def _prox(self, alpha, input):
return thresh.soft_thresh(self.lamda * alpha, input)
def _prox(self, alpha, input):
u, s, vh = np.linalg.svd(input, full_matrices=False)
s_max = np.max(s)
# print('Eigen Value:{}'.format(np.diag(s)))
s_t = thresh.soft_thresh(self.lamda * alpha * s_max, s)
return np.matmul(u, s_t[..., None] * vh)
def test_soft_thresh(self):
x = np.array([-2, -1.5, -1, 0.5, 0, 0.5, 1, 1.5, 2])
y = np.array([-1, -0.5, 0, 0, 0, 0, 0, 0.5, 1])
npt.assert_allclose(thresh.soft_thresh(1, x), y)
def test_soft_thresh_cuda(self):
x = cp.array([-2, -1.5, -1, 0.5, 0, 0.5, 1, 1.5, 2])
y = cp.array([-1, -0.5, 0, 0, 0, 0, 0, 0.5, 1])
lamda = cp.array([1.0])
cp.testing.assert_allclose(thresh.soft_thresh(lamda, x), y)
def _prox(self, alpha, input):
with backend.get_device(input):
return thresh.soft_thresh(self.lamda * alpha, input)
