def test_09(self):
N = 64
M = 2 * N
L = 4
np.random.seed(12345)
D = np.random.randn(N, M)
x0 = np.zeros((M, 1))
si = np.random.permutation(list(range(0, M - 1)))
x0[si[0:L]] = np.random.randn(L, 1)
s0 = D.dot(x0)
lmbda = 5e-3
opt = bpdn.BPDN.Options({
'Verbose': False,
'MaxMainIter': 1000,
'BackTrack': {
'Enabled': True,
'Robust': True
},
'RelStopTol': 5e-8
})
b = bpdn.BPDN(D, s0, lmbda, opt)
b.solve()
x1 = b.Y
assert np.abs(b.itstat[-1].ObjFun - 0.012009) < 1e-5
assert np.abs(b.itstat[-1].DFid - 1.9636082e-06) < 1e-5
assert np.abs(b.itstat[-1].RegL1 - 2.401446) < 2e-4
assert np.linalg.norm(x1 - x0) < 1e-3
def test_02(self):
N = 8
M = 16
D = np.random.randn(N, M)
s = np.random.randn(N, 1)
try:
b = bpdn.BPDN(D, s)
b.solve()
except Exception as e:
print(e)
assert 0
def test_01(self):
N = 8
M = 16
D = np.random.randn(N, M)
s = np.random.randn(N, 1)
lmbda = 1e-1
try:
b = bpdn.BPDN(D, s, lmbda)
b.solve()
except Exception as e:
print(e)
assert 0
def test_10(self):
N = 8
M = 16
D = np.random.randn(N, M)
s = np.random.randn(N, 1)
lmbda = 1e-1
opt = bpdn.BPDN.Options({'Verbose': False, 'MaxMainIter': 10})
b = bpdn.BPDN(D, s, lmbda, opt)
bp = pickle.dumps(b)
c = pickle.loads(bp)
Xb = b.solve()
Xc = c.solve()
assert np.linalg.norm(Xb - Xc) == 0.0
def test_08(self):
N = 8
M = 16
D = np.random.randn(N, M)
s = np.random.randn(N, 1)
dt = np.float64
opt = bpdn.BPDN.Options({
'Verbose': False,
'MaxMainIter': 20,
'BackTrack': {
'Enabled': True
},
'DataType': dt
})
b = bpdn.BPDN(D, s, lmbda=1.0, opt=opt)
b.solve()
assert b.X.dtype == dt
assert b.Y.dtype == dt
def test_05(self):
N = 8
M = 8
D = np.random.randn(N, M)
s = np.random.randn(N, 1)
try:
opt = bpdn.BPDN.Options({
'FastSolve': True,
'Verbose': False,
'MaxMainIter': 10,
'BackTrack': {
'Enabled': False
}
})
b = bpdn.BPDN(D, s, lmbda=1.0, opt=opt)
b.solve()
except Exception as e:
print(e)
assert 0
def test_03(self):
N = 8
M = 16
D = np.random.randn(N, M)
s = np.random.randn(N, 1)
try:
opt = bpdn.BPDN.Options({
'Verbose': False,
'MaxMainIter': 80,
'BackTrack': {
'Enabled': True
},
'RelStopTol': 1e-4
})
b = bpdn.BPDN(D, s, lmbda=1.0, opt=opt)
b.solve()
except Exception as e:
print(e)
assert 0
assert np.array(b.getitstat().Rsdl).min() < 2.e-4
"""
L_sc = 9.e2
opt = bpdn.BPDN.Options({
'Verbose': True,
'MaxMainIter': 50,
'BackTrack': {
'Enabled': True
},
'L': L_sc
})
lmbda = 2.98e1
print("")
b = bpdn.BPDN(D, s, lmbda, opt)
x = b.solve()
print("BPDN standard FISTA backtracking solve time: %.2fs" %
b.timer.elapsed('solve'))
print("")
"""
Plot comparison of reference and recovered representations.
"""
plot.plot(np.hstack((x0, x)),
title='Sparse representation (standard FISTA)',
lgnd=['Reference', 'Reconstructed'])
"""
Plot lmbda error curve, functional value, residuals, and rho
"""