From 3aad63f1699d8f14da4a8b7ad45eb415a7c3bbb8 Mon Sep 17 00:00:00 2001
From: Tiago Freitas Pereira <tiagofrepereira@gmail.com>
Date: Sat, 7 Feb 2015 22:14:52 +0100
Subject: [PATCH] Reorganized the MAP and ML trainers
---
bob/learn/misc/MAP_gmm_trainer.cpp | 175 +++++++++-------
bob/learn/misc/ML_gmm_trainer.cpp | 187 ++++++++++--------
bob/learn/misc/__MAP_gmm_trainer__.py | 22 ++-
bob/learn/misc/__ML_gmm_trainer__.py | 20 +-
bob/learn/misc/cpp/MAP_GMMTrainer.cpp | 34 ++--
bob/learn/misc/cpp/ML_GMMTrainer.cpp | 25 ++-
.../include/bob.learn.misc/GMMBaseTrainer.h | 12 +-
.../include/bob.learn.misc/MAP_GMMTrainer.h | 49 +++--
.../include/bob.learn.misc/ML_GMMTrainer.h | 41 ++--
bob/learn/misc/main.cpp | 2 +-
bob/learn/misc/main.h | 5 +-
bob/learn/misc/test_em.py | 14 +-
setup.py | 2 +-
13 files changed, 357 insertions(+), 231 deletions(-)
diff --git a/bob/learn/misc/MAP_gmm_trainer.cpp b/bob/learn/misc/MAP_gmm_trainer.cpp
index 1ffa4d2..f3a3f4c 100644
--- a/bob/learn/misc/MAP_gmm_trainer.cpp
+++ b/bob/learn/misc/MAP_gmm_trainer.cpp
@@ -13,6 +13,7 @@
/************ Constructor Section *********************************/
/******************************************************************/
+static inline bool f(PyObject* o){return o != 0 && PyObject_IsTrue(o) > 0;} /* converts PyObject to bool and returns false if object is NULL */
static auto MAP_GMMTrainer_doc = bob::extension::ClassDoc(
BOB_EXT_MODULE_PREFIX ".MAP_GMMTrainer",
@@ -24,18 +25,22 @@ static auto MAP_GMMTrainer_doc = bob::extension::ClassDoc(
"",
true
)
-
-
- .add_prototype("gmm_base_trainer,prior_gmm,relevance_factor","")
- .add_prototype("gmm_base_trainer,prior_gmm,alpha","")
+
+ .add_prototype("prior_gmm,relevance_factor, update_means, [update_variances], [update_weights], [mean_var_update_responsibilities_threshold]","")
+ .add_prototype("prior_gmm,alpha, update_means, [update_variances], [update_weights], [mean_var_update_responsibilities_threshold]","")
.add_prototype("other","")
.add_prototype("","")
- .add_parameter("gmm_base_trainer", ":py:class:`bob.learn.misc.GMMBaseTrainer`", "A GMMBaseTrainer object.")
.add_parameter("prior_gmm", ":py:class:`bob.learn.misc.GMMMachine`", "The prior GMM to be adapted (Universal Backgroud Model UBM).")
.add_parameter("reynolds_adaptation", "bool", "Will use the Reynolds adaptation procedure? See Eq (14) from [Reynolds2000]_")
.add_parameter("relevance_factor", "double", "If set the reynolds_adaptation parameters, will apply the Reynolds Adaptation procedure. See Eq (14) from [Reynolds2000]_")
.add_parameter("alpha", "double", "Set directly the alpha parameter (Eq (14) from [Reynolds2000]_), ignoring zeroth order statistics as a weighting factor.")
+
+ .add_parameter("update_means", "bool", "Update means on each iteration")
+ .add_parameter("update_variances", "bool", "Update variances on each iteration")
+ .add_parameter("update_weights", "bool", "Update weights on each iteration")
+ .add_parameter("mean_var_update_responsibilities_threshold", "float", "Threshold over the responsibilities of the Gaussians Equations 9.24, 9.25 of Bishop, `Pattern recognition and machine learning`, 2006 require a division by the responsibilities, which might be equal to zero because of numerical issue. This threshold is used to avoid such divisions.")
+
.add_parameter("other", ":py:class:`bob.learn.misc.MAP_GMMTrainer`", "A MAP_GMMTrainer object to be copied.")
);
@@ -59,43 +64,54 @@ static int PyBobLearnMiscMAPGMMTrainer_init_base_trainer(PyBobLearnMiscMAPGMMTra
char** kwlist1 = MAP_GMMTrainer_doc.kwlist(0);
char** kwlist2 = MAP_GMMTrainer_doc.kwlist(1);
- PyBobLearnMiscGMMBaseTrainerObject* gmm_base_trainer;
PyBobLearnMiscGMMMachineObject* gmm_machine;
bool reynolds_adaptation = false;
double alpha = 0.5;
double relevance_factor = 4.0;
double aux = 0;
- PyObject* keyword_relevance_factor = Py_BuildValue("s", kwlist1[2]);
- PyObject* keyword_alpha = Py_BuildValue("s", kwlist2[2]);
+ PyObject* update_means = 0;
+ PyObject* update_variances = 0;
+ PyObject* update_weights = 0;
+ double mean_var_update_responsibilities_threshold = std::numeric_limits<double>::epsilon();
+
+ PyObject* keyword_relevance_factor = Py_BuildValue("s", kwlist1[1]);
+ PyObject* keyword_alpha = Py_BuildValue("s", kwlist2[1]);
//Here we have to select which keyword argument to read
- if (kwargs && PyDict_Contains(kwargs, keyword_relevance_factor) && (PyArg_ParseTupleAndKeywords(args, kwargs, "O!O!|d", kwlist1,
- &PyBobLearnMiscGMMBaseTrainer_Type, &gmm_base_trainer,
+ if (kwargs && PyDict_Contains(kwargs, keyword_relevance_factor) && (PyArg_ParseTupleAndKeywords(args, kwargs, "O!dO!|O!O!d", kwlist1,
&PyBobLearnMiscGMMMachine_Type, &gmm_machine,
- &aux)))
+ &aux,
+ &PyBool_Type, &update_means,
+ &PyBool_Type, &update_variances,
+ &PyBool_Type, &update_weights,
+ &mean_var_update_responsibilities_threshold)))
reynolds_adaptation = true;
- else if (kwargs && PyDict_Contains(kwargs, keyword_alpha) && (PyArg_ParseTupleAndKeywords(args, kwargs, "O!O!|d", kwlist2,
- &PyBobLearnMiscGMMBaseTrainer_Type, &gmm_base_trainer,
+ else if (kwargs && PyDict_Contains(kwargs, keyword_alpha) && (PyArg_ParseTupleAndKeywords(args, kwargs, "O!dO!|O!O!d", kwlist2,
&PyBobLearnMiscGMMMachine_Type, &gmm_machine,
- &aux)))
+ &aux,
+ &PyBool_Type, &update_means,
+ &PyBool_Type, &update_variances,
+ &PyBool_Type, &update_weights,
+ &mean_var_update_responsibilities_threshold)))
reynolds_adaptation = false;
else{
- PyErr_Format(PyExc_RuntimeError, "%s. The third argument must be a keyword argument.", Py_TYPE(self)->tp_name);
+ PyErr_Format(PyExc_RuntimeError, "%s. The second argument must be a keyword argument.", Py_TYPE(self)->tp_name);
MAP_GMMTrainer_doc.print_usage();
return -1;
}
-
-
if (reynolds_adaptation)
relevance_factor = aux;
else
alpha = aux;
- self->cxx.reset(new bob::learn::misc::MAP_GMMTrainer(gmm_base_trainer->cxx, gmm_machine->cxx, reynolds_adaptation,relevance_factor, alpha));
+ self->cxx.reset(new bob::learn::misc::MAP_GMMTrainer(f(update_means), f(update_variances), f(update_weights),
+ mean_var_update_responsibilities_threshold,
+ reynolds_adaptation,relevance_factor, alpha, gmm_machine->cxx));
return 0;
+
}
@@ -151,47 +167,6 @@ static PyObject* PyBobLearnMiscMAPGMMTrainer_RichCompare(PyBobLearnMiscMAPGMMTra
/************ Variables Section ***********************************/
/******************************************************************/
-
