Ensembles

Multi-model ensemble coordination for SNN networks.

• EnsembleOrchestrator — Coordinate multiple SNN models in parallel, aggregate outputs via voting, averaging, or stacking. Supports heterogeneous architectures (different neuron models, topologies).

from sc_neurocore.ensembles import EnsembleOrchestrator

sc_neurocore.ensembles

sc_neurocore.ensembles -- Tier: research (experimental / research).

EnsembleOrchestrator dataclass

Manages a collective of SC-NeuroCore Agents. Implements ensemble consensus and coordinated action.

Source code in src/sc_neurocore/ensembles/orchestrator.py

@dataclass
class EnsembleOrchestrator:
    """
    Manages a collective of SC-NeuroCore Agents.
    Implements ensemble consensus and coordinated action.
    """

    agents: Dict[str, CognitiveOrchestrator] = field(default_factory=dict)

    def add_agent(self, name: str, agent: CognitiveOrchestrator) -> None:
        self.agents[name] = agent

    def run_consensus(self, pipeline: List[str], initial_input: Any) -> np.ndarray[Any, Any]:
        """
        Runs the same pipeline on all agents and averages results.
        """
        results = []
        for name, agent in self.agents.items():
            out = agent.execute_pipeline(pipeline, initial_input)
            results.append(out.to_prob())

        # Majority vote / Average
        return np.mean(results, axis=0)

    def coordinated_mission(self, goal: str) -> None:
        """
        Assigns sub-tasks to agents based on their capabilities.
        """
        logger.info("Ensemble: Initiating mission '%s'...", goal)
        for name, agent in self.agents.items():
            logger.info("  Agent '%s': Assigned sub-task.", name)
            agent.active_goals = [f"{goal}_subtask"]

run_consensus(pipeline, initial_input)

Runs the same pipeline on all agents and averages results.

Source code in src/sc_neurocore/ensembles/orchestrator.py

def run_consensus(self, pipeline: List[str], initial_input: Any) -> np.ndarray[Any, Any]:
    """
    Runs the same pipeline on all agents and averages results.
    """
    results = []
    for name, agent in self.agents.items():
        out = agent.execute_pipeline(pipeline, initial_input)
        results.append(out.to_prob())

    # Majority vote / Average
    return np.mean(results, axis=0)

coordinated_mission(goal)

Assigns sub-tasks to agents based on their capabilities.

Source code in src/sc_neurocore/ensembles/orchestrator.py

def coordinated_mission(self, goal: str) -> None:
    """
    Assigns sub-tasks to agents based on their capabilities.
    """
    logger.info("Ensemble: Initiating mission '%s'...", goal)
    for name, agent in self.agents.items():
        logger.info("  Agent '%s': Assigned sub-task.", name)
        agent.active_goals = [f"{goal}_subtask"]