chainopy.nn

Functions

divergance_analysis(mc, nn)

KL Divergance between MarkovChain.tpm and MarkovChain().fit(MarkovChainNeuralNetwork.simulate_random_walk).tpm.

Classes

MarkovChainNeuralNetwork(markov_chain, ...)

Neural network for simulating Markov chain behavior.
class chainopy.nn.MarkovChainNeuralNetwork(markov_chain, num_layers)[source]

Neural network for simulating Markov chain behavior.

Parameters:

  • markov_chain (chainopy.MarkovChain) – Markov chain object.

  • num_layers (int) – Number of layers in the neural network.

input_dim

Input shape of the neural network. Calculated using the transition matrix.

Type:

tuple

output_dim

Output shape of the neural network. Calculated using the transition matrix.

Type:

tuple

Raises:

ValueError – If markov_chain is not of type MarkovChain.:

__init__(markov_chain, num_layers)[source]

Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]

Forward pass of the neural network.

Parameters:

x (torch.tensor) – Input data.

Returns:

torch.Tensor: Output data after passing through the network.

get_weights()[source]

Returns the weights of the model.

Returns:

dict: Dictionary containing layer names and corresponding weights.

simulate_random_walk(start_state, steps)[source]

Simulates a random walk based on the trained model.

Parameters:

  • start_state (int) – Starting state for the random walk.

  • steps (int) – Number of steps to simulate.

Returns:

list: List of states representing the random walk.

train_model(num_samples, epochs, learning_rate, momentum=0.9, verbose=True, patience=500, factor=0.5)[source]

Trains the neural network model.

Parameters:

  • num_samples (int) – Number of training samples.

  • epochs (int) – Number of training epochs.

  • learning_rate (float) – Learning rate for optimization.

  • momentum (float) – Momentum factor (default is 0.9).

  • verbose (bool, optional) – If True, prints training progress (default is True).

  • patience (int, optional) – Patience parameter for learning rate scheduler (default is 500).

  • factor (float, optional) – Factor by which the learning rate will be reduced (default is 0.5).

chainopy.nn.divergance_analysis(mc, nn)[source]

KL Divergance between MarkovChain.tpm and MarkovChain().fit(MarkovChainNeuralNetwork.simulate_random_walk).tpm.

Parameters:
Return type:

float

Returns:

float: KL-Divergance

Lower the KL-Divergance, better the fit.

Notes

KL-Divergance<https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence>_.