Gibbs with Gradients demo #524
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| (MkImage rows cols pixels) = fromJust $ loadImageP6 "examples/peace.ppm" | ||
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| -- Convert to binary image. | ||
| def pixelToBool (x:Char) : Bool = (W8ToI x) < 0 |
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This is confusing isn't this already going to return false?
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You'd think so, but casting an unsigned W8 to a signed int means that half the values are negative. This is evidently a confusing way to have written this, though.
| addnoise = rand (ixkey2 (newKey 0) i j) < noisefrac | ||
| case addnoise of | ||
| True -> not image_bool.i.j | ||
| False -> image_bool.i.j |
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Can tje above 3 lines be written
(not addnoise) & image_bool.i.j ?
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I think it's xor, but the syntax for that is .^. and I thought this would be easier to read.
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This is awesome. I especially enjoyed the commentary. IMO this is good to go, except that I'd like to reduce the size of the peace.ppm image. Checking in binary files is generally a bad practice and I'd like to avoid doing that as much as possible. Plus, are there any licensing constraints on it?
| which is a Boolean vector `n=>Bool`, | ||
| and an unnormalized probability mass function `f`. | ||
| The pmf maps Boolean vectors of the same size as `x` and maps | ||
| them to an unnormalized log probability density. |
| 'The Gibbs with Gradients sampler has a slightly different function signature | ||
| than standard Gibbs. Instead of its log probability function taking in a | ||
| discrete array, it takes in an array of floats of the same size. | ||
| This is because Gibbs with Gradients needs to be able to differentiate the energy function |
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is pmf/pdf the same as the energy function?
| of Algorithm 1 from the paper. In fact, the reason this demo got | ||
| started was because I wasn't sure I understood the pseudocode | ||
| in our own paper. Writing the algorithm in this terse (but machine-checked) | ||
| format made me realize we were missing a line from the algorithm box. |
Yes, I realize this. How about if I host the image somewhere, and use the shell to download it?
Nope, it's in the public domain. Source |
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Downloading via the shell sounds good! Alternatively you could assume that it's in the file system and add the download instructions (+ insert them in CI). That's what we do for the tutorial IIRC. |
'Main algorithm from "Oops I Took A Gradient: Scalable Sampling for Discrete Distributions"
by Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison
Arxiv Link demonstrated on an Ising model.
This one already works - the main thing that needs to be done is make the colors nicer.