def getSuccessors( self, state ):
""" For a given state, this should return a list of triples,
(successor, action, stepCost), where 'successor' is a
successor to the current state, 'action' is the action
required to get there, and 'stepCost' is the incremental
cost of expanding to that successor """
utils.raiseNotDefined()
def retrieve(input):
#Use your trained hopfield network to retrieve and return a pattern based on the
#input pattern.
#HopfieldNotes.pdf on canvas is a good reference for asynchronous updating which
#has generally better convergence properties than synchronous updating.
# pattern = inputPattern
# num_iterate = len(inputPattern)*len(inputPattern)
# for _ in range(num_iterate) :
# np.random.shuffle(v_in)
# step = 0
# for index in v_in:
# if np.dot(self.h[index], pattern) < 0:
# if pattern[index] == 1:
# step += 1
# pattern[index] = 0
# else :
# if pattern[index] == 0:
# step += 1
# pattern[index] = 1
# if step == 0:
# return pattern
# return None
print("TODO")
utils.raiseNotDefined()
def addExperience(self, experience):
"""
Called by environment after
observing a transition and reward
experience should be in some format like (state, action, reward)
"""
utils.raiseNotDefined("addExperience")
def getAction( self, gameState ):
"""
Returns the minimax action from the current gameState using self.depth
and self.evaluationFunction.
"""
" *** YOUR CODE HERE ***"
utils.raiseNotDefined()
def classify(inputPattern):
#Classify should consider the input and classify as either, five or two
#You will call your retrieve function passing the input
#Compare the returned pattern to the 'perfect' instances
#return a string classification 'five', 'two' or 'unknown'
print("TODO")
utils.raiseNotDefined()
def betterEvaluationFunction( currentGameState ): """ Your extreme ghost-hunting, pellet-nabbing, food-gobbling, unstoppable evaluation function. """ " *** YOUR CODE HERE ***" utils.raiseNotDefined()
def betterEvaluationFunction( currentGameState ): """ Your extreme ghost-hunting, pellet-nabbing, food-gobbling, unstoppable evaluation function. """ " *** YOUR CODE HERE ***" utils.raiseNotDefined()
def getDistribution(self, state):
"Returns a Counter encoding a distribution over actions from the provided state."
utils.raiseNotDefined()
def getDistribution(self, state):
"Returns a Counter encoding a distribution over actions from the provided state."
utils.raiseNotDefined()
def EndGame(self,game_state,scores):
utils.raiseNotDefined()
pass
def TimeOutWarning(self,runner,id):
utils.raiseNotDefined()
pass
def getGoalState( self ):
""" Returns the goal state for the search problem """
utils.raiseNotDefined()
def GetLegalMoves(self, state, moves):
utils.raiseNotDefined()
def final(self, state):
"""
Called by environment at the end
"""
utils.raiseNotDefined("final")
def startEpisode(self):
"""
Called by environment when new episode is starting
"""
utils.raiseNotDefined("startEpisode")
def initialGameState(self):
utils.raiseNotDefined()
return 0
def isGoalState( self, state ):
""" Returns True if and only if the state is a valid goal state """
utils.raiseNotDefined()
def distort_input(instance, percent_distortion):
#percent distortion should be a float from 0-1
#Should return a distorted version of the instance, relative to distoriton Rate
print("TODO")
utils.raiseNotDefined()
def getAction(self, state):
"""
The Agent will receive a GameState and
must return an action
"""
utils.raiseNotDefined("getAction")
def generateSuccessor(self, game_state, action, agent_id):
utils.raiseNotDefined()
return 0
def getCostOfActions( self, actions ):
""" This method returns the total cost of a particular sequence
of actions. The sequence must be composed of legal moves """
utils.raiseNotDefined()
def stopEpisode(self):
"""
Called by environment when episode is done
"""
utils.raiseNotDefined("stopEpisode")
def getLegalActions(self, game_state, agent_id):
utils.raiseNotDefined()
return []
def calScore(self, game_state,agent_id):
utils.raiseNotDefined()
return 0
def gameEnds(self):
utils.raiseNotDefined()
return False
def getAction(self, state, board):
"""
The Agent will receive a GameState (scotlandyard.py) and
must return an action from one of the valid actions
"""
utils.raiseNotDefined()
def ExcuteAction(self,i,move,game_state):
utils.raiseNotDefined()
pass
def visualizeWeight(): #You can change these parameters if you need to utils.raiseNotDefined()
def getStartState( self ):
""" Returns the start state for the search problem """
utils.raiseNotDefined()
