import copy
import random

import numpy as np
import requests
import theano.tensor as T

from project3.module6.game2048.gui import *
from project3.module6.deeplearning.ann import ANN


class AiWindow(GameWindow):
    # Initializes gui and board class
    def __init__(self, player, control):
        super(AiWindow, self).__init__()
        self.control = control
        self.player = player
        self.movedict = {0: 'up', 1: 'down', 2: 'left', 3: 'right'}
        self.weight_matrix = [
            [200, 150, 100, 50],
            [150, 100, 50, 30],
            [100, 50, 30, 20],
            [50, 30, 20, 10]
        ]

    # Handles game and gui logic, returns true if move was legal, false if not
    def onKeyPress(self, direction):
        if self.board.move(direction, self.board.state):
            self.update_view(self.board.state.board)
            self.board.place_tile(self.board.state)
            self.update_view(self.board.state.board)
            return True
        return False

    # AI plays until play_best_move returns false, at which point there are no legal moves
    def play(self):
        legal_moves = True
        while legal_moves:
            directions = list(self.player.net.predict([self.convert_board()])[0])
            legal_moves = self.play_best_move(directions)

    # Goes through a list of direction values from the nnet.predict function
    # Returns false if no moves are legal
    def play_best_move(self, directions):
        dir_val_tuples = [(directions[i], i) for i in range(len(directions))]
        dir_val_tuples.sort()

        for elem in dir_val_tuples:
            if self.onKeyPress(self.movedict[elem[1]]):
                return True

        self.restart(self.board.get_highest_tile())
        return False

    # Plays a random move, until there are no legal moves
    def play_random(self):
        legal_moves = True
        while legal_moves:
            legal_moves = self.play_random_move()

    # Randomly chooses a move and tries to perform it. If no moves are legal, return false
    def play_random_move(self):
        directions = [0, 1, 2, 3]
        moves = []
        for _ in range(4):
            choice = random.choice(directions)
            moves.append(choice)
            directions.remove(choice)

        for num in moves:
            if self.onKeyPress(self.movedict[num]):
                return True
        self.restart(self.board.get_highest_tile())
        return False

    def calculate_heuristic(self, state):
        value = 0
        for i in range(len(state.board)):
            for j in range(len(state.board[i])):
                value += self.weight_matrix[i][j] * state.board[i][j]
        return value

    # Converts boards to match cases from their respective training cases
    def convert_board(self):
        returnboard = []
        #16 dim vector scaled
        if self.control.training_set <3:
            for i in range(len(self.board.state.board)):
                for j in range(len(self.board.state.board[i])):
                    if self.board.state.board[i][j] != 0:
                        returnboard.append(np.log2(self.board.state.board[i][j]))
                    else:
                        returnboard.append(0)

            for i in range(len(returnboard)):
                if returnboard[i] != 0:
                    returnboard[i] = returnboard[i]/np.log2(self.board.get_highest_tile())

        # 19 dim vector. board + 3 bits to say if highest tile in upper left corner,
        # and if top row and left column are full
        elif self.control.training_set >2:
            highest = self.board.get_highest_tile()
            for i in range(len(self.board.state.board)):
                for j in range(len(self.board.state.board[i])):
                    if self.board.state.board[i][j]!=0:
                        returnboard.append(np.log2(self.board.state.board[i][j]))
                    else:
                        returnboard.append(0)

            for i in range (len(returnboard)):
                if returnboard[i]!=0:
                    returnboard[i] = returnboard[i]/np.log2(self.board.get_highest_tile())





            if self.board.state.board[3][3] ==highest:
                returnboard.append(1)
            else:
                returnboard.append(0)


            if 0 in list(self.board.state.board[3]):
                returnboard.append(1)
            else:
                returnboard.append(0)

            full = True
            for i in range(4):
                if self.board.state.board[i][3] == 0:
                    full = False
            if full:
                returnboard.append(1)
            else:
                returnboard.append(0)

