diff --git a/doc/guide.rst b/doc/guide.rst
index 1ce2e6d148319641d6c1922537fb16ac1ebc05c7..fecf54b4b773a454b9cd60855a2c3bb81110fec8 100644
--- a/doc/guide.rst
+++ b/doc/guide.rst
@@ -58,7 +58,8 @@ Follow bellow an snippet on how to train a KMeans using Bob_.
.. doctest::
:options: +NORMALIZE_WHITESPACE
- >>> import bob.learn.em
+ >>> from bob.learn.em.cluster import KMeansMachine
+ >>> from bob.learn.em.cluster import KMeansTrainer
>>> import numpy
>>> data = numpy.array(
... [[3,-3,100],
@@ -66,18 +67,14 @@ Follow bellow an snippet on how to train a KMeans using Bob_.
... [3.5,-3.5,99],
... [-7,7,-100],
... [-5,5,-101]], dtype='float64')
- >>> # Create a kmeans m with k=2 clusters with a dimensionality equal to 3
- >>> kmeans_machine = bob.learn.em.KMeansMachine(2, 3)
- >>> kmeans_trainer = bob.learn.em.KMeansTrainer()
- >>> max_iterations = 200
- >>> convergence_threshold = 1e-5
+ >>> # Create a k-means machine with k=2 clusters
+ >>> kmeans_machine = KMeansMachine(2, convergence_threshold=1e-5)
+ >>> kmeans_trainer = KMeansTrainer(max_iter=200)
>>> # Train the KMeansMachine
- >>> bob.learn.em.train(kmeans_trainer, kmeans_machine, data,
- ... max_iterations=max_iterations,
- ... convergence_threshold=convergence_threshold)
- >>> print(kmeans_machine.means)
- [[ -6. 6. -100.5]
- [ 3.5 -3.5 99. ]]
+ >>> kmeans_machine = kmeans_machine.fit(data)
+ >>> print(numpy.array(kmeans_machine.centroids_))
+ [[ 3.5 -3.5 99. ]
+ [ -6. 6. -100.5]]
Bellow follow an intuition (source code + plot) of a kmeans training using the
@@ -171,8 +168,10 @@ Bellow follow an intuition of the GMM trained the maximum likelihood estimator
using the Iris flower
`dataset <https://en.wikipedia.org/wiki/Iris_flower_data_set>`_.
-.. plot:: plot/plot_ML.py
- :include-source: False
+..
+ TODO uncomment when implemented
+ .. plot:: plot/plot_ML.py
+ :include-source: False
Maximum a posteriori Estimator (MAP)
@@ -232,8 +231,10 @@ Follow bellow an snippet on how to train a GMM using the MAP estimator.
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>`_.
-.. plot:: plot/plot_MAP.py
- :include-source: False
+..
+ TODO uncomment when implemented
+ .. plot:: plot/plot_MAP.py
+ :include-source: False
Session Variability Modeling with Gaussian Mixture Models
@@ -326,8 +327,10 @@ The arrows :math:`U_{1}`, :math:`U_{2}` and :math:`U_{3}` are the directions of
the within class variations, with respect to each Gaussian component, that will
be suppressed a posteriori.
-.. plot:: plot/plot_ISV.py
- :include-source: False
+..
+ TODO uncomment when implemented
+ .. plot:: plot/plot_ISV.py
+ :include-source: False
The ISV statistical model is stored in this container
@@ -400,8 +403,10 @@ between class variations with respect to each Gaussian component that will be
added a posteriori.
-.. plot:: plot/plot_JFA.py
- :include-source: False
+..
+ TODO uncomment when implemented
+ .. plot:: plot/plot_JFA.py
+ :include-source: False
The JFA statistical model is stored in this container
:py:class:`bob.learn.em.JFABase` and the training is performed by
@@ -472,8 +477,10 @@ Follow bellow an intuition of the data from the Iris flower
`dataset <https://en.wikipedia.org/wiki/Iris_flower_data_set>`_, embedded in
the iVector space.