-/***** gmm_base_trainer *****/
-static auto gmm_base_trainer = bob::extension::VariableDoc(
- "gmm_base_trainer",
- ":py:class:`bob.learn.misc.GMMBaseTrainer`",
- "This class that implements the E-step of the expectation-maximisation algorithm.",
- ""
-);
-PyObject* PyBobLearnMiscMAPGMMTrainer_getGMMBaseTrainer(PyBobLearnMiscMAPGMMTrainerObject* self, void*){
- BOB_TRY
-
- boost::shared_ptr<bob::learn::misc::GMMBaseTrainer> gmm_base_trainer = self->cxx->getGMMBaseTrainer();
-
- //Allocating the correspondent python object
- PyBobLearnMiscGMMBaseTrainerObject* retval =
- (PyBobLearnMiscGMMBaseTrainerObject*)PyBobLearnMiscGMMBaseTrainer_Type.tp_alloc(&PyBobLearnMiscGMMBaseTrainer_Type, 0);
-
- retval->cxx = gmm_base_trainer;
-
- return Py_BuildValue("O",retval);
- BOB_CATCH_MEMBER("GMMBaseTrainer could not be read", 0)
-}
-int PyBobLearnMiscMAPGMMTrainer_setGMMBaseTrainer(PyBobLearnMiscMAPGMMTrainerObject* self, PyObject* value, void*){
- BOB_TRY
-
- if (!PyBobLearnMiscGMMBaseTrainer_Check(value)){
- PyErr_Format(PyExc_RuntimeError, "%s %s expects a :py:class:`bob.learn.misc.GMMBaseTrainer`", Py_TYPE(self)->tp_name, gmm_base_trainer.name());
- return -1;
- }
-
- PyBobLearnMiscGMMBaseTrainerObject* gmm_base_trainer = 0;
- PyArg_Parse(value, "O!", &PyBobLearnMiscGMMBaseTrainer_Type,&gmm_base_trainer);
-
- self->cxx->setGMMBaseTrainer(gmm_base_trainer->cxx);
-
- return 0;
- BOB_CATCH_MEMBER("gmm_base_trainer could not be set", -1)
-}
-
-
-
/***** relevance_factor *****/
static auto relevance_factor = bob::extension::VariableDoc(
"relevance_factor",
@@ -246,13 +221,6 @@ int PyBobLearnMiscMAPGMMTrainer_setAlpha(PyBobLearnMiscMAPGMMTrainerObject* self
static PyGetSetDef PyBobLearnMiscMAPGMMTrainer_getseters[] = {
- {
- gmm_base_trainer.name(),
- (getter)PyBobLearnMiscMAPGMMTrainer_getGMMBaseTrainer,
- (setter)PyBobLearnMiscMAPGMMTrainer_setGMMBaseTrainer,
- gmm_base_trainer.doc(),
- 0
- },
{
alpha.name(),
(getter)PyBobLearnMiscMAPGMMTrainer_getAlpha,
@@ -304,6 +272,40 @@ static PyObject* PyBobLearnMiscMAPGMMTrainer_initialize(PyBobLearnMiscMAPGMMTrai
}
+/*** eStep ***/
+static auto eStep = bob::extension::FunctionDoc(
+ "eStep",
+ "Calculates and saves statistics across the dataset,"
+ "and saves these as m_ss. ",
+
+ "Calculates the average log likelihood of the observations given the GMM,"
+ "and returns this in average_log_likelihood."
+ "The statistics, m_ss, will be used in the mStep() that follows.",
+
+ true
+)
+.add_prototype("gmm_machine,data")
+.add_parameter("gmm_machine", ":py:class:`bob.learn.misc.GMMMachine`", "GMMMachine Object")
+.add_parameter("data", "array_like <float, 2D>", "Input data");
+static PyObject* PyBobLearnMiscMAPGMMTrainer_eStep(PyBobLearnMiscMAPGMMTrainerObject* self, PyObject* args, PyObject* kwargs) {
+ BOB_TRY
+
+ /* Parses input arguments in a single shot */
+ char** kwlist = eStep.kwlist(0);
+
+ PyBobLearnMiscGMMMachineObject* gmm_machine;
+ PyBlitzArrayObject* data = 0;
+ if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!O&", kwlist, &PyBobLearnMiscGMMMachine_Type, &gmm_machine,
+ &PyBlitzArray_Converter, &data)) Py_RETURN_NONE;
+ auto data_ = make_safe(data);
+
+ self->cxx->eStep(*gmm_machine->cxx, *PyBlitzArrayCxx_AsBlitz<double,2>(data));
+
+ BOB_CATCH_MEMBER("cannot perform the eStep method", 0)
+
+ Py_RETURN_NONE;
+}
+
/*** mStep ***/
static auto mStep = bob::extension::FunctionDoc(
@@ -335,6 +337,31 @@ static PyObject* PyBobLearnMiscMAPGMMTrainer_mStep(PyBobLearnMiscMAPGMMTrainerOb
}
+/*** computeLikelihood ***/
+static auto compute_likelihood = bob::extension::FunctionDoc(
+ "compute_likelihood",
+ "This functions returns the average min (Square Euclidean) distance (average distance to the closest mean)",
+ 0,
+ true
+)
+.add_prototype("gmm_machine")
+.add_parameter("gmm_machine", ":py:class:`bob.learn.misc.GMMMachine`", "GMMMachine Object");
+static PyObject* PyBobLearnMiscMAPGMMTrainer_compute_likelihood(PyBobLearnMiscMAPGMMTrainerObject* self, PyObject* args, PyObject* kwargs) {
+ BOB_TRY
+
+ /* Parses input arguments in a single shot */
+ char** kwlist = compute_likelihood.kwlist(0);
+
+ PyBobLearnMiscGMMMachineObject* gmm_machine;
+ if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!", kwlist, &PyBobLearnMiscGMMMachine_Type, &gmm_machine)) Py_RETURN_NONE;
+
+ double value = self->cxx->computeLikelihood(*gmm_machine->cxx);
+ return Py_BuildValue("d", value);
+
+ BOB_CATCH_MEMBER("cannot perform the computeLikelihood method", 0)
+}
+
+
static PyMethodDef PyBobLearnMiscMAPGMMTrainer_methods[] = {
{
@@ -343,13 +370,25 @@ static PyMethodDef PyBobLearnMiscMAPGMMTrainer_methods[] = {
METH_VARARGS|METH_KEYWORDS,
initialize.doc()
},
+ {
+ eStep.name(),
+ (PyCFunction)PyBobLearnMiscMAPGMMTrainer_eStep,
+ METH_VARARGS|METH_KEYWORDS,
+ eStep.doc()
+ },
{
mStep.name(),
(PyCFunction)PyBobLearnMiscMAPGMMTrainer_mStep,
METH_VARARGS|METH_KEYWORDS,
mStep.doc()
},
-
+ {
+ compute_likelihood.name(),
+ (PyCFunction)PyBobLearnMiscMAPGMMTrainer_compute_likelihood,
+ METH_VARARGS|METH_KEYWORDS,
+ compute_likelihood.doc()
+ },
+
{0} /* Sentinel */
};
@@ -379,7 +418,7 @@ bool init_BobLearnMiscMAPGMMTrainer(PyObject* module)
PyBobLearnMiscMAPGMMTrainer_Type.tp_richcompare = reinterpret_cast<richcmpfunc>(PyBobLearnMiscMAPGMMTrainer_RichCompare);
PyBobLearnMiscMAPGMMTrainer_Type.tp_methods = PyBobLearnMiscMAPGMMTrainer_methods;
PyBobLearnMiscMAPGMMTrainer_Type.tp_getset = PyBobLearnMiscMAPGMMTrainer_getseters;
- //PyBobLearnMiscMAPGMMTrainer_Type.tp_call = reinterpret_cast<ternaryfunc>(PyBobLearnMiscMAPGMMTrainer_compute_likelihood);
+ PyBobLearnMiscMAPGMMTrainer_Type.tp_call = reinterpret_cast<ternaryfunc>(PyBobLearnMiscMAPGMMTrainer_compute_likelihood);
// check that everything is fine
diff --git a/bob/learn/misc/ML_gmm_trainer.cpp b/bob/learn/misc/ML_gmm_trainer.cpp
index f9a4598..ff72609 100644
--- a/bob/learn/misc/ML_gmm_trainer.cpp
+++ b/bob/learn/misc/ML_gmm_trainer.cpp
@@ -13,6 +13,8 @@
/************ Constructor Section *********************************/
/******************************************************************/
+static inline bool f(PyObject* o){return o != 0 && PyObject_IsTrue(o) > 0;} /* converts PyObject to bool and returns false if object is NULL */
+
static auto ML_GMMTrainer_doc = bob::extension::ClassDoc(
BOB_EXT_MODULE_PREFIX ".ML_GMMTrainer",
"This class implements the maximum likelihood M-step of the expectation-maximisation algorithm for a GMM Machine."
