sync: update from blackroad-operator 2026-03-14

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human_machine/adaptation.py

@@ -0,0 +1,81 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Callable, Any, List
+
+@dataclass
+class AdaptationRule:
+    """Represents a single adaptation rule for human-machine interaction.
+
+    Attributes
+    ----------
+    condition : Callable[[Any], bool]
+        A predicate that determines whether the rule should fire for a given state.
+    action : Callable[[Any], Any]
+        A transformation to apply when the condition is met.
+    description : str
+        Human-friendly summary of the rule's purpose.
+    """
+    condition: Callable[[Any], bool]
+    action: Callable[[Any], Any]
+    description: str = ""
+
+
+class AdaptiveSystem:
+    """
+    Framework for applying adaptation rules based on conditions.
+
+    This simple system iterates through registered rules and applies the
+    action for the first rule whose condition is true. If no rule
+    matches, it returns the state unchanged.
+    """
+
+    def __init__(self) -> None:
+        self.rules: List[AdaptationRule] = []
+
+    def add_rule(self, rule: AdaptationRule) -> None:
+        """Register a new adaptation rule."""
+        self.rules.append(rule)
+
+    def adapt(self, state: Any) -> Any:
+        """
+        Apply the first matching adaptation rule to the given state.
+
+        Parameters
+        ----------
+        state : Any
+            The current state or input value to adapt.
+
+        Returns
+        -------
+        Any
+            The adapted state if a rule matched, otherwise the original state.
+        """
+        for rule in self.rules:
+            if rule.condition(state):
+                return rule.action(state)
+        return state
+
+
+if __name__ == "__main__":
+    # Example: adapt temperature values to a comfortable range
+    adapt_sys = AdaptiveSystem()
+
+    def too_cold(x: float) -> bool:
+        return x < 20
+
+    def warm_action(x: float) -> float:
+        return x + 5
+
+    def too_hot(x: float) -> bool:
+        return x > 25
+
+    def cool_action(x: float) -> float:
+        return x - 5
+
+    adapt_sys.add_rule(AdaptationRule(too_cold, warm_action, "Warm up if too cold"))
+    adapt_sys.add_rule(AdaptationRule(too_hot, cool_action, "Cool down if too hot"))
+
+    temps = [18.0, 22.0, 28.0]
+    for t in temps:
+        print(f"{t} -> {adapt_sys.adapt(t)}")