diff --git a/bob/learn/misc/main.cpp b/bob/learn/misc/main.cpp
index 0627cfb8cba5fcecd5f92ada95af4a8dad094cb2..0bb13e2a03cbc7b732276536657074763806ba5b 100644
--- a/bob/learn/misc/main.cpp
+++ b/bob/learn/misc/main.cpp
@@ -10,7 +10,27 @@
#endif
#include "main.h"
-static PyMethodDef module_methods[] = {};
+static PyMethodDef module_methods[] = {
+ {
+ zt_norm.name(),
+ (PyCFunction)PyBobLearnMisc_ztNorm,
+ METH_VARARGS|METH_KEYWORDS,
+ zt_norm.doc()
+ },
+ {
+ t_norm.name(),
+ (PyCFunction)PyBobLearnMisc_tNorm,
+ METH_VARARGS|METH_KEYWORDS,
+ t_norm.doc()
+ },
+ {
+ z_norm.name(),
+ (PyCFunction)PyBobLearnMisc_zNorm,
+ METH_VARARGS|METH_KEYWORDS,
+ z_norm.doc()
+ },
+ {0}//Sentinel
+};
PyDoc_STRVAR(module_docstr, "Bob EM based Machine Learning Routines");
diff --git a/bob/learn/misc/main.h b/bob/learn/misc/main.h
index 45759fa765565b044489625da3811b192d000c44..a500855b07d8c2a0a68963c41a0adf2ad391e6d5 100644
--- a/bob/learn/misc/main.h
+++ b/bob/learn/misc/main.h
@@ -34,6 +34,9 @@
#include <bob.learn.misc/ISVMachine.h>
#include <bob.learn.misc/IVectorMachine.h>
#include <bob.learn.misc/PLDAMachine.h>
+#include <bob.learn.misc/ZTNorm.h>
+
+#include "ztnorm.cpp"
#if PY_VERSION_HEX >= 0x03000000
diff --git a/bob/learn/misc/test_ztnorm.py b/bob/learn/misc/test_ztnorm.py
index 40d7efa630ffe05ffd3305df8df4a5420b87c10d..ee74a446998feded7ff624c5645db1eb4c240675 100644
--- a/bob/learn/misc/test_ztnorm.py
+++ b/bob/learn/misc/test_ztnorm.py
@@ -14,7 +14,8 @@ import numpy
from bob.io.base.test_utils import datafile
import bob.io.base
-from . import znorm, tnorm, ztnorm
+#from . import znorm, tnorm, ztnorm
+import bob.learn.misc
def sameValue(vect_A, vect_B):
sameMatrix = numpy.zeros((vect_A.shape[0], vect_B.shape[0]), 'bool')
@@ -59,8 +60,8 @@ def test_ztnorm_simple():
znorm_id = numpy.array([1, 2, 3, 4],'uint32')
# 2x1
tnorm_id = numpy.array([1, 5],'uint32')
-
- scores = ztnorm(my_A, my_B, my_C, my_D,
+
+ scores = bob.learn.misc.ztnorm(my_A, my_B, my_C, my_D,
sameValue(tnorm_id, znorm_id))
ref_scores = numpy.array([[-4.45473107e+00, -3.29289322e+00, -1.50519101e+01, -8.42086557e-01, 6.46544511e-03], [-8.27619927e-01, 7.07106781e-01, 1.13757710e+01, 2.01641412e+00, 7.63765080e-01], [ 2.52913570e+00, 2.70710678e+00, 1.24400233e+01, 7.07106781e-01, 6.46544511e-03]], 'float64')
@@ -75,7 +76,7 @@ def test_ztnorm_big():
# ZT-Norm
ref_scores = bob.io.base.load(datafile("ztnorm_result.hdf5", __name__))
- scores = ztnorm(my_A, my_B, my_C, my_D)
+ scores = bob.learn.misc.ztnorm(my_A, my_B, my_C, my_D)
assert (abs(scores - ref_scores) < 1e-7).all()
# T-Norm
@@ -101,7 +102,7 @@ def test_tnorm_simple():
assert (abs(zC - zC_py) < 1e-7).all()
empty = numpy.zeros(shape=(0,0), dtype=numpy.float64)
- zC = ztnorm(my_A, empty, my_C, empty)
+ zC = bob.learn.misc.ztnorm(my_A, empty, my_C, empty)
assert (abs(zC - zC_py) < 1e-7).all()
def test_znorm_simple():
@@ -117,5 +118,5 @@ def test_znorm_simple():
assert (abs(zA - zA_py) < 1e-7).all()
empty = numpy.zeros(shape=(0,0), dtype=numpy.float64)
- zA = ztnorm(my_A, my_B, empty, empty)
+ zA = bob.learn.misc.ztnorm(my_A, my_B, empty, empty)