@@ -23,11 +25,16 @@ static auto ML_GMMTrainer_doc = bob::extension::ClassDoc(
"",
true
)
- .add_prototype("gmm_base_trainer","")
+ .add_prototype("update_means, [update_variances], [update_weights], [mean_var_update_responsibilities_threshold]","")
.add_prototype("other","")
.add_prototype("","")
- .add_parameter("gmm_base_trainer", ":py:class:`bob.learn.misc.GMMBaseTrainer`", "A set GMMBaseTrainer object.")
+ .add_parameter("update_means", "bool", "Update means on each iteration")
+ .add_parameter("update_variances", "bool", "Update variances on each iteration")
+ .add_parameter("update_weights", "bool", "Update weights on each iteration")
+ .add_parameter("mean_var_update_responsibilities_threshold", "float", "Threshold over the responsibilities of the Gaussians Equations 9.24, 9.25 of Bishop, `Pattern recognition and machine learning`, 2006 require a division by the responsibilities, which might be equal to zero because of numerical issue. This threshold is used to avoid such divisions.")
+
+
.add_parameter("other", ":py:class:`bob.learn.misc.ML_GMMTrainer`", "A ML_GMMTrainer object to be copied.")
);
@@ -48,15 +55,24 @@ static int PyBobLearnMiscMLGMMTrainer_init_copy(PyBobLearnMiscMLGMMTrainerObject
static int PyBobLearnMiscMLGMMTrainer_init_base_trainer(PyBobLearnMiscMLGMMTrainerObject* self, PyObject* args, PyObject* kwargs) {
- char** kwlist = ML_GMMTrainer_doc.kwlist(1);
- PyBobLearnMiscGMMBaseTrainerObject* o;
+ char** kwlist = ML_GMMTrainer_doc.kwlist(0);
- if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!", kwlist, &PyBobLearnMiscGMMBaseTrainer_Type, &o)){
+ PyObject* update_means = 0;
+ PyObject* update_variances = 0;
+ PyObject* update_weights = 0;
+ double mean_var_update_responsibilities_threshold = std::numeric_limits<double>::epsilon();
+
+ if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!|O!O!d", kwlist,
+ &PyBool_Type, &update_means,
+ &PyBool_Type, &update_variances,
+ &PyBool_Type, &update_weights,
+ &mean_var_update_responsibilities_threshold)){
ML_GMMTrainer_doc.print_usage();
return -1;
}
- self->cxx.reset(new bob::learn::misc::ML_GMMTrainer(o->cxx));
+ self->cxx.reset(new bob::learn::misc::ML_GMMTrainer(f(update_means), f(update_variances), f(update_weights),
+ mean_var_update_responsibilities_threshold));
return 0;
}
@@ -65,31 +81,24 @@ static int PyBobLearnMiscMLGMMTrainer_init_base_trainer(PyBobLearnMiscMLGMMTrain
static int PyBobLearnMiscMLGMMTrainer_init(PyBobLearnMiscMLGMMTrainerObject* self, PyObject* args, PyObject* kwargs) {
BOB_TRY
- int nargs = (args?PyTuple_Size(args):0) + (kwargs?PyDict_Size(kwargs):0);
-
- if (nargs==1){ //default initializer ()
- //Reading the input argument
- PyObject* arg = 0;
- if (PyTuple_Size(args))
- arg = PyTuple_GET_ITEM(args, 0);
- else {
- PyObject* tmp = PyDict_Values(kwargs);
- auto tmp_ = make_safe(tmp);
- arg = PyList_GET_ITEM(tmp, 0);
- }
-
- // If the constructor input is GMMBaseTrainer object
- if (PyBobLearnMiscGMMBaseTrainer_Check(arg))
- return PyBobLearnMiscMLGMMTrainer_init_base_trainer(self, args, kwargs);
- else
- return PyBobLearnMiscMLGMMTrainer_init_copy(self, args, kwargs);
- }
- else{
- PyErr_Format(PyExc_RuntimeError, "number of arguments mismatch - %s requires 1, but you provided %d (see help)", Py_TYPE(self)->tp_name, nargs);
- ML_GMMTrainer_doc.print_usage();
- return -1;
+ //Reading the input argument
+ PyObject* arg = 0;
+ if (PyTuple_Size(args))
+ arg = PyTuple_GET_ITEM(args, 0);
+ else {
+ PyObject* tmp = PyDict_Values(kwargs);
+ auto tmp_ = make_safe(tmp);
+ arg = PyList_GET_ITEM(tmp, 0);
}
+ // If the constructor input is GMMBaseTrainer object
+ if (PyBobLearnMiscMLGMMTrainer_Check(arg))
+ return PyBobLearnMiscMLGMMTrainer_init_copy(self, args, kwargs);
+ else
+ return PyBobLearnMiscMLGMMTrainer_init_base_trainer(self, args, kwargs);
+
+
+
BOB_CATCH_MEMBER("cannot create GMMBaseTrainer_init_bool", 0)
return 0;
}
@@ -131,54 +140,7 @@ static PyObject* PyBobLearnMiscMLGMMTrainer_RichCompare(PyBobLearnMiscMLGMMTrain
/************ Variables Section ***********************************/
/******************************************************************/
-
-/***** gmm_base_trainer *****/
-static auto gmm_base_trainer = bob::extension::VariableDoc(
- "gmm_base_trainer",
- ":py:class:`bob.learn.misc.GMMBaseTrainer`",
- "This class that implements the E-step of the expectation-maximisation algorithm.",
- ""
-);
-PyObject* PyBobLearnMiscMLGMMTrainer_getGMMBaseTrainer(PyBobLearnMiscMLGMMTrainerObject* self, void*){
- BOB_TRY
-
- boost::shared_ptr<bob::learn::misc::GMMBaseTrainer> gmm_base_trainer = self->cxx->getGMMBaseTrainer();
-
- //Allocating the correspondent python object
- PyBobLearnMiscGMMBaseTrainerObject* retval =
- (PyBobLearnMiscGMMBaseTrainerObject*)PyBobLearnMiscGMMBaseTrainer_Type.tp_alloc(&PyBobLearnMiscGMMBaseTrainer_Type, 0);
-
- retval->cxx = gmm_base_trainer;
-
- return Py_BuildValue("O",retval);
- BOB_CATCH_MEMBER("GMMBaseTrainer could not be read", 0)
-}
-int PyBobLearnMiscMLGMMTrainer_setGMMBaseTrainer(PyBobLearnMiscMLGMMTrainerObject* self, PyObject* value, void*){
- BOB_TRY
-
- if (!PyBobLearnMiscGMMBaseTrainer_Check(value)){
- PyErr_Format(PyExc_RuntimeError, "%s %s expects a :py:class:`bob.learn.misc.GMMBaseTrainer`", Py_TYPE(self)->tp_name, gmm_base_trainer.name());
- return -1;
- }
-
- PyBobLearnMiscGMMBaseTrainerObject* gmm_base_trainer = 0;
- PyArg_Parse(value, "O!", &PyBobLearnMiscGMMBaseTrainer_Type,&gmm_base_trainer);
-
- self->cxx->setGMMBaseTrainer(gmm_base_trainer->cxx);
-
- return 0;
- BOB_CATCH_MEMBER("gmm_base_trainer could not be set", -1)
-}
-
-
static PyGetSetDef PyBobLearnMiscMLGMMTrainer_getseters[] = {
- {
- gmm_base_trainer.name(),
- (getter)PyBobLearnMiscMLGMMTrainer_getGMMBaseTrainer,
- (setter)PyBobLearnMiscMLGMMTrainer_setGMMBaseTrainer,
- gmm_base_trainer.doc(),
- 0
- },
{0} // Sentinel
};
@@ -215,6 +177,40 @@ static PyObject* PyBobLearnMiscMLGMMTrainer_initialize(PyBobLearnMiscMLGMMTraine
}
+/*** eStep ***/
+static auto eStep = bob::extension::FunctionDoc(
+ "eStep",
+ "Calculates and saves statistics across the dataset,"
+ "and saves these as m_ss. ",
+
+ "Calculates the average log likelihood of the observations given the GMM,"
+ "and returns this in average_log_likelihood."
