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How to use successor_states method in hypothesis

Best Python code snippet using hypothesis

search.py

Source:search.py

1# search.py2# ---------3# Licensing Information:  You are free to use or extend these projects for4# educational purposes provided that (1) you do not distribute or publish5# solutions, (2) you retain this notice, and (3) you provide clear6# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.7# 8# Attribution Information: The Pacman AI projects were developed at UC Berkeley.9# The core projects and autograders were primarily created by John DeNero10# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).11# Student side autograding was added by Brad Miller, Nick Hay, and12# Pieter Abbeel (pabbeel@cs.berkeley.edu).13"""14In search.py, you will implement generic search algorithms which are called by15Pacman agents (in searchAgents.py).16"""17import util18class SearchProblem:19    """20    This class outlines the structure of a search problem, but doesn't implement21    any of the methods (in object-oriented terminology: an abstract class).22    You do not need to change anything in this class, ever.23    """24    def getStartState(self):25        """26        Returns the start state for the search problem.27        """28        util.raiseNotDefined()29    def isGoalState(self, state):30        """31          state: Search state32        Returns True if and only if the state is a valid goal state.33        """34        util.raiseNotDefined()35    def getSuccessors(self, state):36        """37          state: Search state38        For a given state, this should return a list of triples, (successor,39        action, stepCost), where 'successor' is a successor to the current40        state, 'action' is the action required to get there, and 'stepCost' is41        the incremental cost of expanding to that successor.42        """43        util.raiseNotDefined()44    def getCostOfActions(self, actions):45        """46         actions: A list of actions to take47        This method returns the total cost of a particular sequence of actions.48        The sequence must be composed of legal moves.49        """50        util.raiseNotDefined()51def tinyMazeSearch(problem):52    """53    Returns a sequence of moves that solves tinyMaze.  For any other maze, the54    sequence of moves will be incorrect, so only use this for tinyMaze.55    """56    from game import Directions57    s = Directions.SOUTH58    w = Directions.WEST59    return  [w,w,w,w,w,w,w,w,w,w,w,w,w,w,w,w,w,w,w]60def depthFirstSearch(problem):61    """62    Search the deepest nodes in the search tree first.63    Your search algorithm needs to return a list of actions that reaches the64    goal. Make sure to implement a graph search algorithm.65    To get started, you might want to try some of these simple commands to66    understand the search problem that is being passed in:67    print "Start:", problem.getStartState()68    print "Is the start a goal?", problem.isGoalState(problem.getStartState())69    print "Start's successors:", problem.getSuccessors(problem.getStartState())70    """71    "*** YOUR CODE HERE ***"72    #The following lines are mine73    74    from game import Directions75    #b76    #Problem is an object of class SearchProblem77    start_state=problem.getStartState()78    state=start_state #-1 and -100 is to indicate that this is the start state79    80    fringe_stack=util.Stack()81    explored_states=util.Stack()82    is_state_in_fringe={start_state:True}83    successor_state_for_this_state={}84    85    successor_states=problem.getSuccessors(state)86    successor_state_for_this_state[state]=successor_states87    ancestors={state:[]} #Root state has no ancestors88    89    for i in successor_states:90        fringe_stack.push(i)91        is_state_in_fringe[i]=True92        ancestors[i[0]]=[start_state]93    state=fringe_stack.pop()94    #Explore all states to find the goal state. Don't expand the goal state itself95    while(not problem.isGoalState(state[0])):96        #ipdb.set_trace()97        98        #print state[0]99        successor_states=problem.getSuccessors(state[0])100        successor_state_for_this_state[state[0]]=successor_states101        #ipdb.set_trace()102        103        for i in successor_states:104            next_state=i[0]105            if(next_state in ancestors): #If its ancestors are already listed106                if(state[0] not in ancestors[next_state] and next_state not in ancestors[state[0]]): #Ancestors don't include current state, and no cycle is being formed107                    