Generalizing minimal initializers

Project.toml

@@ -1,7 +1,7 @@
 name = "ReservoirComputing"
 uuid = "7c2d2b1e-3dd4-11ea-355a-8f6a8116e294"
 authors = ["Francesco Martinuzzi"]
-version = "0.10.13"
+version = "0.11.0"
 
 [deps]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"

docs/Project.toml

@@ -17,5 +17,5 @@ Documenter = "1"
 OrdinaryDiffEq = "6"
 Plots = "1"
 PredefinedDynamicalSystems = "1"
-ReservoirComputing = "0.10.5"
+ReservoirComputing = "0.11.0"
 StatsBase = "0.34.4"

docs/src/api/inits.md

@@ -30,3 +30,14 @@
     selfloop_forward_connection
     forward_connection
 ```
+
+## Building functions
+
+```@docs
+    scale_radius!
+    delay_line!
+    backward_connection!
+    simple_cycle!
+    self_loop!
+    add_jumps!
+```

docs/src/esn_tutorials/change_layers.md

@@ -1,6 +1,10 @@
 # Using different layers
 
-A great deal of efforts in the ESNs field are devoted to finding an ideal construction for the reservoir matrices. ReservoirComputing.jl offers multiple implementation of reservoir and input matrices initializations found in the literature. The API is standardized, and follows by [WeightInitializers.jl](https://github.com/LuxDL/Lux.jl/tree/main/lib/WeightInitializers):
+A great deal of efforts in the ESNs field are devoted to finding an ideal construction
+for the reservoir matrices. ReservoirComputing.jl offers multiple implementation of
+reservoir and input matrices initializations found in the literature.
+The API is standardized, and follows 
+[WeightInitializers.jl](https://github.com/LuxDL/Lux.jl/tree/main/lib/WeightInitializers):
 
 ```julia
 weights = init(rng, dims...)
@@ -22,9 +26,13 @@ Custom layers only need to follow these APIs to be compatible with ReservoirComp
 
 ## Example of minimally complex ESN
 
-Using [^rodan2012] and [^rodan2010] as references this section will provide an example on how to change both the input layer and the reservoir for ESNs.
+Using [^rodan2012] and [^rodan2010] as references this section will provide an
+example on how to change both the input layer and the reservoir for ESNs.
 
-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 [PredefinedDynamicalSystems.jl](https://juliadynamics.github.io/PredefinedDynamicalSystems.jl/dev/). The data is scaled to be between -1 and 1.
+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
+[PredefinedDynamicalSystems.jl](https://juliadynamics.github.io/PredefinedDynamicalSystems.jl/dev/).
+The data is scaled to be between -1 and 1.
 
 ```@example minesn
 using PredefinedDynamicalSystems
@@ -43,14 +51,16 @@ testing_input = data[:, (shift + train_len):(shift + train_len + predict_len - 1
 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.
+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.
 
 ```@example minesn
 using ReservoirComputing, StatsBase
 
 res_size = 300
-input_layer = [minimal_init(; weight=0.85, sampling_type=:irrational),
-    minimal_init(; weight=0.95, sampling_type=:irrational)]
+input_layer = [minimal_init(; weight=0.85, sampling_type=:irrational_sample!),
+    minimal_init(; weight=0.95, sampling_type=:irrational_sample!)]
 reservoirs = [simple_cycle(; weight=0.7),
     cycle_jumps(; cycle_weight=0.7, jump_weight=0.2, jump_size=5)]
 
@@ -64,9 +74,14 @@ for i in 1:length(reservoirs)
 end
 ```
 
-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.
+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.
 
 ## Bibliography
 
-[^rodan2012]: Rodan, Ali, and Peter Tiňo. “Simple deterministically constructed cycle reservoirs with regular jumps.” Neural computation 24.7 (2012): 1822-1852.
-[^rodan2010]: Rodan, Ali, and Peter Tiňo. “Minimum complexity echo state network.” IEEE transactions on neural networks 22.1 (2010): 131-144.
+[^rodan2012]: Rodan, Ali, and Peter Tiňo.
+    “Simple deterministically constructed cycle reservoirs with regular jumps.”
+    Neural computation 24.7 (2012): 1822-1852.
+[^rodan2010]: Rodan, Ali, and Peter Tiňo.
+    “Minimum complexity echo state network.”
+    IEEE transactions on neural networks 22.1 (2010): 131-144.

src/ReservoirComputing.jl

@@ -22,6 +22,7 @@ include("predict.jl")
 include("train/linear_regression.jl")
 
 #esn
+include("esn/inits_components.jl")
 include("esn/esn_inits.jl")
 include("esn/esn_reservoir_drivers.jl")
 include("esn/esn.jl")
@@ -42,6 +43,8 @@ export rand_sparse, delay_line, delay_line_backward, cycle_jumps,
        simple_cycle, pseudo_svd, chaotic_init, low_connectivity, double_cycle,
        selfloop_cycle, selfloop_feedback_cycle, selfloop_delayline_backward,
        selfloop_forward_connection, forward_connection
+export scale_radius!, delay_line!, backward_connection!, simple_cycle!, add_jumps!,
+       self_loop!
 export RNN, MRNN, GRU, GRUParams, FullyGated, Minimal
 export train
 export ESN, HybridESN, KnowledgeModel, DeepESN