Commit 61cc2540 authored by Tiago de Freitas Pereira's avatar Tiago de Freitas Pereira
Browse files

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 <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);
......
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