296e65ff95
- Add Factions model (optimists/fearful/neutral) - Implement classify_faction() based on stance thresholds - Add update_factions() to track faction distribution - Add apply_faction_influence() for faction→mood feedback - Integrate faction system into tick flow 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
41 lines
1.2 KiB
Python
41 lines
1.2 KiB
Python
"""派系系统 - 基于立场分类角色并影响世界"""
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from typing import Dict
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from .models import WorldState, AgentState, Factions
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# 派系分类阈值
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OPTIMIST_THRESHOLD = 0.6
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FEARFUL_THRESHOLD = 0.6
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def classify_faction(agent: AgentState) -> str:
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"""根据 stance 分类角色所属派系"""
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if agent.stance.optimism > OPTIMIST_THRESHOLD:
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return "optimists"
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elif agent.stance.fear > FEARFUL_THRESHOLD:
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return "fearful"
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else:
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return "neutral"
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def update_factions(state: WorldState) -> None:
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"""统计各派系人数并更新 world_state"""
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counts = {"optimists": 0, "fearful": 0, "neutral": 0}
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for agent in state.agents.values():
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faction = classify_faction(agent)
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counts[faction] += 1
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state.factions = Factions(**counts)
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def apply_faction_influence(state: WorldState) -> None:
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"""派系分布影响世界情绪"""
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optimists = state.factions.optimists
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fearful = state.factions.fearful
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if optimists > fearful:
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state.town_mood = min(10, state.town_mood + 1)
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elif fearful > optimists:
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state.town_mood = max(-10, state.town_mood - 1)
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# 平局时不变化
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