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#!/usr/bin/env python3
"""Deterministic contract checks for the physical coupled-learning adapter."""
from __future__ import annotations
from pathlib import Path
import sys
import numpy as np
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
from sdil.physical_coupled import (
Circuit,
LocalAffineBias,
LocalPredictor,
Task,
calibrate_predictor,
free_output,
joint_solution,
local_replay_update,
overclamped_clean_rate,
simulate_alternating_tasks,
standard_clean_rate,
)
def main() -> None:
circuit = Circuit()
tasks = (
Task("alpha", circuit.high, 0.31),
Task("beta", circuit.low, 0.14),
)
joint = joint_solution(circuit, tasks)
for task in tasks:
assert abs(free_output(circuit, joint, task.input_voltage) - task.label_voltage) < 1e-12
rate, _, _ = standard_clean_rate(circuit, joint, task)
assert np.linalg.norm(rate) < 1e-10
field = LocalAffineBias(
reference_gate=np.asarray((3.0, 3.5)),
bias_at_reference=np.asarray((2.9, 4.7)),
local_slopes=np.asarray((0.6, -0.2)),
)
states = np.column_stack((
np.linspace(2.2, 4.8, 40),
np.linspace(2.8, 5.0, 40),
))
scale = np.ptp(states, axis=0)
affine = LocalPredictor.zeros(field.reference_gate, scale, affine=True)
constant = LocalPredictor.zeros(field.reference_gate, scale, affine=False)
observations_affine = calibrate_predictor(
affine, states, field, epochs=30, learning_rate=0.2)
observations_constant = calibrate_predictor(
constant, states, field, epochs=30, learning_rate=0.2)
assert observations_affine == observations_constant
affine_error = np.mean([
np.linalg.norm(field(state) - affine.predict(state)) for state in states
])
constant_error = np.mean([
np.linalg.norm(field(state) - constant.predict(state)) for state in states
])
assert affine_error < 1e-4
assert constant_error > 0.1
gates = states[7]
teaching = np.asarray((3.2, -1.7))
eligibility = np.asarray((0.4, 0.8))
update_a = local_replay_update(affine, gates, teaching, eligibility, 0.03)
update_b = 0.03 * (teaching - affine.predict(gates)) * eligibility
assert np.array_equal(update_a, update_b)
downstream = np.random.default_rng(19).normal(size=(100, 100))
downstream[:] = np.random.default_rng(29).normal(size=downstream.shape)
update_c = local_replay_update(affine, gates, teaching, eligibility, 0.03)
assert np.array_equal(update_a, update_c)
_, output_free, output_clamped = overclamped_clean_rate(
circuit,
joint,
tasks[0],
nudging=0.25,
clamp_magnitude=circuit.high,
exact_target=True,
)
expected_clamped = output_free + 0.25 * (
circuit.high * np.sign(tasks[0].label_voltage - output_free)
- output_free
)
assert output_clamped == expected_clamped
exact_predictor = LocalPredictor.zeros(
field.reference_gate, scale, affine=True)
exact_predictor.coefficients[:, 0] = field.bias_at_reference
exact_predictor.coefficients[:, 1] = field.local_slopes * scale
shared = {
"period_seconds": 0.01,
"cycles": 8,
"initial_gates": np.asarray((4.0, 4.0)),
"overclamp_nudging": 0.25,
"overclamp_magnitude": circuit.high,
"overclamp_exact_target": True,
}
clean_overclamp = simulate_alternating_tasks(
circuit,
tasks,
field,
method="overclamp",
bias_strength=0.0,
**shared,
)
debiased_overclamp = simulate_alternating_tasks(
circuit,
tasks,
field,
method="overclamp_sdil",
bias_strength=1.0,
predictor=exact_predictor,
**shared,
)
assert np.allclose(
clean_overclamp["final_gates"],
debiased_overclamp["final_gates"],
atol=1e-12,
rtol=0.0,
)
assert debiased_overclamp["max_abs_clamp_displacement_v"] > 0.0
print({
"joint_solution": joint.tolist(),
"affine_calibration_mae": float(affine_error),
"constant_calibration_mae": float(constant_error),
"neutral_observations_each": observations_affine,
"local_replay_exact": True,
"sdil_overclamp_composition_exact": True,
"autodiff_used": False,
})
if __name__ == "__main__":
main()
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