ancestors[next_state].append(state[0])108                pass109            else:110                ancestors[next_state]=[state[0]]111                ancestors[next_state].extend(ancestors[state[0]])112            113            if(problem.isGoalState(next_state)):114                fringe_stack.push(i)115                is_state_in_fringe[next_state]=True116                break #If there is a goal state, it should be the first to pop out of the stack117            if(next_state not in is_state_in_fringe or next_state not in ancestors[state[0]] ): #Add the next state in the fringe if and only if 118                fringe_stack.push(i)119                is_state_in_fringe[next_state]=True120        #print state    121        122        explored_states.push(state)123        state=fringe_stack.pop()124        noSuccessorIsGoal=True125        #pass126    #print "Goal Found at", state[0]127    #If loop has ended, that means state=GoalState. From explored state, backtrack to obtain the start state128    if(problem.isGoalState(state[0]) and noSuccessorIsGoal):129        explored_states.push(state) #Since it wasn't added in the loop130    planning_stack=util.Stack()131    #ipdb.set_trace()132    current_state=explored_states.pop()133    planning_stack.push(current_state)134    while(len(explored_states.list)>0):135        136        predecessor_state=explored_states.pop()137        successor_states=successor_state_for_this_state[predecessor_state[0]]138        for i in successor_states:139            #if(problem.isGoalState(predecessor_state[0])):140                #noSuccessorIsGoal=False141            if(i==current_state): #Both the predecessor state and the action are the same142                current_state=predecessor_state143                planning_stack.push(current_state)144    #Now the planning stack is full. The state next to the start state is on top of the stack, stored with the action that got it there145    action_sequence=[]146    while(len(planning_stack.list)>0):147        current_state=planning_stack.pop()148        action_sequence.append(current_state[1])149    return action_sequence150        151    152    #return [Directions.SOUTH]153    util.raiseNotDefined()154def breadthFirstSearch(problem):155    """Search the shallowest nodes in the search tree first."""156    "*** YOUR CODE HERE ***"157    #The following lines are mine158    159    from game import Directions160    #b161    162    #Problem is an object of class SearchProblem163    start_state=problem.getStartState()164    state=(start_state,-1,0) #-1 and -100 is to indicate that this is the start state165    166    fringe_queue=util.Queue()167    #fringe_queue.push([state],0)168    explored_states=util.Stack()169    170    successor_states=problem.getSuccessors(state[0])171    #successor_state_for_this_state[state]=successor_states172    173    for i in successor_states:174        temp=[state]175        temp.append(i)176        fringe_queue.push(temp)177        #costs[i[0]]=i[2]178    #ipdb.set_trace()    179        180    traj=fringe_queue.pop()181    curr_state=traj[-1]182    nodes_expanded=[state[0]]183    184    while(not problem.isGoalState(curr_state[0])):185        186        if(curr_state[0] not in nodes_expanded):187            nodes_expanded.append(curr_state[0])188            successor_states=problem.getSuccessors(curr_state[0])189              190            for next_state in successor_states:191                temp=traj[:]192                temp.append(next_state)193                cost=0194                if next_state[0] not in nodes_expanded:195                    196                    fringe_queue.push(temp)197        #if curr_state[0]==[5,1,1,1,1,1]:198        #    ipdb.set_trace()199        #ipdb.set_trace()   200        #print curr_state     201        traj=fringe_queue.pop()202        curr_state=traj[-1]203            204    #Loop has exited=> curr_state=goalState205    action_sequence=[]206    for i in traj[1:]:207        action_sequence.append(i[1])208    209    return action_sequence210    util.raiseNotDefined()211def uniformCostSearch(problem):212    """Search the node of least total cost first."""213    "*** YOUR CODE HERE ***"214    #The following lines are mine215    216    from game import Directions217    #b218    219    #Problem is an object of class SearchProblem220    start_state=problem.getStartState()221    state=(start_state,-1,0) #-1 and -100 is to indicate that this is the start state222    223    fringe_queue=util.PriorityQueue()224    #fringe_queue.push([state],0)225    explored_states=util.Stack()226    227    successor_states=problem.getSuccessors(state[0])228    #successor_state_for_this_state[state]=successor_states229    230    for i in successor_states:231        temp=[state]232        temp.append(i)233        fringe_queue.update(temp,i[2])234        #costs[i[0]]=i[2]235    #ipdb.set_trace()    236        237    traj=fringe_queue.pop()238    curr_state=traj[-1]239    nodes_expanded=[state[0]]240    241    while(not problem.isGoalState(curr_state[0])):242        243        if(curr_state[0] not in nodes_expanded):244            nodes_expanded.append(curr_state[0])245            successor_states=problem.getSuccessors(curr_state[0])246              247            for next_state in successor_states:248                temp=traj[:]249                temp.append(next_state)250                cost=0251                if next_state[0] not in nodes_expanded:252                    #Calculate traj_cost253                    #ipdb.set_trace()    254                    for past_states in traj:255                        cost+=past_states[2]256                    fringe_queue.update(temp,cost+next_state[2])257            #ipdb.set_trace()        258        traj=fringe_queue.pop()259        curr_state=traj[-1]260            261    #Loop has exited=> curr_state=goalState262    action_sequence=[]263    for i in traj[1:]:264        action_sequence.append(i[1])265    266    return action_sequence267            268def nullHeuristic(state, problem=None):269    """270    A heuristic function estimates the cost from the current state to the nearest271    goal in the provided SearchProblem.  This heuristic is trivial.272    """273    return 0274def aStarSearch(problem, heuristic=nullHeuristic):275    """Search the node that has the lowest combined cost and heuristic first."""276    "*** YOUR CODE HERE ***"277    "*** YOUR CODE HERE ***"278    #The following lines are mine279    280    from game import Directions281    #b282    283    #Problem is an object of class SearchProblem284    start_state=problem.getStartState()285    286    287    state=(start_state,-1,0) #-1 and -100 is to indicate that this is the start state288    289    fringe_queue=util.PriorityQueue()290    #fringe_queue.push([state],0)291    explored_states=util.Stack()292    successor_states=problem.getSuccessors(state[0])293    #successor_state_for_this_state[state]=successor_states294    295    for i in successor_states:296        temp=[state]297        temp.append(i)298        #ipdb.set_trace()299        fringe_queue.update(temp,i[2]+heuristic(i[0],problem))#This is the only difference between UCS and A*    300        301    traj=fringe_queue.pop()302    curr_state=traj[-1]303    nodes_expanded=[state[0]]304    305    while(not problem.isGoalState(curr_state[0])):306        307        if(curr_state[0] not in nodes_expanded):308            nodes_expanded.append(curr_state[0])309            successor_states=problem.getSuccessors(curr_state[0])310              311            for next_state in successor_states:312                temp=traj[:]313                temp.append(next_state)314                cost=0315                if next_state[0] not in nodes_expanded:316                    #Calculate traj_cost317                    318                    for past_states in traj:319                        cost+=past_states[2]320                    fringe_queue.update(temp,cost+next_state[2]+heuristic(next_state[0],problem)) #This is the only difference between UCS and A*321            #ipdb.set_trace()        322        traj=fringe_queue.pop()323        curr_state=traj[-1]324            325    #Loop has exited=> curr_state=goalState326    action_sequence=[]327    for i in traj[1:]:328        action_sequence.append(i[1])329    330    return action_sequence331    util.raiseNotDefined()332def aStarSearchLocallyObservableTunnelVision(problem, heuristic=nullHeuristic):333    """Search the node that has the lowest combined cost and heuristic first."""334    #The following lines are mine335    #Problem is an object of class SearchProblem. Since the goal is fixed at (1,1), this is a PositionSearchProblem336    current_state=problem.getStartState()337    path=[]338    while(not problem.isGoalState(current_state)):339        current_path=aStarSearch(problem,heuristic)340        #follow current path until a wall is detected.341        for action in current_path:342            if(not problem.senseWall(current_state,action)):343                path.append(action)344                current_state=problem.getNextState(current_state,action)345            else:346                break347        if(problem.isGoalState(current_state)):348            return path349        else:350            wall_location=problem.getNextState(current_state,action)351            