Finish project 5 and class.

p5_classification/.vscode/launch.json

@@ -0,0 +1,26 @@
+{
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "run_file",
+            "type": "python",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal"
+        },
+        {
+            "name": "features",
+            "type": "python",
+            "request": "launch",
+            "program": "${workspaceFolder}/dataClassifier.py",
+            "args" : ["-d", "pacman", "-c", "perceptron", "-f", "-g", "ContestAgent", "-t", "1000", "-s", "1000"]
+        },
+        {
+            "name": "autograder",
+            "type": "python",
+            "request": "launch",
+            "program": "${workspaceFolder}/autograder.py",
+            "args" : []
+        }
+    ]
+}

p5_classification/.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "python.pythonPath": "/usr/bin/python2"
+}

p5_classification/dataClassifier.py

@@ -144,6 +144,7 @@ def basicFeatureExtractorPacman(state):
         features[action] = featureCounter
     return features, state.getLegalActions()
 
+
 def enhancedFeatureExtractorPacman(state):
     """
     Your feature extraction playground.
@@ -159,14 +160,30 @@ def enhancedFeatureExtractorPacman(state):
         features[action] = util.Counter(features[action], **enhancedPacmanFeatures(state, action))
     return features, state.getLegalActions()
 
+
 def enhancedPacmanFeatures(state, action):
     """
     For each state, this function is called with each legal action.
     It should return a counter with { <feature name> : <feature value>, ... }
     """
     features = util.Counter()
-    "*** YOUR CODE HERE ***"
-    util.raiseNotDefined()
+    ghostDists = []
+    state = state.generateSuccessor(0, action)
+    pacmanPosition = state.getPacmanPosition()
+    ghostPositions = [ghostState.getPosition() for ghostState in state.getGhostStates()]
+    foodPositions = [foodPos for foodPos in state.getFood().asList()]
+    walls = state.data.layout.walls
+    for x in range(walls.width):
+        for y in range(walls.height):
+            if walls[x][y] is True:
+                # features[("wall", x, y)] = 1
+                pass
+            else:
+                pos = (x, y)
+                features[("wall", x, y)] = 0
+                features[("pacman", x, y)] = 1 if pos == pacmanPosition else 0
+                features[("ghost", x, y)] = 1 if pos in ghostPositions else 0
+                features[("food", x, y)] = 1 if pos in foodPositions else 0
     return features
 
 
@@ -208,17 +225,17 @@ def analysis(classifier, guesses, testLabels, testData, rawTestData, printImage)
 
     # Put any code here...
     # Example of use:
-    # for i in range(len(guesses)):
-    #     prediction = guesses[i]
-    #     truth = testLabels[i]
-    #     if (prediction != truth):
-    #         print "==================================="
-    #         print "Mistake on example %d" % i
-    #         print "Predicted %d; truth is %d" % (prediction, truth)
-    #         print "Image: "
-    #         print rawTestData[i]
-    #         print whiteAreasFeature(rawTestData[i])
-            # break
+    for i in range(len(guesses)):
+        prediction = guesses[i]
+        truth = testLabels[i]
+        if (prediction != truth):
+            print "==================================="
+            print "Mistake on example %d" % i
+            print "Predicted {}; truth is {}".format(prediction, truth)
+            print "Image: "
+            print rawTestData[i]
+            # print whiteAreasFeature(rawTestData[i])
+            break
 
 
 ## =====================