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Merged
InauguralPhysicist merged 1 commit into from
Nov 14, 2025
Merged

style: apply Black formatting to all files #38

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160 changes: 86 additions & 74 deletions benchmark_accuracy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,8 @@

import numpy as np
import sys
sys.path.insert(0, '/home/user/Eigen-Geometric-Control')

sys.path.insert(0, "/home/user/Eigen-Geometric-Control")

from src import (
run_arm_simulation,
Expand All @@ -20,7 +21,7 @@
forward_kinematics,
compute_ds2,
compute_gradient,
compute_change_stability
compute_change_stability,
)
import pandas as pd

Expand All @@ -37,7 +38,7 @@ def run_arm_with_lightlike_damping(
eps_change=1e-3,
L1=0.9,
L2=0.9,
window=3
window=3,
):
"""Run arm simulation WITH lightlike observer damping"""
theta1, theta2 = theta_init
Expand Down Expand Up @@ -80,28 +81,30 @@ def run_arm_with_lightlike_damping(
C, S, ds2_CS = compute_change_stability(delta, eps_change)

# Record state
rows.append({
'tick': t,
'theta1_rad': theta1,
'theta2_rad': theta2,
'x': x,
'y': y,
'ds2_total': ds2_total,
'target_term': components['target_term'],
'obs_term': components['obs_term'],
'reg_term': components['reg_term'],
'grad_norm': grad_norm,
'C': C,
'S': S,
'ds2_CS': ds2_CS,
'd_obs': components['d_obs'],
'delta_theta1': delta[0],
'delta_theta2': delta[1],
'delta_theta_sq': float(np.sum(delta**2)),
'oscillating': oscillating,
'osc_strength': osc_strength,
'damping': damping,
})
rows.append(
{
"tick": t,
"theta1_rad": theta1,
"theta2_rad": theta2,
"x": x,
"y": y,
"ds2_total": ds2_total,
"target_term": components["target_term"],
"obs_term": components["obs_term"],
"reg_term": components["reg_term"],
"grad_norm": grad_norm,
"C": C,
"S": S,
"ds2_CS": ds2_CS,
"d_obs": components["d_obs"],
"delta_theta1": delta[0],
"delta_theta2": delta[1],
"delta_theta_sq": float(np.sum(delta**2)),
"oscillating": oscillating,
"osc_strength": osc_strength,
"damping": damping,
}
)

# Update for next iteration
theta1, theta2 = theta1_new, theta2_new
Expand All @@ -111,34 +114,34 @@ def run_arm_with_lightlike_damping(

def compute_metrics(df, target, name=""):
"""Compute accuracy metrics from simulation results"""
final_x = df['x'].iloc[-1]
final_y = df['y'].iloc[-1]
final_dist = np.sqrt((final_x - target[0])**2 + (final_y - target[1])**2)
final_x = df["x"].iloc[-1]
final_y = df["y"].iloc[-1]
final_dist = np.sqrt((final_x - target[0]) ** 2 + (final_y - target[1]) ** 2)

# Convergence: first tick where distance < 0.1m
target_dist = np.sqrt((df['x'] - target[0])**2 + (df['y'] - target[1])**2)
target_dist = np.sqrt((df["x"] - target[0]) ** 2 + (df["y"] - target[1]) ** 2)
converged_ticks = target_dist[target_dist < 0.1]
ticks_to_converge = converged_ticks.index[0] if len(converged_ticks) > 0 else len(df)

# Oscillation metrics
grad_norms = df['grad_norm'].values
grad_norms = df["grad_norm"].values
osc_count = 0
for i in range(10, len(grad_norms) - 1):
# Check if gradient reverses (sign of oscillation)
if grad_norms[i] > grad_norms[i-1] * 1.5: # Gradient increased significantly
if grad_norms[i] > grad_norms[i - 1] * 1.5: # Gradient increased significantly
osc_count += 1

metrics = {
'name': name,
'final_distance_to_target_m': final_dist,
'ticks_to_converge': ticks_to_converge,
'final_ds2': df['ds2_total'].iloc[-1],
'initial_ds2': df['ds2_total'].iloc[0],
'ds2_reduction': df['ds2_total'].iloc[0] - df['ds2_total'].iloc[-1],
'min_obstacle_dist_m': df['d_obs'].min(),
'final_grad_norm': df['grad_norm'].iloc[-1],
'oscillation_events': osc_count,
'mean_step_size': df['delta_theta_sq'].mean(),
"name": name,
"final_distance_to_target_m": final_dist,
"ticks_to_converge": ticks_to_converge,
"final_ds2": df["ds2_total"].iloc[-1],
"initial_ds2": df["ds2_total"].iloc[0],
"ds2_reduction": df["ds2_total"].iloc[0] - df["ds2_total"].iloc[-1],
"min_obstacle_dist_m": df["d_obs"].min(),
"final_grad_norm": df["grad_norm"].iloc[-1],
"oscillation_events": osc_count,
"mean_step_size": df["delta_theta_sq"].mean(),
}

return metrics
Expand All @@ -159,21 +162,14 @@ def main():
# Run baseline (v1.0.0 algorithm - no lightlike observer)
print("1. Running baseline (v1.0.0 algorithm)...")
df_baseline = run_arm_simulation(
theta_init=(-1.4, 1.2),
target=tuple(target),
n_ticks=140,
eta=0.12
theta_init=(-1.4, 1.2), target=tuple(target), n_ticks=140, eta=0.12
)
metrics_baseline = compute_metrics(df_baseline, target, "v1.0.0 Baseline")

