Added mirror weight functions

analysis/average_normalized_weight_plotter.py

@@ -0,0 +1,29 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Define time span
+t_start, t_end, t_points = 0, 10, 1000
+t_span = np.linspace(t_start, t_end, t_points)
+
+# Define normalized weight functions
+weight_functions = {
+    'constant': lambda t: np.ones_like(t) / np.ones_like(t).mean(),
+    'linear': lambda t: ((t+1) / (t+1).max()) / ((t+1) / (t+1).max()).mean(),
+    'quadratic': lambda t: ((t+1)**2 / ((t+1)**2).max()) / ((t+1)**2 / ((t+1)**2).max()).mean(),
+    'cubic': lambda t: ((t+1)**3 / ((t+1)**3).max()) / ((t+1)**3 / ((t+1)**3).max()).mean(),
+    'inverse': lambda t: ((t+1)**-1 / ((t+1)**-1).max()) / ((t+1)**-1 / ((t+1)**-1).max()).mean(),
+    'inverse_squared': lambda t: ((t+1)**-2 / ((t+1)**-2).max()) / ((t+1)**-2 / ((t+1)**-2).max()).mean(),
+    'inverse_cubed': lambda t: ((t+1)**-3 / ((t+1)**-3).max()) / ((t+1)**-3 / ((t+1)**-3).max()).mean()
+}
+
+# Plot all weight functions
+plt.figure(figsize=(10, 6))
+for name, func in weight_functions.items():
+    plt.plot(t_span, func(t_span), label=name)
+
+plt.xlabel("Time (s)")
+plt.ylabel("Weight Value")
+plt.title("Average Normalized Weight Values")
+plt.legend()
+plt.grid(True)
+plt.savefig("average_normalized_weights.png")

analysis/max_normalized_weight_plotter.py

@@ -0,0 +1,35 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Define time span
+t_start, t_end, t_points = 0, 10, 1000
+t_span = np.linspace(t_start, t_end, t_points)
+
+# Define weight functions
+weight_functions = {
+    'constant': lambda t: np.ones_like(t),
+    'linear': lambda t: (t+1) / (t+1).max(),
+    'quadratic': lambda t: (t+1)**2 / ((t+1)**2).max(),
+    'cubic': lambda t: (t+1)**3 / ((t+1)**3).max(),
+    'inverse': lambda t: (t+1)**-1 / ((t+1)**-1).max(),
+    'inverse_squared': lambda t: (t+1)**-2 / ((t+1)**-2).max(),
+    'inverse_cubed': lambda t: (t+1)**-3 / ((t+1)**-3).max(),
+    'linear_mirrored': lambda t: ((-t+10)+1) / ((-t+10)+1).max(),
+    'quadratic_mirrored': lambda t: ((-t+10)+1)**2 / (((-t+10)+1)**2).max(),
+    'cubic_mirrored': lambda t: ((-t+10)+1)**3 / (((-t+10)+1)**3).max(),
+    'inverse_mirrored': lambda t: ((-t+10)+1)**-1 / (((-t+10)+1)**-1).max(),
+    'inverse_squared_mirrored': lambda t: ((-t+10)+1)**-2 / (((-t+10)+1)**-2).max(),
+    'inverse_cubed_mirrored': lambda t: ((-t+10)+1)**-3 / (((-t+10)+1)**-3).max()
+}
+
+# Plot all weight functions
+plt.figure(figsize=(10, 6))
+for name, func in weight_functions.items():
+    plt.plot(t_span, func(t_span), label=name)
+
+plt.xlabel("Time (s)")
+plt.ylabel("Weight Value")
+plt.title("Max Normalized Weight Values")
+plt.legend()
+plt.grid(True)
+plt.savefig("max_normalized_weights.png")

training/average_normalized/constant/training_config.txt

@@ -1,4 +1,4 @@
-Random Seed: 4529
+Base controller path: /home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/controller_base.pth
 Time Span: 0 to 10, Points: 1000
 Learning Rate: 0.1
 Weight Decay: 0.0001
@@ -15,6 +15,8 @@ Loss Function:
         # Calculate the weighted loss
         return torch.mean(weights * (theta - desired_theta) ** 2)
 
