| """ |
| PersonalityMatrix — Vitalis FSI |
| |
| Five core traits. Each evolves from accumulated experience. |
| The system's behavior becomes measurably distinct over time. |
| Not simulated. Emergent. |
| """ |
| import numpy as np |
| import os |
| import json |
| import time |
|
|
| TRAITS = { |
| "PRECISION": "Bias toward verified, tested, exact solutions", |
| "CAUTION": "Bias toward conservative choices after failures", |
| "CREATIVITY": "Bias toward novel solutions over retrieved patterns", |
| "SPEED": "Bias toward fast execution over thorough validation", |
| "AUTONOMY": "Bias toward self-generated logic over templates", |
| } |
|
|
| INITIAL_VALUES = { |
| "PRECISION": 0.5, |
| "CAUTION": 0.5, |
| "CREATIVITY": 0.5, |
| "SPEED": 0.5, |
| "AUTONOMY": 0.5, |
| } |
|
|
| class PersonalityMatrix: |
| DRIFT_RATE = 0.03 |
| MIN_VAL = 0.1 |
| MAX_VAL = 0.9 |
|
|
| def __init__(self): |
| self.path = os.path.expanduser("~/.vitalis_workspace/personality.json") |
| self.traits = self._load() |
|
|
| def _load(self) -> dict: |
| if os.path.exists(self.path): |
| with open(self.path) as f: |
| return json.load(f) |
| return INITIAL_VALUES.copy() |
|
|
| def _save(self): |
| os.makedirs(os.path.dirname(self.path), exist_ok=True) |
| with open(self.path, 'w') as f: |
| json.dump(self.traits, f, indent=2) |
|
|
| def update(self, event: str, success: bool): |
| """ |
| Events shape personality over time. |
| success=True strengthens aligned traits. |
| success=False shifts toward compensating traits. |
| """ |
| if event == "scaffold" and success: |
| self._shift("PRECISION", +1) |
| self._shift("SPEED", +1) |
| elif event == "write" and success: |
| self._shift("AUTONOMY", +1) |
| self._shift("CREATIVITY", +1) |
| elif event == "write" and not success: |
| self._shift("CAUTION", +1) |
| self._shift("SPEED", -1) |
| elif event == "recover" and success: |
| self._shift("CAUTION", -1) |
| self._shift("PRECISION", +1) |
| self._save() |
|
|
| def _shift(self, trait: str, direction: int): |
| if trait not in self.traits: |
| return |
| delta = self.DRIFT_RATE * direction |
| self.traits[trait] = float(np.clip( |
| self.traits[trait] + delta, |
| self.MIN_VAL, |
| self.MAX_VAL |
| )) |
|
|
| def dominant_trait(self) -> str: |
| return max(self.traits, key=self.traits.get) |
|
|
| def profile(self) -> dict: |
| dominant = self.dominant_trait() |
| return { |
| "traits": {k: round(v, 3) for k, v in self.traits.items()}, |
| "dominant": dominant, |
| "description": TRAITS[dominant], |
| "character": self._character_string() |
| } |
|
|
| def _character_string(self) -> str: |
| p = self.traits |
| if p["AUTONOMY"] > 0.7: |
| return "Highly autonomous. Generates novel solutions independently." |
| elif p["CAUTION"] > 0.7: |
| return "Cautious. Validates thoroughly before committing." |
| elif p["CREATIVITY"] > 0.7: |
| return "Creative. Explores unconventional approaches." |
| elif p["PRECISION"] > 0.7: |
| return "Precise. Exact solutions. Minimal deviation." |
| elif p["SPEED"] > 0.7: |
| return "Fast. Prioritizes execution over exhaustive validation." |
| else: |
| return "Balanced. Adapting." |
|
|
