diff --git a/bob/learn/em/cpp/ZTNorm.cpp b/bob/learn/em/cpp/ZTNorm.cpp
deleted file mode 100644
index 0319d0402532d135c7e169681a71ecd2ac60bd24..0000000000000000000000000000000000000000
--- a/bob/learn/em/cpp/ZTNorm.cpp
+++ /dev/null
@@ -1,181 +0,0 @@
-/**
- * @date Tue Jul 19 15:33:20 2011 +0200
- * @author Francois Moulin <Francois.Moulin@idiap.ch>
- * @author Laurent El Shafey <Laurent.El-Shafey@idiap.ch>
- *
- * Copyright (C) Idiap Research Institute, Martigny, Switzerland
- */
-
-#include <bob.learn.em/ZTNorm.h>
-#include <bob.core/assert.h>
-#include <limits>
-
-
-static void _ztNorm(const blitz::Array<double,2>& rawscores_probes_vs_models,
- const blitz::Array<double,2>* rawscores_zprobes_vs_models,
- const blitz::Array<double,2>* rawscores_probes_vs_tmodels,
- const blitz::Array<double,2>* rawscores_zprobes_vs_tmodels,
- const blitz::Array<bool,2>* mask_zprobes_vs_tmodels_istruetrial,
- blitz::Array<double,2>& scores)
-{
- // Rename variables
- const blitz::Array<double,2>& A = rawscores_probes_vs_models;
- const blitz::Array<double,2>* B = rawscores_zprobes_vs_models;
- const blitz::Array<double,2>* C = rawscores_probes_vs_tmodels;
- const blitz::Array<double,2>* D = rawscores_zprobes_vs_tmodels;
-
- // Compute the sizes
- int size_eval = A.extent(0);
- int size_enroll = A.extent(1);
- int size_tnorm = (C ? C->extent(0) : 0);
- int size_znorm = (B ? B->extent(1) : 0);
-
- // Check the inputs
- bob::core::array::assertSameDimensionLength(A.extent(0), size_eval);
- bob::core::array::assertSameDimensionLength(A.extent(1), size_enroll);
-
- if (B) {
- bob::core::array::assertSameDimensionLength(B->extent(1), size_znorm);
- if (size_znorm > 0)
- bob::core::array::assertSameDimensionLength(B->extent(0), size_eval);
- }
-
- if (C) {
- bob::core::array::assertSameDimensionLength(C->extent(0), size_tnorm);
- if (size_tnorm > 0)
- bob::core::array::assertSameDimensionLength(C->extent(1), size_enroll);
- }
-
- if (D && size_znorm > 0 && size_tnorm > 0) {
- bob::core::array::assertSameDimensionLength(D->extent(0), size_tnorm);
- bob::core::array::assertSameDimensionLength(D->extent(1), size_znorm);
- }
-
- if (mask_zprobes_vs_tmodels_istruetrial) {
- bob::core::array::assertSameDimensionLength(mask_zprobes_vs_tmodels_istruetrial->extent(0), size_tnorm);
- bob::core::array::assertSameDimensionLength(mask_zprobes_vs_tmodels_istruetrial->extent(1), size_znorm);
- }
-
- bob::core::array::assertSameDimensionLength(scores.extent(0), size_eval);
- bob::core::array::assertSameDimensionLength(scores.extent(1), size_enroll);
-
- // Declare needed IndexPlaceholder
- blitz::firstIndex ii;
- blitz::secondIndex jj;
-
- // Constant to check if the std is close to 0.