+ "The statistics, m_ss, will be used in the mStep() that follows.",
+
+ true
+)
+.add_prototype("gmm_machine,data")
+.add_parameter("gmm_machine", ":py:class:`bob.learn.misc.GMMMachine`", "GMMMachine Object")
+.add_parameter("data", "array_like <float, 2D>", "Input data");
+static PyObject* PyBobLearnMiscMLGMMTrainer_eStep(PyBobLearnMiscMLGMMTrainerObject* self, PyObject* args, PyObject* kwargs) {
+ BOB_TRY
+
+ /* Parses input arguments in a single shot */
+ char** kwlist = eStep.kwlist(0);
+
+ PyBobLearnMiscGMMMachineObject* gmm_machine;
+ PyBlitzArrayObject* data = 0;
+ if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!O&", kwlist, &PyBobLearnMiscGMMMachine_Type, &gmm_machine,
+ &PyBlitzArray_Converter, &data)) Py_RETURN_NONE;
+ auto data_ = make_safe(data);
+
+ self->cxx->eStep(*gmm_machine->cxx, *PyBlitzArrayCxx_AsBlitz<double,2>(data));
+
+ BOB_CATCH_MEMBER("cannot perform the eStep method", 0)
+
+ Py_RETURN_NONE;
+}
+
/*** mStep ***/
static auto mStep = bob::extension::FunctionDoc(
@@ -246,6 +242,31 @@ static PyObject* PyBobLearnMiscMLGMMTrainer_mStep(PyBobLearnMiscMLGMMTrainerObje
}
+/*** computeLikelihood ***/
+static auto compute_likelihood = bob::extension::FunctionDoc(
+ "compute_likelihood",
+ "This functions returns the average min (Square Euclidean) distance (average distance to the closest mean)",
+ 0,
+ true
+)
+.add_prototype("gmm_machine")
+.add_parameter("gmm_machine", ":py:class:`bob.learn.misc.GMMMachine`", "GMMMachine Object");
+static PyObject* PyBobLearnMiscMLGMMTrainer_compute_likelihood(PyBobLearnMiscMLGMMTrainerObject* self, PyObject* args, PyObject* kwargs) {
+ BOB_TRY
+
+ /* Parses input arguments in a single shot */
+ char** kwlist = compute_likelihood.kwlist(0);
+
+ PyBobLearnMiscGMMMachineObject* gmm_machine;
+ if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!", kwlist, &PyBobLearnMiscGMMMachine_Type, &gmm_machine)) Py_RETURN_NONE;
+
+ double value = self->cxx->computeLikelihood(*gmm_machine->cxx);
+ return Py_BuildValue("d", value);
+
+ BOB_CATCH_MEMBER("cannot perform the computeLikelihood method", 0)
+}
+
+
static PyMethodDef PyBobLearnMiscMLGMMTrainer_methods[] = {
{
@@ -254,12 +275,24 @@ static PyMethodDef PyBobLearnMiscMLGMMTrainer_methods[] = {
METH_VARARGS|METH_KEYWORDS,
initialize.doc()
},
+ {
+ eStep.name(),
+ (PyCFunction)PyBobLearnMiscMLGMMTrainer_eStep,
+ METH_VARARGS|METH_KEYWORDS,
+ eStep.doc()
+ },
{
mStep.name(),
(PyCFunction)PyBobLearnMiscMLGMMTrainer_mStep,
METH_VARARGS|METH_KEYWORDS,
mStep.doc()
},
+ {
+ compute_likelihood.name(),
+ (PyCFunction)PyBobLearnMiscMLGMMTrainer_compute_likelihood,
+ METH_VARARGS|METH_KEYWORDS,
+ compute_likelihood.doc()
+ },
{0} /* Sentinel */
};
@@ -289,7 +322,7 @@ bool init_BobLearnMiscMLGMMTrainer(PyObject* module)
PyBobLearnMiscMLGMMTrainer_Type.tp_richcompare = reinterpret_cast<richcmpfunc>(PyBobLearnMiscMLGMMTrainer_RichCompare);
PyBobLearnMiscMLGMMTrainer_Type.tp_methods = PyBobLearnMiscMLGMMTrainer_methods;
PyBobLearnMiscMLGMMTrainer_Type.tp_getset = PyBobLearnMiscMLGMMTrainer_getseters;
- //PyBobLearnMiscMLGMMTrainer_Type.tp_call = reinterpret_cast<ternaryfunc>(PyBobLearnMiscMLGMMTrainer_compute_likelihood);
+ PyBobLearnMiscMLGMMTrainer_Type.tp_call = reinterpret_cast<ternaryfunc>(PyBobLearnMiscMLGMMTrainer_compute_likelihood);
// check that everything is fine
diff --git a/bob/learn/misc/__MAP_gmm_trainer__.py b/bob/learn/misc/__MAP_gmm_trainer__.py
index 18f215d..4258b08 100644
--- a/bob/learn/misc/__MAP_gmm_trainer__.py
+++ b/bob/learn/misc/__MAP_gmm_trainer__.py
@@ -11,13 +11,17 @@ import numpy
# define the class
class MAP_GMMTrainer(_MAP_GMMTrainer):
- def __init__(self, gmm_base_trainer, prior_gmm, convergence_threshold=0.001, max_iterations=10, converge_by_likelihood=True, **kwargs):
+ def __init__(self, prior_gmm, update_means=True, update_variances=False, update_weights=False, convergence_threshold=0.001, max_iterations=10, converge_by_likelihood=True, **kwargs):
"""
:py:class:`bob.learn.misc.MAP_GMMTrainer` constructor
Keyword Parameters:
- gmm_base_trainer
- The base trainer (:py:class:`bob.learn.misc.GMMBaseTrainer`)
+ update_means
+
+ update_variances
+
+ update_weights
+
prior_gmm
A :py:class:`bob.learn.misc.GMMMachine` to be adapted
convergence_threshold
@@ -34,10 +38,10 @@ class MAP_GMMTrainer(_MAP_GMMTrainer):
if kwargs.get('alpha')!=None:
alpha = kwargs.get('alpha')
- _MAP_GMMTrainer.__init__(self, gmm_base_trainer, prior_gmm, alpha=alpha)
+ _MAP_GMMTrainer.__init__(self, prior_gmm,alpha=alpha, update_means=update_means, update_variances=update_variances,update_weights=update_weights)
else:
relevance_factor = kwargs.get('relevance_factor')
- _MAP_GMMTrainer.__init__(self, gmm_base_trainer, prior_gmm, relevance_factor=relevance_factor)
