Restructure files. Changed weight functions to be normalized from 0-1ns to always be normalized from 0-1 (aka max normalization). Also updated average normalization

analysis/controller_across_epochs.py

@@ -85,16 +85,16 @@ loss_functions = ["constant", "linear", "quadratic", "exponential", "inverse", "
 
 
 epoch_start = 0   # Start of the epoch range
-epoch_end = 500  # End of the epoch range
+epoch_end = 1000  # End of the epoch range
-epoch_step = 5    # Interval between epochs
+epoch_step = 10    # Interval between epochs
 
 if __name__ == "__main__":
     for condition_name, initial_condition in initial_conditions.items():
-        full_path = f"/home/judson/Neural-Networks-in-GNC/inverted_pendulum/analysis/max_normalized/{condition_name}"
+        full_path = f"/home/judson/Neural-Networks-in-GNC/inverted_pendulum/analysis/average_normalized/{condition_name}"
         os.makedirs(full_path, exist_ok=True)  # Create directory if it does not exist
         
         for loss_function in loss_functions:
-            controller_dir = f"/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/normalized/max_normalized/{loss_function}/controllers"
+            controller_dir = f"/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/normalized/average_normalized/{loss_function}/controllers"
             controller_files = sorted([f for f in os.listdir(controller_dir) if f.startswith("controller_") and f.endswith(".pth")])
             # Extract epoch numbers and filter based on the defined range and interval
             epoch_numbers = [int(f.split('_')[1].split('.')[0]) for f in controller_files]