- const double eps = std::numeric_limits<double>::min();
-
- // zA
- blitz::Array<double,2> zA(A.shape());
- if (B && size_znorm > 0) {
- // Znorm --> zA = (A - mean(B) ) / std(B) [znorm on oringinal scores]
- // mean(B)
- blitz::Array<double,1> mean_B(blitz::mean(*B, jj));
- // std(B)
- blitz::Array<double,2> B2n(B->shape());
- B2n = blitz::pow2((*B)(ii, jj) - mean_B(ii));
- blitz::Array<double,1> std_B(B->extent(0));
- if(size_znorm>1)
- std_B = blitz::sqrt(blitz::sum(B2n, jj) / (size_znorm - 1));
- else // 1 single value -> std = 0
- std_B = 0;
- std_B = blitz::where( std_B <= eps, 1., std_B);
-
- zA = (A(ii, jj) - mean_B(ii)) / std_B(ii);
- }
- else
- zA = A;
-
- blitz::Array<double,2> zC(size_tnorm, size_enroll);
- if (D && size_tnorm > 0 && size_znorm > 0) {
- blitz::Array<double,1> mean_Dimp(size_tnorm);
- blitz::Array<double,1> std_Dimp(size_tnorm);
-
- // Compute mean_Dimp and std_Dimp = D only with impostors
- for (int i = 0; i < size_tnorm; ++i) {
- double sum = 0;
- double sumsq = 0;
- double count = 0;
- for (int j = 0; j < size_znorm; ++j) {
- bool keep;
- // The second part is never executed if mask_zprobes_vs_tmodels_istruetrial==NULL
- keep = (mask_zprobes_vs_tmodels_istruetrial == NULL) || !(*mask_zprobes_vs_tmodels_istruetrial)(i, j); //tnorm_models_spk_ids(i) != znorm_tests_spk_ids(j);
-
- double value = keep * (*D)(i, j);
- sum += value;
- sumsq += value*value;
- count += keep;
- }
-
- double mean = sum / count;
- mean_Dimp(i) = mean;
- if (count > 1)
- std_Dimp(i) = sqrt((sumsq - count * mean * mean) / (count -1));
- else // 1 single value -> std = 0
- std_Dimp(i) = 0;
- }
-
- // zC = (C - mean(D)) / std(D) [znorm the tnorm scores]
- std_Dimp = blitz::where( std_Dimp <= eps, 1., std_Dimp);
- zC = ((*C)(ii, jj) - mean_Dimp(ii)) / std_Dimp(ii);
- }
- else if (C && size_tnorm > 0)
- zC = *C;
-
- if (C && size_tnorm > 0)
- {
- blitz::Array<double,1> mean_zC(size_enroll);
- blitz::Array<double,1> std_zC(size_enroll);
-
- // ztA = (zA - mean(zC)) / std(zC) [ztnorm on eval scores]
- mean_zC = blitz::mean(zC(jj, ii), jj);
- if (size_tnorm > 1)
- std_zC = sqrt(blitz::sum(pow(zC(jj, ii) - mean_zC(ii), 2) , jj) / (size_tnorm - 1));
- else // 1 single value -> std = 0
- std_zC = 0;
- std_zC = blitz::where( std_zC <= eps, 1., std_zC);
-
- // Normalised scores
- scores = (zA(ii, jj) - mean_zC(jj)) / std_zC(jj);
- }
- else
- scores = zA;
-}
-
-void bob::learn::em::ztNorm(const blitz::Array<double,2>& rawscores_probes_vs_models,
- const blitz::Array<double,2>& rawscores_zprobes_vs_models,
- const blitz::Array<double,2>& rawscores_probes_vs_tmodels,
- const blitz::Array<double,2>& rawscores_zprobes_vs_tmodels,
- const blitz::Array<bool,2>& mask_zprobes_vs_tmodels_istruetrial,
- blitz::Array<double,2>& scores)
-{
- _ztNorm(rawscores_probes_vs_models, &rawscores_zprobes_vs_models, &rawscores_probes_vs_tmodels,
- &rawscores_zprobes_vs_tmodels, &mask_zprobes_vs_tmodels_istruetrial, scores);
-}
-
-void bob::learn::em::ztNorm(const blitz::Array<double,2>& rawscores_probes_vs_models,
- const blitz::Array<double,2>& rawscores_zprobes_vs_models,
- const blitz::Array<double,2>& rawscores_probes_vs_tmodels,
- const blitz::Array<double,2>& rawscores_zprobes_vs_tmodels,
- blitz::Array<double,2>& scores)
-{
- _ztNorm(rawscores_probes_vs_models, &rawscores_zprobes_vs_models, &rawscores_probes_vs_tmodels,
- &rawscores_zprobes_vs_tmodels, NULL, scores);
-}
-
-void bob::learn::em::tNorm(const blitz::Array<double,2>& rawscores_probes_vs_models,
