Merge pull request #473 from ArnoStrouwen/format

reapply formatter

.JuliaFormatter.toml

@@ -1 +1,3 @@
-style = "sciml"
+style = "sciml"
+format_markdown = true
+format_docstrings = true

CONTRIBUTING.md

@@ -1,21 +1,13 @@
 # Contributing
 
-1. Fork the repository on github. (Click the `Fork` button in the top-right corner)
-
-2. Clone the repository you have just forked. `git clone https://github.com/YOUR_USERNAME/Surrogates.jl.git`
-
-3. `cd Surrogates.jl` Enter the repository's directory.
-
-4. `julia` Open the Julia REPL.
-
-5. `]` Enter package mode
-
-6. Activate the local environment `activate .`
-
-7. Install the dependencies. `instantiate`
-
-8. Perform your edits (Atom with Juno, or VSCode with the Julia plugin are good editor choices)
-
-9. Stage, Commit, and Push your changes
-
+ 1. Fork the repository on github. (Click the `Fork` button in the top-right corner)
+
+ 2. Clone the repository you have just forked. `git clone https://github.com/YOUR_USERNAME/Surrogates.jl.git`
+ 3. `cd Surrogates.jl` Enter the repository's directory.
+ 4. `julia` Open the Julia REPL.
+ 5. `]` Enter package mode
+ 6. Activate the local environment `activate .`
+ 7. Install the dependencies. `instantiate`
+ 8. Perform your edits (Atom with Juno, or VSCode with the Julia plugin are good editor choices)
+ 9. Stage, Commit, and Push your changes
 10. [Open a Pull Request](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request-from-a-fork)

LICENSE.md

@@ -1,18 +1,18 @@
 The Surrogates.jl package is licensed under the MIT "Expat" License:
 
 > Copyright (c) 2019-20: Ludovico Bessi, Julia Computing.
->
+> 
 > Permission is hereby granted, free of charge, to any person obtaining
 > a copy of this software and associated documentation files (the
 > "Software"), to deal in the Software without restriction, including
 > without limitation the rights to use, copy, modify, merge, publish,
 > distribute, sublicense, and/or sell copies of the Software, and to
 > permit persons to whom the Software is furnished to do so, subject to
 > the following conditions:
->
+> 
 > The above copyright notice and this permission notice shall be
 > included in all copies or substantial portions of the Software.
->
+> 
 > THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
 > EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
 > MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.

README.md

@@ -6,47 +6,48 @@
 [![codecov](https://codecov.io/gh/SciML/Surrogates.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/SciML/Surrogates.jl)
 [![Build Status](https://github.com/SciML/Surrogates.jl/workflows/CI/badge.svg)](https://github.com/SciML/Surrogates.jl/actions?query=workflow%3ACI)
 
-[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
+[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor%27s%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
 [![SciML Code Style](https://img.shields.io/static/v1?label=code%20style&message=SciML&color=9558b2&labelColor=389826)](https://github.com/SciML/SciMLStyle)
 
 A surrogate model is an approximation method that mimics the behavior of a computationally
 expensive simulation. In more mathematical terms: suppose we are attempting to optimize a function
 `f(p)`, but each calculation of `f` is very expensive. It may be the case we need to solve a PDE for each point or use advanced numerical linear algebra machinery, which is usually costly. The idea is then to develop a surrogate model `g` which approximates `f` by training on previous data collected from evaluations of `f`.
 The construction of a surrogate model can be seen as a three-step process:
 
-1. Sample selection
-2. Construction of the surrogate model
-3. Surrogate optimization
+ 1. Sample selection
+ 2. Construction of the surrogate model
+ 3. Surrogate optimization
 
 Sampling can be done through [QuasiMonteCarlo.jl](https://github.com/SciML/QuasiMonteCarlo.jl), all the functions available there can be used in Surrogates.jl.
 
 ## ALL the currently available surrogate models:
 
-- Kriging
-- Kriging using Stheno
-- Radial Basis
-- Wendland
-- Linear
-- Second Order Polynomial
-- Support Vector Machines (Wait for LIBSVM resolution)
-- Neural Networks
-- Random Forests
-- Lobachevsky
-- Inverse-distance
-- Polynomial expansions
-- Variable fidelity
-- Mixture of experts (Waiting GaussianMixtures package to work on v1.5)
-- Earth
-- Gradient Enhanced Kriging
+  - Kriging
+  - Kriging using Stheno
+  - Radial Basis
+  - Wendland
+  - Linear
+  - Second Order Polynomial
+  - Support Vector Machines (Wait for LIBSVM resolution)
+  - Neural Networks
+  - Random Forests
+  - Lobachevsky
+  - Inverse-distance
+  - Polynomial expansions
+  - Variable fidelity
+  - Mixture of experts (Waiting GaussianMixtures package to work on v1.5)
+  - Earth
+  - Gradient Enhanced Kriging
 
