chore: Game Theory algorithms are added #11864

isatyamks · 2024-10-07T16:40:11Z

Describe your change:

#fixed issue #11804

Add an algorithm?
Fix a bug or typo in an existing algorithm?
Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
Documentation change?

Checklist:

… in game theory

…ash equilibrium

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algorithms-keeper · 2024-10-07T16:43:01Z

game_theory/best_response_dynamics.py

@@ -0,0 +1,28 @@
+import numpy as np
+def best_response_dynamics(payoff_matrix_a, payoff_matrix_b, iterations=10):


Please provide return type hint for the function: best_response_dynamics. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file game_theory/best_response_dynamics.py, please provide doctest for the function best_response_dynamics

Please provide type hint for the parameter: payoff_matrix_a

Please provide type hint for the parameter: payoff_matrix_b

Please provide type hint for the parameter: iterations

algorithms-keeper · 2024-10-07T16:43:01Z

game_theory/fictitious_play.py

@@ -0,0 +1,33 @@
+def fictitious_play(payoff_matrix_a, payoff_matrix_b, iterations=100):


Please provide return type hint for the function: fictitious_play. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file game_theory/fictitious_play.py, please provide doctest for the function fictitious_play

Please provide type hint for the parameter: payoff_matrix_a

Please provide type hint for the parameter: payoff_matrix_b

Please provide type hint for the parameter: iterations

algorithms-keeper · 2024-10-07T16:43:01Z

game_theory/minimax_algorithm.py

@@ -0,0 +1,29 @@
+def minimax(depth, node_index, is_maximizing_player, values, alpha, beta):


Please provide return type hint for the function: minimax. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file game_theory/minimax_algorithm.py, please provide doctest for the function minimax

Please provide type hint for the parameter: depth

Please provide type hint for the parameter: node_index

Please provide type hint for the parameter: is_maximizing_player

Please provide type hint for the parameter: values

Please provide type hint for the parameter: alpha

Please provide type hint for the parameter: beta

algorithms-keeper · 2024-10-07T16:43:01Z

game_theory/nash_equlibrium.py

+from scipy.optimize import linprog
+
+
+def find_nash_equilibrium(payoff_matrix_a, payoff_matrix_b):


Please provide return type hint for the function: find_nash_equilibrium. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file game_theory/nash_equlibrium.py, please provide doctest for the function find_nash_equilibrium

Please provide type hint for the parameter: payoff_matrix_a

Please provide type hint for the parameter: payoff_matrix_b

algorithms-keeper · 2024-10-07T16:43:01Z

game_theory/shapley_value.py

+import numpy as np
+
+
+def shapley_value(payoff_matrix):


Please provide return type hint for the function: shapley_value. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file game_theory/shapley_value.py, please provide doctest for the function shapley_value

Please provide type hint for the parameter: payoff_matrix

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Python:

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doctest

unittest

pytest

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algorithms-keeper · 2024-10-07T16:47:00Z

game_theory/best_response_dynamics.py

+import numpy as np
+
+
+def best_response_dynamics(payoff_matrix_a, payoff_matrix_b, iterations=10):


Please provide return type hint for the function: best_response_dynamics. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file game_theory/best_response_dynamics.py, please provide doctest for the function best_response_dynamics

Please provide type hint for the parameter: payoff_matrix_a

Please provide type hint for the parameter: payoff_matrix_b

Please provide type hint for the parameter: iterations

algorithms-keeper · 2024-10-07T16:47:00Z

game_theory/fictitious_play.py

@@ -0,0 +1,34 @@
+import numpy as np
+def fictitious_play(payoff_matrix_a, payoff_matrix_b, iterations=100):


Please provide return type hint for the function: fictitious_play. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file game_theory/fictitious_play.py, please provide doctest for the function fictitious_play

Please provide type hint for the parameter: payoff_matrix_a

Please provide type hint for the parameter: payoff_matrix_b

Please provide type hint for the parameter: iterations

algorithms-keeper · 2024-10-07T16:47:00Z

game_theory/minimax_algorithm.py

@@ -0,0 +1,29 @@
+def minimax(depth, node_index, is_maximizing_player, values, alpha, beta):


Please provide return type hint for the function: minimax. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file game_theory/minimax_algorithm.py, please provide doctest for the function minimax