- const blitz::Array<double,2>& rawscores_probes_vs_tmodels,
- blitz::Array<double,2>& scores)
-{
- _ztNorm(rawscores_probes_vs_models, NULL, &rawscores_probes_vs_tmodels,
- NULL, NULL, scores);
-}
-
-void bob::learn::em::zNorm(const blitz::Array<double,2>& rawscores_probes_vs_models,
- const blitz::Array<double,2>& rawscores_zprobes_vs_models,
- blitz::Array<double,2>& scores)
-{
- _ztNorm(rawscores_probes_vs_models, &rawscores_zprobes_vs_models, NULL,
- NULL, NULL, scores);
-}
diff --git a/bob/learn/em/include/bob.learn.em/ZTNorm.h b/bob/learn/em/include/bob.learn.em/ZTNorm.h
deleted file mode 100644
index d8e953d3b7d44ec66fd60e5acd652479e6733410..0000000000000000000000000000000000000000
--- a/bob/learn/em/include/bob.learn.em/ZTNorm.h
+++ /dev/null
@@ -1,89 +0,0 @@
-/**
- * @date Tue Jul 19 15:33:20 2011 +0200
- * @author Francois Moulin <Francois.Moulin@idiap.ch>
- * @author Laurent El Shafey <Laurent.El-Shafey@idiap.ch>
- *
- * Copyright (C) Idiap Research Institute, Martigny, Switzerland
- */
-
-#ifndef BOB_LEARN_EM_ZTNORM_H
-#define BOB_LEARN_EM_ZTNORM_H
-
-#include <blitz/array.h>
-
-namespace bob { namespace learn { namespace em {
-
-/**
- * Normalise raw scores with ZT-Norm
- *
- * @exception std::runtime_error matrix sizes are not consistent
- *
- * @param rawscores_probes_vs_models
- * @param rawscores_zprobes_vs_models
- * @param rawscores_probes_vs_tmodels
- * @param rawscores_zprobes_vs_tmodels
- * @param mask_zprobes_vs_tmodels_istruetrial
- * @param[out] normalizedscores normalized scores
- * @warning The destination score array should have the correct size
- * (Same size as rawscores_probes_vs_models)
- */
-void ztNorm(const blitz::Array<double, 2>& rawscores_probes_vs_models,
- const blitz::Array<double, 2>& rawscores_zprobes_vs_models,
- const blitz::Array<double, 2>& rawscores_probes_vs_tmodels,
- const blitz::Array<double, 2>& rawscores_zprobes_vs_tmodels,
- const blitz::Array<bool, 2>& mask_zprobes_vs_tmodels_istruetrial,
- blitz::Array<double, 2>& normalizedscores);
-
-/**
- * Normalise raw scores with ZT-Norm.
- * Assume that znorm and tnorm have no common subject id.
- *
- * @exception std::runtime_error matrix sizes are not consistent
- *
- * @param rawscores_probes_vs_models
- * @param rawscores_zprobes_vs_models
- * @param rawscores_probes_vs_tmodels
- * @param rawscores_zprobes_vs_tmodels
- * @param[out] normalizedscores normalized scores
- * @warning The destination score array should have the correct size
- * (Same size as rawscores_probes_vs_models)
- */
-void ztNorm(const blitz::Array<double,2>& rawscores_probes_vs_models,
- const blitz::Array<double,2>& rawscores_zprobes_vs_models,
- const blitz::Array<double,2>& rawscores_probes_vs_tmodels,
- const blitz::Array<double,2>& rawscores_zprobes_vs_tmodels,
- blitz::Array<double,2>& normalizedscores);
-
-/**
- * Normalise raw scores with T-Norm.
- *
- * @exception std::runtime_error matrix sizes are not consistent
- *
- * @param rawscores_probes_vs_models
- * @param rawscores_probes_vs_tmodels
- * @param[out] normalizedscores normalized scores
- * @warning The destination score array should have the correct size
- * (Same size as rawscores_probes_vs_models)
- */
-void tNorm(const blitz::Array<double,2>& rawscores_probes_vs_models,
- const blitz::Array<double,2>& rawscores_probes_vs_tmodels,
- blitz::Array<double,2>& normalizedscores);
-
-/**
- * Normalise raw scores with Z-Norm.