-.. plot:: plot/plot_iVector.py
- :include-source: False
+..
+ TODO uncomment when implemented
+ .. plot:: plot/plot_iVector.py
+ :include-source: False
The iVector statistical model is stored in this container
diff --git a/doc/plot/plot_ISV.py b/doc/plot/plot_ISV.py
deleted file mode 100755
index 876d71b8da5b61a53b944ecfcd21cbaa1e17155f..0000000000000000000000000000000000000000
--- a/doc/plot/plot_ISV.py
+++ /dev/null
@@ -1,130 +0,0 @@
-import bob.db.iris
-import bob.learn.em
-import bob.learn.linear
-import matplotlib.pyplot as plt
-import numpy
-numpy.random.seed(2) # FIXING A SEED
-
-
-def train_ubm(features, n_gaussians):
- """
- Train UBM
-
- **Parameters**
- features: 2D numpy array with the features
-
- n_gaussians: Number of Gaussians
-
- """
- input_size = features.shape[1]
-
- kmeans_machine = bob.learn.em.KMeansMachine(int(n_gaussians), input_size)
- ubm = bob.learn.em.GMMMachine(int(n_gaussians), input_size)
-
- # The K-means clustering is firstly used to used to estimate the initial
- # means, the final variances and the final weights for each gaussian
- # component
- kmeans_trainer = bob.learn.em.KMeansTrainer('RANDOM_NO_DUPLICATE')
- bob.learn.em.train(kmeans_trainer, kmeans_machine, features)
-
- # Getting the means, weights and the variances for each cluster. This is a
- # very good estimator for the ML
- (variances, weights) = kmeans_machine.get_variances_and_weights_for_each_cluster(features)
- means = kmeans_machine.means
-
- # initialize the UBM with the output of kmeans
- ubm.means = means
- ubm.variances = variances
- ubm.weights = weights
-
- # Creating the ML Trainer. We will adapt only the means
- trainer = bob.learn.em.ML_GMMTrainer(
- update_means=True, update_variances=False, update_weights=False)
- bob.learn.em.train(trainer, ubm, features)
-
- return ubm
-
-
-def isv_train(features, ubm):
- """
- Train U matrix
-
- **Parameters**
- features: List of :py:class:`bob.learn.em.GMMStats` organized by class
-
- n_gaussians: UBM (:py:class:`bob.learn.em.GMMMachine`)
-
- """
-
- stats = []
- for user in features:
- user_stats = []
- for f in user:
- s = bob.learn.em.GMMStats(ubm.shape[0], ubm.shape[1])
- ubm.acc_statistics(f, s)
- user_stats.append(s)
- stats.append(user_stats)
-
- relevance_factor = 4
- subspace_dimension_of_u = 1
-
- isvbase = bob.learn.em.ISVBase(ubm, subspace_dimension_of_u)
- trainer = bob.learn.em.ISVTrainer(relevance_factor)
- # trainer.rng = bob.core.random.mt19937(int(self.init_seed))
- bob.learn.em.train(trainer, isvbase, stats, max_iterations=50)
-
- return isvbase
-
-
-# GENERATING DATA
-data_per_class = bob.db.iris.data()
-setosa = numpy.column_stack(
- (data_per_class['setosa'][:, 0], data_per_class['setosa'][:, 3]))
-versicolor = numpy.column_stack(
- (data_per_class['versicolor'][:, 0], data_per_class['versicolor'][:, 3]))
-virginica = numpy.column_stack(
- (data_per_class['virginica'][:, 0], data_per_class['virginica'][:, 3]))
-data = numpy.vstack((setosa, versicolor, virginica))
-
-# TRAINING THE PRIOR
-ubm = train_ubm(data, 3)
-isvbase = isv_train([setosa, versicolor, virginica], ubm)
-
-# Variability direction
-u0 = isvbase.u[0:2, 0] / numpy.linalg.norm(isvbase.u[0:2, 0])
