def test_constraints(): y, X, beta = sample_lasso(100, 50, 10) las = lasso(y, X, 4.) las.fit() las.form_constraints() active = las.active_constraints inactive = las.inactive_constraints const = las.constraints
def test_intervals(n=100, p=20, m=5, n_test = 10): t = [] for i in range(n_test): y, X, beta = sample_lasso(n, p, m) las = lasso(y, X, 4., sigma = .25) las.fit() las.form_constraints() intervals = las.intervals t.append([(beta[I[0]], I[3]) for I in intervals]) return t
def test_intervals(n=100, p=20, m=5, n_test=10): t = [] for i in range(n_test): y, X, beta = sample_lasso(n, p, m) las = lasso(y, X, 4., sigma=.25) las.fit() las.form_constraints() intervals = las.intervals t.append([(beta[I[0]], I[3]) for I in intervals]) return t
def test_class(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)) L = lasso(y,X,lam=0.5*lam_theor) L.fit(tol=1.e-7) L.form_constraints() C = L.constraints np.testing.assert_array_less( \ np.dot(L.constraints.linear_part, L.y), L.constraints.offset) I = L.intervals P = L.active_pvalues return L, C, I, P
def test_class(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)) L = lasso(y, X, lam=0.5 * lam_theor) L.fit(tol=1.e-7) L.form_constraints() C = L.constraints np.testing.assert_array_less( \ np.dot(L.constraints.linear_part, L.y), L.constraints.offset) I = L.intervals P = L.active_pvalues return L, C, I, P
def test_nominal_intervals(): y, X, beta = sample_lasso(100, 50, 10) las = lasso(y, X, 4.) nom_int = las.nominal_intervals
def test_pvalue(): y, X, beta = sample_lasso(100, 50, 10) las = lasso(y, X, 4.) las.form_constraints() pval = las.active_pvalues
def test_soln(): y, X, bet = sample_lasso(100, 50, 10) las = lasso(y, X, 4.) beta2 = las.soln