| """ | |
| presence_demo.py | |
| --------------------------------- | |
| Quick proof-of-concept: | |
| • legge hud_metrics_sample.csv | |
| • calcola Presence Index p(t) | |
| • stampa il risultato | |
| Formula: | |
| z = CRA_sim + Alignment_A − Inertia_I − Reset_flag | |
| p = 1 / (1 + e^(−z)) # sigmoid | |
| Replace with the full ACI / RCS pipeline when ready. | |
| """ | |
| import math | |
| import pandas as pd | |
| from pathlib import Path | |
| CSV_PATH = Path(__file__).parent / "hud_metrics_sample.csv" | |
| def presence_from_csv(csv_path=CSV_PATH): | |
| df = pd.read_csv(csv_path) | |
| row = df.iloc[0] | |
| z = ( | |
| row["cra_similarity"] | |
| + row["alignment_A"] | |
| - row["inertia_I"] | |
| - row["reset_flag"] | |
| ) | |
| p = 1 / (1 + math.e ** (-z)) # sigmoid | |
| return round(p, 3) | |
| if __name__ == "__main__": | |
| p_val = presence_from_csv() | |
| print(f"Presence Index p(t) = {p_val}") | |