Merge branch '0.7-prpack' into develop

Conflicts:
	interfaces/R/types-C.def
	interfaces/R/types-R.def
	src/Makefile.am

Author: gaborcsardi

igraph/__init__.py

@@ -768,7 +768,8 @@ def path_length_hist(self, directed=True):
         return hist
 
     def pagerank(self, vertices=None, directed=True, damping=0.85,
-            weights=None, arpack_options=None):
+                 weights=None, arpack_options=None, implementation="prpack",
+                 niter=1000, eps=0.001):
         """Calculates the Google PageRank values of a graph.
         
         @param vertices: the indices of the vertices being queried.
@@ -781,13 +782,30 @@ def pagerank(self, vertices=None, directed=True, damping=0.85,
           or even an edge attribute name.
         @param arpack_options: an L{ARPACKOptions} object used to fine-tune
           the ARPACK eigenvector calculation. If omitted, the module-level
-          variable called C{arpack_options} is used.
+          variable called C{arpack_options} is used. This argument is
+          ignored if not the ARPACK implementation is used, see the
+          I{implementation} argument.
+        @param implementation: which implementation to use to solve the
+          PageRank eigenproblem. Possible values are:
+            - C{"prpack"}: use the PRPACK library. This is a new
+              implementation in igraph 0.7
+            - C{"arpack"}: use the ARPACK library. This implementation
+              was used from version 0.5, until version 0.7.
+            - C{"power"}: use a simple power method. This is the
+              implementation that was used before igraph version 0.5.
+        @param niter: The number of iterations to use in the power method
+          implementation. It is ignored in the other implementations
+        @param eps: The power method implementation will consider the
+          calculation as complete if the difference of PageRank values between
+          iterations change less than this value for every node. It is
+          ignored by the other implementations.
         @return: a list with the Google PageRank values of the specified
           vertices."""
         if arpack_options is None:
             arpack_options = _igraph.arpack_options
-        return self.personalized_pagerank(vertices, directed, damping, None, \
-                None, weights, arpack_options)
+        return self.personalized_pagerank(vertices, directed, damping, None,\
+                                          None, weights, arpack_options, \
+                                          implementation, niter, eps)
 
     def spanning_tree(self, weights=None, return_tree=True):
         """Calculates a minimum spanning tree for a graph.

src/convert.c

@@ -2880,3 +2880,19 @@ int igraphmodule_PyObject_to_attribute_combination_t(PyObject* object,
   return 0;
 }
 
+/**
+ * \ingroup python_interface_conversion
+ * \brief Converts a Python object to an igraph \c igraph_pagerank_algo_t
+ */
+int igraphmodule_PyObject_to_pagerank_algo_t(PyObject *o,
+					     igraph_pagerank_algo_t *result) {
+  static igraphmodule_enum_translation_table_entry_t pagerank_algo_tt[] = {
+        {"prpack", IGRAPH_PAGERANK_ALGO_PRPACK},
+        {"arpack", IGRAPH_PAGERANK_ALGO_ARPACK},
+        {"power",  IGRAPH_PAGERANK_ALGO_POWER},
+        {0,0}
+    };
+
+  return igraphmodule_PyObject_to_enum(o, pagerank_algo_tt, (int*)result);
+}
+

src/convert.h

@@ -61,6 +61,7 @@ int igraphmodule_PyObject_to_connectedness_t(PyObject *o, igraph_connectedness_t
 int igraphmodule_PyObject_to_degseq_t(PyObject *o, igraph_degseq_t *result);
 int igraphmodule_PyObject_to_fas_algorithm_t(PyObject *o, igraph_fas_algorithm_t *result);
 int igraphmodule_PyObject_to_neimode_t(PyObject *o, igraph_neimode_t *result);
+int igraphmodule_PyObject_to_pagerank_algo_t(PyObject *o, igraph_pagerank_algo_t *result);
 int igraphmodule_PyObject_to_reciprocity_t(PyObject *o, igraph_reciprocity_t *result);
 int igraphmodule_PyObject_to_rewiring_t(PyObject *o, igraph_rewiring_t *result);
 int igraphmodule_PyObject_to_spinglass_implementation_t(PyObject *o, igraph_spinglass_implementation_t *result);

src/graphobject.c

@@ -4784,7 +4784,7 @@ PyObject *igraphmodule_Graph_personalized_pagerank(igraphmodule_GraphObject *sel
 {
   static char *kwlist[] =
     { "vertices", "directed", "damping", "reset", "reset_vertices", "weights",
-      "arpack_options", NULL };
+      "arpack_options", "implementation", "niter", "eps", NULL };
   PyObject *directed = Py_True;
   PyObject *vobj = Py_None, *wobj = Py_None, *robj = Py_None, *rvsobj = Py_None;
   PyObject *list;
@@ -4796,12 +4796,21 @@ PyObject *igraphmodule_Graph_personalized_pagerank(igraphmodule_GraphObject *sel
   igraph_vector_t weights;
   igraph_bool_t return_single = 0;
   igraph_vs_t vs, reset_vs;
+  igraph_pagerank_algo_t algo=IGRAPH_PAGERANK_ALGO_PRPACK;
+  PyObject *algo_o = Py_None;
+  long niter=1000;
+  float eps=0.001f;
+  igraph_pagerank_power_options_t popts;
+  void *opts;
   int retval;
 
