Binded IVector Trainer

bob/learn/misc/cpp/IVectorMachine.cpp

@@ -229,6 +229,7 @@ void bob::learn::misc::IVectorMachine::computeTtSigmaInvFnorm(
     m_tmp_d = gs.sumPx(c,rall) - gs.n(c) * m_ubm->getGaussian(c)->getMean();
     blitz::Array<double,2> Tct_sigmacInv = m_cache_Tct_sigmacInv(c, rall, rall);
     bob::math::prod(Tct_sigmacInv, m_tmp_d, m_tmp_t2);
+
     output += m_tmp_t2;
   }
 }

bob/learn/misc/cpp/IVectorTrainer.cpp

@@ -49,6 +49,7 @@ bob::learn::misc::IVectorTrainer::~IVectorTrainer()
 void bob::learn::misc::IVectorTrainer::initialize(
   bob::learn::misc::IVectorMachine& machine)
 {
+
   const int C = machine.getNGaussians();
   const int D = machine.getNInputs();
   const int Rt = machine.getDimRt();
@@ -67,6 +68,7 @@ void bob::learn::misc::IVectorTrainer::initialize(
   m_tmp_wij2.resize(Rt,Rt);
   m_tmp_d1.resize(D);
   m_tmp_t1.resize(Rt);
+
   m_tmp_dt1.resize(D,Rt);
   m_tmp_tt1.resize(Rt,Rt);
   m_tmp_tt2.resize(Rt,Rt);
@@ -105,6 +107,7 @@ void bob::learn::misc::IVectorTrainer::eStep(
     // b. Computes \f$Id + T^{T} \Sigma^{-1} T\f$
     machine.computeIdTtSigmaInvT(*it, m_tmp_tt1);
     // c. Computes \f$(Id + T^{T} \Sigma^{-1} T)^{-1}\f$
+
     bob::math::inv(m_tmp_tt1, m_tmp_tt2);
     // d. Computes \f$E{wij} = (Id + T^{T} \Sigma^{-1} T)^{-1} T^{T} \Sigma^{-1} F_{norm}\f$
     bob::math::prod(m_tmp_tt2, m_tmp_t1, m_tmp_wij); // E{wij}

bob/learn/misc/ivector_trainer.cpp

@@ -26,9 +26,8 @@ static int extract_GMMStats_1d(PyObject *list,
       PyErr_Format(PyExc_RuntimeError, "Expected GMMStats objects");
       return -1;
     }
-    bob::learn::misc::GMMStats *stats_pointer = stats->cxx.get();
-    std::cout << " #### " << std::endl;
-    training_data.push_back(*(stats_pointer));
+    training_data.push_back(*stats->cxx);
+
   }
   return 0;
 }
@@ -360,9 +359,8 @@ static PyObject* PyBobLearnMiscIVectorTrainer_e_step(PyBobLearnMiscIVectorTraine
   if(extract_GMMStats_1d(stats ,training_data)==0)
     self->cxx->eStep(*ivector_machine->cxx, training_data);
 
-  BOB_CATCH_MEMBER("cannot perform the e_step method", 0)
-
   Py_RETURN_NONE;
+  BOB_CATCH_MEMBER("cannot perform the e_step method", 0)
 }
 
 

bob/learn/misc/test_ivector_trainer.py

@@ -11,7 +11,7 @@ import numpy
 import numpy.linalg
 import numpy.random
 
-from . import GMMMachine, GMMStats, IVectorMachine, IVectorTrainer
+from bob.learn.misc import GMMMachine, GMMStats, IVectorMachine, IVectorTrainer
 
 ### Test class inspired by an implementation of Chris McCool
 ### Chris McCool (chris.mccool@nicta.com.au)
@@ -229,7 +229,7 @@ def test_trainer_nosigma():
 
   # Initialization
   trainer = IVectorTrainer()
-  trainer.initialize(m, data)
+  trainer.initialize(m)
   m.t = t
   m.sigma = sigma
   for it in range(2):
@@ -241,7 +241,7 @@ def test_trainer_nosigma():
       assert numpy.allclose(acc_Fnorm_Sigma_wij_ref[it][k], trainer.acc_fnormij_wij[k], 1e-5)
 
     # M-Step
-    trainer.m_step(m, data)
+    trainer.m_step(m)
     assert numpy.allclose(t_ref[it], m.t, 1e-5)
 
 def test_trainer_update_sigma():
@@ -343,7 +343,7 @@ def test_trainer_update_sigma():
 
   # Initialization
   trainer = IVectorTrainer(update_sigma=True)
-  trainer.initialize(m, data)
+  trainer.initialize(m)
   m.t = t
   m.sigma = sigma
   for it in range(2):
@@ -357,7 +357,7 @@ def test_trainer_update_sigma():
     assert numpy.allclose(N_ref[it], trainer.acc_nij, 1e-5)
 
     # M-Step
-    trainer.m_step(m, data)
+    trainer.m_step(m)
     assert numpy.allclose(t_ref[it], m.t, 1e-5)
     assert numpy.allclose(sigma_ref[it], m.sigma, 1e-5)