            if self.control.training_set==3:
                returnboard.append(len(self.board.get_free_cells(self.board.state)))
                returnboard.append(self.board.count_horizontal_moves(self.board.state))
                returnboard.append(self.board.count_vertical_moves(self.board.state))


            if self.control.training_set>4:
                returnboard.append(self.board.count_horizontal_moves(self.board.state))
                returnboard.append(self.board.count_vertical_moves(self.board.state))
                up = copy.deepcopy(self.board.state)
                down = copy.deepcopy(self.board.state)
                left = copy.deepcopy(self.board.state)
                right = copy.deepcopy(self.board.state)
                states = [self.calculate_heuristic(up),self.calculate_heuristic(down)
                    ,self.calculate_heuristic(left),self.calculate_heuristic(right)]
                maxnum = np.max(states)
                for i in range(len(states)):
                    states[i] =states[i]/maxnum
                    returnboard.append(states[i])
                freecells = [len(self.board.get_free_cells(up)), len(self.board.get_free_cells(down)),
                             len(self.board.get_free_cells(left)),len(self.board.get_free_cells(right))]
                maxnum = np.max(freecells)

                for i in range(len(freecells)):
                    if maxnum >0:
                        freecells[i] /= maxnum

                    returnboard.append(freecells[i])
        print(returnboard)

        return np.array(returnboard)

    # To be called when game is done
    def restart(self, result):
        self.control.new_game(result)


class NNplayer(object):
    # Initialises values and takes parameters from user
    def __init__(self, training_set, topology, lr, epochs, batch_size, activation_functions):
        self.training_set = training_set
        self.topology = topology
        self.lr = lr
        self.epochs = epochs
        self.batch_size = batch_size
        self.activation_functions = activation_functions
        self.train_boards = None
        self.train_moves = None
        # Different number of in nodes because of different test set
        if self.training_set == 1:
            self.input = 16
        elif self.training_set ==2:
            self.input = 16
        elif self.training_set ==3:
            self.input =19
        elif self.training_set ==4:
            self.input =22
        elif self.training_set ==5:
            self.input=29

        self.net = ANN(self.input, self.topology, activation_functions, 4, lr)

    # Loads the different test cases and scales the ones that have not been scaled when they were made
    def load_test_cases(self):
        if self.training_set == 1:
            f = open('myfile1.txt', 'r')
        elif self.training_set ==2:
            f = open('Gradient16dim.txt', 'r')
        elif self.training_set ==3:
            f= open('Gradient19dim.txt', 'r')
        elif self.training_set ==4:
            f= open('Gradient22dim.txt', 'r')
        elif self.training_set ==5:
            f= open('Gradient30dim.txt','r')
        boards = []
        moves = []
        lines = f.readlines()
        for line in lines:
            case = line.split('$')
            board = case[0]
            move = case[1]
            board = board.split(',')
            move = move.strip('\n').split(',')
            boards.append(board)
            moves.append(move)
        boards = np.asarray(boards, dtype=np.double)

        if self.training_set < 3:
            scale(boards)


        moves = np.array(moves)

        # Sets assignes the set to the objects variables
        self.train_boards = boards
        self.train_moves = moves

    # Trains batches of size "minibatch_size" for x epochs.
    def train_model(self, epochs, minibatch_size):
        for epoch in range(epochs):
            print("Training epoch number {}...".format(epoch))
            cost = 0
            for i in range(0, len(self.train_boards), minibatch_size):
                board_batch = self.train_boards[i:i+minibatch_size]
                move_batch = self.train_moves[i:i+minibatch_size]
                cost += self.net.train(board_batch, move_batch)

            print("Cost after epoch {}: {}".format(epoch, cost/len(self.train_boards)))


class main:
    #Takes input from user
    #Creates an NNplayer
    #Creates a GUI window
    #Initializes statistic variables

