diff --git a/bob/learn/misc/MAP_gmm_trainer.cpp b/bob/learn/misc/MAP_gmm_trainer.cpp
index e9b0fde0b2a70f0a9c3d4ac347930b72a73cafaf..66b3f04eee3ad02261476d13bc9e61316fa32574 100644
--- a/bob/learn/misc/MAP_gmm_trainer.cpp
+++ b/bob/learn/misc/MAP_gmm_trainer.cpp
@@ -34,8 +34,8 @@ static auto MAP_GMMTrainer_doc = bob::extension::ClassDoc(
.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 factor? See Eq (14) from [Reynolds2000]_")
- .add_parameter("relevance_factor", "double", "If set the reynolds_adaptation parameters, will apply the Reynolds Adaptation Factor. See Eq (14) from [Reynolds2000]_")
+ .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("other", ":py:class:`bob.learn.misc.MAP_GMMTrainer`", "A MAP_GMMTrainer object to be copied.")
);
diff --git a/bob/learn/misc/ML_gmm_trainer.cpp b/bob/learn/misc/ML_gmm_trainer.cpp
index bddc9dee3749e9d09d0d0511bd3b06b343714ae6..b0b2379fe9cc85692c69f701b7eb56700b30b93a 100644
--- a/bob/learn/misc/ML_gmm_trainer.cpp
+++ b/bob/learn/misc/ML_gmm_trainer.cpp
@@ -221,7 +221,7 @@ static PyObject* PyBobLearnMiscMLGMMTrainer_initialize(PyBobLearnMiscMLGMMTraine
/*** mStep ***/
static auto mStep = bob::extension::FunctionDoc(
"mStep",
- "Performs a maximum likelihood (ML) update of the GMM parameters"
+ "Performs a maximum likelihood (ML) update of the GMM parameters "
"using the accumulated statistics in :py:class:`bob.learn.misc.GMMBaseTrainer.m_ss`",
"See Section 9.2.2 of Bishop, \"Pattern recognition and machine learning\", 2006",
diff --git a/bob/learn/misc/__MAP_gmm_trainer__.py b/bob/learn/misc/__MAP_gmm_trainer__.py
index d20efb52311623425d4ff408444b2acfbf744c29..a41855bcf5650012ab60ea11ae79393c5d541b4b 100644
--- a/bob/learn/misc/__MAP_gmm_trainer__.py
+++ b/bob/learn/misc/__MAP_gmm_trainer__.py
@@ -13,30 +13,28 @@ class MAP_GMMTrainer(_MAP_GMMTrainer):
def __init__(self, gmm_base_trainer, prior_gmm, convergence_threshold=0.001, max_iterations=10, converge_by_likelihood=True, reynolds_adaptation=False, relevance_factor=4., alpha=0.5):
"""
- :py:class:bob.learn.misc.MAP_GMMTrainer constructor
+ :py:class:`bob.learn.misc.MAP_GMMTrainer` constructor
Keyword Parameters:
gmm_base_trainer
- The base trainer (:py:class:`bob.learn.misc.GMMBaseTrainer`
+ The base trainer (:py:class:`bob.learn.misc.GMMBaseTrainer`)
prior_gmm
-
+ A :py:class:`bob.learn.misc.GMMMachine` to be adapted
convergence_threshold
Convergence threshold
max_iterations
Number of maximum iterations
converge_by_likelihood
Tells whether we compute log_likelihood as a convergence criteria, or not
-
reynolds_adaptation
-
+ Will use the Reynolds adaptation procedure? See Eq (14) from [Reynolds2000]_
relevance_factor
-
+ If set the :py:class:`bob.learn.misc.MAP_GMMTrainer.reynolds_adaptation` parameters, will apply the Reynolds Adaptation procedure. See Eq (14) from [Reynolds2000]_
alpha
-
+ Set directly the alpha parameter (Eq (14) from [Reynolds2000]_), ignoring zeroth order statistics as a weighting factor.
"""
- #_MAP_GMMTrainer.__init__(self, gmm_base_trainer, prior_gmm, reynolds_adaptation=reynolds_adaptation, relevance_factor=relevance_factor, alpha=alpha)
- _MAP_GMMTrainer.__init__(self, gmm_base_trainer, prior_gmm, reynolds_adaptation, relevance_factor=relevance_factor, alpha=alpha)
+ _MAP_GMMTrainer.__init__(self, gmm_base_trainer, prior_gmm, reynolds_adaptation=reynolds_adaptation, relevance_factor=relevance_factor, alpha=alpha)
self.convergence_threshold = convergence_threshold
self.max_iterations = max_iterations
diff --git a/bob/learn/misc/gmm_stats.cpp b/bob/learn/misc/gmm_stats.cpp
index f7f38019506dfb3ef5b7a4fb2f6d93b4fc427baf..9f859ffbebe56bd0e367082d9e4a47fdbd074056 100644
--- a/bob/learn/misc/gmm_stats.cpp
+++ b/bob/learn/misc/gmm_stats.cpp
@@ -177,7 +177,7 @@ int PyBobLearnMiscGMMStats_Check(PyObject* o) {
/***** n *****/
static auto n = bob::extension::VariableDoc(
"n",
- "array_like <double, 1D>",
+ "array_like <float, 1D>",
"For each Gaussian, the accumulated sum of responsibilities, i.e. the sum of :math:`P(gaussian_i|x)`"
);
PyObject* PyBobLearnMiscGMMStats_getN(PyBobLearnMiscGMMStatsObject* self, void*){
@@ -204,7 +204,7 @@ int PyBobLearnMiscGMMStats_setN(PyBobLearnMiscGMMStatsObject* self, PyObject* va
/***** sum_px *****/
static auto sum_px = bob::extension::VariableDoc(
"sum_px",
- "array_like <double, 2D> ",
+ "array_like <float, 2D>",
"For each Gaussian, the accumulated sum of responsibility times the sample"
);
PyObject* PyBobLearnMiscGMMStats_getSum_px(PyBobLearnMiscGMMStatsObject* self, void*){
@@ -231,7 +231,7 @@ int PyBobLearnMiscGMMStats_setSum_px(PyBobLearnMiscGMMStatsObject* self, PyObjec
/***** sum_pxx *****/
static auto sum_pxx = bob::extension::VariableDoc(
"sum_pxx",
- "array_like <double, 2D> ",
+ "array_like <float, 2D>",
"For each Gaussian, the accumulated sum of responsibility times the sample squared"
);
PyObject* PyBobLearnMiscGMMStats_getSum_pxx(PyBobLearnMiscGMMStatsObject* self, void*){
@@ -288,7 +288,7 @@ int PyBobLearnMiscGMMStats_setT(PyBobLearnMiscGMMStatsObject* self, PyObject* va
/***** log_likelihood *****/
static auto log_likelihood = bob::extension::VariableDoc(
"log_likelihood",
- "double ",
+ "double",
"The accumulated log likelihood of all samples"
);
PyObject* PyBobLearnMiscGMMStats_getLog_likelihood(PyBobLearnMiscGMMStatsObject* self, void*){