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Remove the GaussianProcesses based and Loess interpolations #65

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Merged
merged 3 commits into from
Jun 23, 2020
Merged

Remove the GaussianProcesses based and Loess interpolations #65

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d3d4edb
Remove html version of example doc
andreasnoack Jun 23, 2020
8c63ee1
Remove GaussianProcesses based interpolation
andreasnoack Jun 23, 2020
b9e4cbe
Remove Loess based interpolation
andreasnoack Jun 23, 2020
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2 changes: 0 additions & 2 deletions Project.toml
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Expand Up @@ -3,15 +3,13 @@ uuid = "82cc6244-b520-54b8-b5a6-8a565e85f1d0"
version = "2.0.1"

[deps]
GaussianProcesses = "891a1506-143c-57d2-908e-e1f8e92e6de9"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
Optim = "429524aa-4258-5aef-a3af-852621145aeb"
RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
Reexport = "189a3867-3050-52da-a836-e630ba90ab69"

[compat]
GaussianProcesses = "0.11, 0.12"
Optim = "0.19, 0.20, 0.21"
RecipesBase = "0.8, 1.0"
RecursiveArrayTools = "2"
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20 changes: 0 additions & 20 deletions README.md
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Expand Up @@ -60,25 +60,6 @@ corresponding to `(u,t)` pairs.

- `BSplineApprox(u,t,d,h,pVec,knotVec)` - A regression B-spline which smooths the fitting curve. The argument choices are the same as the `BSplineInterpolation`, with the additional parameter `h<length(t)` which is the number of control points to use, with smaller `h` indicating more smoothing.

- `Loess(u,t,d,α)` - A local least square regression. `d` is the degree of the polynomial to use and `α` is a smoothing parameter.

- `GPInterpolation(u,t,m,k,n=-2.0)` - A Gaussian Process interpolation via GaussianProcess.jl. Stochastic: each trajectory is different. `m` is mean function. `k` is kernel function. `n` is log standard deviation of observation noise and defaults to `-2.0`.

- Available Mean functions
- `MeanZero` - Zero mean function
- `MeanConst` - Constant mean function
- `MeanLin` - Linear mean function
- `MeanPoly` - Polynomial mean function
- Available Kernel functions (covariance functions)
- `Lin` - Linear covariance function
- `SE` - Squared exponential covariance function
- `RQ` - Rational quadratic covariance function
- `Matern` - Matern covariance function
- `Periodic` - Periodic covariance function
- `Poly` - Polnomial covariance function
- `Noise` - White noise covariance function
- `Const` - Constant (bias) covariance function

- `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.

## Plotting
Expand All @@ -94,6 +75,5 @@ The series types defined are:
- `:lagrange_interp`
- `:quadratic_spline`
- `:cubic_spline`
- `:loess`

By and large, these accept the same keywords as their function counterparts.
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