-
-
Notifications
You must be signed in to change notification settings - Fork 50
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.
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
CDF of GenExtreme gives zero instead of one for larger values #347
Comments
The logcdf implementation seems to have no special logic for out of bounds x: https://github.com/pymc-devs/pymc-experimental/blob/main/pymc_experimental/distributions/continuous.py#L203-L213 CC @ccaprani |
Want to open a PR to contribute the fix to the codebase? |
@souvikpaul33 you should! It's a great find & fix. I'll do it otherwise, but really appreciate your work on it 👍 |
@ricardoV94 @ccaprani thank you very much! It would be great if I could open a PR and contribute. What do I need to do? |
This guide may help: https://www.pymc.io/projects/docs/en/stable/contributing/pr_tutorial.html It's for pymc, but instructions should be similar for pymc-experimental. |
@ricardoV94 Thank you! I was trying it. Firstly, I fork the repository to my Github (souvikpaul33). Then when I tried to clone it using the command: git clone git@github.com:souvikpaul33/pymc-experimental.git , it's showing some 'access denied' and need for access. How can I get the access ? |
Hi @ricardoV94 @ccaprani Please review! Thank you very much!! |
Hi
I am using GEV for classification. Here in the example below, I wanted to check a few things like logcdf and cdf works for GenExtreme. Though, now I am able to find logcdf or cdf, some logcdf values are coming -inf (i.e. cdf 0), which should be 0 (i.e. cdf 1).
My Random Variable is GEV($\mu$ = 0, $\sigma$ = 1, $\xi$ = -0.5). So, range of the RV is ($-\infty, -1/\xi$ )=($-\infty$ , 2). So, logcdf should come as 0 (i.e. cdf 1) for RV>=2. But see the below example it’s -\infty$ for RV>=2.
Please help to find out what is wrong and how solve it! I could make some conditions for checking and could make 0 to 1, but I will run it inside pm.Model(),this kind of condition can make it slow. Thank you.
import numpy as np
import pymc as pm
import pymc_experimental.distributions as pmx
rv = pmx.GenExtreme.dist(mu=0, sigma=1, xi=-0.5) # range of x: (-inf, 2)
x = np.array([-100,-50,-40, -30, -10,-5,-2,-1,0,1,1.5,1.7,1.9,1.99,2,3,4,5])
lcdf_gev = pm.logcdf(rv, x)
print(x)
[-100. -50. -40. -30. -10. -5. -2. -1. 0.
print(lcdf_gev.eval())
[-2.601e+03 -6.760e+02 -4.410e+02 -2.560e+02 -3.600e+01 -1.225e+01
-4.000e+00 -2.250e+00 -1.000e+00 -2.500e-01 -6.250e-02 -2.250e-02
-2.500e-03 -2.500e-05 -inf -inf -inf -inf]
print(np.exp(lcdf_gev.eval()))
[0.00000000e+000 2.61174176e-294 2.99318445e-192 6.61626106e-112
2.31952283e-016 4.78511739e-006 1.83156389e-002 1.05399225e-001
3.67879441e-001 7.78800783e-001 9.39413063e-001 9.77751237e-001
9.97503122e-001 9.99975000e-001 0.00000000e+000 0.00000000e+000
0.00000000e+000 0.00000000e+000]
The text was updated successfully, but these errors were encountered: