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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)
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