From aec805cca7fa25f371433379d7cb9492b7fa9ee1 Mon Sep 17 00:00:00 2001
From: Tiago Freitas Pereira <tiagofrepereira@gmail.com>
Date: Mon, 22 May 2017 22:32:44 -0700
Subject: [PATCH] Fixed python 3 issues
---
doc/guide.rst | 10 +++++-----
1 file changed, 5 insertions(+), 5 deletions(-)
diff --git a/doc/guide.rst b/doc/guide.rst
index 661809d..6f4da1d 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]]
--
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