problem.addWall(wall_location)352            problem.setStartState(current_state)353           354    return path355def aStarSearchLocallyObservable(problem, heuristic=nullHeuristic):356    """Search the node that has the lowest combined cost and heuristic first."""357    #The following lines are mine358    #Problem is an object of class SearchProblem. Since the goal is fixed at (1,1), this is a PositionSearchProblem359    current_state=problem.getStartState()360    path=[]361    while(not problem.isGoalState(current_state)):362        current_path=aStarSearch(problem,heuristic)363        #follow current path until a wall is detected.364        for action in current_path:365            if(not problem.getNeighboringWalls(current_state)):366                path.append(action)367                current_state=problem.getNextState(current_state,action)368            else:369                break370        if(problem.isGoalState(current_state)):371            return path372        else:373            problem.setStartState(current_state)           374    return path375def dStarSearch(problem):376    def manhattanDistance(s1,s2):377        return abs(s1[0]-s2[0])+abs(s1[1]-s2[1])378    def CalculateKey(u):379        return [min(g[u],rhs[u])+manhattanDistance(start_state,u),min(g[u],rhs[u])]380    def UpdateVertex(u):381        if(not problem.isGoalState(u)): 382            rhs[u]=float('inf')383            for s,a,d in problem.getSuccessors(u):384                rhs[u]=min(rhs[u],g[s]+1)385        if(U.CheckPresence(u)):386            U.Remove(u)387        if(g[u]!=rhs[u]):388            U.update(u,CalculateKey(u))389    def ComputeShortestPath():390        while(U.TopKey()<CalculateKey(start_state) or rhs[start_state]!=g[start_state]):391                392            u=U.Top()393            k_old=U.TopKey()394            k_new=CalculateKey(u)395            U.pop()396            #print(rhs[start_state])397            if(k_old<k_new): #this means some edges have become unusable398                U.update(u,k_new)399            elif(g[u]>rhs[u]): #This means a shorter path has been found400                g[u]=rhs[u]401                for s,a,d in problem.getSuccessors(u):402                    UpdateVertex(s)403            else:  #This means g[u]=rhs[u] i.e vertex is locally consistent404                g_old=g[u]405                g[u]=float('inf')406                UpdateVertex(u)407                for s,a,d in problem.getSuccessors(u):408                    UpdateVertex(s)409    def senseWallAt(problem,s):410        return problem.actual_walls[s[0]][s[1]]411    def knowWallAt(problem,s):412        return problem.walls[s[0]][s[1]]413    from util import PriorityQueue414    415    #Initialize416    U=PriorityQueue()417    km=0418    start_state=problem.getStartState()419    last_state=start_state #A copy is created, not a reference420    problem.getNeighboringWalls(start_state) #Update the agent model421    rhs={}422    g={}423    for state in problem.AllStates():424        rhs[state]=float('inf')425        g[state]=float('inf')426    goal=(1,1) #Predefined427    rhs[goal]=0428    U.push(goal,[manhattanDistance(goal,start_state),0])429    ComputeShortestPath()430    431    actions=[]432    while(not problem.isGoalState(start_state)):433        min_successor_value=float('inf')434        current_action=[]435        for s,a,discombombulation in problem.getSuccessors(start_state):436           437            if(1+g[s]<min_successor_value):438                start_state=s439                current_action=a440                min_successor_value=1+g[s]441        actions.append(current_action)442        443        #Scan for edge-weight changes after moving to the new start_state444        changes=problem.getNeighboringWalls(start_state)445        446        if(changes):447            km+=manhattanDistance(last_state,start_state)448            last_state=start_state449            for wall in changes:450                g[wall]=float('inf') #Since there's a wall, it's impossible to reach from the goal node451                452                #When a wall is detected, upto 4 edges become infinitely weighted. Update all their successors (predecessors)453                successors=problem.getSuccessors(wall) #No edge weight has gone down454                for s1,a1,discombombulation in successors:455                    UpdateVertex(s1)456        ComputeShortestPath()457    return actions458# Abbreviations459bfs = breadthFirstSearch460dfs = depthFirstSearch461astar = aStarSearch462ucs = uniformCostSearch463astar2=aStarSearchLocallyObservable464astartv=aStarSearchLocallyObservableTunnelVision...

