81 lines
4.2 KiB
Python
81 lines
4.2 KiB
Python
from multiprocessing import Pool, cpu_count
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import os
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import numpy as np
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from simulation import run_simulation
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from data_processing import get_controller_files
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from plotting import plot_3d_epoch_evolution, plot_theta_vs_epoch
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# Constants and setup
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initial_conditions = {
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"small_perturbation": (0.1*np.pi, 0.0, 0.0, 0.0),
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"large_perturbation": (-np.pi, 0.0, 0.0, 0),
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"overshoot_vertical_test": (-0.1*np.pi, 2*np.pi, 0.0, 0.0),
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"overshoot_angle_test": (0.2*np.pi, 2*np.pi, 0.0, 0.3*np.pi),
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"extreme_perturbation": (4*np.pi, 0.0, 0.0, 0),
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}
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loss_functions = ["constant", "linear", "quadratic", "cubic", "inverse", "inverse_squared", "inverse_cubed"]
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epoch_range = (0, 3) # Start and end of epoch range
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epoch_step = 1 # Interval between epochs
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dt = 0.02 # Time step for simulation
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num_steps = 500 # Number of steps in each simulation
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# Main execution
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if __name__ == "__main__":
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all_results = {} # Dictionary to store results by loss function
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for condition_name, initial_condition in initial_conditions.items():
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condition_text = f"IC_{'_'.join(map(lambda x: str(round(x, 2)), initial_condition))}"
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desired_theta = initial_condition[-1]
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condition_path = f"/home/judson/Neural-Networks-in-GNC/inverted_pendulum/analysis/max_normalized/{condition_name}"
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os.makedirs(condition_path, exist_ok=True) # Create directory if it does not exist
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for loss_function in loss_functions:
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# Construct the path to the controller directory
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directory = f"/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/max_normalized/{loss_function}/controllers"
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# Fetch the controller files according to the specified range and interval
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controllers = get_controller_files(directory, epoch_range, epoch_step)
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# Pack parameters for parallel processing
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tasks = [(c, initial_condition, directory, dt, num_steps) for c in controllers]
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# Execute simulations in parallel
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print("Starting worker processes")
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with Pool(min(cpu_count(), 16)) as pool:
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results = pool.map(run_simulation, tasks)
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# Sorting the results
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results.sort(key=lambda x: x[0]) # Assuming x[0] is the epoch number
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epochs, state_histories, torque_histories = zip(*results) # Assuming results contain these
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# Convert state_histories to a more manageable form if necessary, e.g., just theta values
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theta_over_epochs = [[state[0] for state in history] for history in state_histories]
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# Store results for later use
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if loss_function not in all_results:
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all_results[loss_function] = {}
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all_results[loss_function][condition_name] = (epochs, theta_over_epochs)
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# continue
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# Plotting the 3D epoch evolution
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print(f"Plotting the 3d epoch evolution for {loss_function} under {condition_text}")
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title = f"Pendulum Angle Evolution for {loss_function} and {condition_text}"
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save_path = os.path.join(condition_path, f"epoch_evolution")
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save_path = os.path.join(save_path, f"{loss_function}.png")
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plot_3d_epoch_evolution(epochs, theta_over_epochs, desired_theta, save_path, title, num_steps, dt)
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print("")
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# Plot the theta as a function of epoch for all loss functions
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continue
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specific_theta_index = num_steps // 2
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save_path = os.path.join(condition_path, f"theta_at_5sec_across_epochs.png")
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plot_theta_vs_epoch(all_results, condition_name, desired_theta, save_path, f"Theta at 5 Seconds across Epochs for {condition_text}", specific_theta_index)
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specific_theta_index = -1
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save_path = os.path.join(condition_path, f"final_theta_across_epochs.png")
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plot_theta_vs_epoch(all_results, condition_name, desired_theta, save_path, f"Final Theta across Epochs for {condition_text}", specific_theta_index)
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print(f"Completed plotting for all loss functions under {condition_name} condition.\n")
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import json
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with open("all_results.json", 'w') as file:
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json.dump(all_results, file) |