def test_01(self):
N = 64
L = 20
x = np.cos(np.linspace(0, np.pi, N))[np.newaxis, :]
y = np.cos(np.linspace(0, np.pi, N))[:, np.newaxis]
U = x * y
V = np.random.randn(N, N)
t = np.sort(np.abs(V).ravel())[V.size - L]
V[np.abs(V) < t] = 0
D = U + V
lmbda = 0.1
opt = spline.SplineL1.Options({
'Verbose': False,
'gEvalY': False,
'MaxMainIter': 250,
'RelStopTol': 5e-4,
'DFidWeight': V == 0,
'AutoRho': {
'Enabled': True
}
})
b = spline.SplineL1(D, lmbda, opt)
X = b.solve()
assert (np.abs(b.itstat[-1].ObjFun - 0.333606246) < 1e-6)
assert (sl.mse(U, X) < 1e-6)
def test_01(self):
N = 64
K = 5
L = 10
u = np.random.randn(N, K)
U = np.dot(u, u.T)
V = np.random.randn(N, N)
t = np.sort(np.abs(V).ravel())[V.size-L]
V[np.abs(V) < t] = 0
D = U + V
opt = rpca.RobustPCA.Options({'Verbose' : False, 'gEvalY' : False,
'MaxMainIter' : 250,
'AutoRho' : {'Enabled' : True}})
b = rpca.RobustPCA(D, None, opt)
X, Y = b.solve()
assert(np.abs(b.itstat[-1].ObjFun - 321.493968419) < 1e-6)
assert(sl.mse(U,X) < 5e-6)
assert(sl.mse(V,Y) < 1e-8)
def test_01(self):
lmbda = 3
opt = tvl1.TVL1Denoise.Options({
'Verbose': False,
'gEvalY': False,
'MaxMainIter': 250
})
b = tvl1.TVL1Denoise(self.D, lmbda, opt, axes=(0, 1, 2))
X = b.solve()
assert (np.abs(b.itstat[-1].ObjFun - 6219.6209699337605) < 1e-6)
assert (sl.mse(self.U, X) < 1e-6)
def test_01(self):
lmbda = 3
opt = tvl1.TVL1Denoise.Options({
'Verbose': False,
'gEvalY': False,
'MaxMainIter': 250
})
b = tvl1.TVL1Denoise(self.D, lmbda, opt)
X = b.solve()
assert (np.abs(b.itstat[-1].ObjFun - 447.78101756451662) < 1e-6)
assert (sl.mse(self.U, X) < 1e-6)
def test_02(self):
lmbda = 1e-1
opt = tvl2.TVL2Deconv.Options({
'Verbose': False,
'gEvalY': False,
'MaxMainIter': 250
})
b = tvl2.TVL2Deconv(np.ones((1)), self.D, lmbda, opt, axes=(0, 1, 2))
X = b.solve()
assert (np.abs(b.itstat[-1].ObjFun - 567.72425227) < 1e-3)
assert (sl.mse(self.U, X) < 1e-3)
def test_02(self):
lmbda = 1e-1
opt = tvl2.TVL2Deconv.Options({
'Verbose': False,
'gEvalY': False,
'MaxMainIter': 250
})
b = tvl2.TVL2Deconv(np.ones((1)), self.D, lmbda, opt)
X = b.solve()
assert (np.abs(b.itstat[-1].ObjFun - 45.45958573088) < 1e-3)
assert (sl.mse(self.U, X) < 1e-3)
def test_02(self):
lmbda = 3
opt = tvl1.TVL1Deconv.Options({
'Verbose': False,
'gEvalY': False,
'MaxMainIter': 250,
'rho': 10.0
})
b = tvl1.TVL1Deconv(np.ones((1, 1)), self.D, lmbda, opt, axes=(0, 1))
X = b.solve()
assert (np.abs(b.itstat[-1].ObjFun - 12364.029061174046) < 1e-5)
assert (sl.mse(self.U, X) < 1e-4)
def test_02(self):
lmbda = 3
opt = tvl1.TVL1Deconv.Options({
'Verbose': False,
'gEvalY': False,
'MaxMainIter': 250,
'rho': 10.0
})
b = tvl1.TVL1Deconv(np.ones((1, 1)), self.D, lmbda, opt)
X = b.solve()
assert (np.abs(b.itstat[-1].ObjFun - 831.88219947939172) < 1e-5)
assert (sl.mse(self.U, X) < 1e-4)
def test_01(self):
lmbda = 1e-1
opt = tvl2.TVL2Denoise.Options({
'Verbose': False,
'gEvalY': False,
'MaxMainIter': 250,
'rho': 10 * lmbda
})
b = tvl2.TVL2Denoise(self.D, lmbda, opt, axes=(0, 1, 2))
X = b.solve()
assert (np.abs(b.itstat[-1].ObjFun - 366.04267554965134) < 1e-3)
assert (sl.mse(self.U, X) < 1e-3)
def test_01(self):
lmbda = 1e-1
opt = tvl2.TVL2Denoise.Options({
'Verbose': False,
'gEvalY': False,
'MaxMainIter': 300,
'rho': 75 * lmbda
})
b = tvl2.TVL2Denoise(self.D, lmbda, opt)
X = b.solve()
assert (np.abs(b.itstat[-1].ObjFun - 32.875710674129564) < 1e-3)
assert (sl.mse(self.U, X) < 1e-3)