update GTFunction #758

src/mlpro/gt/native/basics.py

@@ -68,7 +68,7 @@ class GTFunction (Persistent):
 
 
 ## -------------------------------------------------------------------------------------------------
-    def __init__(self, p_func_type:int, p_dim_elems:list=None, p_logging=Log.C_LOG_ALL):
+    def __init__(self, p_func_type:int, p_dim_elems:list=None, p_num_coalisions:int=None, p_logging=Log.C_LOG_ALL):
 
         super().__init__(p_id=None, p_logging=p_logging)
 
@@ -79,8 +79,11 @@ def __init__(self, p_func_type:int, p_dim_elems:list=None, p_logging=Log.C_LOG_A
 
             if p_dim_elems is None:
                 raise ParamError("p_dim_elems is not defined!")
+
+            if p_num_coalisions is None:
+                raise ParamError("p_num_coalisions is not defined!")
 
-            self._num_coals     = len(p_dim_elems)
+            self._num_coals     = p_num_coalisions
             dim_elems           = [self._num_coals]
             dim_elems.extend(p_dim_elems)
 

src/mlpro/gt/pool/native/games/prisonersdilemma_2p.py

@@ -132,7 +132,8 @@ def _setup(self, p_mode, p_ada:bool, p_visualize:bool, p_logging) -> Model:
         self._payoff = GTPayoffMatrix(
             p_function=PayoffFunction_PD2P(
                 p_func_type=GTFunction.C_FUNC_PAYOFF_MATRIX,
-                p_dim_elems=[2,2]
+                p_dim_elems=[2,2],
+                p_num_coalisions=2
                 ),
             p_player_ids=coal_ids
         )

src/mlpro/gt/pool/native/games/prisonersdilemma_3p.py

@@ -161,7 +161,7 @@ def _setup(self, p_mode, p_ada:bool, p_visualize:bool, p_logging) -> Model:
             p_logging=p_logging
         )
 
-        p2 = GTPlayer(
+        p3 = GTPlayer(
             p_solver=[solver3a,solver3b],
             p_name="Player of Prisoner 3",
             p_visualize=p_visualize,
@@ -173,7 +173,7 @@ def _setup(self, p_mode, p_ada:bool, p_visualize:bool, p_logging) -> Model:
             p_name="Coalition of Prisoner 3",
             p_coalition_type=GTCoalition.C_COALITION_SUM
         )
-        coal2.add_player(p3)
+        coal3.add_player(p3)
 
 
         competition = GTCompetition(
@@ -189,7 +189,8 @@ def _setup(self, p_mode, p_ada:bool, p_visualize:bool, p_logging) -> Model:
         self._payoff = GTPayoffMatrix(
             p_function=PayoffFunction_PD3P(
                 p_func_type=GTFunction.C_FUNC_PAYOFF_MATRIX,
-                p_dim_elems=[2,2,2]
+                p_dim_elems=[2,4],
+                p_num_coalisions=3
                 ),
             p_player_ids=coal_ids
         )

test/howtos/gt/howto_gt_native_002_prisonners_dilemma_3p.py

@@ -0,0 +1,56 @@
+## -------------------------------------------------------------------------------------------------
+## -- Project : MLPro - A Synoptic Framework for Standardized Machine Learning Tasks
+## -- Package : mlpro.gt.examples
+## -- Module  : howto_gt_native_002_prisonners_dilemma_3p.py
+## -------------------------------------------------------------------------------------------------
+## -- History :
+## -- yyyy-mm-dd  Ver.      Auth.    Description
+## -- 2023-12-07  0.0.0     SY       Creation
+## -- 2023-12-07  1.0.0     SY       Release of first version
+## -------------------------------------------------------------------------------------------------
+
+"""
+Ver. 1.0.0 (2023-12-07)
+
+This module shows how to run a game, namely 3P Prisoners' Dilemma with two solvers, such as random
+solver and min greedy policy.
+
+You will learn:
+    
+1) How to set up a game, including solver, competition, coalition, payoff, and more
+    
+2) How to run the game
+
+3) How to analyse the game
+    
+"""
+
+from mlpro.gt.native.basics import *
+from mlpro.gt.pool.native.games.prisonersdilemma_3p import *
+from pathlib import Path
+
+
+
+if __name__ == "__main__":
+    cycle_limit = 10
+    logging     = Log.C_LOG_ALL
+    visualize   = False
+    path        = str(Path.home())
+
+else:
+    cycle_limit = 1
+    logging     = Log.C_LOG_NOTHING
+    visualize   = False
+    path        = None
+
+PD2P_Game = PrisonersDilemma3PGame()
+
+training = GTTraining(
+        p_game_cls=PrisonersDilemma3PGame,
+        p_cycle_limit=cycle_limit,
+        p_path=path,
+        p_visualize=visualize,
+        p_logging=logging
+        )
+
+training.run()