diff --git a/.gitattributes b/.gitattributes
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--- /dev/null
+++ b/.gitattributes
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+# Auto detect text files and perform LF normalization
+* text=auto
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..e62ec04
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,674 @@
+GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
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+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..4fefd0b
--- /dev/null
+++ b/README.md
@@ -0,0 +1,2 @@
+# tic_tac_toe_bot
+ A tic tac toe bot made using a neural network from scratch
diff --git a/badCursesDebugger.py b/badCursesDebugger.py
new file mode 100644
index 0000000..a481bba
--- /dev/null
+++ b/badCursesDebugger.py
@@ -0,0 +1,14 @@
+'A module for printing outputs after a curses program has ended'
+to_print = []
+
+def buffer(*args):
+    'adds the provided arguments to be printed later'
+    to_print.append(args)
+
+def output():
+    'prints everything that was to be printed later'
+    for i in to_print:
+        if type(i) == tuple:
+            print(*i)
+        else:
+            print(i)
\ No newline at end of file
diff --git a/boardDrawer.py b/boardDrawer.py
new file mode 100644
index 0000000..964fb2b
--- /dev/null
+++ b/boardDrawer.py
@@ -0,0 +1,24 @@
+upright = "|"
+sideways = "_"
+empty = " "
+
+def drawBoard(**kwargs):
+    square_length = kwargs.pop("square_length", 5)
+    square_height = kwargs.pop("square_height", 3)
+    board_length = kwargs.pop("board_length", 5)
+    board = []
+    true_board_height = board_length*square_height
+    true_board_length = board_length*square_length+(board_length-1)
+    #print(true_board_height, true_board_length)
+    for rowNo in range(true_board_height):
+        row = [empty] * true_board_length
+        if (rowNo+1) % square_height == 0 and rowNo != true_board_height-1:
+            row = [sideways] * true_board_length
+        for line in range(board_length-1):
+            row[((line+1)*square_length)+line] = upright
+        board.append("".join(row))
+    return board
+
+if __name__ == "__main__":
+    for i in drawBoard():
+        print(i)
\ No newline at end of file
diff --git a/fileOne.pkl b/fileOne.pkl
new file mode 100644
index 0000000..4bf19f8
Binary files /dev/null and b/fileOne.pkl differ
diff --git a/fileTwo.pkl b/fileTwo.pkl
new file mode 100644
index 0000000..1721f03
Binary files /dev/null and b/fileTwo.pkl differ
diff --git a/game.py b/game.py
new file mode 100644
index 0000000..f9df451
--- /dev/null
+++ b/game.py
@@ -0,0 +1,253 @@
+'contains the Game class'
+import curses
+import math
+import time
+
+from badCursesDebugger import buffer
+from boardDrawer import drawBoard
+from nnTrainer import Trainer
+
+char_map = {
+    True: "X",
+    False: "0"
+}
+
+render_map = {
+    True: 1,
+    False: 2
+}
+
+game_movecursor_map = {
+    "KEY_UP": [-1, 0],
+    "KEY_DOWN": [1, 0],
+    "KEY_LEFT": [0, -1],
+    "KEY_RIGHT": [0, 1],
+    "w": [-1, 0],
+    "s": [1, 0],
+    "a": [0, -1],
+    "d": [0, 1]
+}
+
+def board_val_to_char(num):
+    'returns the assigned character of a board value e.g. 1 -> X, 2 -> 0'
+    return char_map[{render_map[k]:k for k in render_map}[num]]
+
+SQUARE_LENGTH = 9
+SQUARE_HEIGHT = 5
+CURSOR_OFFSET = 2
+WIN_POS = [5, 0]
+WIN_WAIT = 1
+
+class Game:
+    'the class for the tic tac toe game'
+    def __init__(self, options, stdscr=None):
+        self.ai = None
+        self.length = options["board length"]
+        if options["ai"]:
+            self.generate_trainer(options, self.length, stdscr)
+            self.training = True
+        else:
+            self.training = False
+        if stdscr is not None:
+            self.stdscr = stdscr
+            stdscr.clear()
+            stdscr.refresh()
