Add Constraint

[adussarps]

Add an option to constraint linear coefficients to a range of values (e.g >0 ).

[cjekel]

I could potentially create a constrained fit where each breakpoint is subjected to per-determined constraints. The following code sets up this constraint lower and upper limits, just as an example.

# for two breakpoints; arbitrary example 
# 0.1 <= breakpoint[0] <= 0.15
# 0.5 <= breakpoint[1] <= 0.7
g = np.zeros((2, 2)  # constraints
g[0, 0] = 0.1
g[0, 1] = 0.15
g[1, 0] = 0.5
g[1, 1] = 0.7

Then the optimization would find the best breakpoint subject to g.

Do I understand this correctly?

[cjekel]

For now this could be done using a custom optimizer like https://jekel.me/piecewise_linear_fit_py/examples.html#use-custom-optimization-routine where you manually specify the bounds.

I'll try to include an optional bounds parameter in the fitting routines for this case.

[cjekel]

@adussarps I'm not sure if I understand your request to constrain linear coefficients, but I added an option to constrain the search space for breakpoints. Does this example solver your issue? or are you looking to add constraints for each slope (and the change from the previous slope)?

[adussarps]

Yes it's fine thanks!!