training/average_normalized_trainer.py

@@ -0,0 +1,159 @@
+import torch
+import torch.optim as optim
+from torchdiffeq import odeint
+import numpy as np
+import os
+import shutil
+import csv
+import inspect
+
+from PendulumController import PendulumController
+from PendulumDynamics import PendulumDynamics
+
+# Device setup
+device = torch.device("cpu")
+
+# Initial conditions (theta0, omega0, alpha0, desired_theta)
+from initial_conditions import initial_conditions
+state_0 = torch.tensor(initial_conditions, dtype=torch.float32, device=device)
+
+# Device setup
+device = torch.device("cpu")
+
+# Constants
+m = 10.0
+g = 9.81
+R = 1.0
+
+# Time grid
+t_start, t_end, t_points = 0, 10, 1000
+t_span = torch.linspace(t_start, t_end, t_points, device=device)
+
+# Specify directory for storing results
+output_dir = "average_normalized"
+os.makedirs(output_dir, exist_ok=True)
+
+# Use a previously generated random seed
+random_seed = 4529
+
+# Set the seeds for reproducibility
+torch.manual_seed(random_seed)
+np.random.seed(random_seed)
+
+# Print the chosen random seed
+print(f"Random seed for torch and numpy: {random_seed}")
+
+# Initialize controller and dynamics
+controller = PendulumController().to(device)
+pendulum_dynamics = PendulumDynamics(controller, m, R, g).to(device)
+
+# Optimizer setup
+learning_rate = 1e-1
+weight_decay = 1e-4
+optimizer = optim.Adam(controller.parameters(), lr=learning_rate, weight_decay=weight_decay)
+
+# Training parameters
+num_epochs = 1001
+
+# Define loss functions
+def make_loss_fn(weight_fn):
+    def loss_fn(state_traj, t_span):
+        theta = state_traj[:, :, 0]            # Size: [batch_size, t_points]
+        desired_theta = state_traj[:, :, 3]    # Size: [batch_size, t_points]
+        weights = weight_fn(t_span)            # Initially Size: [t_points]
+
+        # Reshape or expand weights to match theta dimensions
+        weights = weights.view(-1, 1)  # Now Size: [batch_size, t_points]
+
+        # Calculate the weighted loss
+        return torch.mean(weights * (theta - desired_theta) ** 2)
+
+    return loss_fn
+
+# Define and store weight functions with descriptions, normalized by average weight
+weight_functions = {
+    'constant': {
+        'function': lambda t: torch.ones_like(t) / torch.ones_like(t).mean(),
+        'description': 'Constant weight: All weights are 1, normalized by the average (remains 1)'
+    },
+    'linear': {
+        'function': lambda t: ((t+1) / (t+1).max()) / ((t+1) / (t+1).max()).mean(),
+        'description': 'Linear weight: Weights increase linearly from 0 to 1, normalized by the average weight'
+    },
+    'quadratic': {
+        'function': lambda t: ((t+1)**2 / ((t+1)**2).max()) / ((t+1)**2 / ((t+1)**2).max()).mean(),
+        'description': 'Quadratic weight: Weights increase quadratically from 0 to 1, normalized by the average weight'
+    },
+    'cubic': {
+        'function': lambda t: ((t+1)**3 / ((t+1)**3).max()) / ((t+1)**3 / ((t+1)**3).max()).mean(),
+        'description': 'Quadratic weight: Weights increase cubically from 0 to 1, normalized by the average weight'
+    },
+    'inverse': {
+        'function': lambda t: ((t+1)**-1 / ((t+1)**-1).max()) / ((t+1)**-2 / ((t+1)**-1).max()).mean(),
+        'description': 'Inverse weight: Weights decrease inversely, normalized by the average weight'
+    },
+    'inverse_squared': {
+        'function': lambda t: ((t+1)**-2 / ((t+1)**-2).max()) / ((t+1)**-2 / ((t+1)**-2).max()).mean(),
+        'description': 'Inverse squared weight: Weights decrease inversely squared, normalized by the average weight'
+    },
+    'inverse_cubed': {
+        'function': lambda t: ((t+1)**-3 / ((t+1)**-3).max()) / ((t+1)**-3 / ((t+1)**-3).max()).mean(),
+        'description': 'Inverse cubed weight: Weights decrease inversely cubed, normalized by the average weight'
+    }
+}
+
+# Training loop for each weight function
+for name, weight_info in weight_functions.items():
+    controller = PendulumController().to(device)
+    pendulum_dynamics = PendulumDynamics(controller, m, R, g).to(device)
+    optimizer = optim.Adam(controller.parameters(), lr=learning_rate, weight_decay=weight_decay)
+    loss_fn = make_loss_fn(weight_info['function'])
+
+    # File paths
+    function_output_dir = os.path.join(output_dir, name)
+    controllers_dir = os.path.join(function_output_dir, "controllers")
+
+    # Check if controllers directory exists and remove it
+    if os.path.exists(controllers_dir):
+        shutil.rmtree(controllers_dir)
+    os.makedirs(controllers_dir, exist_ok=True)
+
+    config_file = os.path.join(function_output_dir, "training_config.txt")
+    log_file = os.path.join(function_output_dir, "training_log.csv")
+
+    # Overwrite configuration and log files
+    with open(config_file, "w") as f:
+        f.write(f"Random Seed: {random_seed}\n")
+        f.write(f"Time Span: {t_start} to {t_end}, Points: {t_points}\n")
+        f.write(f"Learning Rate: {learning_rate}\n")
+        f.write(f"Weight Decay: {weight_decay}\n")
+        f.write("\nLoss Function:\n")
+        f.write(inspect.getsource(loss_fn))
+        f.write("\nTraining Cases:\n")
+        f.write("[theta0, omega0, alpha0, desired_theta]\n")
+        for case in state_0.cpu().numpy():
+            f.write(f"{case.tolist()}\n")
+
+    with open(log_file, "w", newline="") as csvfile:
+        csv_writer = csv.writer(csvfile)
+        csv_writer.writerow(["Epoch", "Loss"])
+
+    # Training loop
+    for epoch in range(num_epochs):
+        optimizer.zero_grad()
+        state_traj = odeint(pendulum_dynamics, state_0, t_span, method='rk4')
+        loss = loss_fn(state_traj, t_span)
+        loss.backward()
+        optimizer.step()
+
+        # Logging
+        with open(log_file, "a", newline="") as csvfile:
+            csv_writer = csv.writer(csvfile)
+            csv_writer.writerow([epoch, loss.item()])
+
+        # Save the model
+        model_file = os.path.join(controllers_dir, f"controller_{epoch}.pth")
+        torch.save(controller.state_dict(), model_file)
+        print(f"{model_file} saved with loss: {loss}")
+
+print("Training complete. Models and logs are saved under respective directories for each loss function.")

