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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: 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
64 lines
1.8 KiB
Python
64 lines
1.8 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass
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from typing import Dict, Any, Callable, Optional
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@dataclass
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class Decision:
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"""Represents a decision with a set of options and a selected recommendation.
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Attributes
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----------
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options : Dict[str, Any]
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A mapping of option names to their underlying values.
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recommendation : Optional[str]
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The name of the option that is currently recommended. None if no
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recommendation is available.
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"""
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options: Dict[str, Any]
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recommendation: Optional[str] = None
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class DecisionSupport:
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"""
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Simple decision support system that ranks options based on a scoring function.
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A `scorer` callable is provided to map option values to numeric scores. The
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`evaluate` method selects the option with the highest score.
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"""
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def __init__(self, scorer: Callable[[Any], float]) -> None:
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self.scorer = scorer
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def evaluate(self, options: Dict[str, Any]) -> Decision:
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"""
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Evaluate and recommend the option with the highest score.
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Parameters
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----------
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options : Dict[str, Any]
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A mapping from option names to their raw values.
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Returns
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-------
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Decision
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A Decision object containing the original options and the recommended key.
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"""
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if not options:
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return Decision(options, None)
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scores = {name: self.scorer(val) for name, val in options.items()}
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best = max(scores, key=scores.get)
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return Decision(options, best)
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if __name__ == "__main__":
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# Example: choose the largest number
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def identity_score(x: float) -> float:
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return x
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ds = DecisionSupport(identity_score)
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opts = {"A": 0.5, "B": 0.8, "C": 0.3}
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result = ds.evaluate(opts)
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print("Recommendation:", result.recommendation)
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