mobai
1012d8bb79
Contracts (the shared language): - percept / state / context / utterance / render / audio / capabilities - Capability model instead of vendor schemas (see below) Providers (capability-based abstraction): - LLM + Embedding: OpenAI-compatible shape IS the standard, adopted directly - ASR: internal contract is an event stream (partial -> final), because /v1/audio/transcriptions has no partial and dialogue needs it for endpointing. OpenAI adapter degrades to supports_partial=False. - TTS: internal contract carries tags/emotion/style, because /v1/audio/speech has no paralinguistics and [laughter]/[breath] are the core of feeling alive. Unsupported providers are stripped AND reported via Degradation -- silent degradation is forbidden. - Avatar: no de-facto standard, custom RenderCmd protocol driven by State. Core: - mood: dynamical system, slow variables, negativity bias (grudge), numbing after prolonged negativity - bazi: four pillars -> numeric vector; 'ming' is data, not prose - projection: phi, system event -> in-world statement; uncovered events are counted as projection gaps Storage: - SQLite schema, three memory layers, dyad ledger - accessibility floor 0.03 (not 0): forgettable but still there Config split: - models.yaml is the ONLY place model versions live - character.yaml holds world rules / emotion params, no version numbers 58 tests pass; ruff clean.
134 lines
3.7 KiB
Python
134 lines
3.7 KiB
Python
"""情绪动力学测试。
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守两件事：
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1. 情绪是慢变量（有惯性），不是每轮重置
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2. 负向偏置真实存在（难过比开心久）
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"""
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from __future__ import annotations
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import math
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from cybergril.contracts import State
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from cybergril.core.mood import (
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Baseline,
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Impulse,
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MoodParams,
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NumbingTracker,
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decay,
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make_impulse,
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mood_word,
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step,
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)
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P = MoodParams.default()
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B = Baseline()
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def test_decay_moves_toward_baseline() -> None:
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s = State(ts=0, v=-0.8, a=0.9, d=0.9, e=0.1)
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out = decay(s, B, dt_seconds=3600, params=P)
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# 一小时后应该更接近基线
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assert out.v > s.v
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assert out.a < s.a
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assert out.e > s.e
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def test_negative_bias_slower_recovery() -> None:
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"""负向恢复必须慢于正向。这是"记仇"的技术来源。
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检验方式：比较"剩余距离占初始距离的比例"，再解出时间常数之比。
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纯正向时 tau_v = 3h；负向时 tau_v ×2 = 6h。
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"""
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start_v = 0.5
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pos = State(ts=0, v=start_v)
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neg = State(ts=0, v=-start_v)
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gap0_pos = abs(start_v - B.v)
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gap0_neg = abs(-start_v - B.v)
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after_pos = decay(pos, B, 3600, P)
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after_neg = decay(neg, B, 3600, P)
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pos_gap = abs(after_pos.v - B.v)
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neg_gap = abs(after_neg.v - B.v)
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# 同样的时间，负向剩得更多
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assert neg_gap > pos_gap
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# 剩余比例：exp(-dt/tau)。两比例取对数后相除 = tau_neg / tau_pos
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pos_frac = pos_gap / gap0_pos
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neg_frac = neg_gap / gap0_neg
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assert neg_frac > pos_frac
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tau_ratio = math.log(pos_frac) / math.log(neg_frac)
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# 配置里 neg_bias["v"] = 2.0
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assert 1.95 <= tau_ratio <= 2.05
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def test_decay_is_monotonic_toward_baseline() -> None:
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s = State(ts=0, v=-0.9)
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prev = s.v
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for _ in range(10):
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s = decay(s, B, 1800, P)
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assert s.v > prev
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prev = s.v
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def test_impulse_scales_with_sensitivity() -> None:
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strong = make_impulse(valence=-0.8, salience=0.9, sensitivity=0.65)
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weak = make_impulse(valence=-0.8, salience=0.9, sensitivity=1.35)
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assert abs(weak.v) > abs(strong.v)
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def test_impulse_direction() -> None:
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bad = make_impulse(valence=-0.7, salience=0.8, sensitivity=1.0)
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good = make_impulse(valence=0.7, salience=0.8, sensitivity=1.0)
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assert bad.v < 0 < good.v
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# 好事也会消耗精力
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assert bad.e <= 0 and good.e <= 0
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def test_step_clamps_and_advances_time() -> None:
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s = State(ts=1000, v=0.0)
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huge = Impulse(v=99.0, a=99.0)
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out = step(s, B, dt_seconds=60, params=P, impulses=(huge,))
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assert -1.0 <= out.v <= 1.0
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assert 0.0 <= out.a <= 1.0
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assert out.ts == 1000 + 60_000
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def test_step_no_impulse_returns_valid_state() -> None:
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s = State(ts=0, v=0.2, a=0.4, d=0.5, e=0.6)
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out = step(s, B, 60, P)
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assert isinstance(out, State)
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out.clamp()
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def test_mood_word_bands() -> None:
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assert mood_word(v=0.6, a=0.7, e=0.8) == "雀跃"
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assert mood_word(v=-0.8, a=0.4, e=0.6) == "低落"
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assert mood_word(v=0.0, a=0.3, e=0.5) == "平静"
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assert mood_word(v=0.0, a=0.3, e=0.1) == "疲惫"
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def test_numbing_kicks_in_after_threshold() -> None:
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tracker = NumbingTracker(after_hours=6.0, drop=0.15)
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s = State(ts=0, v=-0.5)
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# 刚开始：无影响
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assert tracker.update(s) == 0.0
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# 推进 8 小时
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s2 = State(ts=8 * 3600 * 1000, v=-0.5)
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offset = tracker.update(s2)
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assert offset < 0
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def test_numbing_resets_on_positive() -> None:
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tracker = NumbingTracker(after_hours=1.0, drop=0.15)
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tracker.update(State(ts=0, v=-0.5))
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tracker.update(State(ts=3 * 3600 * 1000, v=-0.5)) # 已经麻木
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tracker.update(State(ts=4 * 3600 * 1000, v=0.5)) # 转正
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assert tracker.update(State(ts=5 * 3600 * 1000, v=0.5)) == 0.0
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