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()
