if i == 0:
	X = list_features[k][i] - sample_class_mean[k][i]
	else:
	X = torch.cat((X, list_features[k][i] - sample_class_mean[k][i]), 0)
	# find inverse
	group_lasso.fit(X.cpu().numpy())
	temp_precision = group_lasso.precision_
	temp_precision = torch.from_numpy(temp_precision).float().cuda()
	precision.append(temp_precision)

you are using sklearn.covariance.EmpiricalCovariance for all of the data (see line 117 X) but as per formulae you calculate the covariance for each class and then take the average. So I feel that we should apply sklearn.covariance.EmpiricalCovariance per class and then take the sum.

Thanks,