def test_tv1d_dtype():
# check that prox_tv1d preserve 32bit
x = np.arange(5)
for dtype in (np.float32, np.float64):
y = x.astype(dtype, copy=True)
prox_fast.prox_tv1d(y, 0.01)
assert_equal(y.dtype, dtype)
def test_tv1d_dtype():
# check that prox_tv1d preserve 32bit
x = np.arange(5)
for dtype in (np.float32, np.float64):
y = x.astype(dtype, copy=True)
prox_fast.prox_tv1d(y, 0.01)
assert_equal(y.dtype, dtype)
def test_tv1_denoise():
# test the prox of TV1D
# since its not trivial to check the KKT conditions
# we check that the proximal point algorithm converges
# to a solution to the TV minimization
n_iter = 100
n_features = 100
# repeat the test 10 times
for nrun in range(10):
x = np.random.randn(n_features)
for _ in range(n_iter):
prox_fast.prox_tv1d(x, 1.0)
# check that the solution is flat
np.testing.assert_allclose(x, x.mean() * np.ones(n_features))
def test_tv1_denoise():
# test the prox of TV1D
# since its not trivial to check the KKT conditions
# we check that the proximal point algorithm converges
# to a solution to the TV minimization
n_iter = 100
n_features = 100
# repeat the test 10 times
for nrun in range(10):
x = np.random.randn(n_features)
for _ in range(n_iter):
prox_fast.prox_tv1d(x, 1.0)
# check that the solution is flat
np.testing.assert_allclose(x, x.mean() * np.ones(n_features))
def projection(self, coef, alpha, L):
tmp = coef.copy()
for i in range(tmp.shape[0]):
prox_tv1d(tmp[i, :], alpha / L) # operates inplace
return tmp
def projection(self, coef, alpha, L):
tmp = coef.copy()
for i in range(tmp.shape[0]):
prox_tv1d(tmp[i, :], alpha / L) # operates inplace
return tmp
def projection(self, coef, alpha, L):
tmp = np.empty_like(coef)
for i in range(tmp.shape[0]):
tmp[i] = prox_tv1d(coef[i], alpha / L)
return tmp
def test_tv1d_dtype(dtype):
# check that prox_tv1d preserve 32bit
x = np.arange(5)
y = x.astype(dtype, copy=True)
prox_fast.prox_tv1d(y, 0.01)
assert y.dtype == dtype
