lucidia sync: providers registry, db, main updates

human_machine/decision_support.py

@@ -1,63 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass
-from typing import Dict, Any, Callable, Optional
-
-
-@dataclass
-class Decision:
-    """Represents a decision with a set of options and a selected recommendation.
-
-    Attributes
-    ----------
-    options : Dict[str, Any]
-        A mapping of option names to their underlying values.
-    recommendation : Optional[str]
-        The name of the option that is currently recommended. None if no
-        recommendation is available.
-    """
-    options: Dict[str, Any]
-    recommendation: Optional[str] = None
-
-
-class DecisionSupport:
-    """
-    Simple decision support system that ranks options based on a scoring function.
-
-    A `scorer` callable is provided to map option values to numeric scores. The
-    `evaluate` method selects the option with the highest score.
-    """
-
-    def __init__(self, scorer: Callable[[Any], float]) -> None:
-        self.scorer = scorer
-
-    def evaluate(self, options: Dict[str, Any]) -> Decision:
-        """
-        Evaluate and recommend the option with the highest score.
-
-        Parameters
-        ----------
-        options : Dict[str, Any]
-            A mapping from option names to their raw values.
-
-        Returns
-        -------
-        Decision
-            A Decision object containing the original options and the recommended key.
-        """
-        if not options:
-            return Decision(options, None)
-        scores = {name: self.scorer(val) for name, val in options.items()}
-        best = max(scores, key=scores.get)
-        return Decision(options, best)
-
-
-if __name__ == "__main__":
-    # Example: choose the largest number
-    def identity_score(x: float) -> float:
-        return x
-
-    ds = DecisionSupport(identity_score)
-    opts = {"A": 0.5, "B": 0.8, "C": 0.3}
-    result = ds.evaluate(opts)
-    print("Recommendation:", result.recommendation)