def test_ProxTV(self):
"""...Test of ProxTV
"""
coeffs = self.coeffs.copy().astype(self.dtype)
l_tv = 0.5
t = 1.7
out = np.array([
-0.40102846, -0.40102846, -0.40102846, -0.31364696, -0.31364696,
1.03937619, 1.03937619, 1.03937619, -0.21598253, -0.21598253
])
prox = ProxTV(l_tv).astype(self.dtype)
val = l_tv * np.abs(coeffs[1:] - coeffs[:-1]).sum()
self.assertAlmostEqual(prox.value(coeffs), val, delta=self.delta)
assert_almost_equal(prox.call(coeffs, step=t), out, decimal=7)
coeffs = self.coeffs.copy().astype(self.dtype)
out = np.array([
-0.86017247, -0.58127151, -0.6116414, 0.11135304, 0.11135304,
1.32270952, 1.32270952, 1.32270952, -0.27576309, -1.00620197
])
prox = ProxTV(l_tv, (3, 8)).astype(self.dtype)
val = l_tv * np.abs(coeffs[3:8][1:] - coeffs[3:8][:-1]).sum()
self.assertAlmostEqual(prox.value(coeffs), val, delta=self.delta)
assert_almost_equal(prox.call(coeffs, step=t), out, decimal=7)
coeffs = self.coeffs.copy().astype(self.dtype)
out = np.array([
-0.86017247, -0.58127151, -0.6116414, 0.11135304, 0.11135304,
1.32270952, 1.32270952, 1.32270952, -0.27576309, -1.00620197
])
prox = ProxTV(l_tv, (3, 8), positive=True).astype(self.dtype)
prox.call(coeffs, step=t)
val = l_tv * np.abs(coeffs[3:8][1:] - coeffs[3:8][:-1]).sum()
self.assertAlmostEqual(prox.value(coeffs), val, delta=self.delta)
assert_almost_equal(prox.call(coeffs, step=t), out, decimal=7)
def test_prox_multi(self):
"""...Test of ProxMulti
"""
coeffs = self.coeffs.copy().astype(self.dtype)
double_coeffs = np.concatenate([coeffs, coeffs]).astype(self.dtype)
half_size = coeffs.shape[0]
full_size = double_coeffs.shape[0]
l_tv = 0.5
t = 1.7
prox_tv = ProxTV(strength=l_tv).astype(self.dtype)
prox_tv_multi = ProxTV(strength=l_tv,
range=(0, half_size)).astype(self.dtype)
l_enet = 3e-2
ratio = .3
prox_enet = ProxElasticNet(l_enet, ratio=ratio).astype(self.dtype)
prox_enet_multi = ProxElasticNet(l_enet,
ratio=ratio,
range=(half_size,
full_size)).astype(self.dtype)
prox_multi = ProxMulti((prox_tv_multi, prox_enet_multi))
# Test that the value of the prox is correct
val_multi = prox_multi.value(double_coeffs)
val_correct = prox_enet.value(coeffs) + prox_tv.value(coeffs)
self.assertAlmostEqual(val_multi,
val_correct,
places=self.decimal_places)
# Test that the prox is correct
out1 = prox_tv.call(coeffs, step=t)
out2 = prox_enet.call(coeffs, step=t)
out_correct = np.concatenate([out1, out2])
out_multi = prox_multi.call(double_coeffs, step=t)
np.testing.assert_almost_equal(out_multi, out_correct)
# An example with overlapping coefficients
start1 = 5
end1 = 13
start2 = 10
end2 = 17
prox_tv = ProxTV(strength=l_tv,
range=(start1, end1)).astype(self.dtype)
prox_enet = ProxElasticNet(strength=l_enet,
ratio=ratio,
range=(start2, end2)).astype(self.dtype)
prox_multi = ProxMulti((prox_tv, prox_enet))
val_correct = prox_tv.value(double_coeffs)
val_correct += prox_enet.value(double_coeffs)
val_multi = prox_multi.value(double_coeffs)
self.assertAlmostEqual(val_multi, val_correct)
out_correct = prox_tv.call(double_coeffs)
out_correct = prox_enet.call(out_correct)
out_multi = prox_multi.call(double_coeffs)
np.testing.assert_almost_equal(out_multi, out_correct)