Chore: Game Theory algorithms are missing #11804 #11859

isatyamks · 2024-10-07T13:47:12Z

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:

isatyamks · 2024-10-07T14:06:26Z

@zixindh, I noticed the PR has been approved—do you know why it hasn't been merged yet? Any further actions required on my end?

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

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algorithms-keeper · 2024-10-07T15:36:19Z

game_theory/best_response_dynamics.py

@@ -0,0 +1,27 @@
+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

Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: payoff_matrix_A

Please provide type hint for the parameter: payoff_matrix_B

Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: payoff_matrix_B

Please provide type hint for the parameter: iterations

algorithms-keeper · 2024-10-07T15:36:19Z

game_theory/best_response_dynamics.py

+    m = payoff_matrix_A.shape[1]
+
+    # Initialize strategies
+    strategy_A = np.ones(n) / n


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: strategy_A

algorithms-keeper · 2024-10-07T15:36:19Z

game_theory/best_response_dynamics.py

+
+    # Initialize strategies
+    strategy_A = np.ones(n) / n
+    strategy_B = np.ones(m) / m


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: strategy_B

algorithms-keeper · 2024-10-07T15:36:19Z

game_theory/best_response_dynamics.py

+
+    for _ in range(iterations):
+        # Update strategy A
+        response_A = np.argmax(payoff_matrix_A @ strategy_B)


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: response_A

algorithms-keeper · 2024-10-07T15:36:19Z

game_theory/best_response_dynamics.py

+    for _ in range(iterations):
+        # Update strategy A
+        response_A = np.argmax(payoff_matrix_A @ strategy_B)
+        strategy_A = np.zeros(n)


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: strategy_A

algorithms-keeper · 2024-10-07T15:36:20Z

game_theory/fictitious_play.py

+        best_response_B = np.argmax(payoff_matrix_B.T @ strategy_A)
+
+        # Update strategies
+        strategy_A = np.zeros(n)


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: strategy_A

algorithms-keeper · 2024-10-07T15:36:21Z

game_theory/fictitious_play.py

+        # Update strategies
+        strategy_A = np.zeros(n)
+        strategy_A[best_response_A] = 1
+        strategy_B = np.zeros(m)


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: strategy_B

algorithms-keeper · 2024-10-07T15:36:21Z

game_theory/fictitious_play.py

+    return strategy_A, strategy_B
+
+# Example usage
+payoff_A = np.array([[3, 0], [5, 1]])


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: payoff_A

algorithms-keeper · 2024-10-07T15:36:21Z

game_theory/fictitious_play.py

+
+# Example usage
+payoff_A = np.array([[3, 0], [5, 1]])
+payoff_B = np.array([[2, 4], [0, 2]])


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: payoff_B

algorithms-keeper · 2024-10-07T15:36:21Z

game_theory/minimax_algorithm.py

@@ -0,0 +1,28 @@
+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

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algorithms-keeper · 2024-10-07T15:36:55Z

game_theory/best_response_dynamics.py

@@ -0,0 +1,27 @@
+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

Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: payoff_matrix_A

Please provide type hint for the parameter: payoff_matrix_B

Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: payoff_matrix_B

Please provide type hint for the parameter: iterations

algorithms-keeper · 2024-10-07T15:36:55Z

game_theory/best_response_dynamics.py

+    m = payoff_matrix_A.shape[1]
+
+    # Initialize strategies
+    strategy_A = np.ones(n) / n


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: strategy_A

algorithms-keeper · 2024-10-07T15:36:55Z

game_theory/best_response_dynamics.py

+
+    # Initialize strategies
+    strategy_A = np.ones(n) / n
+    strategy_B = np.ones(m) / m


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: strategy_B

algorithms-keeper · 2024-10-07T15:36:55Z

game_theory/best_response_dynamics.py

+
+    for _ in range(iterations):
+        # Update strategy A
+        response_A = np.argmax(payoff_matrix_A @ strategy_B)


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: response_A

algorithms-keeper · 2024-10-07T15:36:56Z

game_theory/best_response_dynamics.py

+    for _ in range(iterations):
+        # Update strategy A
+        response_A = np.argmax(payoff_matrix_A @ strategy_B)
+        strategy_A = np.zeros(n)


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: strategy_A

algorithms-keeper · 2024-10-07T15:36:56Z

game_theory/fictitious_play.py

+    return strategy_A, strategy_B
+
+# Example usage
+payoff_A = np.array([[3, 0], [5, 1]])