-u1 = isvbase.u[2:4, 0] / numpy.linalg.norm(isvbase.u[2:4, 0])
-u2 = isvbase.u[4:6, 0] / numpy.linalg.norm(isvbase.u[4:6, 0])
-
-figure, ax = plt.subplots()
-plt.scatter(setosa[:, 0], setosa[:, 1], c="darkcyan", label="setosa")
-plt.scatter(versicolor[:, 0], versicolor[:, 1],
- c="goldenrod", label="versicolor")
-plt.scatter(virginica[:, 0], virginica[:, 1], c="dimgrey", label="virginica")
-
-plt.scatter(ubm.means[:, 0], ubm.means[:, 1], c="blue",
- marker="x", label="centroids - mle")
-# plt.scatter(ubm.means[:, 0], ubm.means[:, 1], c="blue",
-# marker=".", label="within class varibility", s=0.01)
-
-ax.arrow(ubm.means[0, 0], ubm.means[0, 1], u0[0], u0[1],
- fc="k", ec="k", head_width=0.05, head_length=0.1)
-ax.arrow(ubm.means[1, 0], ubm.means[1, 1], u1[0], u1[1],
- fc="k", ec="k", head_width=0.05, head_length=0.1)
-ax.arrow(ubm.means[2, 0], ubm.means[2, 1], u2[0], u2[1],
- fc="k", ec="k", head_width=0.05, head_length=0.1)
-plt.text(ubm.means[0, 0] + u0[0], ubm.means[0, 1] +
- u0[1] - 0.1, r'$\mathbf{U}_1$', fontsize=15)
-plt.text(ubm.means[1, 0] + u1[0], ubm.means[1, 1] +
- u1[1] - 0.1, r'$\mathbf{U}_2$', fontsize=15)
-plt.text(ubm.means[2, 0] + u2[0], ubm.means[2, 1] +
- u2[1] - 0.1, r'$\mathbf{U}_3$', fontsize=15)
-
-plt.xticks([], [])
-plt.yticks([], [])
-
-# plt.grid(True)
-plt.xlabel('Sepal length')
-plt.ylabel('Petal width')
-plt.legend()
-plt.tight_layout()
-plt.show()
diff --git a/doc/plot/plot_JFA.py b/doc/plot/plot_JFA.py
deleted file mode 100755
index a2b05d5731b3d8aaefd419df0184d0b9daaacf2a..0000000000000000000000000000000000000000
--- a/doc/plot/plot_JFA.py
+++ /dev/null
@@ -1,156 +0,0 @@
-import bob.db.iris
-import bob.learn.em
-import bob.learn.linear
-import matplotlib.pyplot as plt
-import numpy
-numpy.random.seed(2) # FIXING A SEED
-
-
-def train_ubm(features, n_gaussians):
- """
- Train UBM
-
- **Parameters**
- features: 2D numpy array with the features
-
- n_gaussians: Number of Gaussians
-
- """
-
- input_size = features.shape[1]
-
- kmeans_machine = bob.learn.em.KMeansMachine(int(n_gaussians), input_size)
- ubm = bob.learn.em.GMMMachine(int(n_gaussians), input_size)
-
- # The K-means clustering is firstly used to used to estimate the initial
- # means, the final variances and the final weights for each gaussian
- # component
- kmeans_trainer = bob.learn.em.KMeansTrainer('RANDOM_NO_DUPLICATE')
- bob.learn.em.train(kmeans_trainer, kmeans_machine, features)
-
- # Getting the means, weights and the variances for each cluster. This is a
- # very good estimator for the ML
- (variances, weights) = kmeans_machine.get_variances_and_weights_for_each_cluster(features)
- means = kmeans_machine.means
-
- # initialize the UBM with the output of kmeans
- ubm.means = means
- ubm.variances = variances
- ubm.weights = weights
-
- # Creating the ML Trainer. We will adapt only the means
- trainer = bob.learn.em.ML_GMMTrainer(
- update_means=True, update_variances=False, update_weights=False)
- bob.learn.em.train(trainer, ubm, features)
-
- return ubm
-
-
-def jfa_train(features, ubm):
- """
- Trains U and V matrix
-
- **Parameters**
- features: List of :py:class:`bob.learn.em.GMMStats` organized by class
-
- n_gaussians: UBM (:py:class:`bob.learn.em.GMMMachine`)
