def test_nesta_lasso():
n, p = 1000, 20
X = np.random.standard_normal((n, p))
beta = np.zeros(p)
beta[:4] = 30
Y = np.random.standard_normal(n) + np.dot(X, beta)
loss = rr.squared_error(X,Y)
penalty = rr.l1norm(p, lagrange=2.)
# using nesta
z = rr.zero(p)
primal, dual = rr.nesta(loss, z, penalty, tol=1.e-10,
epsilon=2.**(-np.arange(30)),
initial_dual=np.zeros(p))
# using simple problem
problem = rr.simple_problem(loss, penalty)
problem.solve()
nt.assert_true(np.linalg.norm(primal - problem.coefs) / np.linalg.norm(problem.coefs) < 1.e-3)
# test None as smooth_atom
rr.nesta(None, z, penalty, tol=1.e-10,
epsilon=2.**(-np.arange(30)),
initial_dual=np.zeros(p))
# using coefficients to stop
rr.nesta(loss, z, penalty, tol=1.e-10,
epsilon=2.**(-np.arange(30)),
initial_dual=np.zeros(p),
coef_stop=True)
def test_nesta_lasso():
n, p = 1000, 20
X = np.random.standard_normal((n, p))
beta = np.zeros(p)
beta[:4] = 30
Y = np.random.standard_normal(n) + np.dot(X, beta)
loss = rr.squared_error(X,Y)
penalty = rr.l1norm(p, lagrange=2.)
# using nesta
z = rr.zero(p)
primal, dual = rr.nesta(loss, z, penalty, tol=1.e-10,
epsilon=2.**(-np.arange(30)),
initial_dual=np.zeros(p))
# using simple problem
problem = rr.simple_problem(loss, penalty)
problem.solve()
nt.assert_true(np.linalg.norm(primal - problem.coefs) / np.linalg.norm(problem.coefs) < 1.e-3)
# test None as smooth_atom
rr.nesta(None, z, penalty, tol=1.e-10,
epsilon=2.**(-np.arange(30)),
initial_dual=np.zeros(p))
# using coefficients to stop
rr.nesta(loss, z, penalty, tol=1.e-10,
epsilon=2.**(-np.arange(30)),
initial_dual=np.zeros(p),
coef_stop=True)
def test_nesta_nonnegative():
state = np.random.get_state()
np.random.seed(10)
n, p, q = 1000, 20, 5
X = np.random.standard_normal((n, p))
A = np.random.standard_normal((q,p))
coef = 10 * np.fabs(np.random.standard_normal(q)) + 1
coef[:2] = -0.2
beta = np.dot(np.linalg.pinv(A), coef)
print(r'\beta', beta)
print(r'A\beta', np.dot(A, beta))
Y = np.random.standard_normal(n) + np.dot(X, beta)
loss = rr.squared_error(X,Y)
penalty = rr.l1norm(p, lagrange=0.2)
constraint = rr.nonnegative.linear(A)
primal, dual = rr.nesta(loss, penalty, constraint, max_iters=300, coef_tol=1.e-10, tol=1.e-10)
print(r'A \hat{\beta}', np.dot(A, primal))
assert_almost_nonnegative(np.dot(A,primal), tol=1.e-3)
np.random.set_state(state)
def test_nesta_nonnegative():
state = np.random.get_state()
np.random.seed(10)
n, p, q = 1000, 20, 5
X = np.random.standard_normal((n, p))
A = np.random.standard_normal((q, p))
coef = 10 * np.fabs(np.random.standard_normal(q)) + 1
coef[:2] = -0.2
beta = np.dot(np.linalg.pinv(A), coef)
print(r'\beta', beta)
print(r'A\beta', np.dot(A, beta))
Y = np.random.standard_normal(n) + np.dot(X, beta)
loss = rr.squared_error(X, Y)
penalty = rr.l1norm(p, lagrange=0.2)
constraint = rr.nonnegative.linear(A)
primal, dual = rr.nesta(loss,
penalty,
constraint,
max_iters=300,
coef_tol=1.e-10,
tol=1.e-10)
print(r'A \hat{\beta}', np.dot(A, primal))
assert_almost_nonnegative(np.dot(A, primal), tol=1.e-3)
np.random.set_state(state)
def test_nesta_nonnegative():
n, p, q = 1000, 20, 5
X = np.random.standard_normal((n, p))
A = np.random.standard_normal((q, p))
coef = 10 * np.fabs(np.random.standard_normal(q)) + 1
coef[:2] = -0.2
beta = np.dot(np.linalg.pinv(A), coef)
Y = np.random.standard_normal(n) + np.dot(X, beta)
loss = rr.squared_error(X, Y)
penalty = rr.l1norm(p, lagrange=0.2)
constraint = rr.nonnegative.linear(A)
primal, dual = rr.nesta(loss,
penalty,
constraint,
max_iters=300,
coef_tol=1.e-4,
tol=1.e-4)
print np.dot(A, primal)
assert_almost_nonnegative(np.dot(A, primal), tol=1.e-3)
def test_nesta_nonnegative():
n, p, q = 1000, 20, 5
X = np.random.standard_normal((n, p))
beta = np.zeros(p)
beta[:4] = 3
Y = np.random.standard_normal(n) + np.dot(X, beta)
A = np.random.standard_normal((q, p))
loss = rr.squared_error(X, Y)
penalty = rr.l1norm(p, lagrange=0.2)
constraint = rr.nonnegative.linear(A)
primal, dual = rr.nesta(loss, penalty, constraint)
assert_almost_nonnegative(np.dot(A, primal))
def test_nesta_nonnegative():
n, p, q = 1000, 20, 5
X = np.random.standard_normal((n, p))
beta = np.zeros(p)
beta[:4] = 3
Y = np.random.standard_normal(n) + np.dot(X, beta)
A = np.random.standard_normal((q,p))
loss = rr.squared_error(X,Y)
penalty = rr.l1norm(p, lagrange=0.2)
constraint = rr.nonnegative.linear(A)
primal, dual = rr.nesta(loss, penalty, constraint)
assert_almost_nonnegative(np.dot(A,primal))
def test_nesta_nonnegative():
n, p, q = 1000, 20, 5
X = np.random.standard_normal((n, p))
A = np.random.standard_normal((q,p))
coef = 10 * np.fabs(np.random.standard_normal(q)) + 1
coef[:2] = -0.2
beta = np.dot(np.linalg.pinv(A), coef)
Y = np.random.standard_normal(n) + np.dot(X, beta)
loss = rr.squared_error(X,Y)
penalty = rr.l1norm(p, lagrange=0.2)
constraint = rr.nonnegative.linear(A)
primal, dual = rr.nesta(loss, penalty, constraint, max_iters=300, coef_tol=1.e-4, tol=1.e-4)
print np.dot(A, primal)
assert_almost_nonnegative(np.dot(A,primal), tol=1.e-3)