From c545ea66dbad653f364dc2f0f89a6d4a58164268 Mon Sep 17 00:00:00 2001 From: Sathvik Bhagavan Date: Thu, 28 Sep 2023 07:55:32 +0000 Subject: [PATCH] docs: add description for RegularizationSmooth --- README.md | 4 +++- docs/src/index.md | 4 +++- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index d9a64551..ef3500e1 100644 --- a/README.md +++ b/README.md @@ -70,7 +70,9 @@ corresponding to `(u,t)` pairs. The follow methods require extra dependencies and will be loaded as package extensions. - - `Curvefit(u,t,m,p,alg)` - An interpolation which is done by fitting a user-given functional form `m(t,p)` where `p` is the vector of parameters. The user's input `p` is a an initial value for a least-square fitting, `alg` is the algorithm choice to use for optimize the cost function (sum of squared deviations) via `Optim.jl` and optimal `p`s are used in the interpolation. Requires `using RegularizationTools` + - `Curvefit(u,t,m,p,alg)` - An interpolation which is done by fitting a user-given functional form `m(t,p)` where `p` is the vector of parameters. The user's input `p` is a an initial value for a least-square fitting, `alg` is the algorithm choice to use for optimize the cost function (sum of squared deviations) via `Optim.jl` and optimal `p`s are used in the interpolation. Requires `using Optim`. + - `RegularizationSmooth(u,t,d;λ,alg)` - A regularization algorithm (ridge regression) which is done by minimizing an objective function (l2 loss + derivatives of order `d`) integrated in the time span. It is a global method and creates a smooth curve. + Requires `using RegularizationTools`. ## Plotting diff --git a/docs/src/index.md b/docs/src/index.md index 7e31d620..8fa98235 100644 --- a/docs/src/index.md +++ b/docs/src/index.md @@ -39,7 +39,9 @@ corresponding to `(u,t)` pairs. The follow methods require extra dependencies and will be loaded as package extensions. - - `Curvefit(u,t,m,p,alg)` - An interpolation which is done by fitting a user-given functional form `m(t,p)` where `p` is the vector of parameters. The user's input `p` is a an initial value for a least-square fitting, `alg` is the algorithm choice to use for optimize the cost function (sum of squared deviations) via `Optim.jl` and optimal `p`s are used in the interpolation. Requires `using RegularizationTools` + - `Curvefit(u,t,m,p,alg)` - An interpolation which is done by fitting a user-given functional form `m(t,p)` where `p` is the vector of parameters. The user's input `p` is a an initial value for a least-square fitting, `alg` is the algorithm choice to use for optimize the cost function (sum of squared deviations) via `Optim.jl` and optimal `p`s are used in the interpolation. Requires `using Optim`. + - `RegularizationSmooth(u,t,d;λ,alg)` - A regularization algorithm (ridge regression) which is done by minimizing an objective function (l2 loss + derivatives of order `d`) integrated in the time span. It is a global method and creates a smooth curve. + Requires `using RegularizationTools`. ## Plotting