Solved more memory leaks

bob/learn/em/empca_trainer.cpp

@@ -155,7 +155,7 @@ PyObject* PyBobLearnEMEMPCATrainer_getRng(PyBobLearnEMEMPCATrainerObject* self,
     (PyBoostMt19937Object*)PyBoostMt19937_Type.tp_alloc(&PyBoostMt19937_Type, 0);
 
   retval->rng = self->cxx->getRng().get();
-  return Py_BuildValue("O", retval);
+  return Py_BuildValue("N", retval);
   BOB_CATCH_MEMBER("Rng method could not be read", 0)
 }
 int PyBobLearnEMEMPCATrainer_setRng(PyBobLearnEMEMPCATrainerObject* self, PyObject* value, void*) {

bob/learn/em/isv_base.cpp

@@ -267,7 +267,7 @@ PyObject* PyBobLearnEMISVBase_getUBM(PyBobLearnEMISVBaseObject* self, void*){
     (PyBobLearnEMGMMMachineObject*)PyBobLearnEMGMMMachine_Type.tp_alloc(&PyBobLearnEMGMMMachine_Type, 0);
   retval->cxx = ubm_gmmMachine;
 
-  return Py_BuildValue("O",retval);
+  return Py_BuildValue("N",retval);
   BOB_CATCH_MEMBER("ubm could not be read", 0)
 }
 int PyBobLearnEMISVBase_setUBM(PyBobLearnEMISVBaseObject* self, PyObject* value, void*){

bob/learn/em/isv_machine.cpp

@@ -242,7 +242,7 @@ PyObject* PyBobLearnEMISVMachine_getISVBase(PyBobLearnEMISVMachineObject* self,
     (PyBobLearnEMISVBaseObject*)PyBobLearnEMISVBase_Type.tp_alloc(&PyBobLearnEMISVBase_Type, 0);
   retval->cxx = isv_base_o;
 
-  return Py_BuildValue("O",retval);
+  return Py_BuildValue("N",retval);
   BOB_CATCH_MEMBER("isv_base could not be read", 0)
 }
 int PyBobLearnEMISVMachine_setISVBase(PyBobLearnEMISVMachineObject* self, PyObject* value, void*){

bob/learn/em/isv_trainer.cpp

@@ -340,7 +340,7 @@ PyObject* PyBobLearnEMISVTrainer_getRng(PyBobLearnEMISVTrainerObject* self, void
     (PyBoostMt19937Object*)PyBoostMt19937_Type.tp_alloc(&PyBoostMt19937_Type, 0);
 
   retval->rng = self->cxx->getRng().get();
-  return Py_BuildValue("O", retval);
+  return Py_BuildValue("N", retval);
   BOB_CATCH_MEMBER("Rng method could not be read", 0)
 }
 int PyBobLearnEMISVTrainer_setRng(PyBobLearnEMISVTrainerObject* self, PyObject* value, void*) {

bob/learn/em/jfa_base.cpp

@@ -302,7 +302,7 @@ PyObject* PyBobLearnEMJFABase_getUBM(PyBobLearnEMJFABaseObject* self, void*){
     (PyBobLearnEMGMMMachineObject*)PyBobLearnEMGMMMachine_Type.tp_alloc(&PyBobLearnEMGMMMachine_Type, 0);
   retval->cxx = ubm_gmmMachine;
 
-  return Py_BuildValue("O",retval);
+  return Py_BuildValue("N",retval);
   BOB_CATCH_MEMBER("ubm could not be read", 0)
 }
 int PyBobLearnEMJFABase_setUBM(PyBobLearnEMJFABaseObject* self, PyObject* value, void*){

bob/learn/em/jfa_machine.cpp

@@ -271,7 +271,7 @@ PyObject* PyBobLearnEMJFAMachine_getJFABase(PyBobLearnEMJFAMachineObject* self,
     (PyBobLearnEMJFABaseObject*)PyBobLearnEMJFABase_Type.tp_alloc(&PyBobLearnEMJFABase_Type, 0);
   retval->cxx = jfa_base_o;
 
-  return Py_BuildValue("O",retval);
+  return Py_BuildValue("N",retval);
   BOB_CATCH_MEMBER("jfa_base could not be read", 0)
 }
 int PyBobLearnEMJFAMachine_setJFABase(PyBobLearnEMJFAMachineObject* self, PyObject* value, void*){

bob/learn/em/jfa_trainer.cpp

@@ -446,7 +446,7 @@ PyObject* PyBobLearnEMJFATrainer_getRng(PyBobLearnEMJFATrainerObject* self, void
     (PyBoostMt19937Object*)PyBoostMt19937_Type.tp_alloc(&PyBoostMt19937_Type, 0);
 
   retval->rng = self->cxx->getRng().get();
-  return Py_BuildValue("O", retval);
+  return Py_BuildValue("N", retval);
   BOB_CATCH_MEMBER("Rng method could not be read", 0)
 }
 int PyBobLearnEMJFATrainer_setRng(PyBobLearnEMJFATrainerObject* self, PyObject* value, void*) {

bob/learn/em/kmeans_trainer.cpp

@@ -281,7 +281,7 @@ PyObject* PyBobLearnEMKMeansTrainer_getRng(PyBobLearnEMKMeansTrainerObject* self
     (PyBoostMt19937Object*)PyBoostMt19937_Type.tp_alloc(&PyBoostMt19937_Type, 0);
 
   retval->rng = self->cxx->getRng().get();
-  return Py_BuildValue("O", retval);
+  return Py_BuildValue("N", retval);
   BOB_CATCH_MEMBER("Rng method could not be read", 0)
 }
 int PyBobLearnEMKMeansTrainer_setRng(PyBobLearnEMKMeansTrainerObject* self, PyObject* value, void*) {

bob/learn/em/plda_machine.cpp

@@ -248,7 +248,7 @@ PyObject* PyBobLearnEMPLDAMachine_getPLDABase(PyBobLearnEMPLDAMachineObject* sel
     (PyBobLearnEMPLDABaseObject*)PyBobLearnEMPLDABase_Type.tp_alloc(&PyBobLearnEMPLDABase_Type, 0);
   retval->cxx = plda_base_o;
 
