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firestar5683
49 lines
2.2 KiB
Python
49 lines
2.2 KiB
Python
"""Version5 fixed-map support: original information budget over whole events."""
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import math
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PHYSICAL_EVENT_POINTS = 40
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EVENT_CREDIT_CAP = 40
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REQUIRED_EFFECTFUL_EVENTS = 2
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HALF_CREDITS = REQUIRED_EFFECTFUL_EVENTS * EVENT_CREDIT_CAP
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def capped_group_credits(events):
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"""No outcome/gain input. Preserve all original event/endpoint identities.
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Each row is (whole_event_id, ((control_ns, apply_ns, encoded_delta), ...)).
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This counts the original native signal floor; it never creates a model or
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grants replay qualification. Cap prevents one event filling another's budget.
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"""
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ids, endpoints, support_credits = set(), set(), []
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previous_control = 0
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for event_id, points in events:
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if type(event_id) is not str or not event_id or event_id in ids or len(points) < PHYSICAL_EVENT_POINTS:
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raise ValueError('distinct supported whole physical events required')
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ids.add(event_id)
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changed = 0
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for control_ns, apply_ns, delta in points:
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key = control_ns, apply_ns
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if (type(control_ns) is not int or type(apply_ns) is not int or not 0 < control_ns <= apply_ns or
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control_ns <= previous_control or key in endpoints or
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type(delta) not in (int, float) or not math.isfinite(delta) or delta < 0):
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raise ValueError('unique chronological supported native endpoints required')
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endpoints.add(key)
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previous_control = control_ns
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changed += delta > 1e-8
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support_credits.append(min(EVENT_CREDIT_CAP, changed))
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return tuple(support_credits)
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def independent_half_supported(support_credits):
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if any(type(value) is not int or not 0 <= value <= EVENT_CREDIT_CAP for value in support_credits):
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raise ValueError('bounded whole-event support support_credits required')
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return sum(support_credits) >= HALF_CREDITS and sum(value > 0 for value in support_credits) >= REQUIRED_EFFECTFUL_EVENTS
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def all_event_lower(gains, lower):
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"""Original conservative estimator on EVERY original event, without selection."""
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if len(gains) < 2 or any(type(value) not in (int, float) or not math.isfinite(value) for value in gains):
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return None
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bound = lower(tuple(gains))
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return bound if math.isfinite(bound) and bound > 0 else None
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