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bob
bob.learn.em
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f32c0b37
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f32c0b37
authored
2 years ago
by
Yannick DAYER
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Merge branch 'gitlab-test' into 'master'
Remove doctest skips See merge request
!65
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1 merge request
!65
Remove doctest skips
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f32c0b37
...
...
@@ -98,7 +98,7 @@ This statistical model is defined in the class
:py:class:`bob.learn.em.GMMMachine` as bellow.
.. doctest::
:options: +NORMALIZE_WHITESPACE
+SKIP
:options: +NORMALIZE_WHITESPACE
>>> import bob.learn.em
>>> # Create a GMM with k=2 Gaussians
...
...
@@ -145,8 +145,8 @@ estimator.
>>> # In this setup, kmeans is used to initialize the means, variances and weights of the gaussians
>>> gmm_machine = bob.learn.em.GMMMachine(n_gaussians=2, trainer="ml")
>>> # Training
>>> gmm_machine = gmm_machine.fit(data)
# doctest: +SKIP
>>> print(gmm_machine.means)
# doctest: +SKIP
>>> gmm_machine = gmm_machine.fit(data)
>>> print(gmm_machine.means)
[[ 3.5 -3.5 99. ]
[ -6. 6. -100.5]]
...
...
@@ -206,8 +206,8 @@ Follow bellow an snippet on how to train a GMM using the MAP estimator.
>>> # note that we have set `trainer="map"`, so we use the Maximum a posteriori estimator
>>> adapted_gmm = bob.learn.em.GMMMachine(2, ubm=prior_gmm, trainer="map")
>>> # Training
>>> adapted_gmm = adapted_gmm.fit(data)
# doctest: +SKIP
>>> print(adapted_gmm.means)
# doctest: +SKIP
>>> adapted_gmm = adapted_gmm.fit(data)
>>> print(adapted_gmm.means)
[[ -4. 2.3 -10.5 ]
[ 0.944 -1.833 36.889]]
...
...
@@ -271,11 +271,11 @@ prior GMM.
... [1.2, 1.4, 1],
... [0.8, 1., 1]], dtype='float64')
>>> # Training a GMM with 2 Gaussians of dimension 3
>>> prior_gmm = bob.learn.em.GMMMachine(2).fit(data)
# doctest: +SKIP
>>> prior_gmm = bob.learn.em.GMMMachine(2).fit(data)
>>> # Creating the container
>>> gmm_stats = prior_gmm.acc_stats(data)
# doctest: +SKIP
>>> gmm_stats = prior_gmm.acc_stats(data)
>>> # Printing the responsibilities
>>> print(gmm_stats.n/gmm_stats.t)
# doctest: +SKIP
>>> print(gmm_stats.n/gmm_stats.t)
[0.6 0.4]
...
...
@@ -331,7 +331,7 @@ The snippet bellow shows how to:
>>> y = np.hstack((np.zeros(10, dtype=int), np.ones(10, dtype=int)))
>>> # Create an ISV machine with a UBM of 2 gaussians
>>> isv_machine = bob.learn.em.ISVMachine(r_U=2, ubm_kwargs=dict(n_gaussians=2))
>>> _ = isv_machine.fit_using_array(X, y)
# DOCTEST: +SKIP_
>>> _ = isv_machine.fit_using_array(X, y)
>>> # Alternatively, you can create a pipeline of a GMMMachine and an ISVMachine
>>> # and call pipeline.fit(X, y) instead of calling isv.fit_using_array(X, y)
>>> isv_machine.U
...
...
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