[Documentation] Additional Properties listed in RTD output (Disable Output?)

Example: quantecon.markov.core.MarkovChain

It has a table of attributes and a listing immediately following

class quantecon.markov.core.MarkovChain(P)[source]
Bases: object

Class for a finite-state discrete-time Markov chain. It stores useful information such as the stationary distributions, and communication, recurrent, and cyclic classes, and allows simulation of state transitions.

Parameters: 
P : array_like or scipy sparse matrix (float, ndim=2)

The transition matrix. Must be of shape n x n.

Notes

In computing stationary distributions, if the input matrix is a sparse matrix, internally it is converted to a dense matrix.

Attributes

P   (ndarray or scipy.sparse.csr_matrix (float, ndim=2)) See Parameters
stationary_distributions    (array_like(float, ndim=2)) Array containing stationary distributions, one for each recurrent class, as rows.
is_irreducible  (bool) Indicate whether the Markov chain is irreducible.
num_communication_classes   (int) The number of the communication classes.
communication_classes   (list(ndarray(int))) List of numpy arrays containing the communication classes.
num_recurrent_classes   (int) The number of the recurrent classes.
recurrent_classes   (list(ndarray(int))) List of numpy arrays containing the recurrent classes.
is_aperiodic    (bool) Indicate whether the Markov chain is aperiodic.
period  (int) The period of the Markov chain.
cyclic_classes  (list(ndarray(int))) List of numpy arrays containing the cyclic classes. Defined only when the Markov chain is irreducible.
Methods

simulate(ts_length[, init, num_reps, ...])  Simulate time series of state transitions.
cdfs
cdfs1d
communication_classes
cyclic_classes
digraph
is_aperiodic
is_irreducible
num_communication_classes
num_recurrent_classes
period
recurrent_classes

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