+        self.running = False
+        self.won = None
+        if self.length > 4:
+            self.match_length = math.ceil(self.length*0.75)
+        else:
+            self.match_length = 3
+        self.true_length = self.length*SQUARE_LENGTH+self.length
+        self.board = [[0 for i in range(self.length)] \
+                        for j in range(self.length)]
+        self.win = None
+        self.rendered_board = drawBoard(
+            board_length=self.length,
+            square_length=SQUARE_LENGTH,
+            square_height=SQUARE_HEIGHT
+        )
+        self.original_rendered_board = self.rendered_board.copy()
+        self.turn = options["start first"]
+        self.past_game_pos = None
+        self.cursor_pos = [0, 0]
+
+    def generate_trainer(self, options, length, stdscr):
+        'generates player for ai'
+        self.ai_file_name = options["ai file"]
+        self.ai = Trainer(
+            {
+                "board_length": length,
+                "select file1": self.ai_file_name,
+                "select file2": "",
+                "show training": True,
+                "start first": options["start first"],
+                "slowAmt": 0,
+                "new": False,
+                "rounds": 1
+            },
+            stdscr,
+            self
+        )
+
+    def update_rendered_board(self):
+        'updates the rendered board'
+        for row_no, row in enumerate(self.board):
+            for index in range(len(self.board[row_no])):
+                box = row[index]
+                if box != 0:
+                    box = board_val_to_char(box)
+                    true_pos_y = math.ceil(SQUARE_HEIGHT/2) \
+                                            + (row_no-1) \
+                                            * SQUARE_HEIGHT \
+                                            + SQUARE_HEIGHT-1
+                    true_pos_x = math.ceil(SQUARE_LENGTH/2) + ((index) * SQUARE_LENGTH) + index-1
+                    chars = list(self.rendered_board[true_pos_y])
+                    chars[true_pos_x] = box
+                    self.rendered_board[true_pos_y] = "".join(chars)
+
+    def render_board(self):
+        'renders the board'
+        for i,string in enumerate(self.rendered_board):
+            self.win.addstr(i,0,string)
+        self.win.box()
+        self.win.refresh()
+
+    def render_cursor(self):
+        'renders the cursor'
+        cursor = curses.newwin(3, 5, (
+            self.cursor_pos[0]
+            * (SQUARE_HEIGHT))
+            + round(CURSOR_OFFSET/2)
+            + WIN_POS[0],
+            (self.cursor_pos[1]
+            * (SQUARE_LENGTH+1)
+        ) + CURSOR_OFFSET+WIN_POS[1])
+
+        cursor.box()
+        if self.get_cursor_selected_val() != 0:
+            cursor.move(1,2)
+
+            cursor.addch(board_val_to_char(self.get_cursor_selected_val()))
+        cursor.refresh()
+
+    def render(self):
+        'combines all the render methods and renders some additional text'
+        self.win = curses.newwin(
+            self.length*SQUARE_HEIGHT,
+            self.true_length,
+            WIN_POS[0],
+            WIN_POS[1]
+        )
+        self.update_rendered_board()
+        self.render_board()
+        self.render_cursor()
+        self.stdscr.move(WIN_POS[0]-1, round(WIN_POS[1]+self.true_length/3))
+        self.stdscr.addstr(char_map[self.turn]+"'s turn")
+        self.stdscr.move(WIN_POS[0]-2, round(WIN_POS[1]+self.true_length/3))
+        self.stdscr.addstr("match "+str(self.match_length)+" in a row to win")
+        self.stdscr.move(WIN_POS[0]-3, round(WIN_POS[1]+self.true_length/3))
+        self.stdscr.addstr(str(self.length)+"x"+str(self.length)+" grid")
+
+    def get_cursor_selected_val(self):
+        'returns the value at the selected position'
+        return self.board[self.cursor_pos[0]][self.cursor_pos[1]]
+
+    def move_cursor(self, directions):
+        'changes cursor_pos according to the direction'
+        if (0 <= self.cursor_pos[0] + directions[0] <= self.length-1) \
+                and (0 <= self.cursor_pos[1] + directions[1] <= self.length-1):
+            self.cursor_pos[0] += directions[0]
+            self.cursor_pos[1] += directions[1]
+
+    def has_draw(self):
+        'checks if the game has drawn'
+        draw = False
+        for row in self.board:
+            draw = row.count(0) == 0
+            if draw is False:
+                break
+        return draw
+
+    def haswon(self):
+        'checks if the game has ended and its outcome'
+        current_turn = not self.turn
+        if self.has_draw():
+            self.won = 3
+            return
+        for row_no in range(self.length):
+            check_list = [
+                self.board[row_no], # horizontally
+                [self.board[x][row_no] for x in range(self.length)], # vertically
+                [self.board[x][x] for x in range(self.length)], # diagonally downwards
+                [self.board[x][self.length-x-1] for x in range(self.length)] # diagonally upwards
+            ]
+            for compre in check_list:
+                if compre.count(render_map[current_turn]) == self.match_length:
+                    self.won = current_turn
+                    return
+
+    def terminate(self, immediate=False):
+        'terminates the game object'
+        self.stdscr.move(round(self.true_length/3), round(self.true_length/3))
+        if self.won != 3:
+            self.stdscr.addstr(char_map[self.won]+" has won!")
+        else:
+            self.stdscr.addstr("Draw!")
+        self.stdscr.refresh()
+        if not immediate:
+            time.sleep(WIN_WAIT)
+        self.running = False
+
+    def reset(self):
+        'resets the board and everything for playing again'
+        if self.ai:
+            self.ai.save()
+            self.terminate()
+            return
+        self.board = [[0 for i in range(self.length)] for j in range(self.length)]
+        self.turn = True
+        self.cursor_pos = [0, 0]
+        self.won = None
+        self.rendered_board = self.original_rendered_board.copy()
+
+    def place(self):
+        'attempts to place x or 0 according to the cursor_pos'
+        if self.get_cursor_selected_val() == 0:
+            self.board[self.cursor_pos[0]][self.cursor_pos[1]] = render_map[self.turn]
+            self.turn = not self.turn
+            self.haswon()
+            if not self.training:
+                self.render()
+            if (self.won is not None) and (not self.training):
+                self.terminate()
+            return True
+        return False
+
+    def process_input(self):
+        'receives input and makes the game respond accordingly'
+        INPUT = self.stdscr.getkey()
+        if INPUT in game_movecursor_map:
+            directions = game_movecursor_map[INPUT]
+            self.move_cursor(directions)
+        elif INPUT in (" ", "\n"):
+            self.place()
+        elif INPUT == "q":
+            self.won = not self.turn
+            self.terminate(True)
+
+    def run(self):
+        'runs the game'
+        self.running = True
+        if self.ai is not None:
+            self.past_game_pos = []
+            while self.running:
+                self.render()
+                if not self.turn:
+                    self.ai.network_play(self.ai.network1, self, self.past_game_pos, 500)
+                else:
+                    self.process_input()
+        else:
+            while self.running:
+                self.render()
+                self.process_input()
diff --git a/main.py b/main.py
new file mode 100644
index 0000000..9ed7df9
--- /dev/null
+++ b/main.py
@@ -0,0 +1,55 @@
+import curses
+
+from game import Game
+from nnTrainer import Trainer
+from program import Program
+from badCursesDebugger import output
+
+def better_wrapper(func):
+    """wraps curses"""
+    try:
+        stdscr = curses.initscr()
+        curses.filter()
+        curses.noecho()
+        curses.cbreak()
+        #curses.curs_set(False)
+        stdscr.keypad(True)
+        stdscr.clear()
+        return func(stdscr)
+    finally:
+        curses.nocbreak()
+        stdscr.keypad(False)
+        curses.echo()
+        curses.curs_set(True)
+        curses.endwin()
+        output()
+
+def main(stdscr):
+    'main'
+    Program(stdscr, "tic tac toe", {
+        "game": {
+            "options": {
+                "board length": 3,
+                "ai": True,
+                "ai file": "fileOne",
+                "start first": True
+            },
+            "program_class": Game
+        },
+        "AI training": {
+            "options": {
+                "board_length": 3,
+                "select file1": "fileOne",
+                "select file2": "fileTwo",
+                "show training": True,
+                "slowAmt": 0,
+                "new": True,
+                "rounds": 10,
+                "start first": True
+            },
+            "program_class": Trainer
+        }
+    }).run()
+
+if __name__ == "__main__":
+    better_wrapper(main)
diff --git a/nn.py b/nn.py
new file mode 100644
index 0000000..adfe318
--- /dev/null
+++ b/nn.py
@@ -0,0 +1,107 @@
+'neural network'
+import warnings
+import numpy as np
+
+warnings.filterwarnings('ignore')
+
+BOARD_LENGTH = 3
+
+INPUT_LAYER_AMT = BOARD_LENGTH ** 2
+HIDDEN_LAYER_AMT = INPUT_LAYER_AMT + BOARD_LENGTH
+OUTPUT_LAYER_AMT = INPUT_LAYER_AMT
+EPOCHS = 100
+LEARNING_RATE = 1
+
+def sigmoid(z):
+    'returns sigmoid of z'
+    return 1/(1+np.exp(-z))
+
+def deriv_sigmoid(z):
+    'returns the derivative of the sigmoid of z'
+    return sigmoid(z)*(1-sigmoid(z))
+
+def rand(shape):
+    'returns a numpy array of values -1 to 1 with the given shape'
+    return np.random.uniform(-1, 1, shape)
+
+def gradient_descent(layer, prev_layer, y=None, cost=None, learning_rate=1): # i give up
+    'performs gradient descent on the layer'
+    if cost is None:
+        cost = layer.input-y
+    dy_yhat = cost
+    dz_y = deriv_sigmoid(layer.input)
+    dw_z = prev_layer.input.T
+    dw_yhat = dy_yhat * dz_y * dw_z
+    da_yhat = dy_yhat * dz_y * layer.array.sum(axis=1)[:, np.newaxis]
+    da_yhat = da_yhat.sum(axis=1)[:, np.newaxis].T
+    layer.array -= (dw_yhat*learning_rate)
+    db_yhat = dy_yhat * dz_y
+    layer.biases -= (db_yhat.T*learning_rate)
+    return da_yhat
+
+class NetworkLayer:
+    'layer for NeuralNetwork class'
+    def __init__(self, nparray=None, biases=None):
+        self.array = nparray
+        self.input = None
+        self.biases = biases
+
+    def __len__(self):
+        return len(self.array)
+
+class NeuralNetwork:
+    'makes a neural network'
+    def __init__(self, hidden_layers, output_layer, board_length):
+        self.input_layer = NetworkLayer(np.array(0))
+        self.hidden_layers = [NetworkLayer(hidden[0], hidden[1]) for hidden in hidden_layers]
+        self.output_layer = NetworkLayer(output_layer[0], output_layer[1])
+        self.layers = [self.input_layer]
+        self.value = None
+        self.board_length = board_length
+        for hidden_layer in self.hidden_layers:
+            self.layers.append(hidden_layer)
+        self.layers.append(self.output_layer)
+
+    def output(self):
+        'outputs the network weights'
+        for a in self.layers:
+            print(a.array)
+
+    def forward_prop(self, x) -> np.ndarray:
+        'Forward propagates the network'
+        mult1 = x[:, np.newaxis].T
+        mult2 = self.hidden_layers[0].array
+        self.input_layer.input = mult1
+        for index, network_layer in enumerate(self.layers[1:]):
+            dot = mult2 * mult1.T
+            add_bias = dot.T.sum(axis=1) + network_layer.biases.T
+            mult1 = sigmoid(add_bias)
+            network_layer.input = mult1
+            if index == len(self.layers)-2:
+                break
+            mult2 = self.layers[index+2].array
+        return mult1
+
+    def back_prop(self, y, learning_rate, da_yhat=None):
+        'Back propagates the network'
+        if da_yhat is None:
+            da_yhat = gradient_descent(
+                self.output_layer,
+                self.hidden_layers[-1],
+                y,
+                learning_rate=learning_rate
+            )
+        for index, layer in reversed(list(enumerate(self.layers[1:-1]))):
+            index += 1
+            da_yhat = gradient_descent(
+                layer,
+                self.layers[index-1],
+                None,
+                da_yhat,
+                learning_rate=learning_rate
+            )
+
+    def clear_input(self):
+        'clears the input from all the layers'
+        for layer in self.layers:
+            layer.input = None
diff --git a/nnTrainer.py b/nnTrainer.py
new file mode 100644
index 0000000..2608bac
--- /dev/null
+++ b/nnTrainer.py
@@ -0,0 +1,167 @@
+'Trainer class to train neural network to play tic tac toe'
+import math
+import random
+import time
+import pickle as pk
+import numpy as np
+
+from nn import HIDDEN_LAYER_AMT, INPUT_LAYER_AMT, LEARNING_RATE, OUTPUT_LAYER_AMT, rand
+from nn import NeuralNetwork
+
+from badCursesDebugger import buffer
+
+def relish(file_name, obj):
+    'pickles the python object into the provided file'
+    if file_name is None:
+        return False
+    with open(file_name, "wb") as f:
+        pk.dump(obj, f)
+        return True
+
+def unrelish(file_name):
+    'loads the python object from a pickle file'
+    if file_name is None:
+        return False
+    with open(file_name, "rb") as f:
+        return pk.load(f)
+
+def rand_weights_n_biases():
+    'returns random weights and biases for the neural network'
+    return [[rand((INPUT_LAYER_AMT, HIDDEN_LAYER_AMT)), rand((HIDDEN_LAYER_AMT, 1))], \
+            [rand((HIDDEN_LAYER_AMT, HIDDEN_LAYER_AMT)), rand((HIDDEN_LAYER_AMT, 1))]], \
+            [rand((HIDDEN_LAYER_AMT, OUTPUT_LAYER_AMT)), rand((OUTPUT_LAYER_AMT, 1))]
+
+class Trainer:
+    'Trainer class for neural network to play tic tac toe'
+    def __init__(self, options, stdscr, game):
+        self.file1 = options["select file1"]+".pkl"
+        self.file2 = options["select file2"]+".pkl"
+        self.original_file2 = options["select file2"]
+        self.game = game
+
+        if stdscr is not None:
+            self.stdscr = stdscr
+        self.show_training = options["show training"]
+        self.slow_amt = options["slowAmt"]
+        self.new = options["new"]
+        self.board_length = options["board_length"]
+        self.rounds = options["rounds"]
+
+        self.training = False
+
+        if self.new:
+            self.network1 = NeuralNetwork(*rand_weights_n_biases(), self.board_length)
+            self.network2 = NeuralNetwork(*rand_weights_n_biases(), self.board_length)
+            self.network1.value = options["start first"]
+            self.network2.value = not self.network1.value
+        else:
+            self.network1 = unrelish(self.file1)
+            self.network1.value = options["start first"] # determines which network is which
+            if self.original_file2 != "":
+                self.network2 = unrelish(self.file2)
+                self.network2.value = not self.network1.value
+
+    def __del__(self):
+        self.save()
+
+    def save(self):
+        'saves the neural networks into their pickle files'
+        buffer("saving...")
+        relish(self.file1, self.network1)
+        if self.original_file2 != "":
+            relish(self.file2, self.network2)
+
+    def learn(self, network, past_game_pos, y):
+        'makes the network learn through back propagation'
+        learning_rate = LEARNING_RATE
+        change = [{"array": layer.array, "biases": layer.biases} for layer in network.layers]
+        for layers in past_game_pos[::-1]:
+            learning_rate *= 0.6
+            network.input_layer.input = layers[0]["input"].copy()
+            for index, layer in enumerate(layers[1::]):
+                index += 1
+                network.layers[index].array = layer["array"].copy()
+                network.layers[index].input = layer["input"].copy()
+                network.layers[index].biases = layer["biases"].copy()
+            network.back_prop(y, learning_rate)
+            for index, layer in enumerate(network.layers[1::]):
+                index += 1
+                change[index]["array"] += (layer.array - layers[index]["array"])
+                change[index]["biases"] += (layer.biases - layers[index]["biases"])
+        for index, lay in enumerate(change[1::]):
+            index += 1
+            network.layers[index].array -= lay["array"] / len(past_game_pos)
+            network.layers[index].biases -= lay["biases"] / len(past_game_pos)
+
+    def check(self, game, network, past_game_pos, selected, prev_past_game_pos_len, exists):
+        'dishes out "rewards" or "punishments" to the neural network according to how well it performed'
+        y = np.ones(self.board_length**2)
+        y[selected] = 0
+
+        if self.show_training:
+            game.render()
+            game.stdscr.refresh()
+
+        if exists:
+            layers = []
+            for layer in network.layers:
+                layers.append(
+                    {
+                        "array": layer.array,
+                        "input": layer.input,
+                        "biases": layer.biases
+                    }
+                )
+            past_game_pos.append(layers)
+        else:
+            new_output = network.output_layer.input.copy()
+            new_output[0][selected] = 0
+            network.back_prop(new_output, LEARNING_RATE)
+        if game.won is not None:
+            self.rounds -= 1
+            if len(past_game_pos) > prev_past_game_pos_len:
+                self.learn(network, past_game_pos, y)
+        if game.won in (network.value, 3):
+            y = np.zeros(self.board_length**2)
+            y[selected] = 1
+            self.learn(network, past_game_pos, y)
+            game.reset()
+        elif game.won == (not network.value):
+            self.learn(network, past_game_pos, y)
+            game.reset()
+
+    def network_play(self, network, game, past_game_pos, prev_past_game_pos_len):
+        'lets the network select which tile to place and makes it learn from its mistakes'
+        output = network.forward_prop(np.array(game.board).reshape(self.board_length**2))[0]
+        output = np.nan_to_num(output)
+        selected = np.where(output == np.amax(output))[0]
+        game.ai_last_place = selected
+        if len(selected) > 1:
+            rand_index = random.randrange(0, len(selected))
+            selected = selected[rand_index]
+        else:
+            selected = selected[0]
+        selected = selected.tolist()
+        game.cursor_pos = [math.floor(selected / game.length), selected % game.length]
+        self.check(game, network, past_game_pos, selected, prev_past_game_pos_len, game.place())
+
+        time.sleep(self.slow_amt / 10)
+
+    def run(self):
+        'runs the network Trainer'
+        if (self.network1.board_length != self.board_length) or (self.network2.board_length != self.board_length):
+            return "Board length does not match the file provided" # error message
+        self.training = True
+        self.game.training = True
+        past_game_pos1 = []
+        past_game_pos2 = []
+        prev_past_game_pos1_len = 0
+        prev_past_game_pos2_len = 0
+        start = time.time()
+        while self.rounds > 0:
+            self.network_play(self.network1, self.game, past_game_pos1, prev_past_game_pos1_len)
+            self.network_play(self.network2, self.game, past_game_pos2, prev_past_game_pos2_len)
+        end = time.time()
+        buffer("elapsed time:", end - start)
+        self.save()
+        return True
diff --git a/program.py b/program.py
new file mode 100644
index 0000000..88576e6
--- /dev/null
+++ b/program.py
@@ -0,0 +1,138 @@
+'program class for terminal ui, used to adjust settings and use different programs'
+import curses
+from badCursesDebugger import buffer
+
+def space(self):
+    'selects object below cursor'
+    if self.options is None:
+        program_name = list(self.programs.keys())[self.cursor]
+        self.options = self.programs[program_name]["options"]
+        self.selected_program = program_name
+        self.cursor = 0
+
+def down(self):
+    'moves the cursor down'
+    if self.options is not None:
+        if self.cursor+1 < len(self.options.items()):
+            self.cursor += 1
+        return
+    if self.cursor+1 < len(self.programs.items()):
+        self.cursor += 1
+
+def up(self):
+    'moves the cursor up'
+    if self.cursor-1 >= 0:
+        self.cursor -= 1
+
+def out(self):
+    'exits the scope'
+    if self.options:
+        self.options = None
+        self.selected_program = None
+        return
+    self.running = False
+
+def enter(self):
+    'starts the selected program'
+    if self.selected_program is not None:
+        program = self.programs[self.selected_program]["program_class"]
+        if self.selected_program == "game":
+            program(self.options, self.stdscr).run()
+        else:
+            game = self.programs["game"]["program_class"](
+                {
+                    "board length": self.options["board_length"],
+                    "ai": False,
+                    "start first": self.options["start first"]
+                },
+                self.stdscr
+            )
+            program(self.options, self.stdscr, game).run()
+
+input_map = {
+    " ": space,
+    "KEY_UP": up,
+    "KEY_DOWN": down,
+    "q": out,
+    "\n": enter
+}
+
+class Program:
+    'program class'
+    def __init__(self, stdscr, title, programs):
+        self.programs = programs
+        self.selected_program = None
+        self.cursor = 0
+        self.options = None
+        self.title = title
+        self.stdscr = stdscr
+        self.win = None
+        self.running = True
+
+    def render_options(self, options=None):
+        """renders options"""
+        self.stdscr.clear()
+        self.stdscr.move(1, 0)
+        self.stdscr.addstr(self.title)
+        if options is None:
+            options = list(self.programs.keys())
+        self.stdscr.refresh()
+        self.win = curses.newwin(len(options)+2, 30, 2, 0)
+        self.win.box()
+        for index, option in enumerate(options):
+            original_value = option
+            self.win.move(index+1, 2)
+            self.win.addstr(option)
+            if isinstance(options, dict):
+                self.win.move(index+1, 3+len(option))
+                if options[original_value] is not None:
+                    self.win.addstr(str(options[original_value]))
+        self.stdscr.move(self.cursor+3, 1)
+        self.win.refresh()
+
+    def validate_input(self, INPUT):
+        'checks if input is valid and passes it into options if so'
+        if self.options is not None:
+            option_values = list(self.options.values())
+            option_keys = list(self.options.keys())
+            selected_key = option_keys[self.cursor]
+
+            value = option_values[self.cursor]
+
+            if INPUT == "KEY_BACKSPACE" and not isinstance(value, bool):
+                if isinstance(value, str):
+                    self.options[selected_key] = value[:-1]
+                    return
+                if len(str(value)) != 1 and value is not None:
+                    self.options[selected_key] = int(str(value)[:-1])
+                    return
+                self.options[selected_key] = None
+                return
+
+            if len(str(value)) > 10:
+                return
+
+            if isinstance(value, bool):
+                if INPUT in ("KEY_LEFT", "KEY_RIGHT"):
+                    self.options[selected_key] = not value
+                return
+
+            if (not "KEY" in INPUT) and (INPUT != '\n'):
+                if INPUT.isnumeric() or isinstance(INPUT, int):
+                    if isinstance(value, int):
+                        self.options[selected_key] = int(str(value) + INPUT)
+                    elif value is None:
+                        self.options[selected_key] = int(INPUT)
+                    return
+                if isinstance(value, str):
+                    self.options[selected_key] = value + INPUT
+
+    def run(self):
+        'runs the program'
+        while self.running:
+            self.render_options(self.options)
+            INPUT = self.stdscr.getkey()
+            if INPUT in input_map:
+                input_map[INPUT](self)
+            else:
+                self.validate_input(INPUT)