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603204d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | import numpy as np
class DRSPredictor:
def __init__(self):
# Simulated "trained" weights for a trajectory prediction MLP
# 10 points * 3 coordinates = 30 inputs
self.W1 = np.random.randn(30, 32) * 0.01
self.b1 = np.zeros(32)
self.W2 = np.random.randn(32, 16) * 0.01
self.b2 = np.zeros(16)
self.W3 = np.random.randn(16, 3) * 0.01
self.b3 = np.zeros(3)
def relu(self, x):
return np.maximum(0, x)
def predict_next_step(self, recent_history):
"""
Takes recent 10 tracking points (flattened to 30 values)
Returns the predicted next position delta
"""
if not recent_history:
return np.array([0, 0, 0.1])
# Flatten history for input
x = np.array(recent_history).flatten()
# Ensure x is exactly 30 elements
if x.shape[0] != 30:
# Pad or truncate to 30
if x.shape[0] < 30:
x = np.pad(x, (0, 30 - x.shape[0]), 'edge')
else:
x = x[:30]
# Layer 1
h1 = self.relu(np.dot(x, self.W1) + self.b1)
# Layer 2
h2 = self.relu(np.dot(h1, self.W2) + self.b2)
# Output (Delta Prediction)
delta = np.dot(h2, self.W3) + self.b3
return delta
def infer_trajectory(self, tracking_data, steps=120):
"""
Predicts future trajectory by accurately capturing momentum from history.
"""
if not tracking_data:
return []
future_path = []
current_pos = np.array(tracking_data[-1])
# Improved Velocity Estimation (Average over last 5 frames to avoid noise bias)
if len(tracking_data) >= 5:
velocities = [np.array(tracking_data[i]) - np.array(tracking_data[i-1]) for i in range(-4, 0)]
v = np.mean(velocities, axis=0)
elif len(tracking_data) >= 2:
v = np.array(tracking_data[-1]) - np.array(tracking_data[-2])
else:
v = np.array([0, 0, 0.5])
for _ in range(steps):
history = (tracking_data + [p['position'] for p in future_path])[-10:]
while len(history) < 10:
history.insert(0, history[0])
# Neural Delta (Reduced influence to avoid random bias)
ai_delta = self.predict_next_step(history)
v[1] -= 0.001 # Gravity
# Use momentum + subtle AI refinement
current_pos = current_pos + v + (ai_delta * 0.005)
has_bounced = False
if current_pos[1] < 0:
v[1] *= -0.6
current_pos[1] = 0
has_bounced = True
future_path.append({
"position": current_pos.tolist(),
"hasBounced": has_bounced
})
if current_pos[2] > 16: break
return future_path
predictor = DRSPredictor()
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