diff --git a/doc/guide.rst b/doc/guide.rst index 661809d91a4654f4874d2950eac7e01ef276ddd0..6f4da1df5773352924ad87c109ec9a04baa113de 100644 --- a/doc/guide.rst +++ b/doc/guide.rst @@ -228,7 +228,7 @@ The snippet bellow shows how to compute accumulated these statistics given a pri ... prior_gmm.acc_statistics(d, gmm_stats_container) >>> >>> # Printing the responsibilities - >>> print gmm_stats_container.n/gmm_stats_container.t + >>> print(gmm_stats_container.n/gmm_stats_container.t) [ 0.429 0.571] @@ -294,7 +294,7 @@ The snippet bellow shows how to train a Intersession variability modelling. >>> trainer = bob.learn.em.ISVTrainer(relevance_factor) >>> bob.learn.em.train(trainer, isvbase, gmm_stats_per_class, max_iterations=50) >>> # Printing the session offset w.r.t each Gaussian component - >>> print isvbase.u + >>> print(isvbase.u) [[-0.01 -0.027] [-0.002 -0.004] [ 0.028 0.074] @@ -362,7 +362,7 @@ The snippet bellow shows how to train a Intersession variability modelling. >>> bob.learn.em.train_jfa(trainer, jfabase, gmm_stats_per_class, max_iterations=50) >>> # Printing the session offset w.r.t each Gaussian component - >>> print jfabase.v + >>> print(jfabase.v) [[ 0.003 -0.006] [ 0.041 -0.084] [-0.261 0.53 ] @@ -428,7 +428,7 @@ The snippet bellow shows how to train a Total variability modelling. >>> bob.learn.em.train(ivector_trainer, ivector_machine, gmm_stats_per_class, 500) >>> >>> # Printing the session offset w.r.t each Gaussian component - >>> print ivector_machine.t + >>> print(ivector_machine.t) [[ 0.11 -0.203] [-0.124 0.014] [ 0.296 0.674] @@ -478,7 +478,7 @@ The snippet bellow shows how to compute scores using this approximation. >>> #Accumulating statistics of the GMM >>> stats = bob.learn.em.GMMStats(3, 2) >>> prior_gmm.acc_statistics(input, stats) - >>> print bob.learn.em.linear_scoring([adapted_gmm], prior_gmm, [stats], [], frame_length_normalisation=True) + >>> print(bob.learn.em.linear_scoring([adapted_gmm], prior_gmm, [stats], [], frame_length_normalisation=True)) [[ 0.254]]