diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json
index 348ca5a..43394fe 100644
--- a/dev/.documenter-siteinfo.json
+++ b/dev/.documenter-siteinfo.json
@@ -1 +1 @@
-{"documenter":{"julia_version":"1.10.2","generation_timestamp":"2024-04-08T02:19:43","documenter_version":"1.3.0"}}
\ No newline at end of file
+{"documenter":{"julia_version":"1.10.3","generation_timestamp":"2024-05-03T10:37:44","documenter_version":"1.4.1"}}
\ No newline at end of file
diff --git a/dev/api/esn/index.html b/dev/api/esn/index.html
index 031d86c..c3cb357 100644
--- a/dev/api/esn/index.html
+++ b/dev/api/esn/index.html
@@ -3,7 +3,7 @@
train_data = rand(10, 100) # 10 features, 100 time steps
-esn = ESN(train_data, reservoir = RandSparseReservoir(200), washout = 10)source
To train an ESN model, you can use the train
function. It takes the ESN model, training data, and other optional parameters as input and returns a trained model. Here's the documentation for the train function:
train(esn::AbstractEchoStateNetwork, target_data, training_method = StandardRidge(0.0))
Trains an Echo State Network (ESN) using the provided target data and a specified training method.
Parameters
esn::AbstractEchoStateNetwork
: The ESN instance to be trained.target_data
: Supervised training data for the ESN.training_method
: The method for training the ESN (default: StandardRidge(0.0)
).Returns
The trained ESN model. Its type and structure depend on training_method
and the ESN's implementation. Returns
The trained ESN model. The exact type and structure of the return value depends on the training_method
and the specific ESN implementation.
using ReservoirComputing
+esn = ESN(train_data, reservoir = RandSparseReservoir(200), washout = 10)
source To train an ESN model, you can use the train
function. It takes the ESN model, training data, and other optional parameters as input and returns a trained model. Here's the documentation for the train function:
train(esn::AbstractEchoStateNetwork, target_data, training_method = StandardRidge(0.0))
Trains an Echo State Network (ESN) using the provided target data and a specified training method.
Parameters
esn::AbstractEchoStateNetwork
: The ESN instance to be trained.target_data
: Supervised training data for the ESN.training_method
: The method for training the ESN (default: StandardRidge(0.0)
).Returns
The trained ESN model. Its type and structure depend on training_method
and the ESN's implementation. Returns
The trained ESN model. The exact type and structure of the return value depends on the training_method
and the specific ESN implementation.
using ReservoirComputing
# Initialize an ESN instance and target data
esn = ESN(train_data, reservoir = RandSparseReservoir(200), washout = 10)
@@ -13,4 +13,4 @@
trained_esn = train(esn, target_data)
# Train the ESN using a custom training method
-trained_esn = train(esn, target_data, training_method = StandardRidge(1.0))
source With these components and variations, you can configure and train ESN models for various time series and sequential data prediction tasks.
Theme
Automatic (OS) documenter-light documenter-dark
This document was generated with Documenter.jl version 1.3.0 on Monday 8 April 2024 . Using Julia version 1.10.2.