Implement Bfield parameter

src/graph/inference/blockmodel/graph_blockmodel.cc

@@ -161,7 +161,8 @@ void export_blockmodel_state()
         .def_readwrite("degree_dl_kind", &entropy_args_t::degree_dl_kind)
         .def_readwrite("edges_dl", &entropy_args_t::edges_dl)
         .def_readwrite("recs_dl", &entropy_args_t::recs_dl)
-        .def_readwrite("beta_dl", &entropy_args_t::beta_dl);
+        .def_readwrite("beta_dl", &entropy_args_t::beta_dl)
+        .def_readwrite("Bfield", &entropy_args_t::Bfield);
 
     enum_<deg_dl_kind>("deg_dl_kind")
         .value("ent", deg_dl_kind::ENT)

src/graph/inference/blockmodel/graph_blockmodel.hh

@@ -89,6 +89,7 @@ typedef mpl::vector1<std::false_type> rmap_tr;
     ((bclabel,, vmap_t, 0))                                                    \
     ((pclabel,, vmap_t, 0))                                                    \
     ((bfield,, vprop_map_t<std::vector<double>>::type, 0))                     \
+    ((Bfield, &, std::vector<double>&, 0))                                     \
     ((merge_map,, vmap_t, 0))                                                  \
     ((deg_corr,, bool, 0))                                                     \
     ((rec_types,, std::vector<int32_t>, 0))                                    \
@@ -1456,6 +1457,26 @@ public:
             dS_dl += (r < f.size()) ? f[r] : f.back();
         }
 
+        if (!_Bfield.empty() && ea.Bfield)
+        {
+            int dB = 0;
+            if (virtual_remove_size(v) == 0)
+                dB--;
+            if (_wr[nr] == 0)
+                dB++;
+            if (dB != 0)
+            {
+                size_t actual_B = 0;
+                for (auto& ps : _partition_stats)
+                    actual_B += ps.get_actual_B();
+                dS_dl += (actual_B < _Bfield.size()) ?
+                    _Bfield[actual_B] : _Bfield.back();
+                actual_B += dB;
+                dS_dl -= (actual_B < _Bfield.size()) ?
+                    _Bfield[actual_B] : _Bfield.back();
+            }
+        }
+
         int dL = 0;
         if (ea.recs && _rt != weight_type::NONE)
         {
@@ -2091,6 +2112,15 @@ public:
             S_dl += get<1>(rdS);
         }
 
+        if (!_Bfield.empty() && ea.Bfield)
+        {
+            size_t actual_B = 0;
+            for (auto& ps : _partition_stats)
+                actual_B += ps.get_actual_B();
+            S_dl -= (actual_B < _Bfield.size()) ?
+                _Bfield[actual_B] : _Bfield.back();
+        }
+
         if (_coupled_state != nullptr && propagate)
             S_dl += _coupled_state->entropy(_coupled_entropy_args, true);
 

src/graph/inference/blockmodel/graph_blockmodel_entropy.hh

@@ -48,6 +48,7 @@ struct entropy_args_t
     bool edges_dl;
     bool recs_dl;
     double beta_dl;
+    double Bfield;
 };
 
 // Sparse entropy terms

src/graph_tool/inference/blockmodel.py

@@ -157,12 +157,14 @@ def get_entropy_args(kargs, ignore=None):
         ea.recs_dl = False
     ea.degree_dl_kind = kind
     ea.beta_dl = args.beta_dl
+    ea.Bfield = args.Bfield
     del kargs["dl"]
     del kargs["partition_dl"]
     del kargs["degree_dl"]
     del kargs["edges_dl"]
     del kargs["recs_dl"]
     del kargs["beta_dl"]
+    del kargs["Bfield"]
     kargs.pop("callback", None)
     if len(kargs) > 0:
         raise ValueError("unrecognized entropy arguments: " +
@@ -260,7 +262,8 @@ class BlockState(object):
 
     def __init__(self, g, b=None, B=None, eweight=None, vweight=None, recs=[],
                  rec_types=[], rec_params=[], clabel=None, pclabel=None,
-                 bfield=None, deg_corr=True, allow_empty=False, max_BE=1000, **kwargs):
+                 bfield=None, Bfield=None, deg_corr=True, allow_empty=False,
+                 max_BE=1000, **kwargs):
         kwargs = kwargs.copy()
 
         # initialize weights to unity, if necessary
@@ -463,6 +466,11 @@ class BlockState(object):
         self.bfield = g.new_vp("vector<double>") if bfield is None else bfield
         if self.bfield.value_type() != "vector<double>":
             raise ValueError("'bfield' property map must be of type 'vector<double>'")
+        if Bfield is None:
+            self.Bfield = Vector_double()
+        else:
+            self.Bfield = Vector_double(len(Bfield))
+            self.Bfield.a = Bfield
         self.allow_empty = allow_empty
         self._abg = self.bg._get_any()
         self._avweight = self.vweight._get_any()
@@ -480,7 +488,8 @@ class BlockState(object):
                                   partition_dl=True, degree_dl=True,
                                   degree_dl_kind="distributed", edges_dl=True,
                                   dense=False, multigraph=True, exact=True,
-                                  recs=True, recs_dl=True, beta_dl=1.)
+                                  recs=True, recs_dl=True, beta_dl=1.,
+                                  Bfield=True)
 
         if len(kwargs) > 0:
             warnings.warn("unrecognized keyword arguments: " +
@@ -940,8 +949,8 @@ class BlockState(object):
 
     def entropy(self, adjacency=True, dl=True, partition_dl=True,
                 degree_dl=True, degree_dl_kind="distributed", edges_dl=True,
-                dense=False, multigraph=True, deg_entropy=True,
-                recs=True, recs_dl=True, beta_dl=1., exact=True, **kwargs):
+                dense=False, multigraph=True, deg_entropy=True, recs=True,
+                recs_dl=True, beta_dl=1., Bfield=True, exact=True, **kwargs):
         r"""Calculate the entropy (a.k.a. negative log-likelihood) associated
         with the current block partition.
 

src/graph_tool/inference/overlap_blockmodel.py

@@ -268,7 +268,8 @@ class OverlapBlockState(BlockState):
                                   partition_dl=True, degree_dl=True,
                                   degree_dl_kind="distributed", edges_dl=True,
                                   dense=False, multigraph=True, exact=True,
-                                  recs=True, recs_dl=True, beta_dl=1.)
+                                  recs=True, recs_dl=True, beta_dl=1.,
+                                  Bfield=True)
 
         self._coupled_state = None