From 710a581d842cca48dec7512a05e2874a7f5dcdd0 Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Wed, 18 Dec 2024 15:05:27 +0000 Subject: [PATCH] build based on fcd0e56 --- dev/.documenter-siteinfo.json | 2 +- dev/index.html | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index d03b740..12b698c 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.11.2","generation_timestamp":"2024-12-18T15:02:33","documenter_version":"1.8.0"}} \ No newline at end of file +{"documenter":{"julia_version":"1.11.2","generation_timestamp":"2024-12-18T15:05:23","documenter_version":"1.8.0"}} \ No newline at end of file diff --git a/dev/index.html b/dev/index.html index 74c4bfa..96d7162 100644 --- a/dev/index.html +++ b/dev/index.html @@ -5,7 +5,7 @@ (4, 3) julia> rff(rand(2, 3, 5)) |> size # extra batch dim -(4, 3, 5)source
RandomFeatureMaps.RandomOrientationFeatures — Type
RandomOrientationFeatures

Holds two random matrices which are used to embed rigid transformations.

This type has no trainable parameters.

Methods

  • (::RandomOrientationFeatures)(rigid1, rigid2): returns the distances between the corresponding

rigid transformations, embedded using the two random matrices of the random orientation features.

  • (::RandomOrientationFeatures)(rigid1, rigid2; dims::Int): unsqueezes batch dimension dim+1

of rigid1 and dim of rigid2 to broadcast the rof call and produce a pairwise map.

  • (::RandomOrientationFeatures)(rigid1, rigid2, graph::GraphNeuralNetworks.GNNGraph): similar to

the first method, but takes two sets rigid transformations of equal size and unrolls a graph to get the pairs of rigid transformations. Equivalent to the second method (with broadcasted dimensions flattened) when the graph is complete.

Each of these have single rigid argument methods for when rigid1 == rigid2, i.e. rof(rigid)

Examples

julia> rof = RandomOrientationFeatures(10, 0.1f0);
+(4, 3, 5)
source
RandomFeatureMaps.RandomOrientationFeatures — Type
RandomOrientationFeatures

Holds two random matrices which are used to embed rigid transformations.

This type has no trainable parameters.

Methods

  • (::RandomOrientationFeatures)(rigid1, rigid2): returns the distances between the corresponding

rigid transformations, embedded using the two random matrices of the random orientation features.

  • (::RandomOrientationFeatures)(rigid1, rigid2; dims::Int): unsqueezes batch dimension dim+1

of rigid1 and dim of rigid2 to broadcast the rof call and produce a pairwise map.

  • (::RandomOrientationFeatures)(rigid1, rigid2, graph::GraphNeuralNetworks.GNNGraph): similar to

the first method, but takes two sets rigid transformations of equal size and unrolls a graph to get the pairs of rigid transformations. Equivalent to the second method (with broadcasted dimensions flattened) when the graph is complete.

Each of these have single rigid argument methods for when rigid1 == rigid2, i.e. rof(rigid)

Examples

julia> rof = RandomOrientationFeatures(10, 0.1f0);
 
 julia> rigid = rand_rigid(Float32, (4, 3));
 
@@ -27,4 +27,4 @@
 julia> rigid = rand_rigid(Float32, (2,));
 
 julia> rof(rigid, graph) |> size
-(10, 3)
source
RandomFeatureMaps.RandomOrientationFeatures — Method
RandomOrientationFeatures(m, σ)

Creates a RandomOrientationFeatures instance, mapping to m features.

source
RandomFeatureMaps.get_rigid — Method
get_rigid(R::AbstractArray, t::AbstractArray)

Converts a rotation R and translation t to a BatchedTransformations.Rigid, designed to handle batch dimensions.

The transformation gets applied according to NNlib.batched_mul(R, x) .+ t

source
+(10, 3)source
RandomFeatureMaps.RandomOrientationFeatures — Method
RandomOrientationFeatures(m, σ)

Creates a RandomOrientationFeatures instance, mapping to m features.

source
RandomFeatureMaps.get_rigid — Method
get_rigid(R::AbstractArray, t::AbstractArray)

Converts a rotation R and translation t to a BatchedTransformations.Rigid, designed to handle batch dimensions.

The transformation gets applied according to NNlib.batched_mul(R, x) .+ t

source