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: payoff_A

algorithms-keeper · 2024-10-07T15:36:57Z

game_theory/fictitious_play.py

+
+# Example usage
+payoff_A = np.array([[3, 0], [5, 1]])
+payoff_B = np.array([[2, 4], [0, 2]])


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: payoff_B

algorithms-keeper · 2024-10-07T15:36:57Z

game_theory/minimax_algorithm.py

@@ -0,0 +1,28 @@
+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 · 2024-10-07T15:36:57Z

game_theory/shapley_value.py

@@ -0,0 +1,19 @@
+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

algorithms-keeper · 2024-10-07T15:36:57Z

game_theory/shapley_value.py

+            if (S & (1 << i)) == 0:  # i not in S
+                continue
+
+            S_without_i = S & ~(1 << i)


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: S_without_i

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

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algorithms-keeper · 2024-10-07T15:41:23Z

game_theory/best_response_dynamics.py

@@ -0,0 +1,27 @@
+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

Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: payoff_matrix_A

Please provide type hint for the parameter: payoff_matrix_B

Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: payoff_matrix_B

Please provide type hint for the parameter: iterations

algorithms-keeper · 2024-10-07T15:41:23Z

game_theory/best_response_dynamics.py

+    m = payoff_matrix_A.shape[1]
+
+    # Initialize strategies
+    strategy_A = np.ones(n) / n


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: strategy_A

algorithms-keeper · 2024-10-07T15:41:23Z

game_theory/best_response_dynamics.py

+
+    # Initialize strategies
+    strategy_A = np.ones(n) / n
+    strategy_B = np.ones(m) / m


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: strategy_B

algorithms-keeper · 2024-10-07T15:41:23Z

game_theory/best_response_dynamics.py

+
+    for _ in range(iterations):
+        # Update strategy A
+        response_A = np.argmax(payoff_matrix_A @ strategy_B)


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: response_A

algorithms-keeper · 2024-10-07T15:41:24Z

game_theory/best_response_dynamics.py

+    for _ in range(iterations):
+        # Update strategy A
+        response_A = np.argmax(payoff_matrix_A @ strategy_B)
+        strategy_A = np.zeros(n)


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: strategy_A

algorithms-keeper · 2024-10-07T15:41:25Z

game_theory/nash_equilibrium.py

+    b_ub = [-1] * n
+
+    result_B = linprog(c, A_ub=A_ub, b_ub=b_ub, bounds=(0, None))
+    p_B = result_B.x


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: p_B

algorithms-keeper · 2024-10-07T15:41:25Z

game_theory/nash_equilibrium.py

+    return p_A, p_B
+
+# Example usage
+payoff_A = np.array([[3, 0], [5, 1]])


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: payoff_A

algorithms-keeper · 2024-10-07T15:41:26Z

game_theory/nash_equilibrium.py

+
+# Example usage
+payoff_A = np.array([[3, 0], [5, 1]])
+payoff_B = np.array([[2, 4], [0, 2]])


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: payoff_B

algorithms-keeper · 2024-10-07T15:41:26Z

game_theory/shapley_value.py

@@ -0,0 +1,19 @@
+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

algorithms-keeper · 2024-10-07T15:41:26Z

game_theory/shapley_value.py

+            if (S & (1 << i)) == 0:  # i not in S
+                continue
+
+            S_without_i = S & ~(1 << i)


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: S_without_i

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 · 2024-10-07T15:41:54Z

game_theory/best_response_dynamics.py

@@ -0,0 +1,27 @@
+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

Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: payoff_matrix_A

Please provide type hint for the parameter: payoff_matrix_B

Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: payoff_matrix_B

Please provide type hint for the parameter: iterations

algorithms-keeper · 2024-10-07T15:41:54Z

game_theory/best_response_dynamics.py

+    m = payoff_matrix_A.shape[1]
+
+    # Initialize strategies
+    strategy_A = np.ones(n) / n


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: strategy_A

algorithms-keeper · 2024-10-07T15:41:54Z

game_theory/best_response_dynamics.py

+
+    # Initialize strategies
+    strategy_A = np.ones(n) / n
+    strategy_B = np.ones(m) / m


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: strategy_B

algorithms-keeper · 2024-10-07T15:41:54Z

game_theory/best_response_dynamics.py

+
+    for _ in range(iterations):
+        # Update strategy A
+        response_A = np.argmax(payoff_matrix_A @ strategy_B)


