diff --git a/doc/guide.rst b/doc/guide.rst
index 9ca31ec0065a8027c9812736eba0089f80d84a74..661809d91a4654f4874d2950eac7e01ef276ddd0 100644
--- a/doc/guide.rst
+++ b/doc/guide.rst
@@ -171,9 +171,8 @@ Follow bellow an snippet on how to train a GMM using the MAP estimator.
>>> # Training
>>> bob.learn.em.train(gmm_trainer, adapted_gmm, data, max_iterations = max_iterations, convergence_threshold = convergence_threshold) # Train the KMeansMachine
>>> print(adapted_gmm.means)
- [[ -4.66666667 3.53333333 -40.5 ]
- [ 2.92857143 -4.07142857 76.14285714]]
-
+ [[ -4.667 3.533 -40.5 ]
+ [ 2.929 -4.071 76.143]]
Bellow follow an intuition of the GMM trained with the MAP estimator using the Iris flower `dataset <https://en.wikipedia.org/wiki/Iris_flower_data_set>`_.
@@ -223,15 +222,14 @@ The snippet bellow shows how to compute accumulated these statistics given a pri
>>> # All nice and round diagonal covariance
>>> prior_gmm.variances = numpy.ones((2, 3)) * 0.5
>>> prior_gmm.weights = numpy.array([0.3, 0.7])
-
>>> # Creating the container
>>> gmm_stats_container = bob.learn.em.GMMStats(2, 3)
>>> for d in data:
- ... prior_gmm.acc_statistics(d, gmm_stats_container)
+ ... prior_gmm.acc_statistics(d, gmm_stats_container)
>>>
>>> # Printing the responsibilities
>>> print gmm_stats_container.n/gmm_stats_container.t
- [ 0.42861627 0.57138373]
+ [ 0.429 0.571]
Inter-Session Variability
@@ -283,11 +281,11 @@ The snippet bellow shows how to train a Intersession variability modelling.
>>> gmm_stats_per_class = []
>>> for d in data:
... stats = []
- ... for i in d:
- ... gmm_stats_container = bob.learn.em.GMMStats(2, 3)
- ... prior_gmm.acc_statistics(i, gmm_stats_container)
- ... stats.append(gmm_stats_container)
- ... gmm_stats_per_class.append(stats)
+ ... for i in d:
+ ... gmm_stats_container = bob.learn.em.GMMStats(2, 3)
+ ... prior_gmm.acc_statistics(i, gmm_stats_container)
+ ... stats.append(gmm_stats_container)
+ ... gmm_stats_per_class.append(stats)
>>> # Finally doing the ISV training
>>> subspace_dimension_of_u = 2
@@ -297,12 +295,12 @@ The snippet bellow shows how to train a Intersession variability modelling.
>>> 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
- [[-0.01018674 -0.0266506 ]
- [-0.00160621 -0.00420217]
- [ 0.02811708 0.07356007]
- [ 0.01162401 0.0304108 ]
- [ 0.03261834 0.08533628]
- [ 0.04602195 0.1204029 ]]
+ [[-0.01 -0.027]
+ [-0.002 -0.004]
+ [ 0.028 0.074]
+ [ 0.012 0.03 ]
+ [ 0.033 0.085]
+ [ 0.046 0.12 ]]
Joint Factor Analysis
@@ -349,11 +347,11 @@ The snippet bellow shows how to train a Intersession variability modelling.
>>> gmm_stats_per_class = []
>>> for d in data:
... stats = []
- ... for i in d:
- ... gmm_stats_container = bob.learn.em.GMMStats(2, 3)
- ... prior_gmm.acc_statistics(i, gmm_stats_container)
- ... stats.append(gmm_stats_container)
- ... gmm_stats_per_class.append(stats)
+ ... for i in d:
+ ... gmm_stats_container = bob.learn.em.GMMStats(2, 3)
+ ... prior_gmm.acc_statistics(i, gmm_stats_container)
+ ... stats.append(gmm_stats_container)
+ ... gmm_stats_per_class.append(stats)
>>>
>>> # Finally doing the JFA training
>>> subspace_dimension_of_u = 2
@@ -365,12 +363,12 @@ The snippet bellow shows how to train a Intersession variability modelling.
>>> # Printing the session offset w.r.t each Gaussian component
>>> print jfabase.v
- [[ 0.002881 -0.00584226]
- [ 0.04143534 -0.084025 ]
- [-0.26149889 0.53028268]
- [-0.25156799 0.51014422]
- [-0.38687714 0.78453199]
- [-0.36015773 0.73034882]]
+ [[ 0.003 -0.006]
+ [ 0.041 -0.084]
+ [-0.261 0.53 ]
+ [-0.252 0.51 ]
+ [-0.387 0.785]
+ [-0.36 0.73 ]]
Total variability Modelling
***************************
@@ -431,13 +429,12 @@ The snippet bellow shows how to train a Total variability modelling.
>>>
>>> # Printing the session offset w.r.t each Gaussian component
>>> print ivector_machine.t
- [[ 0.1101072 -0.20271139]
- [-0.12426696 0.01402857]
- [ 0.29584642 0.67414389]
- [ 0.44728435 0.1744876 ]
- [ 0.42547226 0.58287138]
- [ 0.39369553 0.79358693]]
-
+ [[ 0.11 -0.203]
+ [-0.124 0.014]
+ [ 0.296 0.674]
+ [ 0.447 0.174]
+ [ 0.425 0.583]
+ [ 0.394 0.794]]
Linear Scoring
**************
@@ -470,26 +467,19 @@ The snippet bellow shows how to compute scores using this approximation.
>>> import bob.learn.em
>>> import numpy
-
>>> # Defining a fake prior
>>> prior_gmm = bob.learn.em.GMMMachine(3, 2)
>>> prior_gmm.means = numpy.array([[1, 1], [2, 2.1], [3, 3]])
-
>>> # Defining a fake prior
>>> adapted_gmm = bob.learn.em.GMMMachine(3,2)
>>> adapted_gmm.means = numpy.array([[1.5, 1.5], [2.5, 2.5], [2, 2]])
-
>>> # Defining an input
>>> input = numpy.array([[1.5, 1.5], [1.6, 1.6]])
-
>>> #Accumulating statistics of the GMM
>>> stats = bob.learn.em.GMMStats(3, 2)
>>> prior_gmm.acc_statistics(input, stats)
-
- >>> score = bob.learn.em.linear_scoring([adapted_gmm], prior_gmm, [stats], [],
- >>> frame_length_normalisation=True)
- >>> print score
- [[ 0.25354909]]
+ >>> print bob.learn.em.linear_scoring([adapted_gmm], prior_gmm, [stats], [], frame_length_normalisation=True)
+ [[ 0.254]]
Probabilistic Linear Discriminant Analysis (PLDA)