def test_chain(): p = 2 n = 1000 samples, cov = network.chain_network(p, n) x = samples[:,0] y = samples[:,1] plot(x, y, 'o') show()
def chain_lasso(r = 20): fps = np.zeros(r) fns = np.zeros(r) norms = np.zeros(r) for i in range(r): samples, cov = network.chain_network() t = 1e-1 fpr, fnr, fnorm = estimate_data(samples, cov, t) fps[i] = fpr fns[i] = fnr norms[i] = fnorm return fps.mean(), fns.mean(), norms.mean()