Fixed bug related to the issue #7

bob/learn/em/test/test_ztnorm.py

@@ -80,12 +80,12 @@ def test_ztnorm_big():
   assert (abs(scores - ref_scores) < 1e-7).all()
 
   # T-Norm
-  scores = tnorm(my_A, my_C)
+  scores = bob.learn.em.tnorm(my_A, my_C)
   scores_py = tnorm(my_A, my_C)
   assert (abs(scores - scores_py) < 1e-7).all()
 
   # Z-Norm
-  scores = znorm(my_A, my_B)
+  scores = bob.learn.em.znorm(my_A, my_B)
   scores_py = znorm(my_A, my_B)
   assert (abs(scores - scores_py) < 1e-7).all()
 
@@ -97,7 +97,7 @@ def test_tnorm_simple():
   # 2x5
   my_C = numpy.array([[5, 4, 3, 2, 1],[2, 1, 2, 3, 4]],'float64')
 
-  zC = tnorm(my_A, my_C)
+  zC = bob.learn.em.tnorm(my_A, my_C)
   zC_py = tnorm(my_A, my_C)
   assert (abs(zC - zC_py) < 1e-7).all()
 
@@ -113,7 +113,7 @@ def test_znorm_simple():
   # 3x4
   my_B = numpy.array([[5, 4, 7, 8],[9, 8, 7, 4],[5, 6, 3, 2]], numpy.float64)
 
-  zA = znorm(my_A, my_B)
+  zA = bob.learn.em.znorm(my_A, my_B)
   zA_py = znorm(my_A, my_B)
   assert (abs(zA - zA_py) < 1e-7).all()
 

bob/learn/em/ztnorm.cpp

@@ -82,13 +82,13 @@ bob::extension::FunctionDoc t_norm = bob::extension::FunctionDoc(
 .add_return("output","array_like <float, 2D>","The scores T Normalized");
 PyObject* PyBobLearnEM_tNorm(PyObject*, PyObject* args, PyObject* kwargs) {
 
-  char** kwlist = zt_norm.kwlist(0);
+  char** kwlist = t_norm.kwlist(0);
   
   PyBlitzArrayObject *rawscores_probes_vs_models_o, *rawscores_probes_vs_tmodels_o;
 
   if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O&O&", kwlist, &PyBlitzArray_Converter, &rawscores_probes_vs_models_o,
                                                                        &PyBlitzArray_Converter, &rawscores_probes_vs_tmodels_o)){
-    zt_norm.print_usage();
+    t_norm.print_usage();
     return 0;
   }
   
@@ -116,16 +116,16 @@ bob::extension::FunctionDoc z_norm = bob::extension::FunctionDoc(
 .add_prototype("rawscores_probes_vs_models,rawscores_zprobes_vs_models", "output")
 .add_parameter("rawscores_probes_vs_models", "array_like <float, 2D>", "Raw set of scores")
 .add_parameter("rawscores_zprobes_vs_models", "array_like <float, 2D>", "Z-Scores (raw scores of the Z probes against the models)")
-.add_return("output","array_like <float, 2D>","The scores T Normalized");
+.add_return("output","array_like <float, 2D>","The scores Z Normalized");
 PyObject* PyBobLearnEM_zNorm(PyObject*, PyObject* args, PyObject* kwargs) {
 
-  char** kwlist = zt_norm.kwlist(0);
+  char** kwlist = z_norm.kwlist(0);
   
   PyBlitzArrayObject *rawscores_probes_vs_models_o, *rawscores_zprobes_vs_models_o;
 
   if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O&O&", kwlist, &PyBlitzArray_Converter, &rawscores_probes_vs_models_o,
                                                                        &PyBlitzArray_Converter, &rawscores_zprobes_vs_models_o)){
-    zt_norm.print_usage();
+    z_norm.print_usage();
     return 0;
   }
   
@@ -135,7 +135,6 @@ PyObject* PyBobLearnEM_zNorm(PyObject*, PyObject* args, PyObject* kwargs) {
   blitz::Array<double,2> rawscores_probes_vs_models = *PyBlitzArrayCxx_AsBlitz<double,2>(rawscores_probes_vs_models_o);
   blitz::Array<double,2> normalized_scores          = blitz::Array<double,2>(rawscores_probes_vs_models.extent(0), rawscores_probes_vs_models.extent(1));
 
-
   bob::learn::em::zNorm(*PyBlitzArrayCxx_AsBlitz<double,2>(rawscores_probes_vs_models_o), 
                            *PyBlitzArrayCxx_AsBlitz<double,2>(rawscores_zprobes_vs_models_o),
                            normalized_scores);