diff --git a/Project.toml b/Project.toml
index 797d116..9fd9c79 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
name = "CuFluxSampler"
uuid = "d04bc951-f907-4aa3-859a-fc6f9fe5eea9"
authors = ["Mirek Kratochvil <miroslav.kratochvil@uni.lu>"]
-version = "0.1.0"
+version = "0.2.0"
[deps]
COBREXA = "babc4406-5200-4a30-9033-bf5ae714c842"
@@ -11,8 +11,8 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
[compat]
-COBREXA = "1"
-CUDA = "4"
+COBREXA = "2"
+CUDA = "5"
DocStringExtensions = "0.9"
julia = "1.6"
diff --git a/src/CuFluxSampler.jl b/src/CuFluxSampler.jl
index af852d3..0ce4fcb 100644
--- a/src/CuFluxSampler.jl
+++ b/src/CuFluxSampler.jl
@@ -7,10 +7,11 @@ using DocStringExtensions
import COBREXA
import CUDA
import SparseArrays
+import StableRNGs: StableRNG
-include("TeaRNG.jl")
-include("FullAffineHR.jl")
-include("AffineHR.jl")
-include("ACHR.jl")
+include("tea_rng.jl")
+include("full_affine_hr.jl")
+include("affine_hr.jl")
+include("achr.jl")
end # module CuFluxSampler
diff --git a/src/ACHR.jl b/src/achr.jl
similarity index 87%
rename from src/ACHR.jl
rename to src/achr.jl
index 6ba73de..981e091 100644
--- a/src/ACHR.jl
+++ b/src/achr.jl
@@ -1,11 +1,3 @@
-module ACHR
-using ..CUDA
-using ..DocStringExtensions
-using SparseArrays
-
-import ..COBREXA
-import ..TeaRNG
-import Random
"""
$(TYPEDSIGNATURES)
@@ -13,20 +5,28 @@ $(TYPEDSIGNATURES)
A traditional artificially-centered hit-and-run algorithm that starts with
`start` points.
+To use this on a model, use [`flux_sample`](@ref) or
+[`sample_constraints`](@ref); most parameters are filled in correctly by these
+functions.
+
Refer to the documentation in module AffineHR for the meaning of arguments.
"""
-function sample(
- m::COBREXA.MetabolicModel,
- start::AbstractMatrix;
- iters::Int,
- bound_stoichiometry::Bool = false,
- check_stoichiometry::Bool = true,
+function sample_chain_achr_cuda(
+ sample_c::COBREXA.M;
+ variable_lower_bounds::COBREXA.V,
+ variable_upper_bounds::COBREXA.V,
+ constraints::COBREXA.SM,
+ lower_bounds::COBREXA.V,
+ upper_bounds::COBREXA.V,
+ epsilon::Float32 = COBREXA.configuration.sampler_tolerance,
+ collect_iterations::Vector{Int},
+ generator::StableRNG,
direction_noise_max::Union{Nothing,Float32} = nothing,
- epsilon::Float32 = 1.0f-5,
- seed = Random.rand(UInt32),
)
+ # TODO
+
# allocate base helper variables
- npts = size(start, 2)
+ npts = size(sample_c, 2)
pts = cu(Matrix{Float32}(start))
dirs = CUDA.zeros(size(start, 1), npts)
lblambdas = CUDA.zeros(size(dirs))
@@ -125,7 +125,7 @@ function sample(
lmax .= min.(lmax, minimum(ifelse.(isfinite.(clmaxs), clmaxs, Inf32), dims = 1))
end
- # generate random lambdas and compute new points
+ # generate random lambdas and compute new points
@cuda threads = 256 blocks = 32 TeaRNG.device_fill_rand!(
lws,
seed + UInt32(iter * 2 + 1),
@@ -145,4 +145,4 @@ function sample(
collect(pts)
end
-end # module AffineHR
+export sample_chain_achr_cuda
diff --git a/src/AffineHR.jl b/src/affine_hr.jl
similarity index 91%
rename from src/AffineHR.jl
rename to src/affine_hr.jl
index b23d0b0..8074bf3 100644
--- a/src/AffineHR.jl
+++ b/src/affine_hr.jl
@@ -1,11 +1,3 @@
-module AffineHR
-using ..CUDA
-using ..DocStringExtensions
-using SparseArrays
-
-import ..COBREXA
-import ..TeaRNG
-import Random
function random_mix_matrix(npts, mix_points)
mtx = sparse(
@@ -48,19 +40,21 @@ present.
If you are generating a sample of the optimal model solution, it is expected
that the optimum bound is already present in `m`.
-Returns a matrix of the same size as `start`.
