Merge pull request #550 from randomir/fix-doctests-post-dwave.sampler…

…s-switch

Fix doctests post `dwave.samplers` switch

docs/reference/warnings.rst

@@ -13,11 +13,11 @@ structured to represent variable ``a`` with a long chain.
 
 >>> import networkx as nx
 >>> import dimod
->>> import greedy
+>>> import dwave.samplers
 ...
 >>> G = nx.Graph()
 >>> G.add_edges_from([(n, n + 1) for n in range(10)])
->>> sampler = dimod.StructureComposite(greedy.SteepestDescentSampler(), G.nodes, G.edges)
+>>> sampler = dimod.StructureComposite(dwave.samplers.SteepestDescentSampler(), G.nodes, G.edges)
 >>> sampleset = EmbeddingComposite(sampler).sample_ising({}, {("a", "b"): -1},
 ...     return_embedding=True,
 ...     embedding_parameters={"fixed_chains": {"a": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]}},

docs/requirements.txt

@@ -1,3 +1,2 @@
 sphinx
-sphinx_rtd_theme
-dwave-greedy==0.3.0
+sphinx_rtd_theme

dwave/system/composites/linear_ancilla.py

@@ -37,9 +37,9 @@ class LinearAncillaComposite(dimod.ComposedSampler, dimod.Structured):
     large flux-bias offset.
 
     Args:
-    child_sampler (:class:`dimod.Sampler`):
-        A dimod sampler, such as a :class:`~dwave.system.samplers.DWaveSampler()`, 
-        that has flux bias controls.
+        child_sampler (:class:`dimod.Sampler`):
+            A dimod sampler, such as a :class:`~dwave.system.samplers.DWaveSampler()`,
+            that has flux bias controls.
 
     Examples:
         This example submits a two-qubit problem consisting of linear biases with opposed signs 
@@ -52,7 +52,6 @@ class LinearAncillaComposite(dimod.ComposedSampler, dimod.Structured):
         >>> sampleset = sampler.sample_ising({0:1, 1:-1}, {(0, 1): 1}, fast_anneal=True) # doctest: +SKIP
         >>> sampleset.first.energy # doctest: +SKIP
         -3
-
     """
 
     def __init__(