6afdb4b148
Lucidia Core - AI reasoning engines for specialized domains: - Physicist (867 lines) - energy modeling, force calculations - Mathematician (760 lines) - symbolic computation, proofs - Geologist (654 lines) - terrain modeling, stratigraphy - Engineer (599 lines) - structural analysis, optimization - Painter (583 lines) - visual generation, graphics - Chemist (569 lines) - molecular analysis, reactions - Analyst (505 lines) - pattern recognition, insights - Plus: architect, researcher, mediator, speaker, poet, navigator Features: - FastAPI wrapper with REST endpoints for each agent - CLI with `lucidia list`, `lucidia run`, `lucidia api` - Codex YAML configurations for agent personalities - Quantum engine extensions 12,512 lines of Python across 91 files. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
"""Quantum kernel helpers with graceful fallbacks for testing."""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Any, Optional
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import numpy as np
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from .backends import AerCPUBackend, QuantumBackend
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try: # pragma: no cover - optional heavy dependency
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from qiskit_machine_learning.algorithms import PegasosQSVC as _PegasosQSVC
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from qiskit_machine_learning.kernels import QuantumKernel
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except Exception: # pragma: no cover - fallback implementation
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_PegasosQSVC = None # type: ignore
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QuantumKernel = None # type: ignore
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@dataclass
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class _NearestNeighborQSVC:
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"""Fallback classifier used when Qiskit is not available."""
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training_x: np.ndarray | None = None
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training_y: np.ndarray | None = None
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def fit(self, x: np.ndarray, y: np.ndarray) -> "_NearestNeighborQSVC":
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self.training_x = np.asarray(x)
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self.training_y = np.asarray(y)
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return self
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def predict(self, x: np.ndarray) -> np.ndarray:
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if self.training_x is None or self.training_y is None:
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raise RuntimeError("Model has not been fitted")
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x = np.asarray(x)
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preds = []
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for row in x:
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distances = np.linalg.norm(self.training_x - row, axis=1)
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index = int(np.argmin(distances))
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preds.append(int(self.training_y[index]))
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return np.asarray(preds)
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def fit_qsvc(
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x: np.ndarray,
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y: np.ndarray,
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kernel_opts: Optional[dict[str, Any]] = None,
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backend: Optional[QuantumBackend] = None,
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):
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"""Train a QSVC model using Qiskit when available."""
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backend = backend or AerCPUBackend()
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if _PegasosQSVC is None or QuantumKernel is None:
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model = _NearestNeighborQSVC()
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return model.fit(x, y)
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kernel = QuantumKernel(quantum_instance=backend.simulator, **(kernel_opts or {}))
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model = _PegasosQSVC(quantum_kernel=kernel)
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model.fit(x, y)
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return model
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__all__ = ["fit_qsvc"]
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