def test_stack_product():
X = np.random.standard_normal((5, 30))
Y = np.random.standard_normal((5, 30))
Z = np.random.standard_normal((5, 31))
U = np.random.standard_normal((6, 30))
stack = vstack([X, Y])
assert_raises(ValueError, vstack, [X, Z])
assert_raises(ValueError, hstack, [X, U])
np.testing.assert_allclose(
stack.linear_map(np.arange(30))[:5], np.dot(X, np.arange(30)))
np.testing.assert_allclose(
stack.linear_map(np.arange(30))[5:], np.dot(Y, np.arange(30)))
np.testing.assert_allclose(
stack.affine_map(np.arange(30))[:5], np.dot(X, np.arange(30)))
np.testing.assert_allclose(
stack.affine_map(np.arange(30))[5:], np.dot(Y, np.arange(30)))
np.testing.assert_allclose(
stack.adjoint_map(np.arange(10)),
np.dot(X.T, np.arange(5)) + np.dot(Y.T, np.arange(5, 10)))
_hstack = hstack([X, Y, Z])
_hstack.linear_map(np.arange(91))
_hstack.affine_map(np.arange(91))
_hstack.adjoint_map(np.arange(5))
b = np.random.standard_normal(5)
XA = rr.affine_transform(X, b)
_product = product([XA, Y])
np.testing.assert_allclose(
_product.linear_map(np.arange(60))[:5], np.dot(X, np.arange(30)))
np.testing.assert_allclose(
_product.linear_map(np.arange(60))[5:], np.dot(Y, np.arange(30, 60)))
np.testing.assert_allclose(
_product.affine_map(np.arange(60))[:5],
np.dot(X, np.arange(30)) + b)
np.testing.assert_allclose(
_product.affine_map(np.arange(60))[5:], np.dot(Y, np.arange(30, 60)))
np.testing.assert_allclose(
_product.adjoint_map(np.arange(10))[:30], np.dot(X.T, np.arange(5)))
np.testing.assert_allclose(
_product.adjoint_map(np.arange(10))[30:],
np.dot(Y.T, np.arange(5, 10)))
scale_prod = scalar_multiply(_product, 2)
np.testing.assert_allclose(scale_prod.linear_map(np.arange(60)),
2 * _product.linear_map(np.arange(60)))
np.testing.assert_allclose(scale_prod.affine_map(np.arange(60)),
2 * _product.affine_map(np.arange(60)))
np.testing.assert_allclose(scale_prod.adjoint_map(np.arange(60)),
2 * _product.adjoint_map(np.arange(60)))
def fit(self, solve_args={'min_its': 200}):
Y, M, p, weights = self.Y, self.M, self.Y.shape[
0], self.feature_weights
Y0 = Y - Y.mean()
coef = solve_sqrt_lasso(M.T,
Y0,
weights=weights,
solve_args=solve_args)[0]
self.active = active = coef != 0
self.inactive = inactive = ~active
if active.sum():
jumps = self.merge_intervals(M.slices[active])
M_inactive = copy(M)
M_inactive.update_slices(M.slices[inactive])
X = M.form_matrix(M.slices[active])[0]
R = np.identity(p) - X.dot(np.linalg.pinv(X))
L_inactive = M_inactive.dot(rr.astransform(R))
L = lasso.sqrt_lasso(X, Y0, weights[active])
L.fit(solve_args={'min_its': 200}, lasso_solution=coef[active])
C = L.constraints.linear_part.dot(X.T)
L_inactive_neg = ra.scalar_multiply(L_inactive, -1.)
irrep = L_inactive.dot(
X.dot(-np.diag(L.active_signs).dot(L.constraints.offset)))
full_lin = ra.vstack([L_inactive, L_inactive_neg, C])
full_offset = np.hstack([
irrep + L._multiplier * weights[inactive],
-irrep + L._multiplier * weights[inactive],
L.constraints.offset
])
full_con = constraints(full_lin,
full_offset,
covariance=L._sigma_hat**2 * np.identity(p))
print full_con(Y - Y.mean())
fit = X.dot(coef[active]) + Y.mean()
segments = np.array([jumps[:-1], jumps[1:]]).T
Xr = M.form_matrix(segments)[0]
relaxed_fit = Xr.dot(np.linalg.pinv(Xr).dot(Y)) + Y.mean()
# S = L.summary('onesided')
# pS = pd.DataFrame(S)
# pS['interval'] = [M.slices[i] for i in np.nonzero(active)[0]]
# pS = pS.reindex(columns=['interval', 'pval', 'lasso', 'onestep'])
pS = None
else:
fit = relaxed_fit = Y.mean() * np.ones(self.p)
pS = None
segments = np.array([[0, self.p]])
return fit, relaxed_fit, pS, segments
def test_stack_product():
X = np.random.standard_normal((5, 30))
Y = np.random.standard_normal((5, 30))
Z = np.random.standard_normal((5, 31))
U = np.random.standard_normal((6, 30))
stack = vstack([X, Y])
assert_raises(ValueError, vstack, [X, Z])
assert_raises(ValueError, hstack, [X, U])
np.testing.assert_allclose(stack.linear_map(np.arange(30))[:5], np.dot(X, np.arange(30)))
np.testing.assert_allclose(stack.linear_map(np.arange(30))[5:], np.dot(Y, np.arange(30)))
np.testing.assert_allclose(stack.affine_map(np.arange(30))[:5], np.dot(X, np.arange(30)))
np.testing.assert_allclose(stack.affine_map(np.arange(30))[5:], np.dot(Y, np.arange(30)))
np.testing.assert_allclose(stack.adjoint_map(np.arange(10)), np.dot(X.T, np.arange(5)) + np.dot(Y.T, np.arange(5, 10)))
_hstack = hstack([X, Y, Z])
_hstack.linear_map(np.arange(91))
_hstack.affine_map(np.arange(91))
_hstack.adjoint_map(np.arange(5))
b = np.random.standard_normal(5)
XA = rr.affine_transform(X, b)
_product = product([XA,Y])
np.testing.assert_allclose(_product.linear_map(np.arange(60))[:5], np.dot(X, np.arange(30)))
np.testing.assert_allclose(_product.linear_map(np.arange(60))[5:], np.dot(Y, np.arange(30, 60)))
np.testing.assert_allclose(_product.affine_map(np.arange(60))[:5], np.dot(X, np.arange(30)) + b)
np.testing.assert_allclose(_product.affine_map(np.arange(60))[5:], np.dot(Y, np.arange(30, 60)))
np.testing.assert_allclose(_product.adjoint_map(np.arange(10))[:30], np.dot(X.T, np.arange(5)))
np.testing.assert_allclose(_product.adjoint_map(np.arange(10))[30:], np.dot(Y.T, np.arange(5, 10)))
scale_prod = scalar_multiply(_product, 2)
np.testing.assert_allclose(scale_prod.linear_map(np.arange(60)), 2 * _product.linear_map(np.arange(60)))
np.testing.assert_allclose(scale_prod.affine_map(np.arange(60)), 2 * _product.affine_map(np.arange(60)))
np.testing.assert_allclose(scale_prod.adjoint_map(np.arange(60)), 2 * _product.adjoint_map(np.arange(60)))