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https://impicnf.github.io/ContinuousNormalizingFlows.jl/

Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia
https://impicnf.github.io/ContinuousNormalizingFlows.jl/

continuous-normalizing-flows flows julia julia-language julialang normalizing-flows

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Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia

Host: GitHub
URL: https://impicnf.github.io/ContinuousNormalizingFlows.jl/
Owner: impICNF
License: mit
Created: 2021-11-10T14:53:12.000Z (almost 3 years ago)
Default Branch: main
Last Pushed: 2024-04-14T01:24:55.000Z (7 months ago)
Last Synced: 2024-04-14T04:55:11.941Z (7 months ago)
Topics: continuous-normalizing-flows, flows, julia, julia-language, julialang, normalizing-flows
Language: Julia
Homepage: https://impicnf.github.io/ContinuousNormalizingFlows.jl/
Size: 1.11 MB
Stars: 21
Watchers: 1
Forks: 1
Open Issues: 6
Metadata Files:
- Readme: README.md
- License: LICENSE.md
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.bib

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awesome-normalizing-flows - [Docs

README

        # ContinuousNormalizingFlows.jl

[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://impICNF.github.io/ContinuousNormalizingFlows.jl/stable)

[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://impICNF.github.io/ContinuousNormalizingFlows.jl/dev)

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Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia

## Citing

See [`CITATION.bib`](CITATION.bib) for the relevant reference(s).

## Installation

```julia

using Pkg

Pkg.add("ContinuousNormalizingFlows")

```

## Usage

```julia

# Enable Logging

using Logging, TerminalLoggers

global_logger(TerminalLogger())

# Parameters

nvars = 1

naugs = nvars

# n_in = nvars # without augmentation

n_in = nvars + naugs # with augmentation

n = 1024

# Model

using ContinuousNormalizingFlows, Lux, ADTypes, Zygote #, CUDA, ComputationalResources

nn = Chain(Dense(n_in => 3 * n_in, tanh), Dense(3 * n_in => n_in, tanh))

# icnf = construct(RNODE, nn, nvars) # use defaults

icnf = construct(

    RNODE,

    nn,

    nvars, # number of variables

    naugs; # number of augmented dimensions

    compute_mode = DIVecJacMatrixMode(AutoZygote()), # process data in batches

    tspan = (0.0f0, 13.0f0), # have bigger time span

    steer_rate = 1.0f-1, # add random noise to end of the time span

    # resource = CUDALibs(), # process data by GPU

    # inplace = true, # use the inplace version of functions

)

# Data

using Distributions

data_dist = Beta{Float32}(2.0f0, 4.0f0)

r = rand(data_dist, nvars, n)

r = convert.(Float32, r)

# Fit It

using DataFrames, MLJBase #, ForwardDiff, ADTypes, OptimizationOptimisers

df = DataFrame(transpose(r), :auto)

# model = ICNFModel(icnf) # use defaults

model = ICNFModel(

    icnf;

    batch_size = 256, # have bigger batchs

    # n_epochs = 100, # have less epochs

    # optimizers = (Adam(),), # use a different optimizer

    # adtype = AutoForwardDiff(), # use ForwardDiff

)

mach = machine(model, df)

fit!(mach)

ps, st = fitted_params(mach)

# Store It

using JLD2, UnPack

jldsave("fitted.jld2"; ps, st) # save

@unpack ps, st = load("fitted.jld2") # load

# Use It

d = ICNFDist(icnf, TestMode(), ps, st) # direct way

# d = ICNFDist(mach, TestMode()) # alternative way

actual_pdf = pdf.(data_dist, vec(r))

estimated_pdf = pdf(d, r)

new_data = rand(d, n)

# Evaluate It

using Distances

mad_ = meanad(estimated_pdf, actual_pdf)

msd_ = msd(estimated_pdf, actual_pdf)

tv_dis = totalvariation(estimated_pdf, actual_pdf) / n

res_df = DataFrame(; mad_, msd_, tv_dis)

display(res_df)

# Plot It

using CairoMakie

f = Figure()

ax = Makie.Axis(f[1, 1]; title = "Result")

lines!(ax, 0.0f0 .. 1.0f0, x -> pdf(data_dist, x); label = "actual")

lines!(ax, 0.0f0 .. 1.0f0, x -> pdf(d, vcat(x)); label = "estimated")

axislegend(ax)

save("result-fig.svg", f)

save("result-fig.png", f)

```