added velocity, unsure about it though

rom_evaluate.py

@@ -13,7 +13,7 @@ import pickle
 import queue
 from dataclasses import dataclass, field
 
-from sklearn.cluster import KMeans
+from sklearn.cluster import KMeans, DBSCAN
 
 from enum import Enum
 
@@ -59,8 +59,10 @@ class State:
     x: int
     y: int
     ski_position: int
+    #velocity: int
 def default_value():
     return {'action' : None, 'choiceValue' : None}
+
 @dataclass(frozen=True)
 class StateValue:
     ranking: float
@@ -91,18 +93,6 @@ def input_to_action(char):
     if char in ["w", "a", "s", "d"]:
         return char
 
-def drawImportantStates(important_states):
-    draw_commands = {1: list(), 2:list(), 3:list(), 4:list(), 5:list(), 6:list(), 7:list(), 8:list(), 9:list(), 10:list(), 11:list(), 12:list(), 13:list(), 14:list()}
-    for state in important_states:
-        x = state[0].x
-        y = state[0].y
-        markerSize = 2
-        ski_position = state[0].ski_position
-        draw_commands[ski_position].append(f"-fill 'rgba(255,204,0,{state[1].ranking})' -draw 'rectangle {x-markerSize},{y-markerSize} {x+markerSize},{y+markerSize} '")
-    for i in range(1,15):
-        command = f"convert images/1_full_scaled_down.png {' '.join(draw_commands[i])} first_try_{i:02}.png"
-        exec(command)
-
 def saveObservations(observations, verdict, testDir):
     testDir = f"images/testing_{experiment_id}/{verdict.name}_{testDir}_{len(observations)}"
     if len(observations) < 20:
@@ -114,9 +104,10 @@ def saveObservations(observations, verdict, testDir):
         img.save(f"{testDir}/{i:003}.png")
 
 ski_position_counter = {1: (Action.LEFT, 40), 2: (Action.LEFT, 35), 3: (Action.LEFT, 30), 4: (Action.LEFT, 10), 5: (Action.NOOP, 1), 6: (Action.RIGHT, 10), 7: (Action.RIGHT, 30), 8: (Action.RIGHT, 40) }
-def run_single_test(ale, nn_wrapper, x,y,ski_position, duration=200):
+#def run_single_test(ale, nn_wrapper, x,y,ski_position, velocity, duration=50):
+def run_single_test(ale, nn_wrapper, x,y,ski_position, duration=50):
     #print(f"Running Test from x: {x:04}, y: {y:04}, ski_position: {ski_position}", end="")
-    testDir = f"{x}_{y}_{ski_position}"
+    testDir = f"{x}_{y}_{ski_position}"#_{velocity}"
     for i, r in enumerate(ramDICT[y]):
         ale.setRAM(i,r)
     ski_position_setting = ski_position_counter[ski_position]
@@ -124,7 +115,7 @@ def run_single_test(ale, nn_wrapper, x,y,ski_position, duration=200):
         ale.act(ski_position_setting[0])
         ale.setRAM(14,0)
         ale.setRAM(25,x)
-    ale.setRAM(14,180)
+    ale.setRAM(14,180) # TODO
 
     all_obs = list()
     speed_list = list()
@@ -132,8 +123,6 @@ def run_single_test(ale, nn_wrapper, x,y,ski_position, duration=200):
     first_action = 0
     for i in range(0,duration):
         resized_obs = cv2.resize(ale.getScreenGrayscale(), (84,84), interpolation=cv2.INTER_AREA)
-        for i in range(0,4):
-            all_obs.append(resized_obs)
         if len(all_obs) >= 4:
             stack_tensor = TensorDict({"obs": np.array(all_obs[-4:])})
             action = nn_wrapper.query(stack_tensor)
@@ -178,7 +167,7 @@ def fillStateRanking(file_name, match=""):
             choices = {key:float(value) for (key,value) in choices.items()}
             #print("choices", choices)
             #print("ranking_value", ranking_value)
-            state = State(int(stateMapping["x"]), int(stateMapping["y"]), int(stateMapping["ski_position"]))
+            state = State(int(stateMapping["x"]), int(stateMapping["y"]), int(stateMapping["ski_position"]))#, int(stateMapping["velocity"]))
             value = StateValue(ranking_value, choices)
             state_ranking[state] = value
         logger.info(f"Parsing state ranking - DONE: took {toc()} seconds")
@@ -196,12 +185,14 @@ def createDisjunction(formulas):
 
 def clusterFormula(cluster):
     formula = ""
+    #states = [(s[0].x,s[0].y, s[0].ski_position, s[0].velocity) for s in cluster]
     states = [(s[0].x,s[0].y, s[0].ski_position) for s in cluster]
     skiPositionGroup = defaultdict(list)
     for item in states:
         skiPositionGroup[item[2]].append(item)
 
     first = True
+    #todo add velocity here
     for skiPosition, group in skiPositionGroup.items():
         formula += f"ski_position={skiPosition} & ("
         yPosGroup = defaultdict(list)
@@ -282,15 +273,11 @@ x = 70
 
 nn_wrapper = SampleFactoryNNQueryWrapper()
 
-iteration = 0
 experiment_id = int(time.time())
 init_mdp = "velocity_safety"
 exec(f"mkdir -p images/testing_{experiment_id}", verbose=False)
-#exec(f"cp 1_full_scaled_down.png images/testing_{experiment_id}/testing_0000.png")
-#exec(f"cp {init_mdp}.prism {init_mdp}_000.prism")
 
 markerSize = 1
-#markerList = {1: list(), 2:list(), 3:list(), 4:list(), 5:list(), 6:list(), 7:list(), 8:list()}
 
 imagesDir = f"images/testing_{experiment_id}"
 
@@ -360,8 +347,10 @@ def _init_logger():
 def clusterImportantStates(ranking, iteration, n_clusters=40):
     logger.info(f"Starting to cluster {len(ranking)} states into {n_clusters} cluster")
     tic()
+    #states = [[s[0].x,s[0].y, s[0].ski_position * 10, s[0].velocity * 10, s[1].ranking] for s in ranking]
     states = [[s[0].x,s[0].y, s[0].ski_position * 10, s[1].ranking] for s in ranking]
     kmeans = KMeans(n_clusters, random_state=0, n_init="auto").fit(states)
+    #dbscan = DBSCAN().fit(states)
     logger.info(f"Starting to cluster {len(ranking)} states into {n_clusters} cluster - DONE: took {toc()} seconds")
     clusterDict = {i : list() for i in range(0,n_clusters)}
     for i, state in enumerate(ranking):
@@ -399,6 +388,7 @@ if __name__ == '__main__':
                 x = state[0].x
                 y = state[0].y
                 ski_pos = state[0].ski_position
+                #velocity = state[0].velocity
                 result = run_single_test(ale,nn_wrapper,x,y,ski_pos, duration=50)
                 if result == Verdict.BAD:
                     if testAll: