[ADD]first push of a model

conversion.py

@@ -0,0 +1,67 @@
+import torch
+import pandas as pd
+import torch.nn as nn
+
+class CSVToTensor:
+    def __init__(self, file_path):
+        self.data_frame = pd.read_csv(file_path)
+        len_dataset = len(self.data_frame)
+        self.game_tensor = torch.zeros((len_dataset, 9), dtype=torch.float32)
+        self.prediction_tensor = torch.zeros((len_dataset, 9), dtype=torch.float32)
+    
+    def csv_to_tensor(self, pos):
+        if pos >= len(self.data_frame):
+            raise ValueError("Position is greater than the number of data in the dataset")
+        
+        data_pos = self.data_frame.iloc[pos]
+        game_stat = data_pos.values[0:9]
+        prediction_stat = data_pos.values[9:18]
+
+        self.game_tensor[pos] = torch.tensor(game_stat, dtype=torch.float32)
+        self.prediction_tensor[pos] = torch.tensor(prediction_stat, dtype=torch.float32)
+
+    def tensor_to_view(self, pos):
+        if pos >= len(self.data_frame):
+            raise ValueError("Position is greater than the number of data in the dataset")
+
+        if torch.equal(self.game_tensor[pos], torch.zeros(9)) or torch.equal(self.prediction_tensor[pos], torch.zeros(9)):
+            raise ValueError("No tensor data found at this position")
+        
+        symbols = {0: ' ', 1: 'x', 2: 'o', 3: 'O'}
+        board = []
+        for i in range(9):
+            if self.game_tensor[pos][i] == 1:
+                board.append(1)
+            elif self.game_tensor[pos][i] == 2:
+                board.append(2)
+            elif self.prediction_tensor[pos][i] == 2:
+                board.append(3)
+            else:
+                board.append(0)
+        
+        print("\nCurrent Game State:")
+        for i in range(0, 9, 3):
+            print(f"{symbols[board[i]]} | {symbols[board[i+1]]} | {symbols[board[i+2]]}")
+            if i < 6:
+                print("---------")
+
+    def print_data(self):
+        print(self.data_frame)
+
+    def create_all_tensor(self):
+        for i in range(len(self.data_frame)):
+            self.csv_to_tensor(i)
+        return self.game_tensor, self.prediction_tensor
+    
+    def create_a_dataset(self):
+        return torch.utils.data.TensorDataset(self.game_tensor, self.prediction_tensor)
+
+    
+if __name__ == '__main__':
+    position = 0
+    tensor = CSVToTensor('./Datasets/example.csv')
+    tensor.print_data()
+    tensor.csv_to_tensor(position)
+    print(f"Input : {tensor.game_tensor[position]}")
+    print(f"Output : {tensor.prediction_tensor[position]}")
+    tensor.tensor_to_view(position)

dataset_states.csv

@@ -0,0 +1,448 @@
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model.py

@@ -0,0 +1,222 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+import csv
+
+
+class Architecture(nn.Module):
+    def __init__(self) -> None:
+        super(Architecture, self).__init__()
+        self.input_size = 9
+        self.hidden_size_1 = 9
+        self.hidden_size_2 = 9
+        self.hidden_size_3 = 9
+        self.hidden_size_4 = 9
+        self.hidden_size_5 = 9
+        self.hidden_size_6 = 9
+        self.hidden_size_7 = 9
+        self.output_size = 9
+
+        self.fc1 = nn.Linear(self.input_size, self.hidden_size_1)
+        self.fc2 = nn.Linear(self.hidden_size_1, self.hidden_size_2)
+        self.fc3 = nn.Linear(self.hidden_size_2, self.hidden_size_3)
+        self.fc4 = nn.Linear(self.hidden_size_3, self.hidden_size_4)
+        self.fc5 = nn.Linear(self.hidden_size_4, self.hidden_size_5)
+        self.fc6 = nn.Linear(self.hidden_size_5, self.hidden_size_6)
+        self.fc7 = nn.Linear(self.hidden_size_6, self.hidden_size_7)
+        self.fc8 = nn.Linear(self.hidden_size_7, self.output_size)
+
+        self.loss = nn.CrossEntropyLoss()
+        self.relu = nn.ReLU()
+
+    def inference(self, x):
+        with torch.no_grad():
+            x1 = self.relu(self.fc1(x))
+            x2 = self.relu(self.fc2(x1))
+            x3 = self.relu(self.fc3(x2))
+            x4 = self.relu(self.fc4(x3))
+            x5 = self.relu(self.fc5(x4))
+            x6 = self.relu(self.fc6(x5))
+            x7 = self.relu(self.fc7(x6))
+            x8 = self.fc8(x7)
+        return x8
+
+def load_model():
+    model = Architecture()
+    model.load_state_dict(torch.load('fine_tune_model_weights.pth'))
+    return model
+
+def inference_model(model, input):
+    return model.inference(input)
+
+def minimax(board, depth, is_maximizing):
+    if check_winner(board) == 2:  # AI wins
+        return 1
+    if check_winner(board) == 1:  # Human wins
+        return -1
+    if check_winner(board) == 3:  # Draw
+        return 0
+    
+    if is_maximizing:
+        best_score = -math.inf
+        for i in range(9):
+            if board[i] == 0:
+                board[i] = 2  # AI's move
+                score = minimax(board, depth + 1, False)
+                board[i] = 0
+                best_score = max(score, best_score)
+        return best_score
+    else:
+        best_score = math.inf
+        for i in range(9):
+            if board[i] == 0:
+                board[i] = 1  # Human's move
+                score = minimax(board, depth + 1, True)
+                board[i] = 0
+                best_score = min(score, best_score)
+        return best_score
+
+def best_move_minimax(board):
+    best_score = -math.inf
+    move = None
+    for i in range(9):
+        if board[i] == 0:
+            board[i] = 2  # AI's move
+            score = minimax(board, 0, False)
+            board[i] = 0
+            if score > best_score:
+                best_score = score
+                move = i
+    return move
+
+def oracle(board, position):
+    output_board = [0] * 9
+    output_board[position] = 2
+
+    # Get minimax prediction
+    minimax_move = best_move_minimax(board)
+
+    # Compare oracle's move with minimax prediction
+    if minimax_move == position:
+        return 1  # Predictions match
+    else:
+        return -1  # Predictions differ
+
+
+def print_in_csv(board, position, comparison_result):
+    output_board = [0] * 9
+    output_board[position] = 2
+    with open('game_state.csv', 'a', newline='') as f:
+        writer = csv.writer(f)
+        writer.writerow([
+            ','.join(map(str, board)),
+            ','.join(map(str, output_board)),
+            comparison_result
+        ])
+
+def decode_prediction(prediction, board):
+    result = torch.argmax(prediction)
+    position = result.item()
+    comparison_result = oracle(board, position)
+    print(f'Oracle comparison result: {comparison_result}')
+    print_in_csv(board, position, comparison_result)
+    board[position] = 2
+    return board
+
+def encode_input(input):
+    return torch.tensor(input, dtype=torch.float32)
+
+
+def play():
+    model = load_model()
+    model.eval()
+    print(model.inference(torch.randn(9)))
+
+
+def print_board(board):
+    
+    visual_board = [' ' if x == 0 else 'X' if x == 1 else 'O' for x in board]
+
+    print(f'{visual_board[0]} | {visual_board[1]} | {visual_board[2]}')
+    print('---------')
+    print(f'{visual_board[3]} | {visual_board[4]} | {visual_board[5]}')
+    print('---------')
+    print(f'{visual_board[6]} | {visual_board[7]} | {visual_board[8]}')
+    print('---------')
+
+
+def check_winner(board):
+    # Check rows
+    for i in range(0, 9, 3):
+        if board[i] == board[i + 1] == board[i + 2] == 1:
+            return 1
+        if board[i] == board[i + 1] == board[i + 2] == 2:
+            return 2
+
+    # Check columns
+    for i in range(3):
+        if board[i] == board[i + 3] == board[i + 6] == 1:
+            return 1
+        if board[i] == board[i + 3] == board[i + 6] == 2:
+            return 2
+
+    # Check diagonals
+    if board[0] == board[4] == board[8] == 1:
+        return 1
+    if board[0] == board[4] == board[8] == 2:
+        return 2
+
+    if board[2] == board[4] == board[6] == 1:
+        return 1
+    if board[2] == board[4] == board[6] == 2:
+        return 2
+
+    # Check for a draw
+    if all(cell != 0 for cell in board):
+        return 3
+
+    return 0
+
+
+def simulate_game():
+
+    board = [0, 0, 0, 0, 0, 0, 0, 0, 0]
+    end = False
+
+    while end != True:
+        print_board(board)
+        move = int(input('Enter move: '))
+        if move <= 9 and move >= 1 and board[move - 1] == 0:
+            board[move - 1] = 1
+            print_board(board)
+            if check_winner(board) == 1:
+                print('You win')
+                end = True
+                break
+            if check_winner(board) == 3:
+                print('Draw')
+                end = True
+                break
+            print('Computer is thinking...')
+            intput = encode_input(board)
+            model = load_model()
+            prediction = inference_model(model, intput)
+            output = decode_prediction(prediction, board)
+            board = output
+            if check_winner(board) == 2:
+                print_board(board)
+                print('Computer wins')
+                end = True
+                break
+            if check_winner(board) == 3:
+                print('Draw')
+                end = True
+                break
+
+        else:
+            print('Invalid move')
+
+
+if __name__ == '__main__':
+    simulate_game()

model_weights.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c13e13f96c58cb16d7e227a05eaa3e3d50031683cd259f557205c7992f83d1b
+size 7966