diff --git a/bob/learn/em/gaussian.cpp b/bob/learn/em/gaussian.cpp
index 8021a718ff735773748763bea64554738d02015d..35c6e94ba391c993a224bc4aba09ed5437b28895 100644
--- a/bob/learn/em/gaussian.cpp
+++ b/bob/learn/em/gaussian.cpp
@@ -375,7 +375,7 @@ static PyObject* PyBobLearnEMGaussian_resize(PyBobLearnEMGaussianObject* self, P
static auto log_likelihood = bob::extension::FunctionDoc(
"log_likelihood",
"Output the log likelihood of the sample, x. The input size is checked.",
- ".. note:: The :py:meth:`__call__` function is an alias for this.",
+ ".. note:: The ``__call__`` function is an alias for this.",
true
)
.add_prototype("input","output")
diff --git a/bob/learn/em/gmm_machine.cpp b/bob/learn/em/gmm_machine.cpp
index f9cec3cc09b0eed4329c249695110677b61f6dbe..061f39f7958f6b668d4640b476caadc14c4ef2eb 100644
--- a/bob/learn/em/gmm_machine.cpp
+++ b/bob/learn/em/gmm_machine.cpp
@@ -643,7 +643,7 @@ static PyObject* PyBobLearnEMGMMMachine_resize(PyBobLearnEMGMMMachineObject* sel
static auto log_likelihood = bob::extension::FunctionDoc(
"log_likelihood",
"Output the log likelihood of the sample, x, i.e. :math:`log(p(x|GMM))`. Inputs are checked.",
- ".. note:: The :py:meth:`__call__` function is an alias for this. \n "
+ ".. note:: The ``__call__`` function is an alias for this. \n "
"If `input` is 2D the average along the samples will be computed (:math:`\\frac{log(p(x|GMM))}{N}`) ",
true
)
diff --git a/bob/learn/em/isv_base.cpp b/bob/learn/em/isv_base.cpp
index 0a615035e2f26982efc0f7ec28b84b02f196c899..a3f09559a52c1e27a9a8f962db7c76c274a10e98 100644
--- a/bob/learn/em/isv_base.cpp
+++ b/bob/learn/em/isv_base.cpp
@@ -182,11 +182,10 @@ PyObject* PyBobLearnEMISVBase_getShape(PyBobLearnEMISVBaseObject* self, void*) {
static auto supervector_length = bob::extension::VariableDoc(
"supervector_length",
"int",
-
- "Returns the supervector length."
- "NGaussians x NInputs: Number of Gaussian components by the feature dimensionality",
-
- "@warning An exception is thrown if no Universal Background Model has been set yet."
+ "Returns the supervector length.",
+ "NGaussians x NInputs: Number of Gaussian components by the feature dimensionality"
+ "WARNING An exception is thrown if no Universal Background Model has been set yet."
+ ""
);
PyObject* PyBobLearnEMISVBase_getSupervectorLength(PyBobLearnEMISVBaseObject* self, void*) {
BOB_TRY
diff --git a/bob/learn/em/isv_machine.cpp b/bob/learn/em/isv_machine.cpp
index 9d5748b4c6991503dfa95a95dcf2f77f282b5d6b..375bf48feb5f49edf1ec16c5128488ce88db0438 100644
--- a/bob/learn/em/isv_machine.cpp
+++ b/bob/learn/em/isv_machine.cpp
@@ -172,10 +172,10 @@ static auto supervector_length = bob::extension::VariableDoc(
"supervector_length",
"int",
- "Returns the supervector length."
- "NGaussians x NInputs: Number of Gaussian components by the feature dimensionality",
-
+ "Returns the supervector length.",
+ "NGaussians x NInputs: Number of Gaussian components by the feature dimensionality"
"@warning An exception is thrown if no Universal Background Model has been set yet."
+ ""
);
PyObject* PyBobLearnEMISVMachine_getSupervectorLength(PyBobLearnEMISVMachineObject* self, void*) {
BOB_TRY
diff --git a/bob/learn/em/ivector_machine.cpp b/bob/learn/em/ivector_machine.cpp
index c29076c51f3242b38b3157def92bae229e546b52..da4de2d89526d8d099431d219568e596d09ee328 100644
--- a/bob/learn/em/ivector_machine.cpp
+++ b/bob/learn/em/ivector_machine.cpp
@@ -187,10 +187,10 @@ static auto supervector_length = bob::extension::VariableDoc(
"supervector_length",
"int",
- "Returns the supervector length."
- "NGaussians x NInputs: Number of Gaussian components by the feature dimensionality",
-
+ "Returns the supervector length.",
+ "NGaussians x NInputs: Number of Gaussian components by the feature dimensionality"
"@warning An exception is thrown if no Universal Background Model has been set yet."
