some docs fies

Author: MartinuzziFrancesco

src/ReservoirComputing.jl

@@ -45,8 +45,25 @@ end
 """
     Generative(prediction_len)
 
-This prediction methodology allows the models to produce an autonomous prediction, feeding the prediction into itself to generate the next step.
-The only parameter needed is the number of steps for the prediction.
+A prediction strategy that enables models to generate autonomous multi-step
+forecasts by recursively feeding their own outputs back as inputs for
+subsequent prediction steps.
+
+# Parameters
+- `prediction_len::Int`: The number of future steps to predict.
+
+# Description
+
+The `Generative` prediction method allows a model to perform multi-step
+forecasting by using its own previous predictions as inputs for future predictions.
+This approach is especially useful in time series analysis, where each prediction
+depends on the preceding data points.
+
+At each step, the model takes the current input, generates a prediction,
+and then incorporates that prediction into the input for the next step.
+This recursive process continues until the specified
+number of prediction steps (`prediction_len`) is reached.
+
 """
 struct Generative{T} <: AbstractPrediction
     prediction_len::T
@@ -60,7 +77,27 @@ end
 """
     Predictive(prediction_data)
 
-Given a set of labels as `prediction_data`, this method of prediction will return the corresponding labels in a standard Machine Learning fashion.
+A prediction strategy for supervised learning tasks,
+where a model predicts labels based on a provided set
+of input features (`prediction_data`).
+
+# Parameters
+- `prediction_data`: The input data used for prediction, typically structured as a matrix
+   where each column represents a sample, and each row represents a feature.
+
+# Description
+
+The `Predictive` prediction method is a standard approach
+in supervised machine learning tasks. It uses the provided input data
+(`prediction_data`) to produce corresponding labels or outputs based
+on the learned relationships in the model. Unlike generative prediction,
+this method does not recursively feed predictions into the model;
+instead, it operates on fixed input data to produce a single batch of predictions.
+
+This method is suitable for tasks like classification,
+regression, or other use cases where the input features
+and the number of steps are predefined.
+
 """
 function Predictive(prediction_data)
     prediction_len = size(prediction_data, 2)

src/esn/deepesn.jl

@@ -55,11 +55,6 @@ temporal features.
   - `matrix_type`: The type of matrix used for storing the training data.
     Default is inferred from `train_data`.
 
-# Returns
-
-  - A `DeepESN` instance configured according to the provided parameters
-    and suitable for further training and prediction tasks.
-
 # Example
 
 ```julia
@@ -73,10 +68,6 @@ deepESN = DeepESN(train_data, 10, 100, depth = 3, washout = 100)
 train(deepESN, target_data)
 prediction = predict(deepESN, new_data)
 ```
-
-The DeepESN model is ideal for tasks requiring the processing of sequences with
-complex temporal dependencies, benefiting from the multiple reservoirs to capture
-different levels of abstraction and temporal dynamics.
 """
 function DeepESN(train_data,
         in_size::Int,

src/esn/esn.jl

@@ -21,9 +21,9 @@ Creates an Echo State Network (ESN) using specified parameters and training data
 
   - `train_data`: Matrix of training data (columns as time steps, rows as features).
   - `variation`: Variation of ESN (default: `Default()`).
-  - `input_layer`: Input layer of ESN (default: `DenseLayer()`).
-  - `reservoir`: Reservoir of the ESN (default: `RandSparseReservoir(100)`).
-  - `bias`: Bias vector for each time step (default: `NullLayer()`).
+  - `input_layer`: Input layer of ESN.
+  - `reservoir`: Reservoir of the ESN.
+  - `bias`: Bias vector for each time step.
   - `reservoir_driver`: Mechanism for evolving reservoir states (default: `RNN()`).
   - `nla_type`: Non-linear activation type (default: `NLADefault()`).
   - `states_type`: Format for storing states (default: `StandardStates()`).