python类manhattanDistance()的实例源码

def canKill( pacmanPosition, ghostPosition ):
        return manhattanDistance( ghostPosition, pacmanPosition ) <= COLLISION_TOLERANCE

def getFurthestCorner(self, pacPos):
        poses = [(1,1), (1, self.height - 2), (self.width - 2, 1), (self.width - 2, self.height - 2)]
        dist, pos = max([(manhattanDistance(p, pacPos), p) for p in poses])
        return pos

def getDistribution( self, state ):
        # Read variables from state
        ghostState = state.getGhostState( self.index )
        legalActions = state.getLegalActions( self.index )
        pos = state.getGhostPosition( self.index )
        isScared = ghostState.scaredTimer > 0

        speed = 1
        if isScared: speed = 0.5

        actionVectors = [Actions.directionToVector( a, speed ) for a in legalActions]
        newPositions = [( pos[0]+a[0], pos[1]+a[1] ) for a in actionVectors]
        pacmanPosition = state.getPacmanPosition()

        # Select best actions given the state
        distancesToPacman = [manhattanDistance( pos, pacmanPosition ) for pos in newPositions]
        if isScared:
            bestScore = max( distancesToPacman )
            bestProb = self.prob_scaredFlee
        else:
            bestScore = min( distancesToPacman )
            bestProb = self.prob_attack
        bestActions = [action for action, distance in zip( legalActions, distancesToPacman ) if distance == bestScore]

        # Construct distribution
        dist = util.Counter()
        for a in bestActions: dist[a] = bestProb / len(bestActions)
        for a in legalActions: dist[a] += ( 1-bestProb ) / len(legalActions)
        dist.normalize()
        return dist

def canKill( pacmanPosition, ghostPosition ):
    return manhattanDistance( ghostPosition, pacmanPosition ) <= COLLISION_TOLERANCE

def getFurthestCorner(self, pacPos):
    poses = [(1,1), (1, self.height - 2), (self.width - 2, 1), (self.width - 2, self.height - 2)]
    dist, pos = max([(manhattanDistance(p, pacPos), p) for p in poses])
    return pos

def getDistribution( self, state ):
    # Read variables from state
    ghostState = state.getGhostState( self.index )
    legalActions = state.getLegalActions( self.index )
    pos = state.getGhostPosition( self.index )
    isScared = ghostState.scaredTimer > 0

    speed = 1
    if isScared: speed = 0.5

    actionVectors = [Actions.directionToVector( a, speed ) for a in legalActions]
    newPositions = [( pos[0]+a[0], pos[1]+a[1] ) for a in actionVectors]
    pacmanPosition = state.getPacmanPosition()

    # Select best actions given the state
    distancesToPacman = [manhattanDistance( pos, pacmanPosition ) for pos in newPositions]
    if isScared:
      bestScore = max( distancesToPacman )
      bestProb = self.prob_scaredFlee
    else:
      bestScore = min( distancesToPacman )
      bestProb = self.prob_attack
    bestActions = [action for action, distance in zip( legalActions, distancesToPacman ) if distance == bestScore]

    # Construct distribution
    dist = util.Counter()
    for a in bestActions: dist[a] = bestProb / len(bestActions)
    for a in legalActions: dist[a] += ( 1-bestProb ) / len(legalActions)
    dist.normalize()
    return dist

def canKill( pacmanPosition, ghostPosition ):
        return manhattanDistance( ghostPosition, pacmanPosition ) <= COLLISION_TOLERANCE

def getFurthestCorner(self, pacPos):
        poses = [(1,1), (1, self.height - 2), (self.width - 2, 1), (self.width - 2, self.height - 2)]
        dist, pos = max([(manhattanDistance(p, pacPos), p) for p in poses])
        return pos

def getDistribution( self, state ):
        # Read variables from state
        ghostState = state.getGhostState( self.index )
        legalActions = state.getLegalActions( self.index )
        pos = state.getGhostPosition( self.index )
        isScared = ghostState.scaredTimer > 0

        speed = 1
        if isScared: speed = 0.5

        actionVectors = [Actions.directionToVector( a, speed ) for a in legalActions]
        newPositions = [( pos[0]+a[0], pos[1]+a[1] ) for a in actionVectors]
        pacmanPosition = state.getPacmanPosition()

        # Select best actions given the state
        distancesToPacman = [manhattanDistance( pos, pacmanPosition ) for pos in newPositions]
        if isScared:
            bestScore = max( distancesToPacman )
            bestProb = self.prob_scaredFlee
        else:
            bestScore = min( distancesToPacman )
            bestProb = self.prob_attack
        bestActions = [action for action, distance in zip( legalActions, distancesToPacman ) if distance == bestScore]