training/max_normalized_trainer.py

@@ -0,0 +1,159 @@
+import torch
+import torch.optim as optim
+from torchdiffeq import odeint
+import numpy as np
+import os
+import shutil
+import csv
+import inspect
+
+from PendulumController import PendulumController
+from PendulumDynamics import PendulumDynamics
+
+# Device setup
+device = torch.device("cpu")
+
+# Initial conditions (theta0, omega0, alpha0, desired_theta)
+from initial_conditions import initial_conditions
+state_0 = torch.tensor(initial_conditions, dtype=torch.float32, device=device)
+
+# Device setup
+device = torch.device("cpu")
+
+# Constants
+m = 10.0
+g = 9.81
+R = 1.0
+
+# Time grid
+t_start, t_end, t_points = 0, 10, 1000
+t_span = torch.linspace(t_start, t_end, t_points, device=device)
+
+# Specify directory for storing results
+output_dir = "max_normalized"
+os.makedirs(output_dir, exist_ok=True)
+
+# Use a previously generated random seed
+random_seed = 4529
+
+# Set the seeds for reproducibility
+torch.manual_seed(random_seed)
+np.random.seed(random_seed)
+
+# Print the chosen random seed
+print(f"Random seed for torch and numpy: {random_seed}")
+
+# Initialize controller and dynamics
+controller = PendulumController().to(device)
+pendulum_dynamics = PendulumDynamics(controller, m, R, g).to(device)
+
+# Optimizer setup
+learning_rate = 1e-1
+weight_decay = 1e-4
+optimizer = optim.Adam(controller.parameters(), lr=learning_rate, weight_decay=weight_decay)
+
+# Training parameters
+num_epochs = 1001
+
+# Define loss functions
+def make_loss_fn(weight_fn):
+    def loss_fn(state_traj, t_span):
+        theta = state_traj[:, :, 0]            # Size: [batch_size, t_points]
+        desired_theta = state_traj[:, :, 3]    # Size: [batch_size, t_points]
+        weights = weight_fn(t_span)            # Initially Size: [t_points]
+
+        # Reshape or expand weights to match theta dimensions
+        weights = weights.view(-1, 1)  # Now Size: [batch_size, t_points]
+
+        # Calculate the weighted loss
+        return torch.mean(weights * (theta - desired_theta) ** 2)
+
+    return loss_fn
+
+# Define and store weight functions with descriptions
+weight_functions = {
+    'constant': {
+        'function': lambda t: torch.ones_like(t),
+        'description': 'Constant weight: All weights are 1'
+    },
+    'linear': {
+        'function': lambda t: (t+1) / (t+1).max(),
+        'description': 'Linear weight: Weights increase linearly, normalized by max'
+    },
+    'quadratic': {
+        'function': lambda t: (t+1)**2 / ((t+1)**2).max(),
+        'description': 'Quadratic weight: Weights increase quadratically, normalized by max'
+    },
+    'cubic': {
+        'function': lambda t: (t+1)**3 / ((t+1)**3).max(),
+        'description': 'Quadratic weight: Weights increase cubically, normalized by max'
+    },
+    'inverse': {
+        'function': lambda t: (t+1)**-1 / ((t+1)**-1).max(),
+        'description': 'Inverse weight: Weights decrease inversely, normalized by max'
+    },
+    'inverse_squared': {
+        'function': lambda t: (t+1)**-2 / ((t+1)**-1).max(),
+        'description': 'Inverse squared weight: Weights decrease inversely squared, normalized by max'
+    },
+    'inverse_cubed': {
+        'function': lambda t: (t+1)**-3 / ((t+1)**-1).max(),
+        'description': 'Inverse cubed weight: Weights decrease inversely cubed, normalized by max'
+    }
+}
+
+# Training loop for each weight function
+for name, weight_info in weight_functions.items():
+    controller = PendulumController().to(device)
+    pendulum_dynamics = PendulumDynamics(controller, m, R, g).to(device)
+    optimizer = optim.Adam(controller.parameters(), lr=learning_rate, weight_decay=weight_decay)
+    loss_fn = make_loss_fn(weight_info['function'])
+
+    # File paths
+    function_output_dir = os.path.join(output_dir, name)
+    controllers_dir = os.path.join(function_output_dir, "controllers")
+
+    # Check if controllers directory exists and remove it
+    if os.path.exists(controllers_dir):
+        shutil.rmtree(controllers_dir)
+    os.makedirs(controllers_dir, exist_ok=True)
+
+    config_file = os.path.join(function_output_dir, "training_config.txt")
+    log_file = os.path.join(function_output_dir, "training_log.csv")
+
+    # Overwrite configuration and log files
+    with open(config_file, "w") as f:
+        f.write(f"Random Seed: {random_seed}\n")
+        f.write(f"Time Span: {t_start} to {t_end}, Points: {t_points}\n")
+        f.write(f"Learning Rate: {learning_rate}\n")
+        f.write(f"Weight Decay: {weight_decay}\n")
+        f.write("\nLoss Function:\n")
+        f.write(inspect.getsource(loss_fn))
+        f.write("\nTraining Cases:\n")
+        f.write("[theta0, omega0, alpha0, desired_theta]\n")
+        for case in state_0.cpu().numpy():
+            f.write(f"{case.tolist()}\n")
+
+    with open(log_file, "w", newline="") as csvfile:
+        csv_writer = csv.writer(csvfile)
+        csv_writer.writerow(["Epoch", "Loss"])
+
+    # Training loop
+    for epoch in range(num_epochs):
+        optimizer.zero_grad()
+        state_traj = odeint(pendulum_dynamics, state_0, t_span, method='rk4')
+        loss = loss_fn(state_traj, t_span)
+        loss.backward()
+        optimizer.step()
+
+        # Logging
+        with open(log_file, "a", newline="") as csvfile:
+            csv_writer = csv.writer(csvfile)
+            csv_writer.writerow([epoch, loss.item()])
+
+        # Save the model
+        model_file = os.path.join(controllers_dir, f"controller_{epoch}.pth")
+        torch.save(controller.state_dict(), model_file)
+        print(f"{model_file} saved with loss: {loss}")
+
+print("Training complete. Models and logs are saved under respective directories for each loss function.")

