From 69f2bb59e98d0f11bb6be072b7bbe49ba44918f1 Mon Sep 17 00:00:00 2001
From: Yannick DAYER <yannick.dayer@idiap.ch>
Date: Fri, 26 Nov 2021 20:39:13 +0100
Subject: [PATCH] [doc] skip doctests of removed C++ modules
---
doc/guide.rst | 23 ++++++++++++++---------
1 file changed, 14 insertions(+), 9 deletions(-)
diff --git a/doc/guide.rst b/doc/guide.rst
index fecf54b..f22277b 100644
--- a/doc/guide.rst
+++ b/doc/guide.rst
@@ -100,7 +100,7 @@ This statistical model is defined in the class
:py:class:`bob.learn.em.GMMMachine` as bellow.
.. doctest::
- :options: +NORMALIZE_WHITESPACE
+ :options: +NORMALIZE_WHITESPACE +SKIP
>>> import bob.learn.em
>>> # Create a GMM with k=2 Gaussians with the dimensionality of 3
@@ -132,7 +132,7 @@ estimator.
.. doctest::
- :options: +NORMALIZE_WHITESPACE
+ :options: +NORMALIZE_WHITESPACE +SKIP
>>> import bob.learn.em
>>> import numpy
@@ -197,7 +197,7 @@ Follow bellow an snippet on how to train a GMM using the MAP estimator.
.. doctest::
- :options: +NORMALIZE_WHITESPACE
+ :options: +NORMALIZE_WHITESPACE +SKIP
>>> import bob.learn.em
>>> import numpy
@@ -275,7 +275,7 @@ prior GMM.
.. doctest::
- :options: +NORMALIZE_WHITESPACE
+ :options: +NORMALIZE_WHITESPACE +SKIP
>>> import bob.learn.em
>>> import numpy
@@ -340,7 +340,7 @@ Intersession variability modeling.
.. doctest::
- :options: +NORMALIZE_WHITESPACE
+ :options: +NORMALIZE_WHITESPACE +SKIP
>>> import bob.learn.em
>>> import numpy
@@ -414,7 +414,7 @@ The JFA statistical model is stored in this container
Intersession variability modeling.
.. doctest::
- :options: +NORMALIZE_WHITESPACE
+ :options: +NORMALIZE_WHITESPACE +SKIP
>>> import bob.learn.em
>>> import numpy
@@ -489,7 +489,7 @@ The iVector statistical model is stored in this container
a Total variability modeling.
.. doctest::
- :options: +NORMALIZE_WHITESPACE
+ :options: +NORMALIZE_WHITESPACE +SKIP
>>> import bob.learn.em
>>> import numpy
@@ -564,7 +564,7 @@ This scoring technique is implemented in :py:func:`bob.learn.em.linear_scoring`.
The snippet bellow shows how to compute scores using this approximation.
.. doctest::
- :options: +NORMALIZE_WHITESPACE
+ :options: +NORMALIZE_WHITESPACE +SKIP
>>> import bob.learn.em
>>> import numpy
@@ -611,7 +611,7 @@ Let us consider a training set of two classes, each with 3 samples of
dimensionality 3.
.. doctest::
- :options: +NORMALIZE_WHITESPACE
+ :options: +NORMALIZE_WHITESPACE +SKIP
>>> data1 = numpy.array(
... [[3,-3,100],
@@ -628,6 +628,7 @@ Learning a PLDA model can be performed by instantiating the class
:py:meth:`bob.learn.em.train` method.
.. doctest::
+ :options: +SKIP
>>> # This creates a PLDABase container for input feature of dimensionality
>>> # 3 and with subspaces F and G of rank 1 and 2, respectively.
@@ -645,6 +646,7 @@ obtained by calling the
:py:meth:`bob.learn.em.PLDAMachine.compute_log_likelihood()` method.
.. doctest::
+ :options: +SKIP
>>> plda = bob.learn.em.PLDAMachine(pldabase)
>>> samples = numpy.array(
@@ -658,6 +660,7 @@ a set of enrollment samples, then, several instances of
the :py:meth:`bob.learn.em.PLDATrainer.enroll()` method as follows.
.. doctest::
+ :options: +SKIP
>>> plda1 = bob.learn.em.PLDAMachine(pldabase)
>>> samples1 = numpy.array(
@@ -675,6 +678,7 @@ several test samples can be computed as previously described, and this
separately for each model.
.. doctest::
+ :options: +SKIP
>>> sample = numpy.array([3.2,-3.3,58], dtype=numpy.float64)
>>> l1 = plda1.compute_log_likelihood(sample)
@@ -691,6 +695,7 @@ computed, which is defined in more formal way by:
:math:`s = \ln(P(x_{test},x_{enroll})) - \ln(P(x_{test})P(x_{enroll}))`
.. doctest::
+ :options: +SKIP
>>> s1 = plda1(sample)
>>> s2 = plda2(sample)
--
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