-  if (!PyArg_ParseTupleAndKeywords(args, kwds, "|OOdOOOO!", kwlist, &vobj,
-                                   &directed, &damping, &robj, &rvsobj, &wobj,
+  if (!PyArg_ParseTupleAndKeywords(args, kwds, "|OOdOOOO!Oid", kwlist, &vobj,
+                                   &directed, &damping, &robj,
+				   &rvsobj, &wobj,
                                    &igraphmodule_ARPACKOptionsType,
-                                   &arpack_options_o))
+                                   &arpack_options_o, &algo_o, &niter, &eps))
+
+
     return NULL;
 
   if (robj != Py_None && rvsobj != Py_None) {
@@ -4814,6 +4823,7 @@ PyObject *igraphmodule_Graph_personalized_pagerank(igraphmodule_GraphObject *sel
     return NULL;
   }
 
+  arpack_options = (igraphmodule_ARPACKOptionsObject*)arpack_options_o;
   if (robj != Py_None) {
     if (igraphmodule_attrib_to_vector_t(robj, self, &reset, ATTRIBUTE_TYPE_VERTEX)) {
       igraph_vs_destroy(&vs);
@@ -4845,13 +4855,25 @@ PyObject *igraphmodule_Graph_personalized_pagerank(igraphmodule_GraphObject *sel
     return igraphmodule_handle_igraph_error();
   }
 
-  arpack_options = (igraphmodule_ARPACKOptionsObject*)arpack_options_o;
+  if (igraphmodule_PyObject_to_pagerank_algo_t(algo_o, &algo))
+    return NULL;
+
+  popts.niter = (igraph_integer_t) niter; popts.eps = eps;
+
+  if (algo == IGRAPH_PAGERANK_ALGO_POWER) {
+    opts = &popts;
+  } else if (algo == IGRAPH_PAGERANK_ALGO_ARPACK) {
+    opts = igraphmodule_ARPACKOptions_get(arpack_options);
+  } else {
+    opts = 0;
+  }
+
   if (rvsobj != Py_None)
-    retval = igraph_personalized_pagerank_vs(&self->g, &res, 0, vs, PyObject_IsTrue(directed),
-        damping, reset_vs, &weights, igraphmodule_ARPACKOptions_get(arpack_options));
+    retval = igraph_personalized_pagerank_vs(&self->g, algo, &res, 0, vs,
+	     PyObject_IsTrue(directed), damping, reset_vs, &weights, opts);
   else
-    retval = igraph_personalized_pagerank(&self->g, &res, 0, vs, PyObject_IsTrue(directed),
-        damping, reset, &weights, igraphmodule_ARPACKOptions_get(arpack_options));
+    retval = igraph_personalized_pagerank(&self->g, algo, &res, 0, vs,
+	     PyObject_IsTrue(directed), damping, reset, &weights, opts);
 
   if (retval) {
     igraphmodule_handle_igraph_error();
@@ -12892,8 +12914,10 @@ struct PyMethodDef igraphmodule_Graph_methods[] = {
   /* interface to igraph_personalized_pagerank */
   {"personalized_pagerank", (PyCFunction) igraphmodule_Graph_personalized_pagerank,
    METH_VARARGS | METH_KEYWORDS,
-   "personalized_pagerank(vertices=None, directed=True, damping=0.85, "
-   "reset=None, reset_vertices=None, weights=None, arpack_options=None)\n\n"
+   "personalized_pagerank(vertices=None, directed=True, damping=0.85,\n"
+   "        reset=None, reset_vertices=None, weights=None, \n"
+   "        arpack_options=None, implementation=\"prpack\", niter=1000,\n"
+   "        eps=0.001)\n\n"
    "Calculates the personalized PageRank values of a graph.\n\n"
    "The personalized PageRank calculation is similar to the PageRank\n"
    "calculation, but the random walk is reset to a non-uniform distribution\n"
@@ -12919,7 +12943,23 @@ struct PyMethodDef igraphmodule_Graph_methods[] = {
    "  even an edge attribute name.\n"
    "@param arpack_options: an L{ARPACKOptions} object used to fine-tune\n"
    "  the ARPACK eigenvector calculation. If omitted, the module-level\n"
-   "  variable called C{arpack_options} is used.\n"
+   "  variable called C{arpack_options} is used. This argument is\n"
+   "  ignored if not the ARPACK implementation is used, see the \n"
+   "  I{implementation} argument.\n"
+   "@param implementation: which implementation to use to solve the \n"
+   "  PageRank eigenproblem. Possible values are:\n\n"
+   "    - C{\"prpack\"}: use the PRPACK library. This is a new \n"
+   "      implementation in igraph 0.7\n\n"
+   "    - C{\"arpack\"}: use the ARPACK library. This implementation\n"
+   "      was used from version 0.5, until version 0.7.\n\n"
+   "    - C{\"power\"}: use a simple power method. This is the\n"
+   "      implementation that was used before igraph version 0.5.\n\n"
+   "@param niter: The number of iterations to use in the power method\n"
+   "  implementation. It is ignored in the other implementations.\n"
+   "@param eps: The power method implementation will consider the\n"
+   "  calculation as complete if the difference of PageRank values between\n"
+   "  iterations change less than this value for every node. It is \n"
+   "  ignored by the other implementations.\n"
    "@return: a list with the personalized PageRank values of the specified\n"
    "  vertices.\n"},