+ ""
);
PyObject* PyBobLearnEMIVectorMachine_getSupervectorLength(PyBobLearnEMIVectorMachineObject* self, void*) {
BOB_TRY
@@ -472,7 +472,7 @@ static PyObject* PyBobLearnEMIVectorMachine_IsSimilarTo(PyBobLearnEMIVectorMachi
static auto project = bob::extension::FunctionDoc(
"project",
"Projects the given GMM statistics into the i-vector subspace",
- ".. note:: The :py:meth:`__call__` function is an alias for this function",
+ ".. note:: The ``__call__`` function is an alias for this function",
true
)
.add_prototype("stats")
diff --git a/bob/learn/em/jfa_base.cpp b/bob/learn/em/jfa_base.cpp
index e15c103c9ee9fd9355296505b23cfef47b65e4f4..8a92d65405cf438c36bf95454df8fea52dba9058 100644
--- a/bob/learn/em/jfa_base.cpp
+++ b/bob/learn/em/jfa_base.cpp
@@ -190,10 +190,10 @@ static auto supervector_length = bob::extension::VariableDoc(
"supervector_length",
"int",
- "Returns the supervector length."
- "NGaussians x NInputs: Number of Gaussian components by the feature dimensionality",
-
+ "Returns the supervector length.",
+ "NGaussians x NInputs: Number of Gaussian components by the feature dimensionality"
"@warning An exception is thrown if no Universal Background Model has been set yet."
+ ""
);
PyObject* PyBobLearnEMJFABase_getSupervectorLength(PyBobLearnEMJFABaseObject* self, void*) {
BOB_TRY
diff --git a/bob/learn/em/jfa_machine.cpp b/bob/learn/em/jfa_machine.cpp
index a90dd02b1c65778568436d7a6c8968501b9dcee7..fce093625fcd17aacd68b240e15137dcad332cf4 100644
--- a/bob/learn/em/jfa_machine.cpp
+++ b/bob/learn/em/jfa_machine.cpp
@@ -172,10 +172,10 @@ static auto supervector_length = bob::extension::VariableDoc(
"supervector_length",
"int",
- "Returns the supervector length."
- "NGaussians x NInputs: Number of Gaussian components by the feature dimensionality",
-
+ "Returns the supervector length.",
+ "NGaussians x NInputs: Number of Gaussian components by the feature dimensionality"
"@warning An exception is thrown if no Universal Background Model has been set yet."
+ ""
);
PyObject* PyBobLearnEMJFAMachine_getSupervectorLength(PyBobLearnEMJFAMachineObject* self, void*) {
BOB_TRY
@@ -621,7 +621,7 @@ static PyObject* PyBobLearnEMJFAMachine_ForwardUx(PyBobLearnEMJFAMachineObject*
static auto log_likelihood = bob::extension::FunctionDoc(
"log_likelihood",
"Computes the log-likelihood of the given samples",
- ".. note:: the :py:meth:`__call__` function is an alias for this function.",
+ ".. note:: the ``__call__`` function is an alias for this function.",
true
)
.add_prototype("stats")
diff --git a/bob/learn/em/plda_machine.cpp b/bob/learn/em/plda_machine.cpp
index acd55004b26d4b96b22a9da6a8ae8f38fa825c64..83c9b4223815ee45d06ce60b92d94abaf3369ffc 100644
--- a/bob/learn/em/plda_machine.cpp
+++ b/bob/learn/em/plda_machine.cpp
@@ -301,7 +301,7 @@ int PyBobLearnEMPLDAMachine_setWeightedSum(PyBobLearnEMPLDAMachineObject* self,
static auto log_likelihood = bob::extension::VariableDoc(
"log_likelihood",
"float",
- "",
+ "Get the current log likelihood",
""
);
static PyObject* PyBobLearnEMPLDAMachine_getLogLikelihood(PyBobLearnEMPLDAMachineObject* self, PyObject* args, PyObject* kwargs) {
diff --git a/doc/conf.py b/doc/conf.py
index ae503321b55e9743d36db318dc935a285ace2512..f314ce9b26482c7e0cb4931a83f87095ac4ef8ed 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -79,7 +79,7 @@ source_suffix = '.rst'
master_doc = 'index'
