Commit aec805cc authored by Tiago de Freitas Pereira's avatar Tiago de Freitas Pereira Committed by Amir MOHAMMADI
Browse files

Fixed python 3 issues

parent ea0ec084
...@@ -228,7 +228,7 @@ The snippet bellow shows how to compute accumulated these statistics given a pri ...@@ -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) ... prior_gmm.acc_statistics(d, gmm_stats_container)
>>> >>>
>>> # Printing the responsibilities >>> # 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] [ 0.429 0.571]
...@@ -294,7 +294,7 @@ The snippet bellow shows how to train a Intersession variability modelling. ...@@ -294,7 +294,7 @@ The snippet bellow shows how to train a Intersession variability modelling.
>>> trainer = bob.learn.em.ISVTrainer(relevance_factor) >>> trainer = bob.learn.em.ISVTrainer(relevance_factor)
>>> bob.learn.em.train(trainer, isvbase, gmm_stats_per_class, max_iterations=50) >>> bob.learn.em.train(trainer, isvbase, gmm_stats_per_class, max_iterations=50)
>>> # Printing the session offset w.r.t each Gaussian component >>> # Printing the session offset w.r.t each Gaussian component
>>> print isvbase.u >>> print(isvbase.u)
[[-0.01 -0.027] [[-0.01 -0.027]
[-0.002 -0.004] [-0.002 -0.004]
[ 0.028 0.074] [ 0.028 0.074]
...@@ -362,7 +362,7 @@ The snippet bellow shows how to train a Intersession variability modelling. ...@@ -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) >>> bob.learn.em.train_jfa(trainer, jfabase, gmm_stats_per_class, max_iterations=50)
>>> # Printing the session offset w.r.t each Gaussian component >>> # Printing the session offset w.r.t each Gaussian component
>>> print jfabase.v >>> print(jfabase.v)
[[ 0.003 -0.006] [[ 0.003 -0.006]
[ 0.041 -0.084] [ 0.041 -0.084]
[-0.261 0.53 ] [-0.261 0.53 ]
...@@ -428,7 +428,7 @@ The snippet bellow shows how to train a Total variability modelling. ...@@ -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) >>> bob.learn.em.train(ivector_trainer, ivector_machine, gmm_stats_per_class, 500)
>>> >>>
>>> # Printing the session offset w.r.t each Gaussian component >>> # Printing the session offset w.r.t each Gaussian component
>>> print ivector_machine.t >>> print(ivector_machine.t)
[[ 0.11 -0.203] [[ 0.11 -0.203]
[-0.124 0.014] [-0.124 0.014]
[ 0.296 0.674] [ 0.296 0.674]
...@@ -478,7 +478,7 @@ The snippet bellow shows how to compute scores using this approximation. ...@@ -478,7 +478,7 @@ The snippet bellow shows how to compute scores using this approximation.
>>> #Accumulating statistics of the GMM >>> #Accumulating statistics of the GMM
>>> stats = bob.learn.em.GMMStats(3, 2) >>> stats = bob.learn.em.GMMStats(3, 2)
>>> prior_gmm.acc_statistics(input, stats) >>> 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]] [[ 0.254]]
......
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