-  return Py_BuildValue("O",retval);
+  return Py_BuildValue("N",retval);
   BOB_CATCH_MEMBER("plda_base could not be read", 0)
 }
 int PyBobLearnEMPLDAMachine_setPLDABase(PyBobLearnEMPLDAMachineObject* self, PyObject* value, void*){
@@ -642,21 +642,28 @@ static PyObject* PyBobLearnEMPLDAMachine_computeLogLikelihood(PyBobLearnEMPLDAMa
   BOB_TRY
 
   char** kwlist = compute_log_likelihood.kwlist(0);
-
+  
   PyBlitzArrayObject* samples;
+  PyArrayObject* numpy_samples; 
   PyObject* with_enrolled_samples = Py_True;
+
+  /*Convert to PyObject first to access the number of dimensions*/
+  if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!|O!", kwlist, &PyArray_Type, &numpy_samples,
+                                                                  &PyBool_Type, &with_enrolled_samples)) return 0;
+  auto numpy_samples_ = make_safe(numpy_samples);
+  Py_INCREF(numpy_samples);
+  int dim = PyArray_NDIM(numpy_samples);
 
-  if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O&|O!", kwlist, &PyBlitzArray_Converter, &samples,
-                                                                 &PyBool_Type, &with_enrolled_samples)) return 0;
+  /*Now converting to PyBlitzArrayObject*/
+  samples = reinterpret_cast<PyBlitzArrayObject*>(PyBlitzArray_FromNumpyArray(numpy_samples));
   auto samples_ = make_safe(samples);
-
-  blitz::Array<double,2>  blitz_test = *PyBlitzArrayCxx_AsBlitz<double,2>(samples);
-
-   //There are 2 methods in C++, one <double,1> and the another <double,2>
-  if (blitz_test.extent(1)==0)
+
+  /*Using the proper method according to the dimension*/
+  if (dim==1)
     return Py_BuildValue("d",self->cxx->computeLogLikelihood(*PyBlitzArrayCxx_AsBlitz<double,1>(samples), f(with_enrolled_samples)));
   else
     return Py_BuildValue("d",self->cxx->computeLogLikelihood(*PyBlitzArrayCxx_AsBlitz<double,2>(samples), f(with_enrolled_samples)));
+
 
   BOB_CATCH_MEMBER("`compute_log_likelihood` could not be read", 0)    
 }
@@ -678,13 +685,20 @@ static PyObject* PyBobLearnEMPLDAMachine_forward(PyBobLearnEMPLDAMachineObject*
   char** kwlist = forward.kwlist(0);
 
   PyBlitzArrayObject* samples;
+  PyArrayObject* numpy_samples; 
 
-  if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O&", kwlist, &PyBlitzArray_Converter, &samples)) return 0;
+  /*Convert to PyObject first to access the number of dimensions*/
+  if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!", kwlist, &PyArray_Type, &numpy_samples)) return 0;
+  auto numpy_samples_ = make_safe(numpy_samples);
+  Py_INCREF(numpy_samples);
+  int dim = PyArray_NDIM(numpy_samples);
+
+  /*Now converting to PyBlitzArrayObject*/
+  samples = reinterpret_cast<PyBlitzArrayObject*>(PyBlitzArray_FromNumpyArray(numpy_samples));
   auto samples_ = make_safe(samples);
-  blitz::Array<double,2>  blitz_test = *PyBlitzArrayCxx_AsBlitz<double,2>(samples);
 
    //There are 2 methods in C++, one <double,1> and the another <double,2>
-  if (blitz_test.extent(1)==0)
+  if(dim==1)
     return Py_BuildValue("d",self->cxx->forward(*PyBlitzArrayCxx_AsBlitz<double,1>(samples)));
   else
     return Py_BuildValue("d",self->cxx->forward(*PyBlitzArrayCxx_AsBlitz<double,2>(samples)));

bob/learn/em/test/test_plda.py

@@ -480,6 +480,10 @@ def test_plda_machine_log_likelihood_Python():
   ar_s = numpy.vstack([ar_e, ar_p])
   assert abs(m.compute_log_likelihood(ar_s, False) - compute_log_likelihood(ar_s, mu, C_F, C_G, sigma)) < 1e-10
   ar_p2d = numpy.reshape(ar_p, (1,C_dim_d))
+
+  print ar_p.shape
+  a = m.compute_log_likelihood(ar_p, False)
+
   assert abs(m.compute_log_likelihood(ar_p, False) - compute_log_likelihood(ar_p2d, mu, C_F, C_G, sigma)) < 1e-10
 
   # Defines (random) samples and check forward method