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: response_A

algorithms-keeper · 2024-10-07T15:41:54Z

game_theory/best_response_dynamics.py

+    for _ in range(iterations):
+        # Update strategy A
+        response_A = np.argmax(payoff_matrix_A @ strategy_B)
+        strategy_A = np.zeros(n)


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: strategy_A

algorithms-keeper · 2024-10-07T15:41:56Z

game_theory/nash_equilibrium.py

+    b_ub = [-1] * n
+
+    result_B = linprog(c, A_ub=A_ub, b_ub=b_ub, bounds=(0, None))
+    p_B = result_B.x


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: p_B

algorithms-keeper · 2024-10-07T15:41:56Z

game_theory/nash_equilibrium.py

+    return p_A, p_B
+
+# Example usage
+payoff_A = np.array([[3, 0], [5, 1]])


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: payoff_A

algorithms-keeper · 2024-10-07T15:41:56Z

game_theory/nash_equilibrium.py

+
+# Example usage
+payoff_A = np.array([[3, 0], [5, 1]])
+payoff_B = np.array([[2, 4], [0, 2]])


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: payoff_B

algorithms-keeper · 2024-10-07T15:41:56Z

game_theory/shapley_value.py

@@ -0,0 +1,19 @@
+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

algorithms-keeper · 2024-10-07T15:41:56Z

game_theory/shapley_value.py

+            if (S & (1 << i)) == 0:  # i not in S
+                continue
+
+            S_without_i = S & ~(1 << i)


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: S_without_i

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

algorithms-keeper

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Python:

Formatted string literals (f-strings)

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algorithms-keeper · 2024-10-07T15:49:34Z

game_theory/best_response_dynamics.py

@@ -0,0 +1,26 @@
+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 · 2024-10-07T15:49:34Z

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 · 2024-10-07T15:49:34Z

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 · 2024-10-07T15:49:34Z

game_theory/nash_equilibrium.py

@@ -0,0 +1,65 @@
+<<<<<<< HEAD
+import numpy as np


An error occurred while parsing the file: game_theory/nash_equilibrium.py

Traceback (most recent call last): File "/opt/render/project/src/algorithms_keeper/parser/python_parser.py", line 146, in parse reports = lint_file( ^^^^^^^^^^ libcst._exceptions.ParserSyntaxError: Syntax Error @ 2:3. parser error: error at 1:2: expected one of (, *, +, -, ..., AWAIT, EOF, False, NAME, NUMBER, None, True, [, break, continue, lambda, match, not, pass, ~ import numpy as np ^

algorithms-keeper · 2024-10-07T15:49:34Z

game_theory/shapley_value.py

+                continue
+
+<<<<<<< HEAD
+            s_without_i = s & ~(1 << i)


An error occurred while parsing the file: game_theory/shapley_value.py

Traceback (most recent call last): File "/opt/render/project/src/algorithms_keeper/parser/python_parser.py", line 146, in parse reports = lint_file( ^^^^^^^^^^ libcst._exceptions.ParserSyntaxError: Syntax Error @ 11:3. parser error: error at 10:2: expected one of (, *, +, -, ..., AWAIT, EOF, False, NAME, NUMBER, None, True, [, break, continue, lambda, match, not, pass, ~ s_without_i = s & ~(1 << i) ^

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

algorithms-keeper

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Repository:

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algorithms-keeper · 2024-10-07T15:53:37Z

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 · 2024-10-07T15:53:37Z

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 · 2024-10-07T15:53:37Z

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 · 2024-10-07T15:53:37Z

game_theory/nash_equilibrium.py

@@ -0,0 +1,65 @@
+<<<<<<< HEAD
+import numpy as np


An error occurred while parsing the file: game_theory/nash_equilibrium.py

Traceback (most recent call last): File "/opt/render/project/src/algorithms_keeper/parser/python_parser.py", line 146, in parse reports = lint_file( ^^^^^^^^^^ libcst._exceptions.ParserSyntaxError: Syntax Error @ 2:3. parser error: error at 1:2: expected one of (, *, +, -, ..., AWAIT, EOF, False, NAME, NUMBER, None, True, [, break, continue, lambda, match, not, pass, ~ import numpy as np ^

algorithms-keeper · 2024-10-07T15:53:38Z

game_theory/shapley_value.py

+                continue
+
+<<<<<<< HEAD
+            s_without_i = s & ~(1 << i)


