def estimate_data(samples, cov, t): # XX is a full data matrix # Cov is the covariance matrix of an underlying Gaussian graphical model T = binarize_edges(np.linalg.inv(cov), t) E = binarize_edges(estimate_precision(samples), t) fpr, fnr, fnorm = compute_statistics(T, E) return fpr, fnr, fnorm