    def __init__(self,welch_parent):
        self.welch_parent = welch_parent
        self.gui_bool = str(input("Gui? y/n: \n"))
        self.training_set = int(input("What training set do you want to use?\n1: 16 dim snake, 2: 16 dim gradient,3: 19 dim : "
                                      "4: 22 dim, 5: 29 dim"))
        self.hidden = [x for x in map(int, input("Please specify a topology description on the form 40,20,60: ").strip().split(","))]
        self.activations = [activation_map(x) for x in list(map(int, input("Please specify activation functions for each layer on the form 0,2,1: ").strip().split(",")))]
        self.lr = float(input("Specify learning rate: "))
        self.epochs = int(input('How many epochs do you want to train?: '))
        self.minibatch = int(input('What batch size do you want to use?: '))
        self.net_result = []
        self.rand_result = []
        self.count = 1
        self.player = NNplayer(self.training_set, self.hidden, self.lr, self.epochs, self.minibatch, self.activations)
        self.player.load_test_cases()
        self.player.train_model(self.epochs, self.minibatch)
        self.root = Tk()
        print('NNET playing round: ' + str(self.count))
        self.game = AiWindow(self.player, self)
        if self.gui_bool == 'y':
            self.game.pack()
            self.root.bind('<a>', lambda x: self.game.play())
            self.root.mainloop()
        if self.gui_bool == 'n':
            self.game.play()

        self.net_result = []
        self.rand_result = []
        self.count = 1

#This method handles the logic of starting a new game (to play 50 runs with AI and 50 random)
#Also calculates score
    def new_game(self, result):

        self.game.pack_forget()
        if self.count < 50:
            self.net_result.append(result)
            print('Highest tile: ' + str(result))
            self.count += 1
            print('NNET playing round: ' + str(self.count))
            self.game = AiWindow(self.player, self)
            if self.gui_bool == 'y':
                self.game.pack()
            self.game.play()

        elif self.count == 50:
            print('Highest tile: ' + str(result))
            self.net_result.append(result)
            self.count += 1
            print('NNET playing round: ' + str(self.count))
            self.game = AiWindow(self.player, self)
            if self.gui_bool == 'y':
                self.game.pack()
            self.game.play_random()

        elif 50 < self.count < 100:
            print('Highest tile: ' + str(result))
            self.rand_result.append(result)
            self.count += 1

            print('Random playing round: ' + str(self.count-50))
            self.game = AiWindow(self.player, self)
            if self.gui_bool == 'y':
                self.game.pack()
            self.game.play_random()

        elif self.count == 100:
            # self.count+=1
            self.rand_result.append(result)

            self.somelist =[]
            self.somelist.append(self.net_result)
            self.welch_parent.results.append(self.net_result)
            welch_result = welch(self.rand_result, self.net_result)
            print(welch_result)
            f = open('statistics.txt','a')
            f.write('Statistics:\n'
                   'Topology:'+ str(self.hidden)+'\n'+
                    'Learning rate: '+ str(self.lr)+'\n'+
                    'Activation functions: '+ str([self.activations.index(x) for x in self.activations])+'\n'+
                    'Batchsize: '+ str(self.minibatch)+'\n'+
                    'Epochs: '+ str(self.epochs)+'\n'+
                    'Dataset: '+ str(self.training_set)+'\n'+
                    welch_result+'\n')


def scale(seq):
        """
        Scales a list of board states by first getting the log2 value of every cell,
        and then dividing every cell by the log2 value of the maximum cell in that board state.