+ _MAP_GMMTrainer.__init__(self, prior_gmm, relevance_factor=relevance_factor, update_means=update_means, update_variances=update_variances,update_weights=update_weights)
self.convergence_threshold = convergence_threshold
self.max_iterations = max_iterations
@@ -67,10 +71,10 @@ class MAP_GMMTrainer(_MAP_GMMTrainer):
#eStep
- self.gmm_base_trainer.eStep(gmm_machine, data);
+ self.eStep(gmm_machine, data);
if(self.converge_by_likelihood):
- average_output = self.gmm_base_trainer.compute_likelihood(gmm_machine);
+ average_output = self.compute_likelihood(gmm_machine);
for i in range(self.max_iterations):
#saves average output from last iteration
@@ -80,11 +84,11 @@ class MAP_GMMTrainer(_MAP_GMMTrainer):
self.mStep(gmm_machine);
#eStep
- self.gmm_base_trainer.eStep(gmm_machine, data);
+ self.eStep(gmm_machine, data);
#Computes log likelihood if required
if(self.converge_by_likelihood):
- average_output = self.gmm_base_trainer.compute_likelihood(gmm_machine);
+ average_output = self.compute_likelihood(gmm_machine);
#Terminates if converged (and likelihood computation is set)
if abs((average_output_previous - average_output)/average_output_previous) <= self.convergence_threshold:
diff --git a/bob/learn/misc/__ML_gmm_trainer__.py b/bob/learn/misc/__ML_gmm_trainer__.py
index 53c5a75..93a3c6c 100644
--- a/bob/learn/misc/__ML_gmm_trainer__.py
+++ b/bob/learn/misc/__ML_gmm_trainer__.py
@@ -11,13 +11,17 @@ import numpy
# define the class
class ML_GMMTrainer(_ML_GMMTrainer):
- def __init__(self, gmm_base_trainer, convergence_threshold=0.001, max_iterations=10, converge_by_likelihood=True):
+ def __init__(self, update_means=True, update_variances=False, update_weights=False, convergence_threshold=0.001, max_iterations=10, converge_by_likelihood=True):
"""
:py:class:bob.learn.misc.ML_GMMTrainer constructor
Keyword Parameters:
- gmm_base_trainer
- The base trainer (:py:class:`bob.learn.misc.GMMBaseTrainer`
+ update_means
+
+ update_variances
+
+ update_weights
+
convergence_threshold
Convergence threshold
max_iterations
@@ -27,7 +31,7 @@ class ML_GMMTrainer(_ML_GMMTrainer):
"""
- _ML_GMMTrainer.__init__(self, gmm_base_trainer)
+ _ML_GMMTrainer.__init__(self, update_means=update_means, update_variances=update_variances, update_weights=update_weights)
self.convergence_threshold = convergence_threshold
self.max_iterations = max_iterations
self.converge_by_likelihood = converge_by_likelihood
@@ -53,10 +57,10 @@ class ML_GMMTrainer(_ML_GMMTrainer):
#eStep
- self.gmm_base_trainer.eStep(gmm_machine, data);
+ self.eStep(gmm_machine, data);
if(self.converge_by_likelihood):
- average_output = self.gmm_base_trainer.compute_likelihood(gmm_machine);
+ average_output = self.compute_likelihood(gmm_machine);
for i in range(self.max_iterations):
#saves average output from last iteration
@@ -66,11 +70,11 @@ class ML_GMMTrainer(_ML_GMMTrainer):
self.mStep(gmm_machine);
#eStep
- self.gmm_base_trainer.eStep(gmm_machine, data);
+ self.eStep(gmm_machine, data);
#Computes log likelihood if required
if(self.converge_by_likelihood):
- average_output = self.gmm_base_trainer.compute_likelihood(gmm_machine);
+ average_output = self.compute_likelihood(gmm_machine);
#Terminates if converged (and likelihood computation is set)
if abs((average_output_previous - average_output)/average_output_previous) <= self.convergence_threshold:
diff --git a/bob/learn/misc/cpp/MAP_GMMTrainer.cpp b/bob/learn/misc/cpp/MAP_GMMTrainer.cpp
index 0645c2c..d20b150 100644
--- a/bob/learn/misc/cpp/MAP_GMMTrainer.cpp
+++ b/bob/learn/misc/cpp/MAP_GMMTrainer.cpp
@@ -8,8 +8,18 @@
#include <bob.learn.misc/MAP_GMMTrainer.h>
#include <bob.core/check.h>
-bob::learn::misc::MAP_GMMTrainer::MAP_GMMTrainer(boost::shared_ptr<bob::learn::misc::GMMBaseTrainer> gmm_base_trainer, boost::shared_ptr<bob::learn::misc::GMMMachine> prior_gmm, const bool reynolds_adaptation, const double relevance_factor, const double alpha):
- m_gmm_base_trainer(gmm_base_trainer),
+bob::learn::misc::MAP_GMMTrainer::MAP_GMMTrainer(
+ const bool update_means,
+ const bool update_variances,
+ const bool update_weights,
+ const double mean_var_update_responsibilities_threshold,
+
+ const bool reynolds_adaptation,
+ const double relevance_factor,
+ const double alpha,
+ boost::shared_ptr<bob::learn::misc::GMMMachine> prior_gmm):
+
+ m_gmm_base_trainer(update_means, update_variances, update_weights, mean_var_update_responsibilities_threshold),
m_prior_gmm(prior_gmm)
{
m_reynolds_adaptation = reynolds_adaptation;
@@ -37,7 +47,7 @@ void bob::learn::misc::MAP_GMMTrainer::initialize(bob::learn::misc::GMMMachine&
throw std::runtime_error("MAP_GMMTrainer: Prior GMM distribution has not been set");
// Allocate memory for the sufficient statistics and initialise
- m_gmm_base_trainer->initialize(gmm);
+ m_gmm_base_trainer.initialize(gmm);
const size_t n_gaussians = gmm.getNGaussians();
// TODO: check size?
