def test_fb_zero():
prox_f = lambda u, la : u
grad_g = lambda u : u * 0
# observations of size (5,1)
y = np.zeros((5,1))
for method in methods:
xRec = forward_backward(prox_f, grad_g, y, 1, method=method)
assert_array_almost_equal(y, xRec)
# observations of size (5,2)
y = np.zeros((5,2))
for method in methods:
xRec = forward_backward(prox_f, grad_g, y, 1, method=method)
assert_array_almost_equal(y, xRec)
def test_fb_zero():
prox_f = lambda u, la: u
grad_g = lambda u: u * 0
# observations of size (5,1)
y = np.zeros((5, 1))
for method in methods:
x_rec = forward_backward(prox_f, grad_g, y, 1, method=method)
assert_array_almost_equal(y, x_rec)
# observations of size (5,2)
y = np.zeros((5, 2))
for method in methods:
x_rec = forward_backward(prox_f, grad_g, y, 1, method=method)
assert_array_almost_equal(y, x_rec)
def test_fb_l1_denoising():
n = 1000
# Use a very sparse vector for the test
x = np.zeros((n,1))
x[1,:] = 100
y = x + 0.06 * np.random.randn(n,1)
la = 0.2
prox_f = lambda x,tau: soft_thresholding(x, la*tau)
grad_g = lambda x: x - y
for method in methods:
xRec = forward_backward(prox_f, grad_g, y, 1, method=method)
#TODO ugly test to change
assert_array_almost_equal(x, xRec, decimal=0)
def test_fb_l1_denoising():
n = 1000
# Use a very sparse vector for the test
x = np.zeros((n, 1))
x[1, :] = 100
y = x + 0.06 * np.random.randn(n, 1)
la = 0.2
prox_f = lambda x, tau: soft_thresholding(x, la * tau)
grad_g = lambda x: x - y
for method in methods:
x_rec = forward_backward(prox_f, grad_g, y, 1, method=method)
#TODO ugly test to change
assert_array_almost_equal(x, x_rec, decimal=0)