        # Construct distribution
        dist = util.Counter()
        for a in bestActions: dist[a] = bestProb / len(bestActions)
        for a in legalActions: dist[a] += ( 1-bestProb ) / len(legalActions)
        dist.normalize()
        return dist

def canKill( pacmanPosition, ghostPosition ):
        return manhattanDistance( ghostPosition, pacmanPosition ) <= COLLISION_TOLERANCE

def getFurthestCorner(self, pacPos):
        poses = [(1,1), (1, self.height - 2), (self.width - 2, 1), (self.width - 2, self.height - 2)]
        dist, pos = max([(manhattanDistance(p, pacPos), p) for p in poses])
        return pos

def isWumpusClose(self, state):
        """
        You SHOULD use this function in your implementation. This function checks
        if the stench of the wumpus can be sensed from the square you are at.
        """
        return util.manhattanDistance(self.wumpus, state) == 1

def isPoisonCapsuleClose(self, state):
        """
        You SHOULD use this function in your implementation. This function checks
        if the poison from the pills can be sensed from the square you are at.
        """
        return any([util.manhattanDistance(capsule, state) == 1 for capsule in self.capsules])

def getDistribution( self, state ):
        # Read variables from state
        ghostState = state.getGhostState( self.index )
        legalActions = state.getLegalActions( self.index )
        pos = state.getGhostPosition( self.index )
        isScared = ghostState.scaredTimer > 0

        speed = 1
        if isScared: speed = 0.5

        actionVectors = [Actions.directionToVector( a, speed ) for a in legalActions]
        newPositions = [( pos[0]+a[0], pos[1]+a[1] ) for a in actionVectors]
        pacmanPosition = state.getPacmanPosition()

        # Select best actions given the state
        distancesToPacman = [manhattanDistance( pos, pacmanPosition ) for pos in newPositions]
        if isScared:
            bestScore = max( distancesToPacman )
            bestProb = self.prob_scaredFlee
        else:
            bestScore = min( distancesToPacman )
            bestProb = self.prob_attack
        bestActions = [action for action, distance in zip( legalActions, distancesToPacman ) if distance == bestScore]

        # Construct distribution
        dist = util.Counter()
        for a in bestActions: dist[a] = bestProb / len(bestActions)
        for a in legalActions: dist[a] += ( 1-bestProb ) / len(legalActions)
        dist.normalize()
        return dist

def canKill( pacmanPosition, ghostPosition ):
        return manhattanDistance( ghostPosition, pacmanPosition ) <= COLLISION_TOLERANCE

def getFurthestCorner(self, pacPos):
        poses = [(1,1), (1, self.height - 2), (self.width - 2, 1), (self.width - 2, self.height - 2)]
        dist, pos = max([(manhattanDistance(p, pacPos), p) for p in poses])
        return pos

def getDistribution( self, state ):
        # Read variables from state
        ghostState = state.getGhostState( self.index )
        legalActions = state.getLegalActions( self.index )
        pos = state.getGhostPosition( self.index )
        isScared = ghostState.scaredTimer > 0

        speed = 1
        if isScared: speed = 0.5

        actionVectors = [Actions.directionToVector( a, speed ) for a in legalActions]
        newPositions = [( pos[0]+a[0], pos[1]+a[1] ) for a in actionVectors]
        pacmanPosition = state.getPacmanPosition()

        # Select best actions given the state
        distancesToPacman = [manhattanDistance( pos, pacmanPosition ) for pos in newPositions]
        if isScared:
            bestScore = max( distancesToPacman )
            bestProb = self.prob_scaredFlee
        else:
            bestScore = min( distancesToPacman )
            bestProb = self.prob_attack
        bestActions = [action for action, distance in zip( legalActions, distancesToPacman ) if distance == bestScore]

        # Construct distribution
        dist = util.Counter()
        for a in bestActions: dist[a] = bestProb / len(bestActions)
        for a in legalActions: dist[a] += ( 1-bestProb ) / len(legalActions)
        dist.normalize()
        return dist

def canKill( pacmanPosition, ghostPosition ):
        return manhattanDistance( ghostPosition, pacmanPosition ) <= COLLISION_TOLERANCE

def getFurthestCorner(self, pacPos):
        poses = [(1,1), (1, self.height - 2), (self.width - 2, 1), (self.width - 2, self.height - 2)]
        dist, pos = max([(manhattanDistance(p, pacPos), p) for p in poses])
        return pos

def getDistribution( self, state ):
        # Read variables from state
        ghostState = state.getGhostState( self.index )
        legalActions = state.getLegalActions( self.index )
        pos = state.getGhostPosition( self.index )
        isScared = ghostState.scaredTimer > 0

        speed = 1
        if isScared: speed = 0.5

        actionVectors = [Actions.directionToVector( a, speed ) for a in legalActions]
        newPositions = [( pos[0]+a[0], pos[1]+a[1] ) for a in actionVectors]
        pacmanPosition = state.getPacmanPosition()

        # Select best actions given the state
        distancesToPacman = [manhattanDistance( pos, pacmanPosition ) for pos in newPositions]
        if isScared:
            bestScore = max( distancesToPacman )
            bestProb = self.prob_scaredFlee
        else:
            bestScore = min( distancesToPacman )
            bestProb = self.prob_attack
        bestActions = [action for action, distance in zip( legalActions, distancesToPacman ) if distance == bestScore]

        # Construct distribution
        dist = util.Counter()
        for a in bestActions: dist[a] = bestProb / len(bestActions)
        for a in legalActions: dist[a] += ( 1-bestProb ) / len(legalActions)
        dist.normalize()
        return dist