Comments (7)
I saw your PR for this process but I think it might be simpler to just subclass PoissonProcess and make use of the already existing code. Additionally, you would need to
- provide init args consisting of an
rate_func
, as well asrate_args
, andrate_kwargs
for that function to ensure it is generalized enough for any rng package (random, numpy, scipy, etc.). - override the rate attribute to call
rate_func(*rate_args, **rate_kwargs)
from stochastic.
Following your comments and suggestions, I have implemented changes:
- MixedPoissonProcess now inherits from Poisson process. I have eliminated some amount of code repetition.
- Eliminated some other repetitions by way of the gen_rate() method.
- *rate_args and **rate_kwargs are implemented.
- New test fixtures for rate_kwargs. The tests seem to be in working order.
- Modified the continuous.rst to include the MixedPoissonProcess class.
Still to be implemented is:
- Making sure the code fits style standards. I will work on that for the implementation of the appropriate features, mostly.
Is the pull request in a more acceptable state?
And thank you for your comments and help.
from stochastic.
This is ok but there are still a lot of issues that need to be addressed.
I think I might merge this into a separate dev branch and clean it up if you don't mind. I'm hoping that maybe you'll track the changes closely and if needed I can explain the reasoning for them. Is that ok?
from stochastic.
This sounds marvelous. When it is up to scratch, I will use it as a template for my future pull requests.
Thank you very much!
from stochastic.
I requested some changes in the PR. Please address them. Thanks.
from stochastic.
All changes were addressed. If this makes this feature acceptable, I will get to upgrading other features also.
from stochastic.
@Gabinou I have merged your PR #8 and made several changes that I think make the implementation a bit cleaner. You may refer to the commits in the mixed branch for the changes. Thanks for contributing.
from stochastic.
Related Issues (19)
- New Continuous Process: Multidimensional Spatial Point Process.
- BrownianMotion sample_at losing the drift and scale HOT 3
- Multi-Dimensional Process
- Support RNG seeding HOT 2
- Interface for iterative sampling
- Hosking method for fgn regenerates the autocovariance matrix at wrong times
- In BernoulliProcess(p=P), bp.p is actually probability of "0" instead of "1"
- Added to conda
- Wrong implementation of _fgn_autocovariance HOT 4
- Generating many samples HOT 2
- Update build badge to gha
- Variance gamma process sample depends on the step number
- Add type annotations
- Geometric Brownian Motion process seemingly can't be seeded / results can't be reproduced HOT 2
- Add a more general diffusion process
- The argument volexp behaves different to other arguments in the Difussion process.
- Sampling from `FractionalGaussianNoise` and `ColoredNoise` leads to arrays of different size
- New Continuous Process: Non-Homogeneous Poisson HOT 1
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from stochastic.