Please provide type hint for the parameter: depth

Please provide type hint for the parameter: node_index

Please provide type hint for the parameter: is_maximizing_player

Please provide type hint for the parameter: values

Please provide type hint for the parameter: alpha

Please provide type hint for the parameter: beta

algorithms-keeper · 2024-10-07T16:47:00Z

game_theory/nash_equlibrium.py

+from scipy.optimize import linprog
+
+
+def find_nash_equilibrium(payoff_matrix_a, payoff_matrix_b):


Please provide return type hint for the function: find_nash_equilibrium. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file game_theory/nash_equlibrium.py, please provide doctest for the function find_nash_equilibrium

Please provide type hint for the parameter: payoff_matrix_a

Please provide type hint for the parameter: payoff_matrix_b

algorithms-keeper · 2024-10-07T16:47:00Z

game_theory/shapley_value.py

+import numpy as np
+
+
+def shapley_value(payoff_matrix):


Please provide return type hint for the function: shapley_value. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file game_theory/shapley_value.py, please provide doctest for the function shapley_value

Please provide type hint for the parameter: payoff_matrix

for more information, see https://pre-commit.ci

Remove unnecessary code and resolve merge conflicts

algorithms-keeper

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🔗 Relevant Links

Repository:

Contributing guidelines

Project Euler solution guidelines

Python:

Formatted string literals (f-strings)

Type hints

doctest

unittest

pytest

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algorithms-keeper · 2024-10-07T17:18:50Z

game_theory/best_response_dynamics.py

+import numpy as np
+
+
+def best_response_dynamics(payoff_matrix_a, payoff_matrix_b, iterations=10):


Please provide return type hint for the function: best_response_dynamics. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file game_theory/best_response_dynamics.py, please provide doctest for the function best_response_dynamics

Please provide type hint for the parameter: payoff_matrix_a

Please provide type hint for the parameter: payoff_matrix_b

Please provide type hint for the parameter: iterations

algorithms-keeper · 2024-10-07T17:18:50Z

game_theory/fictitious_play.py

@@ -0,0 +1,35 @@
+import numpy as np
+
+def fictitious_play(payoff_matrix_a, payoff_matrix_b, iterations=100):


Please provide return type hint for the function: fictitious_play. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file game_theory/fictitious_play.py, please provide doctest for the function fictitious_play

Please provide type hint for the parameter: payoff_matrix_a

Please provide type hint for the parameter: payoff_matrix_b

Please provide type hint for the parameter: iterations

algorithms-keeper · 2024-10-07T17:18:50Z

game_theory/minimax_algorithm.py

@@ -0,0 +1,29 @@
+def minimax(depth, node_index, is_maximizing_player, values, alpha, beta):


Please provide return type hint for the function: minimax. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file game_theory/minimax_algorithm.py, please provide doctest for the function minimax

Please provide type hint for the parameter: depth

Please provide type hint for the parameter: node_index

Please provide type hint for the parameter: is_maximizing_player

Please provide type hint for the parameter: values

Please provide type hint for the parameter: alpha

Please provide type hint for the parameter: beta

algorithms-keeper · 2024-10-07T17:18:51Z

game_theory/nash_equlibrium.py

+from scipy.optimize import linprog
+
+
+def find_nash_equilibrium(payoff_matrix_a, payoff_matrix_b):


Please provide return type hint for the function: find_nash_equilibrium. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file game_theory/nash_equlibrium.py, please provide doctest for the function find_nash_equilibrium

Please provide type hint for the parameter: payoff_matrix_a

Please provide type hint for the parameter: payoff_matrix_b

algorithms-keeper · 2024-10-07T17:18:51Z

game_theory/shapley_value.py

+import numpy as np
+
+
+def shapley_value(payoff_matrix):


Please provide return type hint for the function: shapley_value. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file game_theory/shapley_value.py, please provide doctest for the function shapley_value

Please provide type hint for the parameter: payoff_matrix

for more information, see https://pre-commit.ci

algorithms-keeper

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

Contributing guidelines

Project Euler solution guidelines

Python:

Formatted string literals (f-strings)

Type hints

doctest

unittest

pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

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algorithms-keeper · 2024-10-07T17:41:17Z

game_theory/best_response_dynamics.py

+import numpy as np
+
+
+def best_response_dynamics(payoff_matrix_a, payoff_matrix_b, iterations=10):


