def method_instance(self):
if not hasattr(self, "_method_instance"):
n, p = self.X.shape
self._method_instance = lasso_full.gaussian(
self.X, self.Y, self.lagrange * np.sqrt(n))
self._method_instance.sparse_inverse = True
return self._method_instance
def test_gaussian_full(n=100, p=20):
y = np.random.standard_normal(n)
X = np.random.standard_normal((n, p))
lam_theor = np.mean(
np.fabs(np.dot(X.T, np.random.standard_normal((n, 1000)))).max(0))
Q = rr.identity_quadratic(0.01, 0, np.ones(p), 0)
weights_with_zeros = 0.5 * lam_theor * np.ones(p)
weights_with_zeros[:3] = 0.
L = lasso_full.gaussian(X, y, weights_with_zeros, 1., quadratic=Q)
L.fit()
print(L.summary(compute_intervals=True))