@@ -78,13 +88,13 @@ void bob::learn::misc::MAP_GMMTrainer::mStep(bob::learn::misc::GMMMachine& gmm)
if (!m_reynolds_adaptation)
m_cache_alpha = m_alpha;
else
- m_cache_alpha = m_gmm_base_trainer->getGMMStats().n(i) / (m_gmm_base_trainer->getGMMStats().n(i) + m_relevance_factor);
+ m_cache_alpha = m_gmm_base_trainer.getGMMStats().n(i) / (m_gmm_base_trainer.getGMMStats().n(i) + m_relevance_factor);
// - Update weights if requested
// Equation 11 of Reynolds et al., "Speaker Verification Using Adapted Gaussian Mixture Models", Digital Signal Processing, 2000
- if (m_gmm_base_trainer->getUpdateWeights()) {
+ if (m_gmm_base_trainer.getUpdateWeights()) {
// Calculate the maximum likelihood weights
- m_cache_ml_weights = m_gmm_base_trainer->getGMMStats().n / static_cast<double>(m_gmm_base_trainer->getGMMStats().T); //cast req. for linux/32-bits & osx
+ m_cache_ml_weights = m_gmm_base_trainer.getGMMStats().n / static_cast<double>(m_gmm_base_trainer.getGMMStats().T); //cast req. for linux/32-bits & osx
// Get the prior weights
const blitz::Array<double,1>& prior_weights = m_prior_gmm->getWeights();
@@ -104,35 +114,35 @@ void bob::learn::misc::MAP_GMMTrainer::mStep(bob::learn::misc::GMMMachine& gmm)
// Update GMM parameters
// - Update means if requested
// Equation 12 of Reynolds et al., "Speaker Verification Using Adapted Gaussian Mixture Models", Digital Signal Processing, 2000
- if (m_gmm_base_trainer->getUpdateMeans()) {
+ if (m_gmm_base_trainer.getUpdateMeans()) {
// Calculate new means
for (size_t i=0; i<n_gaussians; ++i) {
const blitz::Array<double,1>& prior_means = m_prior_gmm->getGaussian(i)->getMean();
blitz::Array<double,1>& means = gmm.getGaussian(i)->updateMean();
- if (m_gmm_base_trainer->getGMMStats().n(i) < m_gmm_base_trainer->getMeanVarUpdateResponsibilitiesThreshold()) {
+ if (m_gmm_base_trainer.getGMMStats().n(i) < m_gmm_base_trainer.getMeanVarUpdateResponsibilitiesThreshold()) {
means = prior_means;
}
else {
// Use the maximum likelihood means
- means = m_cache_alpha(i) * (m_gmm_base_trainer->getGMMStats().sumPx(i,blitz::Range::all()) / m_gmm_base_trainer->getGMMStats().n(i)) + (1-m_cache_alpha(i)) * prior_means;
+ means = m_cache_alpha(i) * (m_gmm_base_trainer.getGMMStats().sumPx(i,blitz::Range::all()) / m_gmm_base_trainer.getGMMStats().n(i)) + (1-m_cache_alpha(i)) * prior_means;
}
}
}
// - Update variance if requested
// Equation 13 of Reynolds et al., "Speaker Verification Using Adapted Gaussian Mixture Models", Digital Signal Processing, 2000
- if (m_gmm_base_trainer->getUpdateVariances()) {
+ if (m_gmm_base_trainer.getUpdateVariances()) {
// Calculate new variances (equation 13)
for (size_t i=0; i<n_gaussians; ++i) {
const blitz::Array<double,1>& prior_means = m_prior_gmm->getGaussian(i)->getMean();
blitz::Array<double,1>& means = gmm.getGaussian(i)->updateMean();
const blitz::Array<double,1>& prior_variances = m_prior_gmm->getGaussian(i)->getVariance();
blitz::Array<double,1>& variances = gmm.getGaussian(i)->updateVariance();
- if (m_gmm_base_trainer->getGMMStats().n(i) < m_gmm_base_trainer->getMeanVarUpdateResponsibilitiesThreshold()) {
+ if (m_gmm_base_trainer.getGMMStats().n(i) < m_gmm_base_trainer.getMeanVarUpdateResponsibilitiesThreshold()) {
variances = (prior_variances + prior_means) - blitz::pow2(means);
}
else {
- variances = m_cache_alpha(i) * m_gmm_base_trainer->getGMMStats().sumPxx(i,blitz::Range::all()) / m_gmm_base_trainer->getGMMStats().n(i) + (1-m_cache_alpha(i)) * (prior_variances + prior_means) - blitz::pow2(means);
+ variances = m_cache_alpha(i) * m_gmm_base_trainer.getGMMStats().sumPxx(i,blitz::Range::all()) / m_gmm_base_trainer.getGMMStats().n(i) + (1-m_cache_alpha(i)) * (prior_variances + prior_means) - blitz::pow2(means);
}
gmm.getGaussian(i)->applyVarianceThresholds();
}
diff --git a/bob/learn/misc/cpp/ML_GMMTrainer.cpp b/bob/learn/misc/cpp/ML_GMMTrainer.cpp
index 84ecc5c..f08fb2f 100644
--- a/bob/learn/misc/cpp/ML_GMMTrainer.cpp
+++ b/bob/learn/misc/cpp/ML_GMMTrainer.cpp
@@ -8,8 +8,13 @@
#include <bob.learn.misc/ML_GMMTrainer.h>
#include <algorithm>
-bob::learn::misc::ML_GMMTrainer::ML_GMMTrainer(boost::shared_ptr<bob::learn::misc::GMMBaseTrainer> gmm_base_trainer):
- m_gmm_base_trainer(gmm_base_trainer)
+bob::learn::misc::ML_GMMTrainer::ML_GMMTrainer(
+ const bool update_means,
+ const bool update_variances,
+ const bool update_weights,
+ const double mean_var_update_responsibilities_threshold
+):
+ m_gmm_base_trainer(update_means, update_variances, update_weights, mean_var_update_responsibilities_threshold)
{}
@@ -23,7 +28,7 @@ bob::learn::misc::ML_GMMTrainer::~ML_GMMTrainer()
void bob::learn::misc::ML_GMMTrainer::initialize(bob::learn::misc::GMMMachine& gmm)
{
- m_gmm_base_trainer->initialize(gmm);
+ m_gmm_base_trainer.initialize(gmm);
// Allocate cache
size_t n_gaussians = gmm.getNGaussians();
@@ -38,24 +43,24 @@ void bob::learn::misc::ML_GMMTrainer::mStep(bob::learn::misc::GMMMachine& gmm)
// - Update weights if requested
// Equation 9.26 of Bishop, "Pattern recognition and machine learning", 2006
- if (m_gmm_base_trainer->getUpdateWeights()) {
+ if (m_gmm_base_trainer.getUpdateWeights()) {
blitz::Array<double,1>& weights = gmm.updateWeights();
- weights = m_gmm_base_trainer->getGMMStats().n / static_cast<double>(m_gmm_base_trainer->getGMMStats().T); //cast req. for linux/32-bits & osx
+ weights = m_gmm_base_trainer.getGMMStats().n / static_cast<double>(m_gmm_base_trainer.getGMMStats().T); //cast req. for linux/32-bits & osx
// Recompute the log weights in the cache of the GMMMachine
gmm.recomputeLogWeights();
}
// Generate a thresholded version of m_ss.n
for(size_t i=0; i<n_gaussians; ++i)
- m_cache_ss_n_thresholded(i) = std::max(m_gmm_base_trainer->getGMMStats().n(i), m_gmm_base_trainer->getMeanVarUpdateResponsibilitiesThreshold());
+ m_cache_ss_n_thresholded(i) = std::max(m_gmm_base_trainer.getGMMStats().n(i), m_gmm_base_trainer.getMeanVarUpdateResponsibilitiesThreshold());
// Update GMM parameters using the sufficient statistics (m_ss)
// - Update means if requested
// Equation 9.24 of Bishop, "Pattern recognition and machine learning", 2006
- if (m_gmm_base_trainer->getUpdateMeans()) {
+ if (m_gmm_base_trainer.getUpdateMeans()) {
for(size_t i=0; i<n_gaussians; ++i) {
blitz::Array<double,1>& means = gmm.getGaussian(i)->updateMean();
- means = m_gmm_base_trainer->getGMMStats().sumPx(i, blitz::Range::all()) / m_cache_ss_n_thresholded(i);
+ means = m_gmm_base_trainer.getGMMStats().sumPx(i, blitz::Range::all()) / m_cache_ss_n_thresholded(i);
}
}
@@ -64,11 +69,11 @@ void bob::learn::misc::ML_GMMTrainer::mStep(bob::learn::misc::GMMMachine& gmm)
// ...but we use the "computational formula for the variance", i.e.