An error occurred while parsing the file: game_theory/shapley_value.py

Traceback (most recent call last): File "/opt/render/project/src/algorithms_keeper/parser/python_parser.py", line 146, in parse reports = lint_file( ^^^^^^^^^^ libcst._exceptions.ParserSyntaxError: Syntax Error @ 11:3. parser error: error at 10:2: expected one of (, *, +, -, ..., AWAIT, EOF, False, NAME, NUMBER, None, True, [, break, continue, lambda, match, not, pass, ~ s_without_i = s & ~(1 << i) ^

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algorithms-keeper · 2024-10-07T15:55:38Z

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 · 2024-10-07T15:55:38Z

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 · 2024-10-07T15:55:38Z

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 · 2024-10-07T15:55:38Z

game_theory/nash_equilibrium.py

@@ -0,0 +1,65 @@
+<<<<<<< HEAD
+import numpy as np


An error occurred while parsing the file: game_theory/nash_equilibrium.py

Traceback (most recent call last): File "/opt/render/project/src/algorithms_keeper/parser/python_parser.py", line 146, in parse reports = lint_file( ^^^^^^^^^^ libcst._exceptions.ParserSyntaxError: Syntax Error @ 2:3. parser error: error at 1:2: expected one of (, *, +, -, ..., AWAIT, EOF, False, NAME, NUMBER, None, True, [, break, continue, lambda, match, not, pass, ~ import numpy as np ^

algorithms-keeper · 2024-10-07T15:55:39Z

game_theory/shapley_value.py

+                continue
+
+<<<<<<< HEAD
+            s_without_i = s & ~(1 << i)


An error occurred while parsing the file: game_theory/shapley_value.py

Traceback (most recent call last): File "/opt/render/project/src/algorithms_keeper/parser/python_parser.py", line 146, in parse reports = lint_file( ^^^^^^^^^^ libcst._exceptions.ParserSyntaxError: Syntax Error @ 11:3. parser error: error at 10:2: expected one of (, *, +, -, ..., AWAIT, EOF, False, NAME, NUMBER, None, True, [, break, continue, lambda, match, not, pass, ~ s_without_i = s & ~(1 << i) ^

algorithms-keeper

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Repository:

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Project Euler solution guidelines

Python:

Formatted string literals (f-strings)

Type hints

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algorithms-keeper · 2024-10-07T16:01:26Z

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 · 2024-10-07T16:01:26Z

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 · 2024-10-07T16:01:26Z

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 · 2024-10-07T16:01:26Z

game_theory/nash_equilibrium.py

@@ -0,0 +1,65 @@
+<<<<<<< HEAD
+import numpy as np


An error occurred while parsing the file: game_theory/nash_equilibrium.py

Traceback (most recent call last): File "/opt/render/project/src/algorithms_keeper/parser/python_parser.py", line 146, in parse reports = lint_file( ^^^^^^^^^^ libcst._exceptions.ParserSyntaxError: Syntax Error @ 2:3. parser error: error at 1:2: expected one of (, *, +, -, ..., AWAIT, EOF, False, NAME, NUMBER, None, True, [, break, continue, lambda, match, not, pass, ~ import numpy as np ^

algorithms-keeper · 2024-10-07T16:01:26Z

game_theory/shapley_value.py

@@ -0,0 +1,19 @@
+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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Updated link to docs.opencv.org

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isatyamks mentioned this pull request Oct 7, 2024

computer_vision README.md link not working #11841

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chore: best_respose_dynamics.py added to game theory

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isatyamks changed the title ~~Updated link to docs.opencv.org~~ Chore: Game Theory algorithms are missing #11804 Oct 7, 2024

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Jayavardhan-7 mentioned this pull request Oct 7, 2024

Enhance Documentation in Computer_vision README.md. #11860

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		@@ -0,0 +1,27 @@
		def best_response_dynamics(payoff_matrix_A, payoff_matrix_B, iterations=10):

		@@ -0,0 +1,28 @@
		def minimax(depth, node_index, is_maximizing_player, values, alpha, beta):

		@@ -0,0 +1,26 @@
		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):

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

		def best_response_dynamics(payoff_matrix_a, payoff_matrix_b, iterations=10):

Chore: Game Theory algorithms are missing #11804 #11859

Chore: Game Theory algorithms are missing #11804 #11859

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isatyamks commented Oct 7, 2024 • edited Loading

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isatyamks commented Oct 7, 2024 •

edited

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