Add option for continuous sampling

Keeping `N` samples in memory is expensive if we only want the `k` best ones!

Open question is the interface -- `num_reads` is synonymous with `num_samples` returned in sample set for all samplers in Ocean. Also, `initial_states` (even for random states) are expanded to `num_reads` input samples -- something we would also want to avoid in this case.

To retain compatible behavior with existing samplers, perhaps we could introduce a parameter like `num_resample`. Also, `resample_reduce_method` (`min`/`max`). And to support the `k` best samples use case, we'll need a parameter like `num_samples`.

So, something like:
```
ss = greedy.sample(bqm, num_reads=1, num_resample=1000, num_samples=3, resample_reduce_method='k-best')
```

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Another take on this would be async sampler interface. In which case a caller would yield as many samples are needed, all with minimal memory overhead!