        Example:
            Input   = [[0, 2, 4, 8, 2, 16, 256], [4, 8, 512]]
            Log2    = [[0, 1, 2, 3, 1, 4, 8], [2, 3, 9]
            Scaled  = [[0, 0.125, 0.25, 0.375, 0.125, 0.5, 1], [0.222, 0.333, 1]] (by dividing by max(Log2))

        :param seq: A list of board states, formatted as float (important!)
        :return: A scaled list of board states, ranging from 0 to 1
        """

        for i in range(len(seq)):
            for j in range(len(seq[i])):
                if seq[i][j] != 0:
                    seq[i][j] = np.log2(seq[i][j])
            seq[i] = seq[i] / max(seq[i])


#Maps input from user to a real function
def activation_map(x):
        return {
            0: T.tanh,
            1: T.nnet.sigmoid,
            2: lambda k: T.switch(k > 0, k, 0),  # Equivalent to T.nnet.relu (rectified linear unit)
            3: T.nnet.softmax,
        }.get(x, T.tanh)  # T.tanh is default if x is not found


#Function to determine score
def welch(list1, list2):
        params = {"results": str(list1) + " " + str(list2), "raw": "1"}
        resp = requests.post('http://folk.ntnu.no/valerijf/6/', data=params)
        return resp.text

class boss:
    def __init__(self):
        self.results =[]
        main1 = main(self)
        main2 = main(self)
        print(self.results)
        print(welch(self.results[0],self.results[1]))


if __name__ == '__main__':
    boss = boss()



import copy
import random

import numpy as np
import requests
import theano.tensor as T

from project3.module6.game2048.gui import *
from project3.module6.deeplearning.ann import ANN


class AiWindow(GameWindow):
    # Initializes gui and board class
    def __init__(self, player, control):
        super(AiWindow, self).__init__()
        self.control = control
        self.player = player
        self.movedict = {0: 'up', 1: 'down', 2: 'left', 3: 'right'}
        self.weight_matrix = [
            [200, 150, 100, 50],
            [150, 100, 50, 30],
            [100, 50, 30, 20],
            [50, 30, 20, 10]
        ]

    # Handles game and gui logic, returns true if move was legal, false if not
    def onKeyPress(self, direction):
        if self.board.move(direction, self.board.state):
            self.update_view(self.board.state.board)
            self.board.place_tile(self.board.state)
            self.update_view(self.board.state.board)
            return True
        return False

    # AI plays until play_best_move returns false, at which point there are no legal moves
    def play(self):
        legal_moves = True
        while legal_moves:
            directions = list(self.player.net.predict([self.convert_board()])[0])
            legal_moves = self.play_best_move(directions)

    # Goes through a list of direction values from the nnet.predict function
    # Returns false if no moves are legal
    def play_best_move(self, directions):
        dir_val_tuples = [(directions[i], i) for i in range(len(directions))]
        dir_val_tuples.sort()

        for elem in dir_val_tuples:
            if self.onKeyPress(self.movedict[elem[1]]):
                return True

        self.restart(self.board.get_highest_tile())
        return False

    # Plays a random move, until there are no legal moves
    def play_random(self):
        legal_moves = True
        while legal_moves:
            legal_moves = self.play_random_move()

    # Randomly chooses a move and tries to perform it. If no moves are legal, return false
    def play_random_move(self):
        directions = [0, 1, 2, 3]
        moves = []
        for _ in range(4):
            choice = random.choice(directions)
            moves.append(choice)
            directions.remove(choice)

        for num in moves:
            if self.onKeyPress(self.movedict[num]):
                return True
        self.restart(self.board.get_highest_tile())
        return False

    def calculate_heuristic(self, state):
        value = 0
        for i in range(len(state.board)):
            for j in range(len(state.board[i])):
                value += self.weight_matrix[i][j] * state.board[i][j]
        return value

    # Converts boards to match cases from their respective training cases
    def convert_board(self):
        returnboard = []
        #16 dim vector scaled
        if self.control.training_set <3:
            for i in range(len(self.board.state.board)):
                for j in range(len(self.board.state.board[i])):
                    if self.board.state.board[i][j] != 0:
                        returnboard.append(np.log2(self.board.state.board[i][j]))
                    else:
                        returnboard.append(0)

            for i in range(len(returnboard)):
                if returnboard[i] != 0:
                    returnboard[i] = returnboard[i]/np.log2(self.board.get_highest_tile())