# General information about the project.
-project = u'bob.learn.em'
+project = u'bob.core'
import time
copyright = u'%s, Idiap Research Institute' % time.strftime('%Y')
@@ -131,7 +131,7 @@ pygments_style = 'sphinx'
# Some variables which are useful for generated material
project_variable = project.replace('.', '_')
-short_description = u'Bindings for EM machines and trainers of Bob'
+short_description = u'Core utilities required on all Bob modules'
owner = [u'Idiap Research Institute']
@@ -239,7 +239,8 @@ autodoc_default_flags = [
from bob.extension.utils import link_documentation, load_requirements
sphinx_requirements = "extra-intersphinx.txt"
if os.path.exists(sphinx_requirements):
- intersphinx_mapping = link_documentation(additional_packages=load_requirements(sphinx_requirements))
+ intersphinx_mapping = link_documentation(
+ additional_packages=['python', 'numpy'] + load_requirements(sphinx_requirements))
else:
intersphinx_mapping = link_documentation()
diff --git a/doc/extra-intersphinx.txt b/doc/extra-intersphinx.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e0c2a1af70a9f804401fb9c4654cb2338090983c
--- /dev/null
+++ b/doc/extra-intersphinx.txt
@@ -0,0 +1,2 @@
+# The bob.core>2.0.5 in the requirements.txt is making the bob.core not download
+bob.core
diff --git a/doc/guide.rst b/doc/guide.rst
index 8cf1d57e9154576e0f49eb68419af7642a2a1ce3..95fc5160cada4701feb9f635a53d335569bfa031 100644
--- a/doc/guide.rst
+++ b/doc/guide.rst
@@ -212,7 +212,7 @@ Once the :py:class:`bob.learn.em.JFAMachine` has been configured for a
specific class, the log-likelihood (score) that an input sample belongs to the
enrolled class, can be estimated, by first computing the GMM sufficient
statistics of this input sample, and then calling the
-:py:meth:`bob.learn.em.JFAMachine.forward` on the sufficient statistics.
+:py:meth:`bob.learn.em.JFAMachine.log_likelihood` on the sufficient statistics.
.. doctest::
:options: +NORMALIZE_WHITESPACE
@@ -266,7 +266,7 @@ Once the :py:class:`bob.learn.em.ISVMachine` has been configured for a
specific class, the log-likelihood (score) that an input sample belongs to the
enrolled class, can be estimated, by first computing the GMM sufficient
statistics of this input sample, and then calling the
-:py:meth:`bob.learn.em.ISVMachine.forward` on the sufficient statistics.
+``__call__`` on the sufficient statistics.
.. doctest::
:options: +NORMALIZE_WHITESPACE
@@ -565,7 +565,7 @@ Next, we initialize a trainer, which is an instance of
>>> jfa_trainer = bob.learn.em.JFATrainer()
The training process is started by calling the
-:py:meth:`bob.learn.em.JFATrainer.train`.
+:py:meth:`bob.learn.em.train`.
.. doctest::
:options: +NORMALIZE_WHITESPACE
@@ -626,7 +626,7 @@ Next, we initialize a trainer, which is an instance of
>>> isv_trainer = bob.learn.em.ISVTrainer(relevance_factor=4.) # 4 is the relevance factor
The training process is started by calling the
-:py:meth:`bob.learn.em.ISVTrainer.train`.
+:py:meth:`bob.learn.em.train`.
.. doctest::
:options: +NORMALIZE_WHITESPACE
@@ -675,7 +675,7 @@ Next, we initialize a trainer, which is an instance of
>>> TRAINING_STATS_flatten = [gs11, gs12, gs21, gs22]
The training process is started by calling the
-:py:meth:`bob.learn.em.IVectorTrainer.train`.
+:py:meth:`bob.learn.em.train`.
.. doctest::
:options: +NORMALIZE_WHITESPACE
@@ -717,7 +717,7 @@ dimensionality 3.
Learning a PLDA model can be performed by instantiating the class
:py:class:`bob.learn.em.PLDATrainer`, and calling the
-:py:meth:`bob.learn.em.PLDATrainer.train()` method.
+:py:meth:`bob.learn.em.train` method.
.. doctest::
@@ -769,8 +769,8 @@ separately for each model.
In a verification scenario, there are two possible hypotheses: 1.
:math:`x_{test}` and :math:`x_{enroll}` share the same class. 2.
:math:`x_{test}` and :math:`x_{enroll}` are from different classes. Using the
-methods :py:meth:`bob.learn.em.PLDAMachine.forward` or
-:py:meth:`bob.learn.em.PLDAMachine.__call__` function, the corresponding
+methods :py:meth:`bob.learn.em.PLDAMachine.log_likelihood_ratio` or
+its alias ``__call__`` function, the corresponding
log-likelihood ratio will be computed, which is defined in more formal way by:
:math:`s = \ln(P(x_{test},x_{enroll})) - \ln(P(x_{test})P(x_{enroll}))`