From 1f54060b1249eaafa165c468fcdb4898569f5ed8 Mon Sep 17 00:00:00 2001 From: josemanuel22 Date: Fri, 2 Aug 2024 11:42:25 +0200 Subject: [PATCH] add joss paper.md and paper.bib --- src/CustomLossFunction.jl | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/src/CustomLossFunction.jl b/src/CustomLossFunction.jl index a97bfaf..228e071 100644 --- a/src/CustomLossFunction.jl +++ b/src/CustomLossFunction.jl @@ -320,6 +320,7 @@ function auto_invariant_statistical_loss(nn_model, data, hparams) return losses end; +# COV_EXCL_START # Hyperparameters for the method `ts_adaptative_block_learning` """ HyperParamsTS @@ -388,6 +389,7 @@ hparams = HyperParamsTS(; seed=1234, η=1e-2, epochs=2000, window_size=1000, K=1 losses = ts_invariant_statistical_loss_one_step_prediction(rec, gen, Xₜ, Xₜ₊₁, hparams) ``` """ +# COV_EXCL_STOP function ts_invariant_statistical_loss_one_step_prediction(rec, gen, Xₜ, Xₜ₊₁, hparams) losses = [] optim_rec = Flux.setup(Flux.Adam(hparams.η), rec) @@ -413,6 +415,7 @@ function ts_invariant_statistical_loss_one_step_prediction(rec, gen, Xₜ, Xₜ return losses end +# COV_EXCL_START """ ts_invariant_statistical_loss(rec, gen, Xₜ, Xₜ₊₁, hparams) @@ -438,6 +441,7 @@ This function train a model for time series data with statistical invariance los The function iterates through the provided time series data (`Xₜ` and `Xₜ₊₁`) in batches, with a sliding window of size `window_size`. """ +# COV_EXCL_STOP function ts_invariant_statistical_loss(rec, gen, Xₜ, Xₜ₊₁, hparams) losses = [] optim_rec = Flux.setup(Flux.Adam(hparams.η), rec) @@ -463,6 +467,7 @@ function ts_invariant_statistical_loss(rec, gen, Xₜ, Xₜ₊₁, hparams) return losses end +# COV_EXCL_START """ ts_invariant_statistical_loss_multivariate(rec, gen, Xₜ, Xₜ₊₁, hparams) -> losses @@ -502,6 +507,7 @@ hparams = HyperParamsTS(; seed=1234, η=1e-2, epochs=2000, window_size=1000, K=1 losses = ts_invariant_statistical_loss_multivariate(rec, gen, Xₜ, Xₜ₊₁, hparams) ``` """ +# COV_EXCL_STOP function ts_invariant_statistical_loss_multivariate(rec, gen, Xₜ, Xₜ₊₁, hparams) losses = [] optim_rec = Flux.setup(Flux.Adam(hparams.η), rec)