        # 19 dim vector. board + 3 bits to say if highest tile in upper left corner,
        # and if top row and left column are full
        elif self.control.training_set >2:
            highest = self.board.get_highest_tile()
            for i in range(len(self.board.state.board)):
                for j in range(len(self.board.state.board[i])):
                    if self.board.state.board[i][j]!=0:
                        returnboard.append(np.log2(self.board.state.board[i][j]))
                    else:
                        returnboard.append(0)

            for i in range (len(returnboard)):
                if returnboard[i]!=0:
                    returnboard[i] = returnboard[i]/np.log2(self.board.get_highest_tile())





            if self.board.state.board[3][3] ==highest:
                returnboard.append(1)
            else:
                returnboard.append(0)


            if 0 in list(self.board.state.board[3]):
                returnboard.append(1)
            else:
                returnboard.append(0)

            full = True
            for i in range(4):
                if self.board.state.board[i][3] == 0:
                    full = False
            if full:
                returnboard.append(1)
            else:
                returnboard.append(0)

            if self.control.training_set==3:
                returnboard.append(len(self.board.get_free_cells(self.board.state)))
                returnboard.append(self.board.count_horizontal_moves(self.board.state))
                returnboard.append(self.board.count_vertical_moves(self.board.state))


            if self.control.training_set>4:
                returnboard.append(self.board.count_horizontal_moves(self.board.state))
                returnboard.append(self.board.count_vertical_moves(self.board.state))
                up = copy.deepcopy(self.board.state)
                down = copy.deepcopy(self.board.state)
                left = copy.deepcopy(self.board.state)
                right = copy.deepcopy(self.board.state)
                states = [self.calculate_heuristic(up),self.calculate_heuristic(down)
                    ,self.calculate_heuristic(left),self.calculate_heuristic(right)]
                maxnum = np.max(states)
                for i in range(len(states)):
                    states[i] =states[i]/maxnum
                    returnboard.append(states[i])
                freecells = [len(self.board.get_free_cells(up)), len(self.board.get_free_cells(down)),
                             len(self.board.get_free_cells(left)),len(self.board.get_free_cells(right))]
                maxnum = np.max(freecells)

                for i in range(len(freecells)):
                    if maxnum >0:
                        freecells[i] /= maxnum

                    returnboard.append(freecells[i])
        print(returnboard)

        return np.array(returnboard)

    # To be called when game is done
    def restart(self, result):
        self.control.new_game(result)


class NNplayer(object):
    # Initialises values and takes parameters from user
    def __init__(self, training_set, topology, lr, epochs, batch_size, activation_functions):
        self.training_set = training_set
        self.topology = topology
        self.lr = lr
        self.epochs = epochs
        self.batch_size = batch_size
        self.activation_functions = activation_functions
        self.train_boards = None
        self.train_moves = None
        # Different number of in nodes because of different test set
        if self.training_set == 1:
            self.input = 16
        elif self.training_set ==2:
            self.input = 16
        elif self.training_set ==3:
            self.input =19
        elif self.training_set ==4:
            self.input =22
        elif self.training_set ==5:
            self.input=29

        self.net = ANN(self.input, self.topology, activation_functions, 4, lr)

    # Loads the different test cases and scales the ones that have not been scaled when they were made
    def load_test_cases(self):
        if self.training_set == 1:
            f = open('myfile1.txt', 'r')
        elif self.training_set ==2:
            f = open('Gradient16dim.txt', 'r')
        elif self.training_set ==3:
            f= open('Gradient19dim.txt', 'r')
        elif self.training_set ==4:
            f= open('Gradient22dim.txt', 'r')
        elif self.training_set ==5:
            f= open('Gradient30dim.txt','r')
        boards = []
        moves = []
        lines = f.readlines()
        for line in lines:
            case = line.split('$')
            board = case[0]
            move = case[1]
            board = board.split(',')
            move = move.strip('\n').split(',')
            boards.append(board)
            moves.append(move)
        boards = np.asarray(boards, dtype=np.double)

        if self.training_set < 3:
            scale(boards)


        moves = np.array(moves)

