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https://github.com/blackboxprogramming/lucidia.git
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Alexa Amundson
855585cb0e
Synced from BlackRoad-OS-Inc/blackroad-operator/orgs/personal/lucidia BlackRoad OS — Pave Tomorrow. RoadChain-SHA2048: fe729062952871e7 RoadChain-Identity: alexa@sovereign RoadChain-Full: fe729062952871e77147cf6d938b799096e87d9024d7005a14c9e209e12e8ad0c825b624c7bc649fc7eeb4c284fdcab8231af77980065cc04d9f36fca479ffc2346ed3c1b73de6f240d8f9485f47c995ad5b81142f7179b84932c67914dff1c08db039349ba28fca36cb57688093bf0199268dd1c2f3448c9383000bc77cc9663066ff57b834370afc8838b18466ea9029908018b961555cccaabf2ce21649cf3cabc7f64bdcc4abdf2da259b210c342835a2cecf92bdd3b4e109b4d6e622f6934e13b2b123607bd61ce3d0f20454c9ab594f9284cffe18716619c52db57ce5f4ee2856cb96e1fa3748fe1fe65435bec297c5ab3ab58d570ec1064aea29931dd
82 lines
2.2 KiB
Python
82 lines
2.2 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass
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from typing import Callable, Any, List
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@dataclass
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class AdaptationRule:
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"""Represents a single adaptation rule for human-machine interaction.
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Attributes
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----------
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condition : Callable[[Any], bool]
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A predicate that determines whether the rule should fire for a given state.
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action : Callable[[Any], Any]
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A transformation to apply when the condition is met.
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description : str
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Human-friendly summary of the rule's purpose.
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"""
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condition: Callable[[Any], bool]
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action: Callable[[Any], Any]
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description: str = ""
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class AdaptiveSystem:
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"""
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Framework for applying adaptation rules based on conditions.
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This simple system iterates through registered rules and applies the
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action for the first rule whose condition is true. If no rule
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matches, it returns the state unchanged.
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"""
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def __init__(self) -> None:
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self.rules: List[AdaptationRule] = []
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def add_rule(self, rule: AdaptationRule) -> None:
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"""Register a new adaptation rule."""
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self.rules.append(rule)
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def adapt(self, state: Any) -> Any:
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"""
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Apply the first matching adaptation rule to the given state.
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Parameters
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----------
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state : Any
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The current state or input value to adapt.
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Returns
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-------
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Any
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The adapted state if a rule matched, otherwise the original state.
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"""
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for rule in self.rules:
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if rule.condition(state):
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return rule.action(state)
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return state
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if __name__ == "__main__":
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# Example: adapt temperature values to a comfortable range
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adapt_sys = AdaptiveSystem()
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def too_cold(x: float) -> bool:
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return x < 20
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def warm_action(x: float) -> float:
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return x + 5
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def too_hot(x: float) -> bool:
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return x > 25
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def cool_action(x: float) -> float:
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return x - 5
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adapt_sys.add_rule(AdaptationRule(too_cold, warm_action, "Warm up if too cold"))
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adapt_sys.add_rule(AdaptationRule(too_hot, cool_action, "Cool down if too hot"))
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temps = [18.0, 22.0, 28.0]
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for t in temps:
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print(f"{t} -> {adapt_sys.adapt(t)}")
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