[SciPy-User] Fitting Polynomial With Shape Restrictions

[Jared Vacanti]

Are there other techniques of fitting splines (perhaps using scipy.optimize
?) where I can impose boundary conditions myself, even outside of the
options provided in scipy.interpolate?

On Sun, May 7, 2017 at 2:02 PM, <josef.pktd at gmail.com> wrote:

>
>
> On Sun, May 7, 2017 at 2:45 PM, Jared Vacanti <jaredvacanti at gmail.com>
> wrote:
>
>> I am trying to fit a polynomial to observational data with shape
>> restrictions - in this particular case monotonicity (decreasing) of the
>> function and an always positive second derivative.
>>
>> Some of the interpolation classes have a mathematical "built-in"
>> restriction - like scipy.interpolate.Rbf's thin-plate roughness penalty
>> imposes some restrictions, but it's not explicit or adjustable.
>>
>> What are my options for imposing boundary conditions or shape
>> restrictions on the spline?
>>
>> I have sample data here:
>>
>> import pandas as pd
>> df = pd.read_csv("https://bpaste.net/raw/3e20878b5237")
>>
>> or available independently at https://bpaste.net/raw/3e20878b5237
>>
>> I have tried using a interior point convex optimization solver, but the
>> results seem to be numerically finicky. Are there other alternatives?
>>
>
> As far as I know, pchip is the only one with monotonicity constraints
> https://docs.scipy.org/doc/scipy/reference/generated/
> scipy.interpolate.pchip_interpolate.html
>
> Josef
>
>
>
>>
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>>
>
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