Python Reduce 예제들

예제 #1

파일: qbsolv-like-alt.py 프로젝트: cnxtech/dwave-hybrid

problem = sys.argv[1]
with open(problem) as fp:
    bqm = dimod.BinaryQuadraticModel.from_coo(fp)


# define a qbsolv-like workflow
def merge_substates(_, substates):
    a, b = substates
    return a.updated(
        subsamples=hybrid.hstack_samplesets(a.subsamples, b.subsamples))


subproblems = hybrid.Unwind(
    hybrid.EnergyImpactDecomposer(size=50, rolling_history=0.15))

qpu = hybrid.Map(hybrid.QPUSubproblemAutoEmbeddingSampler()) | hybrid.Reduce(
    hybrid.Lambda(merge_substates)) | hybrid.SplatComposer()

random = hybrid.Map(hybrid.RandomSubproblemSampler()) | hybrid.Reduce(
    hybrid.Lambda(merge_substates)) | hybrid.SplatComposer()

subsampler = hybrid.Parallel(qpu, random, endomorphic=False) | hybrid.ArgMin()

iteration = hybrid.Race(hybrid.InterruptableTabuSampler(),
                        subproblems | subsampler) | hybrid.ArgMin()

main = hybrid.Loop(iteration, max_iter=10, convergence=3)

# run the workflow
init_state = hybrid.State.from_sample(hybrid.min_sample(bqm), bqm)
solution = main.run(init_state).result()

예제 #2

subproblem_size = 100

print("BQM size: {}, subproblem size: {}".format(len(bqm), subproblem_size))
# Classical solvers
#subproblem = hybrid.EnergyImpactDecomposer(size=50, rolling_history=0.15)
#subproblem = hybrid.EnergyImpactDecomposer(size=1024, rolling_history=0.15, traversal="bfs")
# Parallel subproblem
subproblem = hybrid.Unwind(
    #hybrid.EnergyImpactDecomposer(size=subproblem_size, rolling_history=0.15, traversal="bfs")
    hybrid.EnergyImpactDecomposer(size=subproblem_size, rolling_history=0.1))

# QPU
#subsampler = hybrid.QPUSubproblemAutoEmbeddingSampler() | hybrid.SplatComposer()

subsampler = hybrid.Map(
    hybrid.QPUSubproblemAutoEmbeddingSampler()) | hybrid.Reduce(
        hybrid.Lambda(merge_substates)) | hybrid.SplatComposer()

# Define the workflow
# iteration = hybrid.RacingBranches(
#     hybrid.InterruptableTabuSampler(),
#     hybrid.EnergyImpactDecomposer(size=2)
#     | hybrid.QPUSubproblemAutoEmbeddingSampler()
#     | hybrid.SplatComposer()
# ) | hybrid.ArgMin()

#iteration = hybrid.RacingBranches(
iteration = hybrid.Race(
    hybrid.InterruptableTabuSampler(),
    #hybrid.SimulatedAnnealingProblemSampler(),
    subproblem | subsampler) | hybrid.ArgMin()

예제 #3

# define the solver
def merge_substates(component, substates):
    a, b = substates
    return a.updated(
        subsamples=hybrid.utils.meld_samplesets(a.subsamples, b.subsamples)
    )

subproblems = hybrid.Unwind(
    hybrid.EnergyImpactDecomposer(size=50, silent_rewind=False, rolling_history=0.15)
)

qpu = hybrid.Map(
    hybrid.QPUSubproblemAutoEmbeddingSampler()
) | hybrid.Reduce(
    hybrid.Lambda(merge_substates)
) | hybrid.SplatComposer()

random = hybrid.Map(
    hybrid.RandomSubproblemSampler()
) | hybrid.Reduce(
    hybrid.Lambda(merge_substates)
) | hybrid.SplatComposer()

subsampler = hybrid.Parallel(qpu, random, endomorphic=False) | hybrid.ArgMin()

iteration = hybrid.Race(
    hybrid.InterruptableTabuSampler(),
    subproblems | subsampler
) | hybrid.ArgMin()