+Returns blocks of the same size as `sample_c`.
"""
-function sample(
- m::COBREXA.MetabolicModel,
- start::AbstractMatrix;
- iters::Int,
- bound_stoichiometry::Bool = false,
- check_stoichiometry::Bool = true,
- direction_noise_max::Union{Nothing,Float32} = nothing,
- epsilon::Float32 = 1.0f-5,
- seed = Random.rand(UInt32),
+function sample_chain_affine_hr_cuda(
+ sample_c::COBREXA.M;
+ variable_lower_bounds::COBREXA.V,
+ variable_upper_bounds::COBREXA.V,
+ constraints::COBREXA.SM,
+ lower_bounds::COBREXA.V,
+ upper_bounds::COBREXA.V,
+ epsilon::Float32 = COBREXA.configuration.sampler_tolerance,
+ collect_iterations::Vector{Int},
+ generator::StableRNG,
mix_points = 3,
- mix_mtx = random_mix_matrix(size(start, 2), mix_points),
+ mix_mtx = random_mix_matrix(size(sample_c, 2), mix_points),
+ direction_noise_max::Union{Nothing,Float32} = nothing,
)
# allocate base helper variables
npts = size(start, 2)
@@ -163,7 +157,7 @@ function sample(
lmax .= min.(lmax, minimum(ifelse.(isfinite.(clmaxs), clmaxs, Inf32), dims = 1))
end
- # generate random lambdas and compute new points
+ # generate random lambdas and compute new points
@cuda threads = 256 blocks = 32 TeaRNG.device_fill_rand!(
lws,
seed + UInt32(iter * 2 + 1),
@@ -183,5 +177,3 @@ function sample(
collect(pts)
end
-
-end # module AffineHR
diff --git a/src/FullAffineHR.jl b/src/full_affine_hr.jl
similarity index 91%
rename from src/FullAffineHR.jl
rename to src/full_affine_hr.jl
index 8f1444f..b2424a4 100644
--- a/src/FullAffineHR.jl
+++ b/src/full_affine_hr.jl
@@ -1,10 +1,3 @@
-module FullAffineHR
-using ..CUDA
-using ..DocStringExtensions
-
-import ..COBREXA
-import ..TeaRNG
-import Random
"""
$(TYPEDSIGNATURES)
@@ -19,16 +12,17 @@ Returns a matrix of `npts` samples organized in columns.
This algorithm is mostly a toy for comparing the performance. It works, but do
not use it in production.
"""
-function sample(
- m::COBREXA.MetabolicModel,
- warmup::AbstractMatrix;
- npts::Int = size(warmup, 2),
- iters::Int,
- bound_stoichiometry::Bool = false,
- check_stoichiometry::Bool = true,
+function sample_chain_full_affine_hr_cuda(
+ sample_c::COBREXA.M;
+ variable_lower_bounds::COBREXA.V,
+ variable_upper_bounds::COBREXA.V,
+ constraints::COBREXA.SM,
+ lower_bounds::COBREXA.V,
+ upper_bounds::COBREXA.V,
+ epsilon::Float32 = COBREXA.configuration.sampler_tolerance,
+ collect_iterations::Vector{Int},
+ generator::StableRNG,
direction_noise_max::Union{Nothing,Float32} = nothing,
- epsilon::Float32 = 1.0f-5,
- seed = Random.rand(UInt32),
)
# TODO seed and tea iters
@@ -140,7 +134,7 @@ function sample(
lmax .= min.(lmax, minimum(ifelse.(isfinite.(clmaxs), clmaxs, Inf32), dims = 1))
end
- # generate random lambdas and compute new points
+ # generate random lambdas and compute new points
@cuda threads = 256 blocks = 32 TeaRNG.device_fill_rand!(
lws,
seed + UInt32(iter * 3 + 2),
@@ -159,5 +153,3 @@ function sample(
collect(pts)
end
-
-end # module AffineHR
diff --git a/src/TeaRNG.jl b/src/tea_rng.jl
similarity index 91%
rename from src/TeaRNG.jl
rename to src/tea_rng.jl
index 67bbd10..8e72347 100644
--- a/src/TeaRNG.jl
+++ b/src/tea_rng.jl
@@ -1,9 +1,3 @@
-"""
-Fast stateless random number generator for GPUs based on TEA cipher.
-"""
-module TeaRNG
-using ..CUDA
-using ..DocStringExtensions
"""
$(TYPEDSIGNATURES)
@@ -60,5 +54,3 @@ function device_add_unif_rand!(arr, seed::UInt32, offset::Float32, scale::Float3
end
return
end
-
-end # module TeaRng