-
- """
-
- stats = []
- for user in features:
- user_stats = []
- for f in user:
- s = bob.learn.em.GMMStats(ubm.shape[0], ubm.shape[1])
- ubm.acc_statistics(f, s)
- user_stats.append(s)
- stats.append(user_stats)
-
- subspace_dimension_of_u = 1
- subspace_dimension_of_v = 1
-
- jfa_base = bob.learn.em.JFABase(
- ubm, subspace_dimension_of_u, subspace_dimension_of_v)
- trainer = bob.learn.em.JFATrainer()
- # trainer.rng = bob.core.random.mt19937(int(self.init_seed))
- bob.learn.em.train_jfa(trainer, jfa_base, stats, max_iterations=50)
-
- return jfa_base
-
-
-# GENERATING DATA
-data_per_class = bob.db.iris.data()
-setosa = numpy.column_stack(
- (data_per_class['setosa'][:, 0], data_per_class['setosa'][:, 3]))
-versicolor = numpy.column_stack(
- (data_per_class['versicolor'][:, 0], data_per_class['versicolor'][:, 3]))
-virginica = numpy.column_stack(
- (data_per_class['virginica'][:, 0], data_per_class['virginica'][:, 3]))
-data = numpy.vstack((setosa, versicolor, virginica))
-
-# TRAINING THE PRIOR
-ubm = train_ubm(data, 3)
-jfa_base = jfa_train([setosa, versicolor, virginica], ubm)
-
-# Variability direction U
-u0 = jfa_base.u[0:2, 0] / numpy.linalg.norm(jfa_base.u[0:2, 0])
-u1 = jfa_base.u[2:4, 0] / numpy.linalg.norm(jfa_base.u[2:4, 0])
-u2 = jfa_base.u[4:6, 0] / numpy.linalg.norm(jfa_base.u[4:6, 0])
-
-
-# Variability direction V
-v0 = jfa_base.v[0:2, 0] / numpy.linalg.norm(jfa_base.v[0:2, 0])
-v1 = jfa_base.v[2:4, 0] / numpy.linalg.norm(jfa_base.v[2:4, 0])
-v2 = jfa_base.v[4:6, 0] / numpy.linalg.norm(jfa_base.v[4:6, 0])
-
-
-figure, ax = plt.subplots()
-plt.scatter(setosa[:, 0], setosa[:, 1], c="darkcyan", label="setosa")
-plt.scatter(versicolor[:, 0], versicolor[:, 1],
- c="goldenrod", label="versicolor")
-plt.scatter(virginica[:, 0], virginica[:, 1], c="dimgrey", label="virginica")
-
-plt.scatter(ubm.means[:, 0], ubm.means[:, 1], c="blue",
- marker="x", label="centroids - mle")
-# plt.scatter(ubm.means[:, 0], ubm.means[:, 1], c="blue",
-# marker=".", label="within class varibility", s=0.01)
-
-# U
-ax.arrow(ubm.means[0, 0], ubm.means[0, 1], u0[0], u0[1],
- fc="k", ec="k", head_width=0.05, head_length=0.1)
-ax.arrow(ubm.means[1, 0], ubm.means[1, 1], u1[0], u1[1],
- fc="k", ec="k", head_width=0.05, head_length=0.1)
-ax.arrow(ubm.means[2, 0], ubm.means[2, 1], u2[0], u2[1],
- fc="k", ec="k", head_width=0.05, head_length=0.1)
-plt.text(ubm.means[0, 0] + u0[0], ubm.means[0, 1] +
- u0[1] - 0.1, r'$\mathbf{U}_1$', fontsize=15)
-plt.text(ubm.means[1, 0] + u1[0], ubm.means[1, 1] +
- u1[1] - 0.1, r'$\mathbf{U}_2$', fontsize=15)
-plt.text(ubm.means[2, 0] + u2[0], ubm.means[2, 1] +
- u2[1] - 0.1, r'$\mathbf{U}_3$', fontsize=15)
-
-# V
-ax.arrow(ubm.means[0, 0], ubm.means[0, 1], v0[0], v0[1],
- fc="k", ec="k", head_width=0.05, head_length=0.1)
-ax.arrow(ubm.means[1, 0], ubm.means[1, 1], v1[0], v1[1],
- fc="k", ec="k", head_width=0.05, head_length=0.1)
-ax.arrow(ubm.means[2, 0], ubm.means[2, 1], v2[0], v2[1],
- fc="k", ec="k", head_width=0.05, head_length=0.1)
-plt.text(ubm.means[0, 0] + v0[0], ubm.means[0, 1] +
- v0[1] - 0.1, r'$\mathbf{V}_1$', fontsize=15)
-plt.text(ubm.means[1, 0] + v1[0], ubm.means[1, 1] +
- v1[1] - 0.1, r'$\mathbf{V}_2$', fontsize=15)
