multiflip_mcmc(): fix detailed balance

src/graph/inference/blockmodel/graph_blockmodel_multiflip_mcmc.cc

@@ -119,14 +119,6 @@ python::object do_multiflip_mcmc_sweep_parallel(python::object omcmc_states,
     return orets;
 }
 
-namespace graph_tool
-{
-std::ostream& operator<<(std::ostream& os, move_t move)
-{
-    return os << static_cast<int>(move);
-}
-}
-
 void export_blockmodel_multiflip_mcmc()
 {
     using namespace boost::python;

src/graph/inference/loops/mcmc_loop.hh

@@ -64,7 +64,7 @@ auto mcmc_sweep(MCMCState state, RNG& rng)
     auto beta = state.get_beta();
 
     typedef std::remove_const_t<decltype(state._null_move)> move_t;
-    constexpr bool single_step =
+     constexpr bool single_step =
         std::is_same_v<decltype(state.move_proposal(vlist.front(), rng)),
                        move_t>;
 
@@ -87,7 +87,7 @@ auto mcmc_sweep(MCMCState state, RNG& rng)
                     return state.get_N();
             };
 
-        for (size_t vi = 0; vi < get_N(); vi += nsteps)
+        for (size_t vi = 0; vi < get_N(); ++vi)
         {
             auto v = (state.is_sequential()) ?
                 vlist[vi] : uniform_sample(vlist, rng);

src/graph_tool/inference/nested_blockmodel.py

@@ -852,6 +852,9 @@ class NestedBlockState(object):
 
         """
 
+        psingle = kwargs.get("psingle", self.g.num_vertices())
+        kwargs["psingle"] = psingle
+
         c = kwargs.pop("c", 1)
         if not isinstance(c, collections.Iterable):
             c = [c * 2 ** l for l in range(0, len(self.levels))]