// var = 1/n * sum (P(x-mean)(x-mean))
// = 1/n * sum (Pxx) - mean^2
- if (m_gmm_base_trainer->getUpdateVariances()) {
+ if (m_gmm_base_trainer.getUpdateVariances()) {
for(size_t i=0; i<n_gaussians; ++i) {
const blitz::Array<double,1>& means = gmm.getGaussian(i)->getMean();
blitz::Array<double,1>& variances = gmm.getGaussian(i)->updateVariance();
- variances = m_gmm_base_trainer->getGMMStats().sumPxx(i, blitz::Range::all()) / m_cache_ss_n_thresholded(i) - blitz::pow2(means);
+ variances = m_gmm_base_trainer.getGMMStats().sumPxx(i, blitz::Range::all()) / m_cache_ss_n_thresholded(i) - blitz::pow2(means);
gmm.getGaussian(i)->applyVarianceThresholds();
}
}
diff --git a/bob/learn/misc/include/bob.learn.misc/GMMBaseTrainer.h b/bob/learn/misc/include/bob.learn.misc/GMMBaseTrainer.h
index 086fdc2..4015055 100644
--- a/bob/learn/misc/include/bob.learn.misc/GMMBaseTrainer.h
+++ b/bob/learn/misc/include/bob.learn.misc/GMMBaseTrainer.h
@@ -30,9 +30,9 @@ class GMMBaseTrainer
* @brief Default constructor
*/
GMMBaseTrainer(const bool update_means=true,
- const bool update_variances=false, const bool update_weights=false,
- const double mean_var_update_responsibilities_threshold =
- std::numeric_limits<double>::epsilon());
+ const bool update_variances=false,
+ const bool update_weights=false,
+ const double mean_var_update_responsibilities_threshold = std::numeric_limits<double>::epsilon());
/**
* @brief Copy constructor
@@ -47,7 +47,7 @@ class GMMBaseTrainer
/**
* @brief Initialization before the EM steps
*/
- virtual void initialize(bob::learn::misc::GMMMachine& gmm);
+ void initialize(bob::learn::misc::GMMMachine& gmm);
/**
* @brief Calculates and saves statistics across the dataset,
@@ -58,14 +58,14 @@ class GMMBaseTrainer
* The statistics, m_ss, will be used in the mStep() that follows.
* Implements EMTrainer::eStep(double &)
*/
- virtual void eStep(bob::learn::misc::GMMMachine& gmm,
+ void eStep(bob::learn::misc::GMMMachine& gmm,
const blitz::Array<double,2>& data);
/**
* @brief Computes the likelihood using current estimates of the latent
* variables
*/
- virtual double computeLikelihood(bob::learn::misc::GMMMachine& gmm);
+ double computeLikelihood(bob::learn::misc::GMMMachine& gmm);
/**
diff --git a/bob/learn/misc/include/bob.learn.misc/MAP_GMMTrainer.h b/bob/learn/misc/include/bob.learn.misc/MAP_GMMTrainer.h
index 42e4552..c6c7cf7 100644
--- a/bob/learn/misc/include/bob.learn.misc/MAP_GMMTrainer.h
+++ b/bob/learn/misc/include/bob.learn.misc/MAP_GMMTrainer.h
@@ -26,7 +26,15 @@ class MAP_GMMTrainer
/**
* @brief Default constructor
*/
- MAP_GMMTrainer(boost::shared_ptr<bob::learn::misc::GMMBaseTrainer> gmm_base_trainer, boost::shared_ptr<bob::learn::misc::GMMMachine> prior_gmm, const bool reynolds_adaptation=false, const double relevance_factor=4, const double alpha=0.5);
+ MAP_GMMTrainer(
+ const bool update_means=true,
+ const bool update_variances=false,
+ const bool update_weights=false,
+ const double mean_var_update_responsibilities_threshold = std::numeric_limits<double>::epsilon(),
+ const bool reynolds_adaptation=false,
+ const double relevance_factor=4,
+ const double alpha=0.5,
+ boost::shared_ptr<bob::learn::misc::GMMMachine> prior_gmm = 0);
/**
* @brief Copy constructor
@@ -41,7 +49,7 @@ class MAP_GMMTrainer
/**
* @brief Initialization
*/
- virtual void initialize(bob::learn::misc::GMMMachine& gmm);
+ void initialize(bob::learn::misc::GMMMachine& gmm);
/**
* @brief Assigns from a different MAP_GMMTrainer
@@ -71,6 +79,21 @@ class MAP_GMMTrainer
*/
bool setPriorGMM(boost::shared_ptr<bob::learn::misc::GMMMachine> prior_gmm);
+ /**
+ * @brief Calculates and saves statistics across the dataset,
+ * and saves these as m_ss. Calculates the average
+ * log likelihood of the observations given the GMM,
+ * and returns this in average_log_likelihood.
+ *
+ * The statistics, m_ss, will be used in the mStep() that follows.
+ * Implements EMTrainer::eStep(double &)
+ */
+ void eStep(bob::learn::misc::GMMMachine& gmm,
+ const blitz::Array<double,2>& data){
+ m_gmm_base_trainer.eStep(gmm,data);
+ }
+
+
/**
* @brief Performs a maximum a posteriori (MAP) update of the GMM
* parameters using the accumulated statistics in m_ss and the
@@ -80,13 +103,12 @@ class MAP_GMMTrainer
void mStep(bob::learn::misc::GMMMachine& gmm);
/**
- * @brief Use a Torch3-like adaptation rule rather than Reynolds'one
- * In this case, alpha is a configuration variable rather than a function of the zeroth
- * order statistics and a relevance factor (should be in range [0,1])
+ * @brief Computes the likelihood using current estimates of the latent
+ * variables
*/
- //void setT3MAP(const double alpha) { m_T3_adaptation = true; m_T3_alpha = alpha; }
- //void unsetT3MAP() { m_T3_adaptation = false; }
-
+ double computeLikelihood(bob::learn::misc::GMMMachine& gmm){
+ return m_gmm_base_trainer.computeLikelihood(gmm);
+ }
bool getReynoldsAdaptation()
{return m_reynolds_adaptation;}
@@ -109,13 +131,6 @@ class MAP_GMMTrainer
{m_alpha = alpha;}
- boost::shared_ptr<bob::learn::misc::GMMBaseTrainer> getGMMBaseTrainer()
- {return m_gmm_base_trainer;}
-
- void setGMMBaseTrainer(boost::shared_ptr<bob::learn::misc::GMMBaseTrainer> gmm_base_trainer)
- {m_gmm_base_trainer = gmm_base_trainer;}
-
-
protected:
/**
@@ -123,12 +138,10 @@ class MAP_GMMTrainer
*/
double m_relevance_factor;
-
/**
Base Trainer for the MAP algorithm. Basically implements the e-step
*/
- boost::shared_ptr<bob::learn::misc::GMMBaseTrainer> m_gmm_base_trainer;
-
+ bob::learn::misc::GMMBaseTrainer m_gmm_base_trainer;
/**
* The GMM to use as a prior for MAP adaptation.
diff --git a/bob/learn/misc/include/bob.learn.misc/ML_GMMTrainer.h b/bob/learn/misc/include/bob.learn.misc/ML_GMMTrainer.h
index 09b3db1..13cda74 100644
--- a/bob/learn/misc/include/bob.learn.misc/ML_GMMTrainer.h
+++ b/bob/learn/misc/include/bob.learn.misc/ML_GMMTrainer.h
@@ -27,7 +27,10 @@ class ML_GMMTrainer{
/**
* @brief Default constructor
*/
- ML_GMMTrainer(boost::shared_ptr<bob::learn::misc::GMMBaseTrainer> gmm_base_trainer);
+ ML_GMMTrainer(const bool update_means=true,
+ const bool update_variances=false,
+ const bool update_weights=false,
+ const double mean_var_update_responsibilities_threshold = std::numeric_limits<double>::epsilon());
/**
* @brief Copy constructor
@@ -42,14 +45,37 @@ class ML_GMMTrainer{
/**
* @brief Initialisation before the EM steps
*/
- virtual void initialize(bob::learn::misc::GMMMachine& gmm);
+ void initialize(bob::learn::misc::GMMMachine& gmm);
+
+ /**
+ * @brief Calculates and saves statistics across the dataset,
+ * and saves these as m_ss. Calculates the average
+ * log likelihood of the observations given the GMM,
+ * and returns this in average_log_likelihood.
+ *
+ * The statistics, m_ss, will be used in the mStep() that follows.