-plt.text(ubm.means[2, 0] + v2[0], ubm.means[2, 1] +
- v2[1] - 0.1, r'$\mathbf{V}_3$', fontsize=15)
-
-plt.xticks([], [])
-plt.yticks([], [])
-
-# plt.grid(True)
-plt.xlabel('Sepal length')
-plt.ylabel('Petal width')
-plt.legend(loc=2)
-plt.ylim([-1, 3.5])
-
-plt.tight_layout()
-# plt.show()
diff --git a/doc/plot/plot_MAP.py b/doc/plot/plot_MAP.py
deleted file mode 100644
index d873b2ca69f32363e76271070e76a21b81be7b6c..0000000000000000000000000000000000000000
--- a/doc/plot/plot_MAP.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import matplotlib.pyplot as plt
-import bob.db.iris
-import bob.learn.em
-import numpy
-numpy.random.seed(10)
-
-data_per_class = bob.db.iris.data()
-setosa = numpy.column_stack(
- (data_per_class['setosa'][:, 0], data_per_class['setosa'][:, 3]))
-versicolor = numpy.column_stack(
- (data_per_class['versicolor'][:, 0], data_per_class['versicolor'][:, 3]))
-virginica = numpy.column_stack(
- (data_per_class['virginica'][:, 0], data_per_class['virginica'][:, 3]))
-
-data = numpy.vstack((setosa, versicolor, virginica))
-
-# Two clusters with a feature dimensionality of 3
-mle_machine = bob.learn.em.GMMMachine(3, 2)
-mle_machine.means = numpy.array([[5, 3], [4, 2], [7, 3.]])
-
-# Creating some random data centered in
-map_machine = bob.learn.em.GMMMachine(3, 2)
-map_trainer = bob.learn.em.MAP_GMMTrainer(mle_machine, relevance_factor=4)
-bob.learn.em.train(map_trainer, map_machine, data, max_iterations=200,
- convergence_threshold=1e-5) # Train the KMeansMachine
-
-
-figure, ax = plt.subplots()
-# plt.scatter(data[:, 0], data[:, 1], c="olivedrab", label="new data")
-plt.scatter(setosa[:, 0], setosa[:, 1], c="darkcyan", label="setosa")
-plt.scatter(versicolor[:, 0], versicolor[:, 1],
- c="goldenrod", label="versicolor")
-plt.scatter(virginica[:, 0], virginica[:, 1],
- c="dimgrey", label="virginica")
-plt.scatter(mle_machine.means[:, 0],
- mle_machine.means[:, 1], c="blue", marker="x",
- label="prior centroids - mle", s=60)
-plt.scatter(map_machine.means[:, 0], map_machine.means[:, 1], c="red",
- marker="^", label="adapted centroids - map", s=60)
-plt.legend()
-plt.xticks([], [])
-plt.yticks([], [])
-ax.set_xlabel("Sepal length")
-ax.set_ylabel("Petal width")
-plt.tight_layout()
-plt.show()
diff --git a/doc/plot/plot_ML.py b/doc/plot/plot_ML.py
deleted file mode 100644
index 18902c4dd8939e33e537b274f9d3b31deaa3b75b..0000000000000000000000000000000000000000
--- a/doc/plot/plot_ML.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import bob.learn.em
-import bob.db.iris
-import numpy
-import matplotlib.pyplot as plt
-
-data_per_class = bob.db.iris.data()
-setosa = numpy.column_stack(
- (data_per_class['setosa'][:, 0], data_per_class['setosa'][:, 3]))
-versicolor = numpy.column_stack(
- (data_per_class['versicolor'][:, 0], data_per_class['versicolor'][:, 3]))
-virginica = numpy.column_stack(
- (data_per_class['virginica'][:, 0], data_per_class['virginica'][:, 3]))
-
-data = numpy.vstack((setosa, versicolor, virginica))
-
-# Two clusters with a feature dimensionality of 3
-machine = bob.learn.em.GMMMachine(3, 2)
-trainer = bob.learn.em.ML_GMMTrainer(True, True, True)
-machine.means = numpy.array([[5, 3], [4, 2], [7, 3.]])
