File size: 2,514 Bytes
ea8c728 | 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 | """Evaluator for adaptive signal processing."""
import importlib.util
import numpy as np
import time
import traceback
import json
def evaluate(program_path):
try:
spec = importlib.util.spec_from_file_location("program", program_path)
program = importlib.util.module_from_spec(spec)
spec.loader.exec_module(program)
if not hasattr(program, "run_signal_processing"):
return {"combined_score": 0.0, "error": "Missing run_signal_processing"}
np.random.seed(42)
test_signals = []
# Generate 5 diverse test signals
for t_type in range(5):
n = 500
t = np.linspace(0, 10, n)
if t_type == 0:
clean = np.sin(2*np.pi*t) + 0.5*np.sin(6*np.pi*t)
elif t_type == 1:
clean = np.where(t < 5, np.sin(2*np.pi*t), np.sin(10*np.pi*t))
elif t_type == 2:
clean = np.cumsum(np.random.randn(n)) / np.sqrt(n)
elif t_type == 3:
clean = np.sin(2*np.pi*t*(1+t/10))
else:
clean = np.sign(np.sin(2*np.pi*t))
noise = np.random.randn(n) * 0.5
test_signals.append((clean, clean + noise))
scores = []
for clean, noisy in test_signals:
try:
result = program.run_signal_processing(noisy, window_size=20)
filtered = np.array(result.get("filtered_signal", []))
if len(filtered) == 0:
scores.append(0.0); continue
# Align lengths - filter may produce shorter output due to windowing
min_len = min(len(filtered), len(clean))
filtered = filtered[:min_len]
clean_trimmed = clean[:min_len]
noisy_trimmed = noisy[:min_len]
corr = np.corrcoef(clean_trimmed, filtered)[0, 1] if np.std(filtered) > 0 else 0
mse = np.mean((clean_trimmed - filtered) ** 2)
noise_var = np.mean((clean_trimmed - noisy_trimmed) ** 2)
noise_red = 1 - mse / noise_var if noise_var > 0 else 0
scores.append(max(0, 0.5 * max(0, corr) + 0.3 * max(0, noise_red) + 0.2))
except:
scores.append(0.0)
combined = float(np.mean(scores)) if scores else 0.0
return {"combined_score": combined, "per_signal_scores": [float(s) for s in scores]}
except Exception as e:
return {"combined_score": 0.0, "error": str(e)}
|