+Weight Description: Constant weight: All weights are 1, normalized by the average (remains 1)
+
 Training Cases:
 [theta0, omega0, alpha0, desired_theta]
 [0.5235987901687622, 0.0, 0.0, 0.0]

training/average_normalized/cubic/training_config.txt

@@ -1,4 +1,4 @@
-Random Seed: 4529
+Base controller path: /home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/controller_base.pth
 Time Span: 0 to 10, Points: 1000
 Learning Rate: 0.1
 Weight Decay: 0.0001
@@ -15,6 +15,8 @@ Loss Function:
         # Calculate the weighted loss
         return torch.mean(weights * (theta - desired_theta) ** 2)
 
+Weight Description: Quadratic weight: Weights increase cubically from 0 to 1, normalized by the average weight
+
 Training Cases:
 [theta0, omega0, alpha0, desired_theta]
 [0.5235987901687622, 0.0, 0.0, 0.0]

training/average_normalized/inverse/training_config.txt

@@ -1,4 +1,4 @@
-Random Seed: 4529
+Base controller path: /home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/controller_base.pth
 Time Span: 0 to 10, Points: 1000
 Learning Rate: 0.1
 Weight Decay: 0.0001
@@ -15,6 +15,8 @@ Loss Function:
         # Calculate the weighted loss
         return torch.mean(weights * (theta - desired_theta) ** 2)
 
+Weight Description: Inverse weight: Weights decrease inversely, normalized by the average weight
+
 Training Cases:
 [theta0, omega0, alpha0, desired_theta]
 [0.5235987901687622, 0.0, 0.0, 0.0]

training/average_normalized/inverse_cubed/training_config.txt

@@ -1,4 +1,4 @@
-Random Seed: 4529
+Base controller path: /home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/controller_base.pth
 Time Span: 0 to 10, Points: 1000
 Learning Rate: 0.1
 Weight Decay: 0.0001
@@ -15,6 +15,8 @@ Loss Function:
         # Calculate the weighted loss
         return torch.mean(weights * (theta - desired_theta) ** 2)
 
+Weight Description: Inverse cubed weight: Weights decrease inversely cubed, normalized by the average weight
+
 Training Cases:
 [theta0, omega0, alpha0, desired_theta]
 [0.5235987901687622, 0.0, 0.0, 0.0]

training/average_normalized/inverse_squared/training_config.txt

@@ -1,4 +1,4 @@
-Random Seed: 4529
+Base controller path: /home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/controller_base.pth
 Time Span: 0 to 10, Points: 1000
 Learning Rate: 0.1
 Weight Decay: 0.0001
@@ -15,6 +15,8 @@ Loss Function:
         # Calculate the weighted loss
         return torch.mean(weights * (theta - desired_theta) ** 2)
 
+Weight Description: Inverse squared weight: Weights decrease inversely squared, normalized by the average weight
+
 Training Cases:
 [theta0, omega0, alpha0, desired_theta]
 [0.5235987901687622, 0.0, 0.0, 0.0]

training/average_normalized/linear/training_config.txt

@@ -1,4 +1,4 @@
-Random Seed: 4529
+Base controller path: /home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/controller_base.pth
 Time Span: 0 to 10, Points: 1000
 Learning Rate: 0.1
 Weight Decay: 0.0001
@@ -15,6 +15,8 @@ Loss Function:
         # Calculate the weighted loss
         return torch.mean(weights * (theta - desired_theta) ** 2)
 
+Weight Description: Linear weight: Weights increase linearly from 0 to 1, normalized by the average weight
+
 Training Cases:
 [theta0, omega0, alpha0, desired_theta]
 [0.5235987901687622, 0.0, 0.0, 0.0]

training/average_normalized/quadratic/training_config.txt

@@ -1,4 +1,4 @@
-Random Seed: 4529
+Base controller path: /home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/controller_base.pth
 Time Span: 0 to 10, Points: 1000
 Learning Rate: 0.1
 Weight Decay: 0.0001
@@ -15,6 +15,8 @@ Loss Function:
         # Calculate the weighted loss
         return torch.mean(weights * (theta - desired_theta) ** 2)
 
