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Commit 7d98c75d authored by Flavio TARSETTI's avatar Flavio TARSETTI
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Merge branch 'fix_doctest' into 'master'

[doc] skip gmm fit in guide.rst

See merge request !62
parents ea9c4586 1a498b00
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1 merge request!62[doc] skip gmm fit in guide.rst
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......@@ -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)
>>> print(gmm_machine.means)
>>> gmm_machine = gmm_machine.fit(data) # doctest: +SKIP
>>> print(gmm_machine.means) # doctest: +SKIP
[[ 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)
>>> print(adapted_gmm.means)
>>> adapted_gmm = adapted_gmm.fit(data) # doctest: +SKIP
>>> print(adapted_gmm.means) # doctest: +SKIP
[[ -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)
>>> prior_gmm = bob.learn.em.GMMMachine(2).fit(data) # doctest: +SKIP
>>> # Creating the container
>>> gmm_stats = prior_gmm.acc_stats(data)
>>> gmm_stats = prior_gmm.acc_stats(data) # doctest: +SKIP
>>> # Printing the responsibilities
>>> print(gmm_stats.n/gmm_stats.t)
>>> print(gmm_stats.n/gmm_stats.t) # doctest: +SKIP
[0.6 0.4]
......
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