import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

def load_data(file_path):
    data = pd.read_csv(file_path)
    data = np.array(data)
    m, n = data.shape
    
    data_train = data[1000:m].T  
    Y_train = data_train[0].astype(int)
    X_train = data_train[1:n]
    
    data_test = data[0:1000].T
    Y_test = data_test[0].astype(int)
    X_test = data_test[1:n]
    
    return X_train, Y_train, X_test, Y_test

def plot_accuracy(acc_store, save_path=None):
    """
    Plot training and validation accuracy over iterations.

    Parameters:
    acc_store (list of tuples): Each tuple contains (training_accuracy, validation_accuracy).
    save_path (str, optional): If provided, saves the plot to the specified path.
    """
    # Unzip the accuracy data
    training_accuracy, validation_accuracy = zip(*acc_store)

    # Plot
    plt.figure(figsize=(10, 6))
    plt.plot(training_accuracy, label='Training Accuracy')
    plt.plot(validation_accuracy, label='Validation Accuracy')
    plt.title('Training and Validation Accuracy Over Iterations')
    plt.xlabel('Iterations (in steps of 10)')
    plt.ylabel('Accuracy')
    plt.legend()
    plt.grid(True)

    # Save the plot if a path is provided
    if save_path:
        plt.savefig(save_path)
        print(f"Accuracy plot saved to {save_path}")
    
    # Show the plot
    plt.show()


def plot_loss(loss_store, save_path=None):
    """
    Plot training and validation loss over iterations.

    Parameters:
    loss_store (list of tuples): Each tuple contains (training_loss, validation_loss).
    save_path (str, optional): If provided, saves the plot to the specified path.
    """
    # Unzip the loss data
    training_loss, validation_loss = zip(*loss_store)

    # Plot
    plt.figure(figsize=(10, 6))
    plt.plot(training_loss, label='Training Loss')
    plt.plot(validation_loss, label='Validation Loss')
    plt.title('Training and Validation Loss Over Iterations')
    plt.xlabel('Iterations (in steps of 10)')
    plt.ylabel('Loss')
    plt.legend()
    plt.grid(True)

    # Save the plot if a path is provided
    if save_path:
        plt.savefig(save_path)
        print(f"Loss plot saved to {save_path}")
    
    # Show the plot
    plt.show()