Add renormalized laplace and gaussian distribution and kernels - #162
Conversation
horizon-blue
left a comment
horizon-blue
left a comment
There was a problem hiding this comment.
Wow thanks for putting this together in this short amount of time! This looks great to me overall -- I just left some inline comments, most of which are just cosmetics stuff. Feel free to merge this whenever you're ready.
| p_below_low = tfp.distributions.Laplace(loc, scale).cdf(low) | ||
| p_below_high = tfp.distributions.Laplace(loc, scale).cdf(high) | ||
| log_integral_of_laplace_pdf_over_this_range = jnp.log( | ||
| p_below_high - p_below_low | ||
| ) |
There was a problem hiding this comment.
For numerical stability, perhaps we can consider using Laplace.log_cdf and Laplace. log_survival_function?
(btw also just for clarity: I think log_integral_of_laplace_pdf_over_this_range is actually referring to the integral of laplace within the range?)
There was a problem hiding this comment.
@horizon-blue can you please suggest the right code snippet we need to do log_cdf and log_survival_function here?
| def sample(self, key, latent_color, color_scale, *args, **kwargs): | ||
| return jax.vmap( | ||
| genjax.truncated_normal.sample, in_axes=(0, 0, None, None, None) | ||
| )(split(key, latent_color.shape[0]), latent_color, color_scale, 0.0, 1.0) |
There was a problem hiding this comment.
How to you feel about usingCOLOR_MIN_VAL and COLOR_MAX_VAL instead of hard-coding the magic constant?
|
@horizon-blue I made the suggested changes that I knew how to make! |
2 participants
No description provided.