random

Generate MarkovChain and DiscreteDP instances randomly.

quantecon.markov.random.random_discrete_dp(num_states, num_actions, beta=None, k=None, scale=1, sparse=False, sa_pair=False, random_state=None)[source]

Generate a DiscreteDP randomly. The reward values are drawn from the normal distribution with mean 0 and standard deviation scale.

Parameters:

num_statesscalar(int): Number of states.
num_actionsscalar(int): Number of actions.
betascalar(float), optional(default=None): Discount factor. Randomly chosen from [0, 1) if not specified.
kscalar(int), optional(default=None): Number of possible next states for each state-action pair. Equal to num_states if not specified.
scalescalar(float), optional(default=1): Standard deviation of the normal distribution for the reward values.
sparsebool, optional(default=False): Whether to store the transition probability array in sparse matrix form.
sa_pairbool, optional(default=False): Whether to represent the data in the state-action pairs formulation. (If sparse=True, automatically set True.)
random_stateint or np.random.RandomState/Generator, optional: Random seed (integer) or np.random.RandomState or Generator instance to set the initial state of the random number generator for reproducibility. If None, a randomly initialized RandomState is used.

Returns:

ddpDiscreteDP: An instance of DiscreteDP.

quantecon.markov.random.random_markov_chain(n, k=None, sparse=False, random_state=None)[source]

Return a randomly sampled MarkovChain instance with n states, where each state has k states with positive transition probability.

Parameters:

nscalar(int): Number of states.
kscalar(int), optional(default=None): Number of states that may be reached from each state with positive probability. Set to n if not specified.
sparsebool, optional(default=False): Whether to store the transition probability matrix in sparse matrix form.
random_stateint or np.random.RandomState/Generator, optional: Random seed (integer) or np.random.RandomState or Generator instance to set the initial state of the random number generator for reproducibility. If None, a randomly initialized RandomState is used.

Returns:

mcMarkovChain

Examples

>>> import numpy as np
>>> import quantecon as qe
>>> rng = np.random.default_rng(1234)
>>> mc = qe.markov.random_markov_chain(3, random_state=rng)
>>> mc.P
array([[0.38019574, 0.59650403, 0.02330023],
       [0.26169242, 0.66155381, 0.07675377],
       [0.11809123, 0.20100583, 0.68090294]])
>>> rng = np.random.default_rng(1234)
>>> mc = qe.markov.random_markov_chain(3, k=2, random_state=rng)
>>> mc.P
array([[0.97669977, 0.        , 0.02330023],
       [0.38019574, 0.        , 0.61980426],
       [0.92324623, 0.07675377, 0.        ]])

quantecon.markov.random.random_stochastic_matrix(n, k=None, sparse=False, format='csr', random_state=None)[source]

Return a randomly sampled n x n stochastic matrix with k nonzero entries for each row.

Parameters:

nscalar(int): Number of states.
kscalar(int), optional(default=None): Number of nonzero entries in each row of the matrix. Set to n if not specified.
sparsebool, optional(default=False): Whether to generate the matrix in sparse matrix form.
formatstr, optional(default=’csr’): Sparse matrix format, str in {‘bsr’, ‘csr’, ‘csc’, ‘coo’, ‘lil’, ‘dia’, ‘dok’}. Relevant only when sparse=True.
random_stateint or np.random.RandomState/Generator, optional: Random seed (integer) or np.random.RandomState or Generator instance to set the initial state of the random number generator for reproducibility. If None, a randomly initialized RandomState is used.

Returns:

Pnumpy ndarray or scipy sparse matrix (float, ndim=2): Stochastic matrix.