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⚡️ Speed up function _best_dev_gains by 8% #27

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⚡️ Speed up function _best_dev_gains by 8% #27

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18 changes: 12 additions & 6 deletions quantecon/game_theory/repeated_game.py
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Original file line number Diff line number Diff line change
Expand Up @@ -199,12 +199,18 @@ def _best_dev_gains(rpg):
ai and a-i, and player i deviates to the best response action.
"""
sg, delta = rpg.sg, rpg.delta

best_dev_gains = ((1-delta)/delta *
(np.max(sg.payoff_arrays[i], 0) - sg.payoff_arrays[i])
for i in range(2))

return tuple(best_dev_gains)
payoff_arrays = sg.payoff_arrays

# Precompute coefficient
coeff = (1 - delta) / delta

# For each player, rapidly compute the best deviation gain using optimal axis parameterization
# np.max() over axis=0 gives the best response for each (a-i)
# No need to build generator, directly construct tuple for efficiency
return (
coeff * (np.max(payoff_arrays[0], axis=0) - payoff_arrays[0]),
coeff * (np.max(payoff_arrays[1], axis=0) - payoff_arrays[1])
)


@njit
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