        # Sets assignes the set to the objects variables
        self.train_boards = boards
        self.train_moves = moves

    # Trains batches of size "minibatch_size" for x epochs.
    def train_model(self, epochs, minibatch_size):
        for epoch in range(epochs):
            print("Training epoch number {}...".format(epoch))
            cost = 0
            for i in range(0, len(self.train_boards), minibatch_size):
                board_batch = self.train_boards[i:i+minibatch_size]
                move_batch = self.train_moves[i:i+minibatch_size]
                cost += self.net.train(board_batch, move_batch)

            print("Cost after epoch {}: {}".format(epoch, cost/len(self.train_boards)))


class main:
    #Takes input from user
    #Creates an NNplayer
    #Creates a GUI window
    #Initializes statistic variables

    def __init__(self,welch_parent):
        self.welch_parent = welch_parent
        self.gui_bool = str(input("Gui? y/n: \n"))
        self.training_set = int(input("What training set do you want to use?\n1: 16 dim snake, 2: 16 dim gradient,3: 19 dim : "
                                      "4: 22 dim, 5: 29 dim"))
        self.hidden = [x for x in map(int, input("Please specify a topology description on the form 40,20,60: ").strip().split(","))]
        self.activations = [activation_map(x) for x in list(map(int, input("Please specify activation functions for each layer on the form 0,2,1: ").strip().split(",")))]
        self.lr = float(input("Specify learning rate: "))
        self.epochs = int(input('How many epochs do you want to train?: '))
        self.minibatch = int(input('What batch size do you want to use?: '))
        self.net_result = []
        self.rand_result = []
        self.count = 1
        self.player = NNplayer(self.training_set, self.hidden, self.lr, self.epochs, self.minibatch, self.activations)
        self.player.load_test_cases()
        self.player.train_model(self.epochs, self.minibatch)
        self.root = Tk()
        print('NNET playing round: ' + str(self.count))
        self.game = AiWindow(self.player, self)
        if self.gui_bool == 'y':
            self.game.pack()
            self.root.bind('<a>', lambda x: self.game.play())
            self.root.mainloop()
        if self.gui_bool == 'n':
            self.game.play()

        self.net_result = []
        self.rand_result = []
        self.count = 1

#This method handles the logic of starting a new game (to play 50 runs with AI and 50 random)
#Also calculates score
    def new_game(self, result):

        self.game.pack_forget()
        if self.count < 50:
            self.net_result.append(result)
            print('Highest tile: ' + str(result))
            self.count += 1
            print('NNET playing round: ' + str(self.count))
            self.game = AiWindow(self.player, self)
            if self.gui_bool == 'y':
                self.game.pack()
            self.game.play()

        elif self.count == 50:
            print('Highest tile: ' + str(result))
            self.net_result.append(result)
            self.count += 1
            print('NNET playing round: ' + str(self.count))
            self.game = AiWindow(self.player, self)
            if self.gui_bool == 'y':
                self.game.pack()
            self.game.play_random()

        elif 50 < self.count < 100:
            print('Highest tile: ' + str(result))
            self.rand_result.append(result)
            self.count += 1

            print('Random playing round: ' + str(self.count-50))
            self.game = AiWindow(self.player, self)
            if self.gui_bool == 'y':
                self.game.pack()
            self.game.play_random()

        elif self.count == 100:
            # self.count+=1
            self.rand_result.append(result)