-bob.learn.em.train(trainer, machine, data, max_iterations=200,
- convergence_threshold=1e-5) # Train the KMeansMachine
-
-figure, ax = plt.subplots()
-plt.scatter(setosa[:, 0], setosa[:, 1], c="darkcyan", label="setosa")
-plt.scatter(versicolor[:, 0], versicolor[:, 1],
- c="goldenrod", label="versicolor")
-plt.scatter(virginica[:, 0], virginica[:, 1],
- c="dimgrey", label="virginica")
-plt.scatter(machine.means[:, 0],
- machine.means[:, 1], c="blue", marker="x", label="centroids", s=60)
-plt.legend()
-plt.xticks([], [])
-plt.yticks([], [])
-ax.set_xlabel("Sepal length")
-ax.set_ylabel("Petal width")
-plt.tight_layout()
-plt.show()
diff --git a/doc/plot/plot_Tnorm.py b/doc/plot/plot_Tnorm.py
deleted file mode 100644
index 8772a95d885ca0c93be117675a86ae04bf614522..0000000000000000000000000000000000000000
--- a/doc/plot/plot_Tnorm.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import matplotlib.pyplot as plt
-import bob.learn.em
-import numpy
-numpy.random.seed(10)
-
-n_clients = 10
-n_scores_per_client = 200
-
-# Defining some fake scores for genuines and impostors
-impostor_scores = numpy.random.normal(-15.5,
- 5, (n_scores_per_client, n_clients))
-genuine_scores = numpy.random.normal(0.5, 5, (n_scores_per_client, n_clients))
-
-# Defining the scores for the statistics computation
-t_scores = numpy.random.normal(-5., 5, (n_scores_per_client, n_clients))
-
-# T - Normalizing
-t_norm_impostors = bob.learn.em.tnorm(impostor_scores, t_scores)
-t_norm_genuine = bob.learn.em.tnorm(genuine_scores, t_scores)
-
-# PLOTTING
-figure = plt.subplot(2, 1, 1)
-ax = figure.axes
-plt.title("Raw scores", fontsize=8)
-plt.hist(impostor_scores.reshape(n_scores_per_client * n_clients),
- label='Impostors', normed=True,
- color='C1', alpha=0.5, bins=50)
-plt.hist(genuine_scores.reshape(n_scores_per_client * n_clients),
- label='Genuine', normed=True,
- color='C0', alpha=0.5, bins=50)
-plt.legend(fontsize=8)
-plt.yticks([], [])
-
-
-figure = plt.subplot(2, 1, 2)
-ax = figure.axes
-plt.title("T-norm scores", fontsize=8)
-plt.hist(t_norm_impostors.reshape(n_scores_per_client * n_clients),
- label='T-Norm Impostors', normed=True,
- color='C1', alpha=0.5, bins=50)
-plt.hist(t_norm_genuine.reshape(n_scores_per_client * n_clients),
- label='T-Norm Genuine', normed=True,
- color='C0', alpha=0.5, bins=50)
-plt.legend(fontsize=8)
-plt.yticks([], [])
-
-plt.tight_layout()
-plt.show()
diff --git a/doc/plot/plot_ZTnorm.py b/doc/plot/plot_ZTnorm.py
deleted file mode 100644
index 8300fdccb419bdc8843c3396f3e78940b56684d9..0000000000000000000000000000000000000000
--- a/doc/plot/plot_ZTnorm.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import matplotlib.pyplot as plt
-import bob.learn.em
