Adapted RNG C-interface to new bob.core interface

bob/learn/em/empca_trainer.cpp

@@ -166,7 +166,7 @@ static auto initialize = bob::extension::FunctionDoc(
   "",
   true
 )
-.add_prototype("linear_machine,data")
+.add_prototype("linear_machine, data, [rng]")
 .add_parameter("linear_machine", ":py:class:`bob.learn.linear.Machine`", "LinearMachine Object")
 .add_parameter("data", "array_like <float, 2D>", "Input data")
 .add_parameter("rng", ":py:class:`bob.core.random.mt19937`", "The Mersenne Twister mt19937 random generator used for the initialization of subspaces/arrays before the EM loop.");
@@ -186,8 +186,7 @@ static PyObject* PyBobLearnEMEMPCATrainer_initialize(PyBobLearnEMEMPCATrainerObj
   auto data_ = make_safe(data);
 
   if(rng){
-    boost::shared_ptr<boost::mt19937> rng_cpy = (boost::shared_ptr<boost::mt19937>)new boost::mt19937(*rng->rng);
-    self->cxx->setRng(rng_cpy);
+    self->cxx->setRng(rng->rng);
   }
 
 

bob/learn/em/isv_trainer.cpp

@@ -409,7 +409,7 @@ static auto initialize = bob::extension::FunctionDoc(
   "",
   true
 )
-.add_prototype("isv_base, stats, rng")
+.add_prototype("isv_base, stats, [rng]")
 .add_parameter("isv_base", ":py:class:`bob.learn.em.ISVBase`", "ISVBase Object")
 .add_parameter("stats", ":py:class:`bob.learn.em.GMMStats`", "GMMStats Object")
 .add_parameter("rng", ":py:class:`bob.core.random.mt19937`", "The Mersenne Twister mt19937 random generator used for the initialization of subspaces/arrays before the EM loop.");
@@ -428,8 +428,7 @@ static PyObject* PyBobLearnEMISVTrainer_initialize(PyBobLearnEMISVTrainerObject*
                                                                  &PyBoostMt19937_Type, &rng)) return 0;
 
   if(rng){
-    boost::shared_ptr<boost::mt19937> rng_cpy = (boost::shared_ptr<boost::mt19937>)new boost::mt19937(*rng->rng);
-    self->cxx->setRng(rng_cpy);
+    self->cxx->setRng(rng->rng);
   }
 
   std::vector<std::vector<boost::shared_ptr<bob::learn::em::GMMStats> > > training_data;

bob/learn/em/ivector_trainer.cpp

@@ -332,8 +332,7 @@ static PyObject* PyBobLearnEMIVectorTrainer_initialize(PyBobLearnEMIVectorTraine
                                                                    &PyBoostMt19937_Type, &rng)) return 0;
 
   if(rng){
-    boost::shared_ptr<boost::mt19937> rng_cpy = (boost::shared_ptr<boost::mt19937>)new boost::mt19937(*rng->rng);
-    self->cxx->setRng(rng_cpy);
+    self->cxx->setRng(rng->rng);
   }
 
   self->cxx->initialize(*ivector_machine->cxx);

bob/learn/em/jfa_trainer.cpp

@@ -620,7 +620,7 @@ static auto initialize = bob::extension::FunctionDoc(
   "",
   true
 )
-.add_prototype("jfa_base,stats,rng")
+.add_prototype("jfa_base, stats, [rng]")
 .add_parameter("jfa_base", ":py:class:`bob.learn.em.JFABase`", "JFABase Object")
 .add_parameter("stats", ":py:class:`bob.learn.em.GMMStats`", "GMMStats Object")
 .add_parameter("rng", ":py:class:`bob.core.random.mt19937`", "The Mersenne Twister mt19937 random generator used for the initialization of subspaces/arrays before the EM loop.");
@@ -639,8 +639,7 @@ static PyObject* PyBobLearnEMJFATrainer_initialize(PyBobLearnEMJFATrainerObject*
                                                                  &PyBoostMt19937_Type, &rng)) return 0;
 
   if(rng){
-    boost::shared_ptr<boost::mt19937> rng_cpy = (boost::shared_ptr<boost::mt19937>)new boost::mt19937(*rng->rng);
-    self->cxx->setRng(rng_cpy);
+    self->cxx->setRng(rng->rng);
   }
 
   std::vector<std::vector<boost::shared_ptr<bob::learn::em::GMMStats> > > training_data;

bob/learn/em/kmeans_trainer.cpp

@@ -313,7 +313,7 @@ static auto initialize = bob::extension::FunctionDoc(
   "Data is split into as many chunks as there are means, then each mean is set to a random example within each chunk.",
   true
 )
-.add_prototype("kmeans_machine,data, rng")
+.add_prototype("kmeans_machine, data, [rng]")
 .add_parameter("kmeans_machine", ":py:class:`bob.learn.em.KMeansMachine`", "KMeansMachine Object")
 .add_parameter("data", "array_like <float, 2D>", "Input data")
 .add_parameter("rng", ":py:class:`bob.core.random.mt19937`", "The Mersenne Twister mt19937 random generator used for the initialization of subspaces/arrays before the EM loop.");
@@ -349,8 +349,7 @@ static PyObject* PyBobLearnEMKMeansTrainer_initialize(PyBobLearnEMKMeansTrainerO
   }
 
   if(rng){
-    boost::shared_ptr<boost::mt19937> rng_cpy = (boost::shared_ptr<boost::mt19937>)new boost::mt19937(*rng->rng);
-    self->cxx->setRng(rng_cpy);
+    self->cxx->setRng(rng->rng);
   }
 
   self->cxx->initialize(*kmeans_machine->cxx, *PyBlitzArrayCxx_AsBlitz<double,2>(data));

bob/learn/em/plda_trainer.cpp

@@ -424,7 +424,7 @@ static auto initialize = bob::extension::FunctionDoc(
   "",
   true
 )
-.add_prototype("plda_base,data,rng")
+.add_prototype("plda_base, data, [rng]")
 .add_parameter("plda_base", ":py:class:`bob.learn.em.PLDABase`", "PLDAMachine Object")
 .add_parameter("data", "list", "")
 .add_parameter("rng", ":py:class:`bob.core.random.mt19937`", "The Mersenne Twister mt19937 random generator used for the initialization of subspaces/arrays before the EM loop.");
@@ -445,8 +445,7 @@ static PyObject* PyBobLearnEMPLDATrainer_initialize(PyBobLearnEMPLDATrainerObjec
   std::vector<blitz::Array<double,2> > data_vector;
   if(list_as_vector(data ,data_vector)==0){
     if(rng){
-      boost::shared_ptr<boost::mt19937> rng_cpy = (boost::shared_ptr<boost::mt19937>)new boost::mt19937(*rng->rng);
-      self->cxx->setRng(rng_cpy);
+      self->cxx->setRng(rng->rng);
     }
 
     self->cxx->initialize(*plda_base->cxx, data_vector);

requirements.txt

 setuptools
 bob.extension
 bob.blitz
-bob.core
+bob.core>2.0.5
 bob.io.base
 bob.sp
 bob.math