Index _ | A | C | D | E | F | G | I | L | M | N | O | P | R | S | T | V _ __init__() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.markov_switching.MarkovSwitchingModel method), [1] (chainopy.MarkovChain method) (chainopy.MarkovChainNeuralNetwork method) (chainopy.MarkovSwitchingModel method) (chainopy.nn.MarkovChainNeuralNetwork method), [1] A absorbing_states() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.MarkovChain method) absorption_probabilities() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.MarkovChain method) adjacency_matrix() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.MarkovChain method) C chainopy module chainopy._caching module chainopy._exceptions module chainopy._fileio module chainopy._visualizations module chainopy.markov_chain module, [1] chainopy.markov_switching module, [1] chainopy.nn module, [1] D divergance_analysis() (in module chainopy) (in module chainopy.nn), [1] E eigendecom (chainopy.markov_chain.MarkovChain attribute), [1], [2], [3] (chainopy.MarkovChain attribute), [1] eigenvalues (chainopy.markov_chain.MarkovChain attribute), [1], [2], [3] (chainopy.MarkovChain attribute), [1] eigenvectors (chainopy.markov_chain.MarkovChain attribute), [1], [2], [3] (chainopy.MarkovChain attribute), [1] epsilon (chainopy.markov_chain.MarkovChain attribute), [1], [2], [3] (chainopy.MarkovChain attribute), [1] evaluate() (chainopy.markov_switching.MarkovSwitchingModel method), [1] (chainopy.MarkovSwitchingModel method) expected_hitting_time() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.MarkovChain method) expected_number_of_visits() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.MarkovChain method) expected_time_to_absorption() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.MarkovChain method) F fit() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.markov_switching.MarkovSwitchingModel method), [1] (chainopy.MarkovChain method) (chainopy.MarkovSwitchingModel method) fit_from_file() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.MarkovChain method) forward() (chainopy.MarkovChainNeuralNetwork method) (chainopy.nn.MarkovChainNeuralNetwork method), [1] fundamental_matrix() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.MarkovChain method) G get_weights() (chainopy.MarkovChainNeuralNetwork method) (chainopy.nn.MarkovChainNeuralNetwork method), [1] I input_dim (chainopy.MarkovChainNeuralNetwork attribute) (chainopy.nn.MarkovChainNeuralNetwork attribute), [1] is_absorbing() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.MarkovChain method) is_aperiodic() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.MarkovChain method) is_communicating() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.MarkovChain method) is_ergodic() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.MarkovChain method) is_irreducible() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.MarkovChain method) is_recurrent() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.MarkovChain method) is_symmetric() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.MarkovChain method) is_transient() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.MarkovChain method) L load_model() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.MarkovChain method) M marginal_dist() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.MarkovChain method) MarkovChain (class in chainopy) (class in chainopy.markov_chain), [1] MarkovChainNeuralNetwork (class in chainopy) (class in chainopy.nn), [1] MarkovSwitchingModel (class in chainopy) (class in chainopy.markov_switching), [1] models (chainopy.markov_switching.MarkovSwitchingModel attribute), [1] (chainopy.MarkovSwitchingModel attribute) module chainopy chainopy._caching chainopy._exceptions chainopy._fileio chainopy._visualizations chainopy.markov_chain, [1] chainopy.markov_switching, [1] chainopy.nn, [1] N nstep_distribution() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.MarkovChain method) num_models (chainopy.markov_switching.MarkovSwitchingModel attribute), [1] (chainopy.MarkovSwitchingModel attribute) num_regimes (chainopy.markov_switching.MarkovSwitchingModel attribute), [1] (chainopy.MarkovSwitchingModel attribute) O output_dim (chainopy.MarkovChainNeuralNetwork attribute) (chainopy.nn.MarkovChainNeuralNetwork attribute), [1] P period() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.MarkovChain method) predict() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.markov_switching.MarkovSwitchingModel method), [1] (chainopy.MarkovChain method) (chainopy.MarkovSwitchingModel method) R regimes (chainopy.markov_switching.MarkovSwitchingModel attribute), [1] (chainopy.MarkovSwitchingModel attribute) S save_model() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.MarkovChain method) simulate() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.MarkovChain method) simulate_random_walk() (chainopy.MarkovChainNeuralNetwork method) (chainopy.nn.MarkovChainNeuralNetwork method), [1] states (chainopy.markov_chain.MarkovChain attribute), [1], [2], [3] (chainopy.MarkovChain attribute), [1] stationary_dist() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.MarkovChain method) T tpm (chainopy.markov_chain.MarkovChain attribute), [1], [2], [3] (chainopy.MarkovChain attribute), [1] train_model() (chainopy.MarkovChainNeuralNetwork method) (chainopy.nn.MarkovChainNeuralNetwork method), [1] V visualize_chain() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.MarkovChain method) visualize_transition_matrix() (chainopy.markov_chain.MarkovChain method), [1] (chainopy.MarkovChain method)