training/old/normalized/PendulumController.py

@@ -0,0 +1,17 @@
+import torch
+import torch.nn as nn
+
+class PendulumController(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(4, 64),
+            nn.ReLU(),
+            nn.Linear(64, 64),
+            nn.ReLU(),
+            nn.Linear(64, 1)
+        )
+
+    def forward(self, x):
+        raw_torque = self.net(x)
+        return torch.clamp(raw_torque, -250, 250)

training/old/normalized/PendulumDynamics.py

@@ -0,0 +1,26 @@
+import torch
+import torch.nn as nn
+
+class PendulumDynamics(nn.Module):
+    def __init__(self, controller, m:'float'=1, R:'float'=1, g:'float'=9.81):
+        super().__init__()
+        self.controller = controller
+        self.m: 'float' = m
+        self.R: 'float' = R
+        self.g: 'float' = g
+
+    def forward(self, t, state):
+        # Get the current values from the state
+        theta, omega, alpha, desired_theta = state[:, 0], state[:, 1], state[:, 2], state[:, 3]
+
+        # Make the input stack for the controller
+        input = torch.stack([theta, omega, alpha, desired_theta], dim=1)
+
+        # Get the torque (the output of the neural network)
+        tau = self.controller(input).squeeze(-1)
+
+        # Relax alpha
+        alpha_desired = (self.g / self.R) * torch.sin(theta) + tau / (self.m * self.R**2)
+        dalpha = alpha_desired - alpha
+        
+        return torch.stack([omega, alpha, dalpha, torch.zeros_like(desired_theta)], dim=1)