assert (abs(zA - zA_py) < 1e-7).all()
diff --git a/bob/learn/misc/ztnorm.cpp b/bob/learn/misc/ztnorm.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..fa8ec364bbd431436f07bd79ec6783ea8344012c
--- /dev/null
+++ b/bob/learn/misc/ztnorm.cpp
@@ -0,0 +1,151 @@
+/**
+ * @author Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
+ * @date Sat 31 Jan 02:46:48 2015
+ *
+ * @brief Python API for bob::learn::em
+ *
+ * Copyright (C) 2011-2014 Idiap Research Institute, Martigny, Switzerland
+ */
+
+#include "main.h"
+
+#ifndef BOB_LEARN_MISC_ZTNORM_BIND
+#define BOB_LEARN_MISC_ZTNORM_BIND
+
+/*** zt_norm ***/
+static auto zt_norm = bob::extension::FunctionDoc(
+ "ztnorm",
+ "",
+ 0,
+ true
+)
+.add_prototype("rawscores_probes_vs_models,rawscores_zprobes_vs_models,rawscores_probes_vs_tmodels,rawscores_zprobes_vs_tmodels,mask_zprobes_vs_tmodels_istruetrial", "output")
+.add_parameter("rawscores_probes_vs_models", "array_like <float, 2D>", "")
+.add_parameter("rawscores_zprobes_vs_models", "array_like <float, 2D>", "")
+.add_parameter("rawscores_probes_vs_tmodels", "array_like <float, 2D>", "")
+.add_parameter("rawscores_zprobes_vs_tmodels", "array_like <float, 2D>", "")
+.add_parameter("mask_zprobes_vs_tmodels_istruetrial", "array_like <float, 2D>", "")
+.add_return("output","array_like <float, 2D>","");
+static PyObject* PyBobLearnMisc_ztNorm(PyObject*, PyObject* args, PyObject* kwargs) {
+
+ char** kwlist = zt_norm.kwlist(0);
+
+ PyBlitzArrayObject *rawscores_probes_vs_models_o, *rawscores_zprobes_vs_models_o, *rawscores_probes_vs_tmodels_o,
+ *rawscores_zprobes_vs_tmodels_o, *mask_zprobes_vs_tmodels_istruetrial_o;
+
+ if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O&O&O&O&|O&", kwlist, &PyBlitzArray_Converter, &rawscores_probes_vs_models_o,
+ &PyBlitzArray_Converter, &rawscores_zprobes_vs_models_o,
+ &PyBlitzArray_Converter, &rawscores_probes_vs_tmodels_o,
+ &PyBlitzArray_Converter, &rawscores_zprobes_vs_tmodels_o,
+ &PyBlitzArray_Converter, &mask_zprobes_vs_tmodels_istruetrial_o)){
+ zt_norm.print_usage();
+ Py_RETURN_NONE;
+ }
+
+ // get the number of command line arguments
+ auto rawscores_probes_vs_models_ = make_safe(rawscores_probes_vs_models_o);
+ auto rawscores_zprobes_vs_models_ = make_safe(rawscores_zprobes_vs_models_o);
+ auto rawscores_probes_vs_tmodels_ = make_safe(rawscores_probes_vs_tmodels_o);
+ auto rawscores_zprobes_vs_tmodels_ = make_safe(rawscores_zprobes_vs_tmodels_o);
+ //auto mask_zprobes_vs_tmodels_istruetrial_ = make_safe(mask_zprobes_vs_tmodels_istruetrial_o);
+
+ 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));
+
+ int nargs = (args?PyTuple_Size(args):0) + (kwargs?PyDict_Size(kwargs):0);
+
+ if(nargs==4)
+ bob::learn::misc::ztNorm(*PyBlitzArrayCxx_AsBlitz<double,2>(rawscores_probes_vs_models_o),
+ *PyBlitzArrayCxx_AsBlitz<double,2>(rawscores_zprobes_vs_models_o),
+ *PyBlitzArrayCxx_AsBlitz<double,2>(rawscores_probes_vs_tmodels_o),
+ *PyBlitzArrayCxx_AsBlitz<double,2>(rawscores_zprobes_vs_tmodels_o),
+ normalized_scores);
+ else
+ bob::learn::misc::ztNorm(*PyBlitzArrayCxx_AsBlitz<double,2>(rawscores_probes_vs_models_o),