 ## ALL the currently available optimization methods:
 
-- SRBF
-- LCBS
-- DYCORS
-- EI
-- SOP
-- Multi-optimization: SMB and RTEA
+  - SRBF
+  - LCBS
+  - DYCORS
+  - EI
+  - SOP
+  - Multi-optimization: SMB and RTEA
+
 ## Installing Surrogates package
 
 ```julia

docs/pages.jl

@@ -1,40 +1,40 @@
 pages = ["index.md"
-    "Tutorials" => [
-    "Basics" => "tutorials.md",
-    "Radials" => "radials.md",
-    "Kriging" => "kriging.md",
-    "Gaussian Process" => "abstractgps.md",
-    "Lobachevsky" => "lobachevsky.md",
-    "Linear" => "LinearSurrogate.md",
-    "InverseDistance" => "InverseDistance.md",
-    "RandomForest" => "randomforest.md",
-    "SecondOrderPolynomial" => "secondorderpoly.md",
-    "NeuralSurrogate" => "neural.md",
-    "Wendland" => "wendland.md",
-    "Polynomial Chaos" => "polychaos.md",
-    "Variable Fidelity" => "variablefidelity.md",
-    "Gradient Enhanced Kriging" => "gek.md",
-    "GEKPLS" => "gekpls.md",
-    "MOE" => "moe.md",
-    "Parallel Optimization" => "parallel.md",
-]
-    "User guide" => [
-    "Samples" => "samples.md",
-    "Surrogates" => "surrogate.md",
-    "Optimization" => "optimizations.md",
-]
-    "Benchmarks" => [
-    "Sphere function" => "sphere_function.md",
-    "Lp norm" => "lp.md",
-    "Rosenbrock" => "rosenbrock.md",
-    "Tensor product" => "tensor_prod.md",
-    "Cantilever beam" => "cantilever.md",
-    "Water Flow function" => "water_flow.md",
-    "Welded beam function" => "welded_beam.md",
-    "Branin function" => "BraninFunction.md",
-    "Improved Branin function" => "ImprovedBraninFunction.md",
-    "Ackley function" => "ackley.md",
-    "Gramacy & Lee Function" => "gramacylee.md",
-    "Salustowicz Benchmark" => "Salustowicz.md",
-    "Multi objective optimization" => "multi_objective_opt.md",
-]]
+         "Tutorials" => [
+             "Basics" => "tutorials.md",
+             "Radials" => "radials.md",
+             "Kriging" => "kriging.md",
+             "Gaussian Process" => "abstractgps.md",
+             "Lobachevsky" => "lobachevsky.md",
+             "Linear" => "LinearSurrogate.md",
+             "InverseDistance" => "InverseDistance.md",
+             "RandomForest" => "randomforest.md",
+             "SecondOrderPolynomial" => "secondorderpoly.md",
+             "NeuralSurrogate" => "neural.md",
+             "Wendland" => "wendland.md",
+             "Polynomial Chaos" => "polychaos.md",
+             "Variable Fidelity" => "variablefidelity.md",
+             "Gradient Enhanced Kriging" => "gek.md",
+             "GEKPLS" => "gekpls.md",
+             "MOE" => "moe.md",
+             "Parallel Optimization" => "parallel.md"
+         ]
+         "User guide" => [
+             "Samples" => "samples.md",
+             "Surrogates" => "surrogate.md",
+             "Optimization" => "optimizations.md"
+         ]
+         "Benchmarks" => [
+             "Sphere function" => "sphere_function.md",
+             "Lp norm" => "lp.md",
+             "Rosenbrock" => "rosenbrock.md",
+             "Tensor product" => "tensor_prod.md",
+             "Cantilever beam" => "cantilever.md",
+             "Water Flow function" => "water_flow.md",
+             "Welded beam function" => "welded_beam.md",
+             "Branin function" => "BraninFunction.md",
+             "Improved Branin function" => "ImprovedBraninFunction.md",
+             "Ackley function" => "ackley.md",
+             "Gramacy & Lee Function" => "gramacylee.md",
+             "Salustowicz Benchmark" => "Salustowicz.md",
+             "Multi objective optimization" => "multi_objective_opt.md"
+         ]]