- *
- * @exception std::runtime_error matrix sizes are not consistent
- *
- * @param rawscores_probes_vs_models
- * @param rawscores_zprobes_vs_models
- * @param[out] normalizedscores normalized scores
- * @warning The destination score array should have the correct size
- * (Same size as rawscores_probes_vs_models)
- */
-void zNorm(const blitz::Array<double,2>& rawscores_probes_vs_models,
- const blitz::Array<double,2>& rawscores_zprobes_vs_models,
- blitz::Array<double,2>& normalizedscores);
-
-} } } // namespaces
-
-#endif /* BOB_LEARN_EM_ZTNORM_H */
diff --git a/bob/learn/em/main.cpp b/bob/learn/em/main.cpp
index dc413e8c130d53d74bfce658567bdc232f9464cf..c2e7211f47fb44321375a2ffd56640c29c31f97c 100644
--- a/bob/learn/em/main.cpp
+++ b/bob/learn/em/main.cpp
@@ -11,24 +11,6 @@
#include "main.h"
static PyMethodDef module_methods[] = {
- {
- zt_norm.name(),
- (PyCFunction)PyBobLearnEM_ztNorm,
- METH_VARARGS|METH_KEYWORDS,
- zt_norm.doc()
- },
- {
- t_norm.name(),
- (PyCFunction)PyBobLearnEM_tNorm,
- METH_VARARGS|METH_KEYWORDS,
- t_norm.doc()
- },
- {
- z_norm.name(),
- (PyCFunction)PyBobLearnEM_zNorm,
- METH_VARARGS|METH_KEYWORDS,
- z_norm.doc()
- },
{
linear_scoring1.name(),
(PyCFunction)PyBobLearnEM_linear_scoring,
diff --git a/bob/learn/em/main.h b/bob/learn/em/main.h
index 1d61e87055b1fbadba14565cf233aa6656059860..7d2cb43eca159beb3b16bc03430a5c8dc9039584 100644
--- a/bob/learn/em/main.h
+++ b/bob/learn/em/main.h
@@ -49,7 +49,6 @@
#include <bob.learn.em/PLDAMachine.h>
#include <bob.learn.em/PLDATrainer.h>
-#include <bob.learn.em/ZTNorm.h>
/// inserts the given key, value pair into the given dictionaries
static inline int insert_item_string(PyObject* dict, PyObject* entries, const char* key, Py_ssize_t value){
@@ -265,16 +264,6 @@ bool init_BobLearnEMEMPCATrainer(PyObject* module);
int PyBobLearnEMEMPCATrainer_Check(PyObject* o);
-//ZT Normalization
-PyObject* PyBobLearnEM_ztNorm(PyObject*, PyObject* args, PyObject* kwargs);
-extern bob::extension::FunctionDoc zt_norm;
-
-PyObject* PyBobLearnEM_tNorm(PyObject*, PyObject* args, PyObject* kwargs);
-extern bob::extension::FunctionDoc t_norm;
-
-PyObject* PyBobLearnEM_zNorm(PyObject*, PyObject* args, PyObject* kwargs);
-extern bob::extension::FunctionDoc z_norm;
-
//Linear scoring
PyObject* PyBobLearnEM_linear_scoring(PyObject*, PyObject* args, PyObject* kwargs);
diff --git a/bob/learn/em/test/test_ztnorm.py b/bob/learn/em/test/test_ztnorm.py
deleted file mode 100644
index 4599d1a8e14b0187daa076910b0acc029e7ac98a..0000000000000000000000000000000000000000
--- a/bob/learn/em/test/test_ztnorm.py
+++ /dev/null
@@ -1,122 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# Francois Moulin <Francois.Moulin@idiap.ch>
-# Laurent El Shafey <Laurent.El-Shafey@idiap.ch>
-# Tue Jul 19 15:33:20 2011 +0200
-#
-# Copyright (C) 2011-2014 Idiap Research Institute, Martigny, Switzerland
-
-"""Tests on the ZTNorm function
-"""
-
-import numpy
-
-from bob.io.base.test_utils import datafile
-import bob.io.base
-
-#from . import znorm, tnorm, ztnorm
-import bob.learn.em
-
-def sameValue(vect_A, vect_B):
- sameMatrix = numpy.zeros((vect_A.shape[0], vect_B.shape[0]), 'bool')
-
- for j in range(vect_A.shape[0]):
- for i in range(vect_B.shape[0]):
- sameMatrix[j, i] = (vect_A[j] == vect_B[i])
-
- return sameMatrix
-
-def tnorm(A, C):
- Cmean = numpy.mean(C, axis=0)
- if C.shape[1] > 1:
- Cstd = numpy.sqrt(numpy.sum((C - numpy.tile(Cmean.reshape(1,C.shape[1]), (C.shape[0],1))) ** 2, axis=0) / (C.shape[0]-1))
- else:
- Cstd = numpy.ones(shape=(C.shape[1],), dtype=numpy.float64)
- return (A - numpy.tile(Cmean.reshape(1,C.shape[1]), (A.shape[0],1))) / numpy.tile(Cstd.reshape(1,C.shape[1]), (A.shape[0],1))
-
-def znorm(A, B):
- Bmean = numpy.mean(B, axis=1)
- if B.shape[1] > 1:
- Bstd = numpy.sqrt(numpy.sum((B - numpy.tile(Bmean.reshape(B.shape[0],1), (1,B.shape[1]))) ** 2, axis=1) / (B.shape[1]-1))
- else:
- Bstd = numpy.ones(shape=(B.shape[0],), dtype=numpy.float64)
-
- return (A - numpy.tile(Bmean.reshape(B.shape[0],1), (1,A.shape[1]))) / numpy.tile(Bstd.reshape(B.shape[0],1), (1,A.shape[1]))
-
-
-def test_ztnorm_simple():
- # 3x5
- my_A = numpy.array([[1, 2, 3, 4, 5],
- [6, 7, 8, 9, 8],
- [7, 6, 5, 4, 3]],'float64')
- # 3x4
- my_B = numpy.array([[5, 4, 7, 8],[9, 8, 7, 4],[5, 6, 3, 2]],'float64')
- # 2x5
- my_C = numpy.array([[5, 4, 3, 2, 1],[2, 1, 2, 3, 4]],'float64')
- # 2x4
- my_D = numpy.array([[8, 6, 4, 2],[0, 2, 4, 6]],'float64')
-
- # 4x1
- znorm_id = numpy.array([1, 2, 3, 4],'uint32')
- # 2x1
- tnorm_id = numpy.array([1, 5],'uint32')
-
- scores = bob.learn.em.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')
-
- assert (abs(scores - ref_scores) < 1e-7).all()
-
-def test_ztnorm_big():
- my_A = bob.io.base.load(datafile("ztnorm_eval_eval.hdf5", __name__, path="../data/"))
- my_B = bob.io.base.load(datafile("ztnorm_znorm_eval.hdf5", __name__, path="../data/"))
- my_C = bob.io.base.load(datafile("ztnorm_eval_tnorm.hdf5", __name__, path="../data/"))
- my_D = bob.io.base.load(datafile("ztnorm_znorm_tnorm.hdf5", __name__, path="../data/"))
-
- # ZT-Norm
- ref_scores = bob.io.base.load(datafile("ztnorm_result.hdf5", __name__, path="../data/"))
- scores = bob.learn.em.ztnorm(my_A, my_B, my_C, my_D)
- assert (abs(scores - ref_scores) < 1e-7).all()
-
- # T-Norm
- 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 = bob.learn.em.znorm(my_A, my_B)
- scores_py = znorm(my_A, my_B)
- assert (abs(scores - scores_py) < 1e-7).all()
-
-def test_tnorm_simple():
- # 3x5
- my_A = numpy.array([[1, 2, 3, 4, 5],
- [6, 7, 8, 9, 8],
- [7, 6, 5, 4, 3]],'float64')
- # 2x5
- my_C = numpy.array([[5, 4, 3, 2, 1],[2, 1, 2, 3, 4]],'float64')
-
- zC = bob.learn.em.tnorm(my_A, my_C)
- zC_py = tnorm(my_A, my_C)
- assert (abs(zC - zC_py) < 1e-7).all()
-
- empty = numpy.zeros(shape=(0,0), dtype=numpy.float64)
- zC = bob.learn.em.ztnorm(my_A, empty, my_C, empty)
- assert (abs(zC - zC_py) < 1e-7).all()
-
-def test_znorm_simple():
- # 3x5
- my_A = numpy.array([[1, 2, 3, 4, 5],
- [6, 7, 8, 9, 8],
- [7, 6, 5, 4, 3]], numpy.float64)
- # 3x4
- my_B = numpy.array([[5, 4, 7, 8],[9, 8, 7, 4],[5, 6, 3, 2]], numpy.float64)
-
- zA = bob.learn.em.znorm(my_A, my_B)
- zA_py = znorm(my_A, my_B)
- assert (abs(zA - zA_py) < 1e-7).all()
-
- empty = numpy.zeros(shape=(0,0), dtype=numpy.float64)
- zA = bob.learn.em.ztnorm(my_A, my_B, empty, empty)
- assert (abs(zA - zA_py) < 1e-7).all()
diff --git a/bob/learn/em/ztnorm.cpp b/bob/learn/em/ztnorm.cpp
deleted file mode 100644
index 6b1c9c03cc5a80ba6cf497a3a617ea56a9d42d04..0000000000000000000000000000000000000000
--- a/bob/learn/em/ztnorm.cpp
+++ /dev/null
@@ -1,144 +0,0 @@
-/**
- * @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"
-
-/*** zt_norm ***/
-bob::extension::FunctionDoc zt_norm = bob::extension::FunctionDoc(
- "ztnorm",
- "Normalise raw scores with :ref:`ZT-Norm <ztnorm>`."
