Merge pull request #143 from SciML/fm/example

Docs work

README.md

@@ -18,6 +18,7 @@ ReservoirComputing.jl provides an efficient, modular and easy to use implementat
 ## Quick Example
 
 To illustrate the workflow of this library we will showcase how it is possible to train an ESN to learn the dynamics of the Lorenz system. As a first step we will need to gather the data. For the `Generative` prediction we need the target data to be one step ahead of the training data:
+
 ```julia
 using ReservoirComputing, OrdinaryDiffEq
 

docs/Project.toml

@@ -1,6 +1,15 @@
 [deps]
+CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
+CellularAutomata = "878138dc-5b27-11ea-1a71-cb95d38d6b29"
+DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+DynamicalSystems = "61744808-ddfa-5f27-97ff-6e42cc95d634"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 ReservoirComputing = "7c2d2b1e-3dd4-11ea-355a-8f6a8116e294"
+StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 
 [compat]
 Documenter = "0.27"

docs/make.jl

@@ -3,11 +3,21 @@ using Documenter, ReservoirComputing
 cp("./docs/Manifest.toml", "./docs/src/assets/Manifest.toml", force = true)
 cp("./docs/Project.toml", "./docs/src/assets/Project.toml", force = true)
 
+ENV["PLOTS_TEST"] = "true"
+ENV["GKSwstype"] = "100"
 include("pages.jl")
 
 makedocs(modules = [ReservoirComputing],
          clean = true, doctest = false,
          sitename = "ReservoirComputing.jl",
+         strict = [
+             :doctest,
+             :linkcheck,
+             :parse_error,
+             :example_block,
+             # Other available options are
+             # :autodocs_block, :cross_references, :docs_block, :eval_block, :example_block, :footnote, :meta_block, :missing_docs, :setup_block
+         ],
          format = Documenter.HTML(analytics = "UA-90474609-3",
                                   assets = ["assets/favicon.ico"],
                                   canonical = "https://docs.sciml.ai/ReservoirComputing/stable/"),

docs/pages.jl

@@ -4,7 +4,7 @@ pages = [
                               "Altering States" => "general/states_variation.md",
                               "Generative vs Predictive" => "general/predictive_generative.md"],
     "Echo State Network Tutorials" => Any["Lorenz System Forecasting" => "esn_tutorials/lorenz_basic.md",
-                                          "Mackey-Glass Forecasting on GPU" => "esn_tutorials/mackeyglass_basic.md",
+                                          #"Mackey-Glass Forecasting on GPU" => "esn_tutorials/mackeyglass_basic.md",
                                           "Using Different Layers" => "esn_tutorials/change_layers.md",
                                           "Using Different Reservoir Drivers" => "esn_tutorials/different_drivers.md",
                                           #"Using Different Training Methods" => "esn_tutorials/different_training.md",

docs/src/api/esn_drivers.md

@@ -11,7 +11,3 @@ The ```GRU``` driver also provides the user the choice of the possible variant:
 ```
 Please refer to the original papers for more detail about these architectures.
 
-The states are created using the following function
-```@docs
-    create_states
-```

docs/src/api/training.md

@@ -7,9 +7,7 @@
 ```
 
 ## Gaussian Regression
-```@docs
-    GaussianProcess
-```
+Currently (v0.9) unavailable.
 
 ## Support Vector Regression
 Support vector Regression is possible using a direct call to [LIBSVM](https://github.com/JuliaML/LIBSVM.jl) regression methods. Instead of a wrapper please refer to the use of ```LIBSVM.AbstractSVR``` in the original library.

docs/src/esn_tutorials/change_layers.md

@@ -43,7 +43,7 @@ end
 Using [^1] and [^2] as references this section will provide an example on how to change both the input layer and the reservoir for ESNs. The full script for this example can be found [here](https://github.com/MartinuzziFrancesco/reservoir-computing-examples/blob/main/change_layers/layers.jl). This example was run on Julia v1.7.2.
 
 The task for this example will be the one step ahead prediction of the Henon map. To obtain the data one can leverage the package [DynamicalSystems.jl](https://juliadynamics.github.io/DynamicalSystems.jl/dev/). The data is scaled to be between -1 and 1.
-```julia
+```@example mesn
 using DynamicalSystems
 train_len = 3000
 predict_len = 2000
@@ -62,7 +62,7 @@ testing_target = data[:,shift+train_len+1:shift+train_len+predict_len]
 
 Now it is possible to define the input layers and reservoirs we want to compare and run the comparison in a simple for loop. The accuracy will be tested using the mean squared deviation `msd` from [StatsBase](https://juliastats.org/StatsBase.jl/stable/).
 
-```julia
+```@example mesn
 using ReservoirComputing, StatsBase
 
 res_size = 300
@@ -72,17 +72,13 @@ reservoirs = [SimpleCycleReservoir(res_size, 0.7),
 
 for i=1:length(reservoirs)
     esn = ESN(training_input;
-        input_init = input_layer[i],
-        reservoir_init = reservoirs[i])
+        input_layer = input_layer[i],
+        reservoir = reservoirs[i])
     wout = train(esn, training_target, StandardRidge(0.001))
     output = esn(Predictive(testing_input), wout)
     println(msd(testing_target, output))
 end
 ```
-```
-0.003402709948526517
-0.0034463857980330612
-```
 As it is possible to see, changing layers in ESN models is straightforward. Be sure to check the API documentation for a full list of reservoir and layers.