import math import numpy as np import pytest from lhf.core import * from lhf.recovery import * from lhf.memory import prune_dominated @pytest.mark.parametrize('budget',[0.,.01,.1,.3,math.log(2),1.,2.,5.]) def test_flagship_frontier(budget): from lhf.core import flagship_contraction family,_,_=flagship_family() assert robust_floor(family,flagship_contraction(budget))==pytest.approx(flagship_frontier(budget),abs=1e-12) @pytest.mark.parametrize('d',[1,2,3,4,8,12]) @pytest.mark.parametrize('budget',[0.,.1,1.,6.]) def test_determinant_and_contraction(d,budget): y=np.diag(np.geomspace(.25,8,d)) c=contraction_from_lower_bound(y,budget) assert np.linalg.slogdet(c)[1]==pytest.approx(-budget,abs=1e-9) assert np.linalg.norm(c,2)<=1+1e-10 assert np.linalg.eigvalsh(c.T@y@c)[0]>=frontier_from_lower_bound(y,budget)-1e-10 @pytest.mark.parametrize('a,b',[(np.eye(2),2*np.eye(2)),(np.diag([4.,1]),np.diag([1.,4.])), (np.array([[2.,2.],[2.,8.]]),np.array([[2.,-2.],[-2.,8.]]))]) def test_pair_duality(a,b): y,z=envelope_pair(a,b) assert logdet_dual_bound([a,b],z)==pytest.approx(np.linalg.slogdet(y)[1],abs=1e-10) @pytest.mark.parametrize('invalid',[[],[np.zeros((2,2))],[np.array([[1.,2.],[0.,1.]])], [np.eye(2),np.eye(3)],[np.array([[np.nan]])]]) def test_validation_rejects_bad_family(invalid): with pytest.raises(ValueError):validate_family(invalid) @pytest.mark.parametrize('budget',[-1.,float('inf'),float('nan')]) def test_budget_rejection(budget): with pytest.raises(ValueError):contraction_from_lower_bound(np.eye(2),budget) @pytest.mark.parametrize('d',[1,2,10,30]) @pytest.mark.parametrize('h',[0.,.01,.4,1.,2.]) def test_sobolev_radius(d,h): r=sobolev_radius(d,1.,1.,h) expected=1. if h>=1 else np.sqrt(h*h+(1-h*h)/(d+1)**2) assert r==pytest.approx(expected) def test_nonassociativity(): a,b=np.diag([4.,1.]),np.diag([1.,4.]); n=np.array([[4.,3.],[3.,4.]])/5 ab,_=envelope_pair(a,b); left,_=envelope_pair(ab,n) bn,_=envelope_pair(b,n);right,_=envelope_pair(a,bn) assert not np.allclose(left,right) assert np.allclose(right,n) def test_pruning_witnesses(): ms=[3*np.eye(2),np.diag([1.,2.]),np.eye(2),2*np.eye(2)] keep,witnesses=prune_dominated(ms) assert keep==[2] for j,i in witnesses.items(): assert np.linalg.eigvalsh(ms[j]-ms[i])[0]>=-1e-10 def test_bad_dilation(): with pytest.raises(ValueError):unitary_dilation(2*np.eye(2)) def test_bad_requested_floor(): with pytest.raises(ValueError):contraction_from_lower_bound(np.eye(2),1.,floor=1.) with pytest.raises(ValueError):contraction_from_lower_bound(np.eye(2),0.,floor=2.) with pytest.raises(ValueError):contraction_from_lower_bound(np.eye(2),0.,floor=float("nan"))