Please provide return type hint for the function: best_response_dynamics. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file game_theory/best_response_dynamics.py, please provide doctest for the function best_response_dynamics

Please provide type hint for the parameter: payoff_matrix_a

Please provide type hint for the parameter: payoff_matrix_b

Please provide type hint for the parameter: iterations

algorithms-keeper · 2024-10-07T17:41:17Z

game_theory/fictitious_play.py

+import numpy as np
+
+
+def fictitious_play(payoff_matrix_a, payoff_matrix_b, iterations=100):


Please provide return type hint for the function: fictitious_play. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file game_theory/fictitious_play.py, please provide doctest for the function fictitious_play

Please provide type hint for the parameter: payoff_matrix_a

Please provide type hint for the parameter: payoff_matrix_b

Please provide type hint for the parameter: iterations

algorithms-keeper · 2024-10-07T17:41:18Z

game_theory/minimax_algorithm.py

@@ -0,0 +1,29 @@
+def minimax(depth, node_index, is_maximizing_player, values, alpha, beta):


Please provide return type hint for the function: minimax. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file game_theory/minimax_algorithm.py, please provide doctest for the function minimax

Please provide type hint for the parameter: depth

Please provide type hint for the parameter: node_index

Please provide type hint for the parameter: is_maximizing_player

Please provide type hint for the parameter: values

Please provide type hint for the parameter: alpha

Please provide type hint for the parameter: beta

algorithms-keeper · 2024-10-07T17:41:18Z

game_theory/nash_equlibrium.py

+from scipy.optimize import linprog
+
+
+def find_nash_equilibrium(payoff_matrix_a, payoff_matrix_b):


Please provide return type hint for the function: find_nash_equilibrium. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file game_theory/nash_equlibrium.py, please provide doctest for the function find_nash_equilibrium

Please provide type hint for the parameter: payoff_matrix_a

Please provide type hint for the parameter: payoff_matrix_b

algorithms-keeper · 2024-10-07T17:41:18Z

game_theory/shapley_value.py

@@ -0,0 +1,28 @@
+import numpy as np
+
+def shapley_value(payoff_matrix):


Please provide return type hint for the function: shapley_value. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file game_theory/shapley_value.py, please provide doctest for the function shapley_value

Please provide type hint for the parameter: payoff_matrix

for more information, see https://pre-commit.ci

isatyamks and others added 6 commits October 7, 2024 21:50

Update computer_vision/README.md with OpenCV documentation link

849fc80

Update computer_vision/README.md with additional resource link

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Add best_response_dynamics.py for implementing best response dynamics…

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… in game theory

Add game theory algorithms: fictitious play, minimax algorithm, and N…

cdf6320

…ash equilibrium

Add shapley_value.py for calculating Shapley values in game theory

8362b29

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algorithms-keeper bot added the awaiting reviews This PR is ready to be reviewed label Oct 7, 2024

Add numpy import to best_response_dynamics.py

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isatyamks mentioned this pull request Oct 7, 2024

Game Theory algorithms are missing #11804

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algorithms-keeper bot mentioned this pull request Oct 8, 2024

fixed #11871 (incorrect union method implementation in fuzzy operations) #11896

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chore: Game Theory algorithms are added #11864

chore: Game Theory algorithms are added #11864

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		@@ -0,0 +1,28 @@
		import numpy as np
		def best_response_dynamics(payoff_matrix_a, payoff_matrix_b, iterations=10):

		@@ -0,0 +1,33 @@
		def fictitious_play(payoff_matrix_a, payoff_matrix_b, iterations=100):

		@@ -0,0 +1,29 @@
		def minimax(depth, node_index, is_maximizing_player, values, alpha, beta):

		from scipy.optimize import linprog


		def find_nash_equilibrium(payoff_matrix_a, payoff_matrix_b):

		@@ -0,0 +1,34 @@
		import numpy as np
		def fictitious_play(payoff_matrix_a, payoff_matrix_b, iterations=100):

		@@ -0,0 +1,35 @@
		import numpy as np

		def fictitious_play(payoff_matrix_a, payoff_matrix_b, iterations=100):

		@@ -0,0 +1,28 @@
		import numpy as np

		def shapley_value(payoff_matrix):

chore: Game Theory algorithms are added #11864

chore: Game Theory algorithms are added #11864

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isatyamks commented Oct 7, 2024

Describe your change:

Checklist:

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