plot.py

import matplotlib.pyplot as plt
from validation import Validation as Val
import numpy as np

# Example layout and circle settings
layout = [0, 0, 3, 0, 2, 0, 2, 0, 0, 0, 3, 0, 0, 1, 0]
circle = False

# Create an instance of validation
validation_instance = Val(layout, circle)


# Plotting function for strategy comparison
def plot_strategy_comparison(num_games=10000):
    strategy_costs = validation_instance.compare_strategies(num_games=num_games)

    # Bar plot for strategy comparison
    plt.figure(figsize=(10, 6))
    plt.bar(strategy_costs.keys(), strategy_costs.values(), color=['blue', 'green', 'orange', 'red', 'purple'])
    plt.xlabel('Strategies')
    plt.ylabel('Average Cost')
    plt.title('Comparison of Strategies')
    plt.savefig('strategy_comparison.png')  # Save the plot
    plt.show()

# Plotting function for state-based average turns for all strategies on the same plot
def plot_state_based_turns(save=True):
    strategies = [validation_instance.optimal_policy,
                  validation_instance.safe_strategy,
                  validation_instance.normal_strategy,
                  validation_instance.risky_strategy,
                  validation_instance.random_strategy]
    strategy_names = ['Optimal', 'SafeDice', 'NormalDice', 'RiskyDice', 'Random']
    
    plt.figure(figsize=(12, 6))
    for strategy, name in zip(strategies, strategy_names):
        mean_turns = validation_instance.simulate_state(strategy, layout, circle)
        plt.plot(range(len(mean_turns)), mean_turns, marker='o', linestyle='-', label=name)

    plt.xlabel('State')
    plt.ylabel('Average Turns')
    plt.title('Average Turns per State for Different Strategies')
    plt.grid(True)
    plt.legend()

    #if save:
         #plt.savefig('state_based_turns_all_strategies.png')  # Save the plot

    plt.show()

def plot_state_based_comparison(validation_instance, num_games=10000):
    optimal_turns, empirical_turns = validation_instance.compare_state_based_turns(num_games=num_games)

    # Plotting the state-based average turns comparison
    plt.figure(figsize=(12, 6))

    # Plot optimal strategy turns
    plt.plot(range(len(optimal_turns)), optimal_turns, marker='o', linestyle='-', label='ValueIteration')

    # Plot empirical strategy turns
    plt.plot(range(len(empirical_turns)), empirical_turns, marker='x', linestyle='-', label='Empirical')

    plt.xlabel('State')
    plt.ylabel('Average Turns')
    plt.title('Average Turns per State - ValueIteration vs. Empirical')
    plt.grid(True)
    plt.legend()

    plt.show()




# Main function to generate and save plots
if __name__ == '__main__':
    # Example of strategy comparison plot
    plot_strategy_comparison(num_games=10000)

    # Example of state-based average turns plot for all strategies on the same plot
    plot_state_based_turns(save=True)

    plot_state_based_comparison(validation_instance, num_games=10000)