- "Assume that znorm and tnorm have no common subject id.",
- 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>", "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_parameter("rawscores_probes_vs_tmodels", "array_like <float, 2D>", "T-Scores (raw scores of the T probes against the models)")
-.add_parameter("rawscores_zprobes_vs_tmodels", "array_like <float, 2D>", "ZT-Scores (raw scores of the Z probes against the T-models)")
-.add_parameter("mask_zprobes_vs_tmodels_istruetrial", "array_like <float, 2D>", "")
-.add_return("output","array_like <float, 2D>","The scores ZT Normalized");
-PyObject* PyBobLearnEM_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=0;
-
- 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();
- return 0;
- }
-
- // 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_xsafe(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));
-
- if(!mask_zprobes_vs_tmodels_istruetrial_o)
- bob::learn::em::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::em::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 ***/
-bob::extension::FunctionDoc t_norm = bob::extension::FunctionDoc(
- "tnorm",
- "Normalise raw scores with :ref:`T-Norm <tnorm>`",
- 0,
- true
-)
-.add_prototype("rawscores_probes_vs_models,rawscores_probes_vs_tmodels", "output")
-.add_parameter("rawscores_probes_vs_models", "array_like <float, 2D>", "Raw set of scores")
-.add_parameter("rawscores_probes_vs_tmodels", "array_like <float, 2D>", "T-Scores (raw scores of the T probes against the models)")
-.add_return("output","array_like <float, 2D>","The scores T Normalized");
-PyObject* PyBobLearnEM_tNorm(PyObject*, PyObject* args, PyObject* kwargs) {
-
- 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)){
- t_norm.print_usage();
- return 0;
- }
-
- 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::em::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 ***/
-bob::extension::FunctionDoc z_norm = bob::extension::FunctionDoc(
- "znorm",
- "Normalise raw scores with :ref:`Z-Norm <znorm>`",
- 0,
- true
-)
-.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 Z Normalized");
-PyObject* PyBobLearnEM_zNorm(PyObject*, PyObject* args, PyObject* kwargs) {
-
- 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)){
- z_norm.print_usage();
- return 0;
- }
-
- 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::em::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);
-}
-
diff --git a/setup.py b/setup.py
index 2d1961c436466a540601453ef6067ebfb6d3502b..3528a1dcc0624d904f58acbb179f938642fa315f 100644
--- a/setup.py
+++ b/setup.py
@@ -59,7 +59,6 @@ setup(
"bob/learn/em/cpp/KMeansMachine.cpp",
"bob/learn/em/cpp/LinearScoring.cpp",
"bob/learn/em/cpp/PLDAMachine.cpp",
- "bob/learn/em/cpp/ZTNorm.cpp",
"bob/learn/em/cpp/FABase.cpp",
"bob/learn/em/cpp/JFABase.cpp",
@@ -114,8 +113,6 @@ setup(
"bob/learn/em/plda_trainer.cpp",
- "bob/learn/em/ztnorm.cpp",
-
"bob/learn/em/linear_scoring.cpp",
"bob/learn/em/main.cpp",