def test_algorithms():
#=======================================================================
"Check that the value returned by the the grid and Brent algorithms coincide"
t = np.linspace(0, 5, 80)
r = np.linspace(2, 5, 80)
P = dd_gauss(r, 3, 0.2)
K = dipolarkernel(t, r)
L = regoperator(r, 2)
V = K @ P + whitegaussnoise(t, 0.02, seed=1)
alpha_grid = selregparam(V,
K,
cvxnnls,
method='aic',
algorithm='grid',
regop=L)
alpha_brent = selregparam(V,
K,
cvxnnls,
method='aic',
algorithm='brent',
regop=L)
assert abs(1 - alpha_grid / alpha_brent) < 0.15
def assert_full_output(method):
t = np.linspace(0, 5, 80)
r = np.linspace(2, 5, 80)
P = dd_gauss(r, 3, 0.4)
K = dipolarkernel(t, r)
L = regoperator(r, 2)
V = K @ P
alpha, alphas_evaled, functional, residuals, penalties = selregparam(
V,
K,
cvxnnls,
method='aic',
algorithm=method,
full_output=True,
regop=L)
errors = []
if np.size(alpha) != 1:
errors.append("alphaopt is not a scalar")
if len(functional) != len(alphas_evaled):
errors.append(
"The number of elements of functional values and evaluated alphas are different."
)
if len(residuals) != len(penalties):
errors.append(
"The number of elements of evluated residuals and penalties are different"
)
if not alpha in alphas_evaled:
errors.append("The optimal alpha is not part of the evaluated alphas")
assert not errors, f"Errors occured:\n{chr(10).join(errors)}"
def test_manual_candidates():
#=======================================================================
"Check that the alpha-search range can be manually passed"
t = np.linspace(0, 5, 80)
r = np.linspace(2, 5, 80)
P = dd_gauss(r, 3, 0.15)
K = dipolarkernel(t, r)
L = regoperator(r, 2, includeedges=True)
alphas = np.linspace(-8, 2, 60)
V = K @ P
alpha_manual = np.log10(
selregparam(V, K, cvxnnls, method='aic', candidates=alphas, regop=L))
alpha_auto = np.log10(selregparam(V, K, cvxnnls, method='aic', regop=L))
assert abs(alpha_manual - alpha_auto) < 1e-4
def test_compensate_condition():
#=======================================================================
"Check that alpha compensates for larger condition numbers"
r = np.linspace(2, 6, 100)
P = dd_gauss(r, 3, 0.2)
# Lower condition number
t1 = np.linspace(0, 3, 200)
K1 = dipolarkernel(t1, r)
V1 = K1 @ P
alpha1 = selregparam(V1, K1, cvxnnls, method='aic')
# Larger condition number
t2 = np.linspace(0, 3, 400)
K2 = dipolarkernel(t2, r)
V2 = K2 @ P
alpha2 = selregparam(V2, K2, cvxnnls, method='aic')
assert alpha2 > alpha1
def get_alpha_from_method(method):
t = np.linspace(0, 5, 500)
r = np.linspace(2, 5, 80)
P = dd_gauss(r, 3.0, 0.16986436005760383)
K = dipolarkernel(t, r)
L = regoperator(r, 2, includeedges=True)
V = K @ P
alpha = selregparam(V, K, cvxnnls, method=method, noiselvl=0, regop=L)
return np.log10(alpha)
def test_nonuniform_r():
#=======================================================================
"Check the value returned when using a non-uniform distance axis"
t = np.linspace(0, 3, 200)
r = np.sqrt(np.linspace(1, 7**2, 200))
P = dd_gauss(r, 3, 0.2)
K = dipolarkernel(t, r)
L = regoperator(r, 2)
V = K @ P
logalpha = np.log10(selregparam(V, K, cvxnnls, method='aic', regop=L))
logalpharef = -6.8517
assert abs(1 - logalpha / logalpharef) < 0.2
def linear_problem(y, A, optimize_alpha, alpha):
#===========================================================================
"""
Linear problem
------------------
Solves the linear subproblem of the SNLLS objective function via linear LSQ
constrained, unconstrained, or regularized.
"""
# If all linear parameters are frozen, do not optimize
if Nlin_notfrozen == 0:
return lin_parfrozen.astype(float), None, 0
# Remove columns corresponding to frozen linear parameters
Ared = A[:, ~lin_frozen]
# Frozen component of the model response
yfrozen = (A[:, lin_frozen] @ lin_parfrozen[lin_frozen]).astype(float)
# Optimiza the regularization parameter only if needed
if optimize_alpha:
alpha = dl.selregparam((y - yfrozen)[mask],
Ared[mask, :],
linSolver,
regparam,
weights=weights[mask],
regop=L,
candidates=regparamrange,
noiselvl=noiselvl,
searchrange=regparamrange)
# Components for linear least-squares
AtA, Aty = _lsqcomponents((y - yfrozen)[mask],
Ared[mask, :],
L,
alpha,
weights=weights[mask])
Ndof = np.maximum(0, np.trace(Ared @ np.linalg.pinv(AtA)))
# Solve the linear least-squares problem
result = linSolver(AtA, Aty)
result = parseResult(result)
xfit = validateResult(result)
# Insert back the frozen linear parameters
xfit = _insertfrozen(xfit, lin_parfrozen, lin_frozen)
return xfit, alpha, Ndof
def test_unconstrained():
#=======================================================================
"Check the algorithm works with unconstrained disributions"
t = np.linspace(0, 5, 80)
r = np.linspace(2, 5, 80)
P = dd_gauss(r, 3, 0.15)
K = dipolarkernel(t, r)
L = regoperator(r, 2, includeedges=True)
V = K @ P
logalpha = np.log10(
selregparam(V, K, np.linalg.solve, method='aic', regop=L))
logalpharef = -8.87
assert abs(1 - logalpha / logalpharef) < 0.1
def test_tikh_value():
#=======================================================================
"Check that the value returned by Tikhonov regularization"
np.random.seed(1)
t = np.linspace(0, 5, 500)
r = np.linspace(2, 5, 80)
P = dd_gauss(r, 3, 0.15)
K = dipolarkernel(t, r)
V = K @ P + whitegaussnoise(t, 0.01)
L = regoperator(r, 2, includeedges=True)
alpha = selregparam(V, K, cvxnnls, method='aic', regop=L)
loga = np.log10(alpha)
logaref = -3.51 # Computed with DeerLab-Matlab (0.9.2)
assert abs(1 - loga / logaref) < 0.02 # less than 2% error