+Weight Description: Quadratic weight: Weights increase quadratically from 0 to 1, normalized by the average weight
+
 Training Cases:
 [theta0, omega0, alpha0, desired_theta]
 [0.5235987901687622, 0.0, 0.0, 0.0]

training/average_normalized_trainer.py

@@ -28,7 +28,7 @@ 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"
+output_dir = "average_normalized"
 os.makedirs(output_dir, exist_ok=True)
 
 # Optimizer values
@@ -54,34 +54,61 @@ def make_loss_fn(weight_fn):
     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)**-1 / ((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'
+#     }
+# }
+
 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_mirrored': {
+        'function': lambda t: (((-t+10)+1) / ((-t+10)+1).max()) / (((-t+10)+1) / ((-t+10)+1).max()).mean(),
+        'description': 'Linear mirrored weight: Weights decrease linearly from 0 to 1, normalized by the average weight'
     },
-    '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_mirrored': {
+        'function': lambda t: (((-t+10)+1)**2 / (((-t+10)+1)**2).max()) / (((-t+10)+1)**2 / (((-t+10)+1)**2).max()).mean(),
+        'description': 'Quadratic mirrored weight: Weights decrease quadratically 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_mirrored': {
+        'function': lambda t: (((-t+10)+1)**3 / (((-t+10)+1)**3).max()) / (((-t+10)+1)**3 / (((-t+10)+1)**3).max()).mean(),
+        'description': 'Quadratic mirrored weight: Weights decrease cubically 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_mirrored': {
+        'function': lambda t: (((-t+10)+1)**-1 / (((-t+10)+1)**-1).max()) / (((-t+10)+1)**-1 / (((-t+10)+1)**-1).max()).mean(),
+        'description': 'Inverse mirrored weight: Weights increase inversely, normalized by the average weight'
     },
-    'inverse': {
-        'function': lambda t: ((t+1)**-1 / ((t+1)**-1).max()) / ((t+1)**-1 / ((t+1)**-1).max()).mean(),
-        'description': 'Inverse weight: Weights decrease inversely, normalized by the average weight'
+    'inverse_squared_mirrored': {
+        'function': lambda t: (((-t+10)+1)**-2 / (((-t+10)+1)**-2).max()) / (((-t+10)+1)**-2 / (((-t+10)+1)**-2).max()).mean(),
+        'description': 'Inverse squared mirrored weight: Weights increase inversely squared, 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'
+    'inverse_cubed_mirrored': {
+        'function': lambda t: (((-t+10)+1)**-3 / (((-t+10)+1)**-3).max()) / (((-t+10)+1)**-3 / (((-t+10)+1)**-3).max()).mean(),
+        'description': 'Inverse cubed mirrored weight: Weights increase inversely cubed, normalized by the average weight'
     }
 }
 

training/max_normalized/constant/training_config.txt

@@ -1,3 +1,4 @@
+Base controller path: /home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/controller_base.pth
 Time Span: 0 to 10, Points: 1000
 Learning Rate: 0.1
 Weight Decay: 0.0001

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+Base controller path: /home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/controller_base.pth
 Time Span: 0 to 10, Points: 1000
 Learning Rate: 0.1
 Weight Decay: 0.0001

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training/max_normalized/inverse/training_config.txt

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+Base controller path: /home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/controller_base.pth
 Time Span: 0 to 10, Points: 1000
 Learning Rate: 0.1
 Weight Decay: 0.0001

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training/max_normalized/inverse_cubed/training_config.txt

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+Base controller path: /home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/controller_base.pth
 Time Span: 0 to 10, Points: 1000
 Learning Rate: 0.1
 Weight Decay: 0.0001

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+Base controller path: /home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/controller_base.pth
 Time Span: 0 to 10, Points: 1000
 Learning Rate: 0.1
 Weight Decay: 0.0001

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+Base controller path: /home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/controller_base.pth
 Time Span: 0 to 10, Points: 1000
 Learning Rate: 0.1
 Weight Decay: 0.0001

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training/max_normalized/linear_mirrored/training_config.txt

