text: import math
  import numpy as np
  
  EPS = 1e-8
  
  
  class MCTS:
      """
      This class handles the MCTS tree.
      """
  
      def __init__(self, game, nnet, args):
          self.game = game
          self.nnet = nnet
          self.args = args
          self.Qsa = {}  # stores Q values for s,a (as defined in the paper)
          self.Nsa = {}  # stores #times edge s,a was visited
          self.Ns = {}  # stores #times board s was visited
          self.Ps = {}  # stores initial policy (returned by neural net)
  
          self.Es = {}  # stores game.getGameEnded ended for board s
          self.Vs = {}  # stores game.getValidMoves for board s
  
      def get_action_prob(self, canonical_board, temp=1):
          """
          This function performs numMCTSSims simulations of MCTS starting from
          canonicalBoard.
  
          Returns:
              probs: a policy vector where the probability of the ith action is
                     proportional to Nsa[(s,a)]**(1./temp)
          """
          for i in range(self.args.numMCTSSims):
              self.search(canonical_board)
  
          s = self.game.stringRepresentation(canonical_board)
          counts = [self.Nsa[(s, a)] if (s, a) in self.Nsa else 0 for a in range(self.game.getActionSize())]
          # TODO
          if temp == 0:
              bestA = np.argmax(counts)
              probs = [0] * len(counts)
              probs[bestA] = 1
              return probs
  
          counts = [x ** (1. / temp) for x in counts]
          probs = [x / float(sum(counts)) for x in counts]
          return probs
  
      def search(self, canonicalBoard):
          """
          This function performs one iteration of MCTS. It is recursively called
          till a leaf node is found. The action chosen at each node is one that
          has the maximum upper confidence bound as in the paper.
  
          Once a leaf node is found, the neural network is called to return an
          initial policy P and a value v for the state. This value is propogated
          up the search path. In case the leaf node is a terminal state, the
          outcome is propogated up the search path. The values of Ns, Nsa, Qsa are
          updated.
  
          NOTE: the return values are the negative of the value of the current
          state. This is done since v is in [-1,1] and if v is the value of a
          state for the current player, then its value is -v for the other player.
  
          Returns:
              v: the negative of the value of the current canonicalBoard
          """
  
          s = self.game.stringRepresentation(canonicalBoard)
  
          if s not in self.Es:
              self.Es[s] = self.game.getGameEnded(canonicalBoard, 1)
          if self.Es[s] != 0:
              # terminal node
              return -self.Es[s]
  
          if s not in self.Ps:
              # leaf node
              self.Ps[s], v = self.nnet.predict(canonicalBoard)
              valids = self.game.getValidMoves(canonicalBoard, 1)
              self.Ps[s] = self.Ps[s] * valids  # masking invalid moves
              sum_Ps_s = np.sum(self.Ps[s])
              if sum_Ps_s > 0:
                  self.Ps[s] /= sum_Ps_s  # renormalize
              else:
                  # if all valid moves were masked make all valid moves equally probable
  
                  # NB! All valid moves may be masked if either your NNet architecture is insufficient or you've get overfitting or something else.
                  # If you have got dozens or hundreds of these messages you should pay attention to your NNet and/or training process.   
                  print("All valid moves were masked, do workaround.")
                  self.Ps[s] = self.Ps[s] + valids
                  self.Ps[s] /= np.sum(self.Ps[s])
                  # TODO check here
  
              self.Vs[s] = valids
              self.Ns[s] = 0
              return -v
  
          valids = self.Vs[s]
          cur_best = -float('inf')
          best_act = -1
  
          # pick the action with the highest upper confidence bound
          for a in range(self.game.getActionSize()):
              if valids[a]:
                  if (s, a) in self.Qsa:
                      u = self.Qsa[(s, a)] + self.args.cpuct * self.Ps[s][a] * math.sqrt(self.Ns[s]) / (
                                  1 + self.Nsa[(s, a)])
                  else:
                      u = self.args.cpuct * self.Ps[s][a] * math.sqrt(self.Ns[s] + EPS)  # Q = 0 ?
  
                  if u > cur_best:
                      cur_best = u
                      best_act = a
  
          a = best_act
          if a == -1:
              print(1)
          next_s, next_player = self.game.getNextState(canonicalBoard, 1, a)
          next_s = self.game.getCanonicalForm(next_s, next_player)
  
          v = self.search(next_s)
  
          if (s, a) in self.Qsa:
              self.Qsa[(s, a)] = (self.Nsa[(s, a)] * self.Qsa[(s, a)] + v) / (self.Nsa[(s, a)] + 1)
              self.Nsa[(s, a)] += 1
  
          else:
              self.Qsa[(s, a)] = v
              self.Nsa[(s, a)] = 1
  
          self.Ns[s] += 1
          return -v
  