            self.somelist =[]
            self.somelist.append(self.net_result)
            self.welch_parent.results.append(self.net_result)
            welch_result = welch(self.rand_result, self.net_result)
            print(welch_result)
            f = open('statistics.txt','a')
            f.write('Statistics:\n'
                   'Topology:'+ str(self.hidden)+'\n'+
                    'Learning rate: '+ str(self.lr)+'\n'+
                    'Activation functions: '+ str([self.activations.index(x) for x in self.activations])+'\n'+
                    'Batchsize: '+ str(self.minibatch)+'\n'+
                    'Epochs: '+ str(self.epochs)+'\n'+
                    'Dataset: '+ str(self.training_set)+'\n'+
                    welch_result+'\n')


def scale(seq):
        """
        Scales a list of board states by first getting the log2 value of every cell,
        and then dividing every cell by the log2 value of the maximum cell in that board state.

        Example:
            Input   = [[0, 2, 4, 8, 2, 16, 256], [4, 8, 512]]
            Log2    = [[0, 1, 2, 3, 1, 4, 8], [2, 3, 9]
            Scaled  = [[0, 0.125, 0.25, 0.375, 0.125, 0.5, 1], [0.222, 0.333, 1]] (by dividing by max(Log2))

        :param seq: A list of board states, formatted as float (important!)
        :return: A scaled list of board states, ranging from 0 to 1
        """

        for i in range(len(seq)):
            for j in range(len(seq[i])):
                if seq[i][j] != 0:
                    seq[i][j] = np.log2(seq[i][j])
            seq[i] = seq[i] / max(seq[i])


#Maps input from user to a real function
def activation_map(x):
        return {
            0: T.tanh,
            1: T.nnet.sigmoid,
            2: lambda k: T.switch(k > 0, k, 0),  # Equivalent to T.nnet.relu (rectified linear unit)
            3: T.nnet.softmax,
        }.get(x, T.tanh)  # T.tanh is default if x is not found


#Function to determine score
def welch(list1, list2):
        params = {"results": str(list1) + " " + str(list2), "raw": "1"}
        resp = requests.post('http://folk.ntnu.no/valerijf/6/', data=params)
        return resp.text

class boss:
    def __init__(self):
        self.results =[]
        main1 = main(self)
        main2 = main(self)
        print(self.results)
        print(welch(self.results[0],self.results[1]))


if __name__ == '__main__':
    boss = boss()



# Initializes gui and board class # Handles game and gui logic, returns true if move was legal, false if not # AI plays until play_best_move returns false, at which point there are no legal moves # Goes through a list of direction values from the nnet.predict function # Returns false if no moves are legal # Plays a random move, until there are no legal moves # Randomly chooses a move and tries to perform it. If no moves are legal, return false # Converts boards to match cases from their respective training cases #16 dim vector scaled # 19 dim vector. board + 3 bits to say if highest tile in upper left corner, # and if top row and left column are full # To be called when game is done # Initialises values and takes parameters from user # Different number of in nodes because of different test set # Loads the different test cases and scales the ones that have not been scaled when they were made # Sets assignes the set to the objects variables # Trains batches of size "minibatch_size" for x epochs. #Takes input from user #Creates an NNplayer #Creates a GUI window #Initializes statistic variables #This method handles the logic of starting a new game (to play 50 runs with AI and 50 random) #Also calculates score # self.count+=1 Scales a list of board states by first getting the log2 value of every cell,         and then dividing every cell by the log2 value of the maximum cell in that board state.          Example:             Input   = [[0, 2, 4, 8, 2, 16, 256], [4, 8, 512]]             Log2    = [[0, 1, 2, 3, 1, 4, 8], [2, 3, 9]             Scaled  = [[0, 0.125, 0.25, 0.375, 0.125, 0.5, 1], [0.222, 0.333, 1]] (by dividing by max(Log2))          :param seq: A list of board states, formatted as float (important!)         :return: A scaled list of board states, ranging from 0 to 1 #Maps input from user to a real function # Equivalent to T.nnet.relu (rectified linear unit) # T.tanh is default if x is not found #Function to determine score