run.jl

using LSM
using Random  # For setting the seed
using MNIST  # For loading the MNIST dataset

# Set the random seed for reproducibility
Random.seed!(1234)

# Initialize the components of the LSM
reservoir_neurons = [Reservoir.Neuron(rand(), 1.0) for _ in 1:100]  # Example initialization
read_in = ReadIn.adapt_dataset
read_out = ReadOut.Perceptron(randn(100), 0.0, softmax)  # Example initialization

# Create the LSM
lsm = LSM.LiquidStateMachine(reservoir_neurons, read_in, read_out)

# Load and preprocess the MNIST dataset
data, labels = MNIST.traindata()

# Preprocess data - this is a placeholder
# Normally, you would resize or transform the data to match the LSM's input requirements

# Train the LSM
LSM.run_lsm(lsm, data, labels)

# Additional logic for testing and evaluating the LSM