+ * Implements EMTrainer::eStep(double &)
+ */
+ void eStep(bob::learn::misc::GMMMachine& gmm,
+ const blitz::Array<double,2>& data){
+ m_gmm_base_trainer.eStep(gmm,data);
+ }
/**
* @brief Performs a maximum likelihood (ML) update of the GMM parameters
* using the accumulated statistics in m_ss
* Implements EMTrainer::mStep()
*/
- virtual void mStep(bob::learn::misc::GMMMachine& gmm);
+ void mStep(bob::learn::misc::GMMMachine& gmm);
+
+ /**
+ * @brief Computes the likelihood using current estimates of the latent
+ * variables
+ */
+ double computeLikelihood(bob::learn::misc::GMMMachine& gmm){
+ return m_gmm_base_trainer.computeLikelihood(gmm);
+ }
+
/**
* @brief Assigns from a different ML_GMMTrainer
@@ -73,19 +99,12 @@ class ML_GMMTrainer{
const double a_epsilon=1e-8) const;
- boost::shared_ptr<bob::learn::misc::GMMBaseTrainer> getGMMBaseTrainer()
- {return m_gmm_base_trainer;}
-
- void setGMMBaseTrainer(boost::shared_ptr<bob::learn::misc::GMMBaseTrainer> gmm_base_trainer)
- {m_gmm_base_trainer = gmm_base_trainer;}
-
-
protected:
/**
Base Trainer for the MAP algorithm. Basically implements the e-step
*/
- boost::shared_ptr<bob::learn::misc::GMMBaseTrainer> m_gmm_base_trainer;
+ bob::learn::misc::GMMBaseTrainer m_gmm_base_trainer;
private:
diff --git a/bob/learn/misc/main.cpp b/bob/learn/misc/main.cpp
index 9e6dc2c..10e1e8a 100644
--- a/bob/learn/misc/main.cpp
+++ b/bob/learn/misc/main.cpp
@@ -75,7 +75,7 @@ static PyObject* create_module (void) {
if (!init_BobLearnMiscGMMMachine(module)) return 0;
if (!init_BobLearnMiscKMeansMachine(module)) return 0;
if (!init_BobLearnMiscKMeansTrainer(module)) return 0;
- if (!init_BobLearnMiscGMMBaseTrainer(module)) return 0;
+ //if (!init_BobLearnMiscGMMBaseTrainer(module)) return 0;
if (!init_BobLearnMiscMLGMMTrainer(module)) return 0;
if (!init_BobLearnMiscMAPGMMTrainer(module)) return 0;
diff --git a/bob/learn/misc/main.h b/bob/learn/misc/main.h
index 42f5f12..5be119c 100644
--- a/bob/learn/misc/main.h
+++ b/bob/learn/misc/main.h
@@ -26,7 +26,7 @@
#include <bob.learn.misc/KMeansMachine.h>
#include <bob.learn.misc/KMeansTrainer.h>
-#include <bob.learn.misc/GMMBaseTrainer.h>
+//#include <bob.learn.misc/GMMBaseTrainer.h>
#include <bob.learn.misc/ML_GMMTrainer.h>
#include <bob.learn.misc/MAP_GMMTrainer.h>
@@ -145,6 +145,7 @@ int PyBobLearnMiscKMeansTrainer_Check(PyObject* o);
// GMMBaseTrainer
+/*
typedef struct {
PyObject_HEAD
boost::shared_ptr<bob::learn::misc::GMMBaseTrainer> cxx;
@@ -153,7 +154,7 @@ typedef struct {
extern PyTypeObject PyBobLearnMiscGMMBaseTrainer_Type;
bool init_BobLearnMiscGMMBaseTrainer(PyObject* module);
int PyBobLearnMiscGMMBaseTrainer_Check(PyObject* o);
-
+*/
// ML_GMMTrainer
typedef struct {
diff --git a/bob/learn/misc/test_em.py b/bob/learn/misc/test_em.py
index dffb86a..88070de 100644
--- a/bob/learn/misc/test_em.py
+++ b/bob/learn/misc/test_em.py
@@ -14,7 +14,7 @@ import bob.io.base
from bob.io.base.test_utils import datafile
from . import KMeansMachine, GMMMachine, KMeansTrainer, \
- GMMBaseTrainer, ML_GMMTrainer, MAP_GMMTrainer
+ ML_GMMTrainer, MAP_GMMTrainer
#, MAP_GMMTrainer
@@ -50,7 +50,7 @@ def test_gmm_ML_1():
ar = bob.io.base.load(datafile("faithful.torch3_f64.hdf5", __name__))
gmm = loadGMM()
- ml_gmmtrainer = ML_GMMTrainer(GMMBaseTrainer(True, True, True))
+ ml_gmmtrainer = ML_GMMTrainer(True, True, True)
ml_gmmtrainer.train(gmm, ar)
#config = bob.io.base.HDF5File(datafile('gmm_ML.hdf5", __name__), 'w')
@@ -82,7 +82,7 @@ def test_gmm_ML_2():
prior = 0.001
max_iter_gmm = 25
accuracy = 0.00001
- ml_gmmtrainer = ML_GMMTrainer(GMMBaseTrainer(True, True, True, prior), converge_by_likelihood=True)
+ ml_gmmtrainer = ML_GMMTrainer(True, True, True, prior, converge_by_likelihood=True)
ml_gmmtrainer.max_iterations = max_iter_gmm
ml_gmmtrainer.convergence_threshold = accuracy
@@ -97,8 +97,6 @@ def test_gmm_ML_2():
weightsML_ref = bob.io.base.load(datafile('weightsAfterML.hdf5', __name__))
- print sum(sum(gmm.means - meansML_ref))
-
# Compare to current results
assert equals(gmm.means, meansML_ref, 3e-3)
assert equals(gmm.variances, variancesML_ref, 3e-3)
@@ -115,7 +113,7 @@ def test_gmm_MAP_1():
gmm = GMMMachine(bob.io.base.HDF5File(datafile("gmm_ML.hdf5", __name__)))
gmmprior = GMMMachine(bob.io.base.HDF5File(datafile("gmm_ML.hdf5", __name__)))
- map_gmmtrainer = MAP_GMMTrainer(GMMBaseTrainer(True, False, False),gmmprior, relevance_factor=4.)
+ map_gmmtrainer = MAP_GMMTrainer(update_means=True, update_variances=False, update_weights=False, prior_gmm=gmmprior, relevance_factor=4.)
#map_gmmtrainer.set_prior_gmm(gmmprior)
map_gmmtrainer.train(gmm, ar)
@@ -142,7 +140,7 @@ def test_gmm_MAP_2():
gmm.variances = variances
gmm.weights = weights
- map_adapt = MAP_GMMTrainer(GMMBaseTrainer(True, False, False, mean_var_update_responsibilities_threshold=0.),gmm, relevance_factor=4.)
+ map_adapt = MAP_GMMTrainer(update_means=True, update_variances=False, update_weights=False, mean_var_update_responsibilities_threshold=0.,prior_gmm=gmm, relevance_factor=4.)
#map_adapt.set_prior_gmm(gmm)
gmm_adapted = GMMMachine(2,50)
@@ -186,7 +184,7 @@ def test_gmm_MAP_3():
max_iter_gmm = 1
accuracy = 0.00001
map_factor = 0.5
- map_gmmtrainer = MAP_GMMTrainer(GMMBaseTrainer(True, False, False, prior), prior_gmm, alpha=map_factor)
+ map_gmmtrainer = MAP_GMMTrainer(update_means=True, update_variances=False, update_weights=False, convergence_threshold=prior, prior_gmm=prior_gmm, alpha=map_factor)
map_gmmtrainer.max_iterations = max_iter_gmm
map_gmmtrainer.convergence_threshold = accuracy
diff --git a/setup.py b/setup.py
index aa58b9c..875085d 100644
--- a/setup.py
+++ b/setup.py
@@ -113,7 +113,7 @@ setup(
"bob/learn/misc/gmm_machine.cpp",
"bob/learn/misc/kmeans_machine.cpp",
"bob/learn/misc/kmeans_trainer.cpp",
- "bob/learn/misc/gmm_base_trainer.cpp",
+
"bob/learn/misc/ML_gmm_trainer.cpp",
"bob/learn/misc/MAP_gmm_trainer.cpp",
--
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