nim_game.py

Source:nim_game.py

1import math2import time3import random4import itertools5def successors(s):6    player = s[1]7    piles = s[0]8    successor_states = []9    successors = []10    if player == 1:11        player = 212    if player == 2:13        player == 114    for i, pile in enumerate(piles):15        for remove in range(1, pile + 1):16            result = pile - remove17            if result != 0:18                next_piles = sorted(piles[:i] + [result] + piles[i + 1:])19            else:20                next_piles = sorted(piles[:i] + piles[i + 1:])21            successor_states.append(next_piles)22    successor_states.sort()23    successor_states = list(successor_states for successor_states, _ in itertools.groupby(successor_states))24    print(successor_states)25    for i in range(len(successor_states)):26        successors.append([successor_states[i], player])27    return successors28def terminal_test(state):29    if state in [([1], 1), ([], 2)]:30        terminal_state = True31    elif state in [([1], 2), ([], 1)]:32        terminal_state = True33    else:34        terminal_state = False35    return terminal_state36def utility_test(state):37    if state in [([1], 1), ([], 2)]:38        utility = 139    elif state in [([1], 2), ([], 1)]:40        utility = -141    else:42        if state[1] == -1:43            utility = 144        else:45            utility = -146    return utility47def max_value(max_state):48    v = math.inf49    terminal_state, utility = terminal_test(max_state)50    if not terminal_state:51        for s in successors(max_state):52            v = min(v, min_value(s))53        return v54    else:55        return terminal_test(max_state)56def min_value(min_state):57    v = -math.inf58    terminal_state, utility = terminal_test(min_state)59    if not terminal_state:60        for s in successors(min_state):61            v = max(v, max_value(s))62        return v63    else:64        return terminal_test(min_state)65def min_max(state):66    if state[1] == 1:67        utility = max_value(state)68    else:69        utility = min_value(state)70    if utility == 1:71        print("Win for Max!")72    if utility == -1:73        print("Win for Min!")74    return utility75def max_value_ab(min_state, a, b):76    v = 177    terminal_state = terminal_test(min_state)78    utility = utility_test(min_state)79    if not terminal_state:80        for s in successors(min_state):81            if v > utility:82                utility = v83            if v >= b:84                return utility85            if v > a:86                a = v87        v = min(v, min_value_ab(min_state, a, b))88    return utility89def min_value_ab(max_state, a, b):90    v = -191    terminal_state = terminal_test(max_state)92    utility = utility_test(max_state)93    if not terminal_state:94        for s in successors(max_state):95            if v < utility:96                utility = v97            if v <= a:98                return utility99            if v < b:100                b = v101        v = max(v, max_value_ab(max_state, a, b))102    return utility103def minimax_ab(state):104    start = time.time()105    alpha = 0106    beta = 0107    if state[1] == 1:108        utility_value = min_value_ab(state, alpha, beta)109    else:110        utility_value = max_value_ab(state, alpha, beta)111    end = time.time()112    return utility_value113def minimax_game():114    number_of_piles = int(input("Number of piles: "))115    maximum_pile_size = int(input("Maximum number of sticks: "))116    first_player = int(input("First player: 1 for computer, 2 for human: "))117    initial_piles = []118    for pile in range(0, number_of_piles):119        pile_size = random.randrange(1, maximum_pile_size + 1)120        initial_piles.append(pile_size)121    state = (sorted(initial_piles), first_player)122    while True:123        # Print game state124        print("state", state)125        if state[1] == 2:126            piles = state[0]127            pile_number = (int(input("Enter the number of pile to remove sticks from: ")) - 1)128            pile = piles[pile_number]129            pick = int(input("Number of sticks to remove: "))130            if pick <= pile:131                result = pile - pick132                if result == 0:133                    new_piles = sorted(piles[:pile_number] + piles[pile_number + 1:])134                else:135                    new_piles = sorted(piles[:pile_number] + [result] + piles[pile_number + 1:])136                state = (new_piles, 1)137            else:138                print("out of bound")139                break140        elif state[1] == 1:141            list_of_successors = successors(state)142            number_of_next_states = len(list_of_successors)143            for s, next_state in enumerate(list_of_successors):144                utility_value = minimax_ab(next_state)145                if utility_value == -1:146                    state = next_state147                elif utility_value == 1:148                    state = list_of_successors[random.randrange(0, number_of_next_states)]149                    state = next_state150        if state in [([1], 1), ([], 2), ([1], 2), ([], 1)]:151            util = utility_test(state)152            if util == -1:153                print("You lost")154                break155            elif util == 1:156                print("You won")157                break...

untitled.py

Source:untitled.py

1   #****************2    def getAction(self, gamestate)3        next_value, next_action = self.minimax_value(gameState, self.index, 0)4        return next_value5    def minimax_value(self, state, agent, depth):6        num_agents = state.getNumAgents()7        #fully explored, score8        if depth == self.depth and agent % num_agents == 0:9            return self.evaluationFunction(state), None10        #its pacman, or a maximizer11        if agent % num_agents == 0:12            return self.maximize_value(state, agent % num_agents, depth)13        #its a ghost, a minimizer14        return self.minimize_value(state, agent % num_agents, depth)15    def minimize_value(self, state, agent, depth):16        successor_states = [(state.generateSuccessor(agent, action), action) for action in state.getLegalActions(agent)]17        if len(successor_states) == 0:18            return self.evaluationFunction(state), None19        value = float("inf")20        value_action = None21        next_agent = agent + 122        for successor_state, action in successor_states:23            next_value, next_action = self.minimax_value(successor_state, next_agent, depth)24            if next_value < value:25                value = next_value26                value_action = action27        return value, value_action28    def maximize_value(self, state, agent, depth):29        successor_states = [(state.generateSuccessor(agent, action), action) for action in state.getLegalActions(agent)]30        if len(successor_states) == 0:31            return self.evaluationFunction(state), None32        value = -float("inf")33        value_action = None34        next_agent = agent + 135        next_depth = depth + 136        for successor_state, action in successor_states:37            next_value, next_action = self.minimax_value(successor_state, next_agent, next_depth)38            if next_value > value:39                value = next_value40                value_action = action41        return value, value_action...

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