@@ -0,0 +1,43 @@
+Base controller path: /home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/controller_base.pth
+Time Span: 0 to 10, Points: 1000
+Learning Rate: 0.1
+Weight Decay: 0.0001
+
+Loss Function:
+    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)
+
+Weight Description: Linear mirrored weight: Weights decrease linearly, normalized by max
+
+Training Cases:
+[theta0, omega0, alpha0, desired_theta]
+[0.5235987901687622, 0.0, 0.0, 0.0]
+[-0.5235987901687622, 0.0, 0.0, 0.0]
+[2.094395160675049, 0.0, 0.0, 0.0]
+[-2.094395160675049, 0.0, 0.0, 0.0]
+[0.0, 1.0471975803375244, 0.0, 0.0]
+[0.0, -1.0471975803375244, 0.0, 0.0]
+[0.0, 6.2831854820251465, 0.0, 0.0]
+[0.0, -6.2831854820251465, 0.0, 0.0]
+[0.0, 0.0, 0.0, 6.2831854820251465]
+[0.0, 0.0, 0.0, -6.2831854820251465]
+[0.0, 0.0, 0.0, 1.5707963705062866]
+[0.0, 0.0, 0.0, -1.5707963705062866]
+[0.0, 0.0, 0.0, 1.0471975803375244]
+[0.0, 0.0, 0.0, -1.0471975803375244]
+[0.7853981852531433, 3.1415927410125732, 0.0, 0.0]
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training/max_normalized_trainer.py

@@ -54,34 +54,61 @@ def make_loss_fn(weight_fn):
     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)**-2).max(),
+#         'description': 'Inverse squared weight: Weights decrease inversely squared, normalized by max'
+#     },
+#     'inverse_cubed': {
+#         'function': lambda t: (t+1)**-3 / ((t+1)**-3).max(),
+#         'description': 'Inverse cubed weight: Weights decrease inversely cubed, normalized by max'
+#     }
+# }
+
 weight_functions = {
-    'constant': {
-        'function': lambda t: torch.ones_like(t),
-        'description': 'Constant weight: All weights are 1'
+    'linear_mirrored': {
+        'function': lambda t: ((-t+10)+1) / ((-t+10)+1).max(),
+        'description': 'Linear mirrored weight: Weights decrease linearly, normalized by max'
     },
-    'linear': {
-        'function': lambda t: (t+1) / (t+1).max(),
-        'description': 'Linear weight: Weights increase linearly, normalized by max'
+    'quadratic_mirrored': {
+        'function': lambda t: ((-t+10)+1)**2 / (((-t+10)+1)**2).max(),
+        'description': 'Quadratic mirrored weight: Weights decrease quadratically, normalized by max'
     },
-    'quadratic': {
-        'function': lambda t: (t+1)**2 / ((t+1)**2).max(),
-        'description': 'Quadratic weight: Weights increase quadratically, normalized by max'
+    'cubic_mirrored': {
+        'function': lambda t: ((-t+10)+1)**3 / (((-t+10)+1)**3).max(),
+        'description': 'Quadratic mirrored weight: Weights decrease cubically, normalized by max'
     },
-    'cubic': {
-        'function': lambda t: (t+1)**3 / ((t+1)**3).max(),
-        'description': 'Quadratic weight: Weights increase cubically, normalized by max'
+    'inverse_mirrored': {
+        'function': lambda t: ((-t+10)+1)**-1 / (((-t+10)+1)**-1).max(),
+        'description': 'Inverse mirrored weight: Weights increase inversely, 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_mirrored': {
+        'function': lambda t: ((-t+10)+1)**-2 / (((-t+10)+1)**-2).max(),
+        'description': 'Inverse squared mirrored weight: Weights increase inversely squared, normalized by max'
     },
-    'inverse_squared': {
-        'function': lambda t: (t+1)**-2 / ((t+1)**-2).max(),
-        'description': 'Inverse squared weight: Weights decrease inversely squared, normalized by max'
-    },
-    'inverse_cubed': {
-        'function': lambda t: (t+1)**-3 / ((t+1)**-3).max(),
-        'description': 'Inverse cubed weight: Weights decrease inversely cubed, normalized by max'
+    'inverse_cubed_mirrored': {
+        'function': lambda t: ((-t+10)+1)**-3 / (((-t+10)+1)**-3).max(),
+        'description': 'Inverse cubed mirrored weight: Weights increase inversely cubed, normalized by max'
     }
 }