# Run with lightlike observer
print("2. Running with lightlike observer damping...")
df_lightlike = run_arm_with_lightlike_damping(
theta_init=(-1.4, 1.2),
target=tuple(target),
n_ticks=140,
eta=0.12,
window=3
theta_init=(-1.4, 1.2), target=tuple(target), n_ticks=140, eta=0.12, window=3
)
metrics_lightlike = compute_metrics(df_lightlike, target, "With Lightlike Observer")

Expand All @@ -189,40 +185,56 @@ def main():
print("-" * 90)

# Final accuracy
baseline_dist = metrics_baseline['final_distance_to_target_m']
lightlike_dist = metrics_lightlike['final_distance_to_target_m']
change_dist = ((lightlike_dist - baseline_dist) / baseline_dist * 100) if baseline_dist > 0 else 0
print(f"{'Final distance to target (m)':<40} {baseline_dist:<20.4f} {lightlike_dist:<20.4f} {change_dist:+.1f}%")
baseline_dist = metrics_baseline["final_distance_to_target_m"]
lightlike_dist = metrics_lightlike["final_distance_to_target_m"]
change_dist = (
((lightlike_dist - baseline_dist) / baseline_dist * 100) if baseline_dist > 0 else 0
)
print(
f"{'Final distance to target (m)':<40} {baseline_dist:<20.4f} {lightlike_dist:<20.4f} {change_dist:+.1f}%"
)

# Convergence speed
baseline_conv = metrics_baseline['ticks_to_converge']
lightlike_conv = metrics_lightlike['ticks_to_converge']
change_conv = ((lightlike_conv - baseline_conv) / baseline_conv * 100) if baseline_conv > 0 else 0
print(f"{'Ticks to converge (<0.1m)':<40} {baseline_conv:<20} {lightlike_conv:<20} {change_conv:+.1f}%")
baseline_conv = metrics_baseline["ticks_to_converge"]
lightlike_conv = metrics_lightlike["ticks_to_converge"]
change_conv = (
((lightlike_conv - baseline_conv) / baseline_conv * 100) if baseline_conv > 0 else 0
)
print(
f"{'Ticks to converge (<0.1m)':<40} {baseline_conv:<20} {lightlike_conv:<20} {change_conv:+.1f}%"
)

# ds2 reduction
baseline_ds2 = metrics_baseline['final_ds2']
lightlike_ds2 = metrics_lightlike['final_ds2']
change_ds2 = ((lightlike_ds2 - baseline_ds2) / abs(baseline_ds2) * 100) if baseline_ds2 != 0 else 0
baseline_ds2 = metrics_baseline["final_ds2"]
lightlike_ds2 = metrics_lightlike["final_ds2"]
change_ds2 = (
((lightlike_ds2 - baseline_ds2) / abs(baseline_ds2) * 100) if baseline_ds2 != 0 else 0
)
print(f"{'Final ds²':<40} {baseline_ds2:<20.4f} {lightlike_ds2:<20.4f} {change_ds2:+.1f}%")

# Obstacle clearance
baseline_obs = metrics_baseline['min_obstacle_dist_m']
lightlike_obs = metrics_lightlike['min_obstacle_dist_m']
baseline_obs = metrics_baseline["min_obstacle_dist_m"]
lightlike_obs = metrics_lightlike["min_obstacle_dist_m"]
change_obs = ((lightlike_obs - baseline_obs) / baseline_obs * 100) if baseline_obs > 0 else 0
print(f"{'Min obstacle distance (m)':<40} {baseline_obs:<20.4f} {lightlike_obs:<20.4f} {change_obs:+.1f}%")
print(
f"{'Min obstacle distance (m)':<40} {baseline_obs:<20.4f} {lightlike_obs:<20.4f} {change_obs:+.1f}%"
)

# Oscillation
baseline_osc = metrics_baseline['oscillation_events']
lightlike_osc = metrics_lightlike['oscillation_events']
baseline_osc = metrics_baseline["oscillation_events"]
lightlike_osc = metrics_lightlike["oscillation_events"]
change_osc = lightlike_osc - baseline_osc
print(f"{'Oscillation events':<40} {baseline_osc:<20} {lightlike_osc:<20} {change_osc:+d}")

# Final gradient
baseline_grad = metrics_baseline['final_grad_norm']
lightlike_grad = metrics_lightlike['final_grad_norm']
change_grad = ((lightlike_grad - baseline_grad) / baseline_grad * 100) if baseline_grad > 0 else 0
print(f"{'Final gradient norm':<40} {baseline_grad:<20.6f} {lightlike_grad:<20.6f} {change_grad:+.1f}%")
baseline_grad = metrics_baseline["final_grad_norm"]
lightlike_grad = metrics_lightlike["final_grad_norm"]
change_grad = (
((lightlike_grad - baseline_grad) / baseline_grad * 100) if baseline_grad > 0 else 0
)
print(
f"{'Final gradient norm':<40} {baseline_grad:<20.6f} {lightlike_grad:<20.6f} {change_grad:+.1f}%"
)

print()
print("=" * 80)
Expand Down Expand Up @@ -257,15 +269,15 @@ def main():
print("=" * 80)

# Check for lightlike observer activations
if 'damping' in df_lightlike.columns:
damping_used = (df_lightlike['damping'] > 0).sum()
max_damping = df_lightlike['damping'].max()
if "damping" in df_lightlike.columns:
damping_used = (df_lightlike["damping"] > 0).sum()
max_damping = df_lightlike["damping"].max()
print(f"\nLightlike observer activations: {damping_used} / {len(df_lightlike)} ticks")
print(f"Maximum damping applied: {max_damping:.4f}")

if damping_used == 0:
print("\n→ Lightlike observer was NEVER activated (no oscillation detected)")


if __name__ == '__main__':
if __name__ == "__main__":
main()
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