+ *PyBlitzArrayCxx_AsBlitz<double,2>(rawscores_zprobes_vs_models_o),
+ *PyBlitzArrayCxx_AsBlitz<double,2>(rawscores_probes_vs_tmodels_o),
+ *PyBlitzArrayCxx_AsBlitz<double,2>(rawscores_zprobes_vs_tmodels_o),
+ *PyBlitzArrayCxx_AsBlitz<bool,2>(mask_zprobes_vs_tmodels_istruetrial_o),
+ normalized_scores);
+
+ return PyBlitzArrayCxx_AsConstNumpy(normalized_scores);
+}
+
+
+
+/*** t_norm ***/
+static auto t_norm = bob::extension::FunctionDoc(
+ "tnorm",
+ "",
+ 0,
+ true
+)
+.add_prototype("rawscores_probes_vs_models,rawscores_probes_vs_tmodels", "output")
+.add_parameter("rawscores_probes_vs_models", "array_like <float, 2D>", "")
+.add_parameter("rawscores_probes_vs_tmodels", "array_like <float, 2D>", "")
+.add_return("output","array_like <float, 2D>","");
+static PyObject* PyBobLearnMisc_tNorm(PyObject*, PyObject* args, PyObject* kwargs) {
+
+ char** kwlist = zt_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();
+ Py_RETURN_NONE;
+ }
+
+ auto rawscores_probes_vs_models_ = make_safe(rawscores_probes_vs_models_o);
+ auto rawscores_probes_vs_tmodels_ = make_safe(rawscores_probes_vs_tmodels_o);
+
+ 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::misc::tNorm(*PyBlitzArrayCxx_AsBlitz<double,2>(rawscores_probes_vs_models_o),
+ *PyBlitzArrayCxx_AsBlitz<double,2>(rawscores_probes_vs_tmodels_o),
+ normalized_scores);
+
+ return PyBlitzArrayCxx_AsConstNumpy(normalized_scores);
+}
+
+
+/*** z_norm ***/
+static auto z_norm = bob::extension::FunctionDoc(
+ "znorm",
+ "",
+ 0,
+ true
+)
+.add_prototype("rawscores_probes_vs_models,rawscores_zprobes_vs_models", "output")
+.add_parameter("rawscores_probes_vs_models", "array_like <float, 2D>", "")
+.add_parameter("rawscores_zprobes_vs_models", "array_like <float, 2D>", "")
+.add_return("output","array_like <float, 2D>","");
+static PyObject* PyBobLearnMisc_zNorm(PyObject*, PyObject* args, PyObject* kwargs) {
+
+ char** kwlist = zt_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();
+ Py_RETURN_NONE;
+ }
+
+ auto rawscores_probes_vs_models_ = make_safe(rawscores_probes_vs_models_o);
+ auto rawscores_zprobes_vs_models_ = make_safe(rawscores_zprobes_vs_models_o);
+
+ 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::misc::zNorm(*PyBlitzArrayCxx_AsBlitz<double,2>(rawscores_probes_vs_models_o),
+ *PyBlitzArrayCxx_AsBlitz<double,2>(rawscores_zprobes_vs_models_o),
+ normalized_scores);
+
+ return PyBlitzArrayCxx_AsConstNumpy(normalized_scores);
+}
+
+#endif
+
diff --git a/setup.py b/setup.py
index 5fe11fe0703fb87dcaaf24eb2ffe8e31e766aad6..903a69ca9f4488c7d13025509cd334e4d4a75029 100644
--- a/setup.py
+++ b/setup.py
@@ -61,7 +61,7 @@ setup(
"bob/learn/misc/cpp/KMeansMachine.cpp",
"bob/learn/misc/cpp/LinearScoring.cpp",
"bob/learn/misc/cpp/PLDAMachine.cpp",
- #"bob/learn/misc/cpp/ZTNorm.cpp",
+ "bob/learn/misc/cpp/ZTNorm.cpp",
"bob/learn/misc/cpp/FABase.cpp",
"bob/learn/misc/cpp/JFABase.cpp",
@@ -120,7 +120,8 @@ setup(
"bob/learn/misc/ivector_machine.cpp",
"bob/learn/misc/plda_base.cpp",
- "bob/learn/misc/plda_machine.cpp",
+ "bob/learn/misc/plda_machine.cpp",
+ "bob/learn/misc/ztnorm.cpp",
"bob/learn/misc/main.cpp",
],