Merge pull request #477 from okuchap/random

FEAT: Add `random_pure_actions` and `random_mixed_actions`

quantecon/game_theory/__init__.py

@@ -5,7 +5,9 @@
 """
 from .normal_form_game import Player, NormalFormGame
 from .normal_form_game import pure2mixed, best_response_2p
-from .random import random_game, covariance_game
+from .random import (
+    random_game, covariance_game, random_pure_actions, random_mixed_actions
+)
 from .pure_nash import pure_nash_brute, pure_nash_brute_gen
 from .support_enumeration import support_enumeration, support_enumeration_gen
 from .lemke_howson import lemke_howson

quantecon/game_theory/random.py

@@ -2,10 +2,12 @@
 Generate random NormalFormGame instances.
 
 """
+
 import numpy as np
 
 from .normal_form_game import Player, NormalFormGame
 from ..util import check_random_state
+from ..random import probvec
 
 
 def random_game(nums_actions, random_state=None):
@@ -92,3 +94,60 @@ def covariance_game(nums_actions, rho, random_state=None):
         random_state.multivariate_normal(mean, cov, nums_actions)
     g = NormalFormGame(payoff_profile_array)
     return g
+
+
+def random_pure_actions(nums_actions, random_state=None):
+    """
+    Return a tuple of random pure actions (integers).
+
+    Parameters
+    ----------
+    nums_actions : tuple(int)
+        Tuple of the numbers of actions, one for each player.
+
+    random_state : int or np.random.RandomState, optional
+        Random seed (integer) or np.random.RandomState instance to set
+        the initial state of the random number generator for
+        reproducibility. If None, a randomly initialized RandomState is
+        used.
+
+    Returns
+    -------
+    action_profile : Tuple(int)
+        Tuple of actions, one for each player.
+
+    """
+    random_state = check_random_state(random_state)
+    action_profile = tuple(
+        [random_state.randint(num_actions) for num_actions in nums_actions]
+    )
+    return action_profile
+
+
+def random_mixed_actions(nums_actions, random_state=None):
+    """
+    Return a tuple of random mixed actions (vectors of floats).
+
+    Parameters
+    ----------
+    nums_actions : tuple(int)
+        Tuple of the numbers of actions, one for each player.
+
+    random_state : int or np.random.RandomState, optional
+        Random seed (integer) or np.random.RandomState instance to set
+        the initial state of the random number generator for
+        reproducibility. If None, a randomly initialized RandomState is
+        used.
+
+    Returns
+    -------
+    action_profile : tuple(ndarray(float, ndim=1))
+        Tuple of mixed_actions, one for each player.
+
+    """
+    random_state = check_random_state(random_state)
+    action_profile = tuple(
+        [probvec(1, num_actions, random_state).ravel()
+         for num_actions in nums_actions]
+    )
+    return action_profile

quantecon/game_theory/tests/test_random.py

@@ -4,9 +4,11 @@
 """
 import numpy as np
 from numpy.testing import assert_allclose, assert_raises
-from nose.tools import eq_
+from nose.tools import eq_, ok_
 
-from quantecon.game_theory import random_game, covariance_game
+from quantecon.game_theory import (
+    random_game, covariance_game, random_pure_actions, random_mixed_actions
+)
 
 
 def test_random_game():
@@ -59,6 +61,25 @@ def test_covariance_game_value_error():
     assert_raises(ValueError, covariance_game, nums_actions, rho)
 
 
+def test_random_pure_actions():
+    nums_actions = (2, 3, 4)
+    N = len(nums_actions)
+    seed = 1234
+    action_profiles = [
+        random_pure_actions(nums_actions, seed) for i in range(2)
+    ]
+    for i in range(N):
+        ok_(action_profiles[0][i] < nums_actions[i])
+    eq_(action_profiles[0], action_profiles[1])
+
+
+def test_random_mixed_actions():
+    nums_actions = (2, 3, 4)
+    seed = 1234
+    action_profile = random_mixed_actions(nums_actions, seed)
+    eq_(tuple([len(action) for action in action_profile]), nums_actions)
+
+
 if __name__ == '__main__':
     import sys
     import nose