Commit 61cc2540 by Tiago de Freitas Pereira

### Fixed bug related to the issue #7

parent b35b293e
 ... ... @@ -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() ... ...
 ... ... @@ -82,13 +82,13 @@ bob::extension::FunctionDoc t_norm = bob::extension::FunctionDoc( .add_return("output","array_like ","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 ", "Raw set of scores") .add_parameter("rawscores_zprobes_vs_models", "array_like ", "Z-Scores (raw scores of the Z probes against the models)") .add_return("output","array_like ","The scores T Normalized"); .add_return("output","array_like ","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 rawscores_probes_vs_models = *PyBlitzArrayCxx_AsBlitz(rawscores_probes_vs_models_o); blitz::Array normalized_scores = blitz::Array(rawscores_probes_vs_models.extent(0), rawscores_probes_vs_models.extent(1)); bob::learn::em::zNorm(*PyBlitzArrayCxx_AsBlitz(rawscores_probes_vs_models_o), *PyBlitzArrayCxx_AsBlitz(rawscores_zprobes_vs_models_o), normalized_scores); ... ...
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