Support sampling normal distribution in expressions #362
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
djeedai
reviewed
Aug 16, 2024
|
I don't fully understand this review comment, did you leave something out? |
|
Yes no surprise because GitHub didn't post my actual comment. The sampling for vec2 (and others) is too much. Box-Muller can create 2 random normal vars from 2 uniform ones, so you don't need to sample 2 vec2 vars, only 1 is enough. That would save some instructions. Same for vec3 and vec4. |
|
Hey, sorry for the slow turnaround on this. I pushed a fix just now |
djeedai
approved these changes
Sep 1, 2024
|
@msvbg there's a linter error, but should be fine after that. Thanks! |
djeedai
approved these changes
Sep 2, 2024
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Closes #353. I decided to look at this issue to build some familiarity with the code base. It mostly just copies what was done for the uniform distribution, with some minor tweaks.
I tested it by replacing various usages of
uniformwithnormalin the examples, and it looked right to my eyes but I'm not sure how to test it more rigorously. I considered making a specialized example but that felt like overkill. Let me know what would pass the quality bar for you in terms of testing.