latent_multigraph(): Add `verbose` parameter

src/graph/inference/graph_latent_multigraph.cc

@@ -25,7 +25,8 @@ using namespace boost;
 using namespace graph_tool;
 
 void latent_multigraph(GraphInterface& gi, boost::any aw, boost::any atheta_out,
-                       boost::any atheta_in, double epsilon, size_t max_niter)
+                       boost::any atheta_in, double epsilon, size_t max_niter,
+                       bool verbose)
 {
     typedef eprop_map_t<double>::type emap_t;
     typedef vprop_map_t<double>::type vmap_t;
@@ -35,7 +36,7 @@ void latent_multigraph(GraphInterface& gi, boost::any aw, boost::any atheta_out,
 
     run_action<>()
         (gi, [&](auto& g){ get_latent_multigraph(g, w, theta_out, theta_in,
-                                                 epsilon, max_niter); })();
+                                                 epsilon, max_niter, verbose); })();
 }
 
 using namespace boost::python;

src/graph/inference/graph_latent_multigraph.hh

@@ -34,7 +34,7 @@ using namespace boost;
 
 template <class Graph, class WMap, class TMap>
 void  get_latent_multigraph(Graph& g, WMap w, TMap theta_out, TMap theta_in,
-                            double epsilon, size_t max_niter)
+                            double epsilon, size_t max_niter, bool verbose)
 {
     auto wc = w.get_checked();
     for (auto v : vertices_range(g))
@@ -101,6 +101,8 @@ void  get_latent_multigraph(Graph& g, WMap w, TMap theta_out, TMap theta_in,
                  }
              });
         niter++;
+        if (verbose)
+            cout << delta << endl;
     }
 };
 

src/graph_tool/inference/latent_multigraph.py

@@ -25,7 +25,7 @@ dl_import("from . import libgraph_tool_inference as libinference")
 
 from numpy import sqrt
 
-def latent_multigraph(g, epsilon=1e-8, max_niter=0):
+def latent_multigraph(g, epsilon=1e-8, max_niter=0, verbose=False):
     r"""Infer latent Poisson multigraph model given an "erased" simple graph.
 
     Parameters
@@ -36,6 +36,8 @@ def latent_multigraph(g, epsilon=1e-8, max_niter=0):
         Convergence criterion.
     max_niter : ``int`` (optional, default: ``0``)
         Maximum number of iterations allowed (if ``0``, no maximum is assumed).
+    verbose : ``boolean`` (optional, default: ``False``)
+        If ``True``, display verbose information.
 
     Returns
     -------
@@ -69,6 +71,5 @@ def latent_multigraph(g, epsilon=1e-8, max_niter=0):
                                    _prop("e", g, w),
                                    _prop("v", g, theta_out),
                                    _prop("v", g, theta_in),
-                                   epsilon,
-                                   max_niter)
+                                   epsilon, max_niter, verbose)
     return g, w