Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

snlls: Fix violation of boundaries due to float-point round-off errors #188

Merged
merged 5 commits into from
Jul 2, 2021
Merged

snlls: Fix violation of boundaries due to float-point round-off errors #188

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
cbc570d
Jacobian: add rounding to avoid float-point round-off errors
Jun 28, 2021
8ac210d
Jacobian: use exception handling to avoid rounding in all cases
Jun 28, 2021
a353814
Merge branch 'main' into fix/fitmultimodel_bounds_violation
luisfabib Jun 28, 2021
61a2824
Merge branch 'main' into fix/fitmultimodel_bounds_violation
luisfabib Jun 29, 2021
9809f7b
snlls: shield linear LSQ results against float-point errors
Jul 2, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
10 changes: 6 additions & 4 deletions deerlab/snlls.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -293,6 +293,9 @@ def snlls(y, Amodel, par0, lb=None, ub=None, lbl=None, ubl=None, nnlsSolver='cvx
elif nnlsSolver == 'cvx':
linSolver = lambda AtA, Aty: cvxnnls(AtA, Aty, tol=lin_tol, maxiter=lin_maxiter)
parseResult = lambda result: result

# Ensure correct formatting and shield against float-point errors
validateResult = lambda result: np.maximum(lbl,np.minimum(ubl,np.atleast_1d(result)))
# ----------------------------------------------------------

# Containers for alpha-update checks
Expand All @@ -319,9 +322,9 @@ def linear_problem(A,optimize_alpha,alpha):

# Solve the linear least-squares problem
result = linSolver(AtA, Aty)
linfit = parseResult(result)
linfit = np.atleast_1d(linfit)
result = parseResult(result)
linfit = validateResult(result)

return linfit, alpha
#===========================================================================

Expand Down Expand Up @@ -566,4 +569,3 @@ def _plot(subsets,y,yfit,show):
plt.close()
return fig
# ===========================================================================================

2 changes: 1 addition & 1 deletion deerlab/utils/utils.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -409,7 +409,7 @@ def Jacobian(fcn, x0, lb, ub):

"""
J = opt._numdiff.approx_derivative(fcn,x0,method='2-point',bounds=(lb,ub))
J = np.atleast_2d(J)
J = np.atleast_2d(J)
return J
#===============================================================================

Expand Down