Comments (1)
I don't have a nice way to do this, because the model parameters are fit with a least squares fit. The model parameters aren't the slope themselves, but the change in slope from the previous slopes.
That is not to say that this formulation is impossible.
It might be possible to write a constraint on the breakpoints such that the slope never hits 3 -- but I would think you would also want fits for known breakpoints where the slope never hits 3. These are two very different formulations.
Anyways, I just hacked up a constrained optimization problem that moves breakpoints such that the slope is less than a max_slope
value. This still solve the least squares fit for given breakpoint locations. Take a look at https://github.com/cjekel/piecewise_linear_fit_py/blob/master/examples/slope_constraint_demo.ipynb and let me know if this works and if you have questions.
from piecewise_linear_fit_py.
Related Issues (20)
- .fit() fails with 1 segment HOT 5
- How to force the fit process to have a fixed Intercept? HOT 1
- Can I get y_values if I have only x and slopes values? HOT 1
- Re-constructing Piecewise PWLF HOT 2
- Set Slope of Segment to 0 HOT 2
- How to fit multiple functions simultaneously HOT 3
- Hi, i want to make sure that there are no fitted lines between points that are too far apart i.e. set a min value( fragment optimization) how to achieve this? HOT 1
- fix cron ci jobs such that they don't push codecov
- Coefficient access HOT 1
- Create a pwlf using custom coefficient HOT 1
- p values does not seem accurate HOT 3
- Error for coefficients of linear equations HOT 7
- pwlf with unknown line segments HOT 9
- assure the slopes to be lower and lower HOT 1
- divide by zero error in calc slopes if two break points are the same, or if a breakpoint is on the boundary HOT 2
- Issue using .fit() HOT 5
- How to plot segments with fit_breaks information HOT 4
- support random seed on init
- How to calculate prediction intervals? HOT 1
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