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snlls: allow extrapenalty to take linear parameters as input #175

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May 10, 2021
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snlls: allow extrapenalty to take linear parameters as input #175

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863eaa9
snlls: allow `extrapenalty` to take linear parameters as input
luisfabib May 10, 2021
1335808
snlls: fix order of concatenation of Jacobians
luisfabib May 10, 2021
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2 changes: 1 addition & 1 deletion deerlab/fitmodel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -568,7 +568,7 @@ def separable_nonlinear_lsq_analysis(Vexp):
prescales = [1 for V in Vexp]
Vexp_ = [Vexp[i]/prescales[i] for i in range(nSignals)]

def scale_constraint(nonlinpar):
def scale_constraint(nonlinpar,linpar):
# --------------------------------------------------------
penalty = np.zeros(nSignals)
for i in range(nSignals):
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16 changes: 9 additions & 7 deletions deerlab/snlls.py
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Expand Up @@ -87,9 +87,10 @@ def snlls(y, Amodel, par0, lb=None, ub=None, lbl=None, ubl=None, nnlsSolver='cvx
The regularization parameter can be manually specified by passing a scalar value
instead of a string. The default ``'aic'``.

custom_penalty: callable
Custom penalty function to impose upon the solution. Must return a vector to be
added to the residual vector.
extrapenalty: callable
Custom penalty function to impose upon the solution. Must take two inputs, a vector of non-linear parameters
and a vector of linear parameters, and return a vector to be added to the residual vector (``pen = fcn(pnonlin,plin)``).
The square of the penalty is computed internally.

alphareopt : float scalar, optional
Relative parameter change threshold for reoptimizing the regularization parameter
Expand Down Expand Up @@ -383,7 +384,7 @@ def ResidualsFcn(p):

# Compute residual from custom penalty
if callable(extrapenalty):
penres = extrapenalty(p)
penres = extrapenalty(p,linfit)
penres = np.atleast_1d(penres)
res = np.concatenate((res,penres))

Expand Down Expand Up @@ -434,10 +435,11 @@ def ResidualsFcn(p):
Jnonlin = Jacobian(ResidualsFcn,nonlinfit,lb,ub)

# Jacobian (linear part)
Jlin = np.zeros((len(res),len(linfit)))
Jlin[:len(y),:] = scales_vec[:,np.newaxis]*Amodel(nonlinfit)
Jlin = scales_vec[:,np.newaxis]*Amodel(nonlinfit)
if callable(extrapenalty):
Jlin = np.concatenate((Jlin, Jacobian(lambda plin: extrapenalty(nonlinfit,plin),linfit,lbl,ubl)))
if includeRegularization:
Jlin[len(res)-Nlin:,:] = reg_penalty(regtype, alpha, L, linfit, huberparam, Nnonlin)[1]
Jlin = np.concatenate((Jlin, reg_penalty(regtype, alpha, L, linfit, huberparam, Nnonlin)[1]))

# Full Jacobian
J = np.concatenate((Jnonlin,Jlin),axis=1)
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