diff --git a/doc/guide.rst b/doc/guide.rst index d9ae724ab7e67cc0b7489bd2a4e96f9db05987bb..56d0526d7e75419b5e152450b9c342f6ada9fcbb 100644 --- a/doc/guide.rst +++ b/doc/guide.rst @@ -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]