def test_changepoint_scaled():
p = 150
M = multiscale(p)
M.minsize = 10
X = ra.adjoint(M)
Y = np.random.standard_normal(p)
Y[20:50] += 8
Y += 2
meanY = Y.mean()
lammax = np.fabs(np.sqrt(M.sizes) * X.adjoint_map(Y) / (1 + np.sqrt(np.log(M.sizes)))).max()
penalty = rr.weighted_l1norm((1 + np.sqrt(np.log(M.sizes))) / np.sqrt(M.sizes), lagrange=0.5*lammax)
loss = rr.squared_error(X, Y - meanY)
problem = rr.simple_problem(loss, penalty)
soln = problem.solve()
Yhat = X.linear_map(soln)
Yhat += meanY
if INTERACTIVE:
plt.scatter(np.arange(p), Y)
plt.plot(np.arange(p), Yhat)
plt.show()
def test_misc():
X = np.random.standard_normal((40, 5))
power_L(X)
Xa = rr.astransform(X)
np.testing.assert_allclose(todense(Xa), X)
reshapeA = adjoint(reshape((30, ), (6, 5)))
assert_raises(NotImplementedError, todense, reshapeA)
def test_misc():
X = np.random.standard_normal((40, 5))
power_L(X)
Xa = rr.astransform(X)
np.testing.assert_allclose(todense(Xa), X)
reshapeA = adjoint(reshape((30,), (6,5)))
assert_raises(NotImplementedError, todense, reshapeA)
def test_adjoint():
X = np.random.standard_normal((20, 30))
b = np.random.standard_normal(20)
L = affine_transform(X, b)
z = np.random.standard_normal(30)
w = np.random.standard_normal(20)
A = adjoint(L)
assert_array_almost_equal(A.linear_map(w), L.adjoint_map(w))
assert_array_almost_equal(A.affine_map(w), L.adjoint_map(w))
assert_array_almost_equal(A.adjoint_map(z), L.linear_map(z))
def test_adjoint():
X = np.random.standard_normal((20,30))
b = np.random.standard_normal(20)
L = affine_transform(X, b)
z = np.random.standard_normal(30)
w = np.random.standard_normal(20)
A = adjoint(L)
assert_array_equal(A.linear_map(w), L.adjoint_map(w))
assert_array_equal(A.affine_map(w), L.adjoint_map(w))
assert_array_equal(A.adjoint_map(z), L.linear_map(z))
def test_choose_parameter(delta=2, p=60):
signal = np.zeros(p)
signal[(p//2):] += delta
Z = np.random.standard_normal(p) + signal
p = Z.shape[0]
M = multiscale(p)
M.scaling = np.sqrt(M.sizes)
lam = choose_tuning_parameter(M)
weights = (lam + np.sqrt(2 * np.log(p / M.sizes))) / np.sqrt(p)
Z0 = Z - Z.mean()
loss = rr.squared_error(ra.adjoint(M), Z0)
penalty = rr.weighted_l1norm(weights, lagrange=1.)
problem = rr.simple_problem(loss, penalty)
coef = problem.solve()
active = coef != 0
if active.sum():
X = M.form_matrix(M.slices[active])[0]
def test_changepoint():
p = 150
M = multiscale(p)
M.minsize = 10
X = ra.adjoint(M)
Y = np.random.standard_normal(p)
Y[20:50] += 8
Y += 2
meanY = Y.mean()
lammax = np.fabs(X.adjoint_map(Y)).max()
penalty = rr.l1norm(X.input_shape, lagrange=0.5*lammax)
loss = rr.squared_error(X, Y - meanY)
problem = rr.simple_problem(loss, penalty)
soln = problem.solve()
Yhat = X.linear_map(soln)
Yhat += meanY
plt.scatter(np.arange(p), Y)
plt.plot(np.arange(p), Yhat)
def test_changepoint():
p = 150
M = multiscale(p)
M.minsize = 10
X = ra.adjoint(M)
Y = np.random.standard_normal(p)
Y[20:50] += 8
Y += 2
meanY = Y.mean()
lammax = np.fabs(X.adjoint_map(Y)).max()
penalty = rr.l1norm(X.input_shape, lagrange=0.5*lammax)
loss = rr.squared_error(X, Y - meanY)
problem = rr.simple_problem(loss, penalty)
soln = problem.solve()
Yhat = X.linear_map(soln)
Yhat += meanY
plt.scatter(np.arange(p), Y)
plt.plot(np.arange(p), Yhat)
plt.show()