-import numpy
-numpy.random.seed(10)
-
-n_clients = 10
-n_scores_per_client = 200
-
-# Defining some fake scores for genuines and impostors
-impostor_scores = numpy.random.normal(-15.5,
- 5, (n_scores_per_client, n_clients))
-genuine_scores = numpy.random.normal(0.5, 5, (n_scores_per_client, n_clients))
-
-# Defining the scores for the statistics computation
-z_scores = numpy.random.normal(-5., 5, (n_scores_per_client, n_clients))
-t_scores = numpy.random.normal(-6., 5, (n_scores_per_client, n_clients))
-
-# T-normalizing the Z-scores
-zt_scores = bob.learn.em.tnorm(z_scores, t_scores)
-
-# ZT - Normalizing
-zt_norm_impostors = bob.learn.em.ztnorm(
- impostor_scores, z_scores, t_scores, zt_scores)
-zt_norm_genuine = bob.learn.em.ztnorm(
- genuine_scores, z_scores, t_scores, zt_scores)
-
-# PLOTTING
-figure = plt.subplot(2, 1, 1)
-ax = figure.axes
-plt.title("Raw scores", fontsize=8)
-plt.hist(impostor_scores.reshape(n_scores_per_client * n_clients),
- label='Impostors', normed=True,
- color='C1', alpha=0.5, bins=50)
-plt.hist(genuine_scores.reshape(n_scores_per_client * n_clients),
- label='Genuine', normed=True,
- color='C0', alpha=0.5, bins=50)
-plt.legend(fontsize=8)
-plt.yticks([], [])
-
-
-figure = plt.subplot(2, 1, 2)
-ax = figure.axes
-plt.title("T-norm scores", fontsize=8)
-plt.hist(zt_norm_impostors.reshape(n_scores_per_client * n_clients),
- label='T-Norm Impostors', normed=True,
- color='C1', alpha=0.5, bins=50)
-plt.hist(zt_norm_genuine.reshape(n_scores_per_client * n_clients),
- label='T-Norm Genuine', normed=True,
- color='C0', alpha=0.5, bins=50)
-plt.legend(fontsize=8)
-plt.yticks([], [])
-
-plt.tight_layout()
-plt.show()
diff --git a/doc/plot/plot_Znorm.py b/doc/plot/plot_Znorm.py
deleted file mode 100644
index 8c16ebbd08dc62864040205012b4682b8de96562..0000000000000000000000000000000000000000
--- a/doc/plot/plot_Znorm.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import matplotlib.pyplot as plt
-import bob.learn.em
-import numpy
-numpy.random.seed(10)
-
-
-n_clients = 10
-n_scores_per_client = 200
-
-# Defining some fake scores for genuines and impostors
-impostor_scores = numpy.random.normal(-15.5,
- 5, (n_scores_per_client, n_clients))
-genuine_scores = numpy.random.normal(0.5, 5, (n_scores_per_client, n_clients))
-
-# Defining the scores for the statistics computation
-z_scores = numpy.random.normal(-5., 5, (n_scores_per_client, n_clients))
-
-# Z - Normalizing
-z_norm_impostors = bob.learn.em.znorm(impostor_scores, z_scores)
-z_norm_genuine = bob.learn.em.znorm(genuine_scores, z_scores)
-
-# PLOTTING
-figure = plt.subplot(2, 1, 1)
-ax = figure.axes
-plt.title("Raw scores", fontsize=8)
-plt.hist(impostor_scores.reshape(n_scores_per_client * n_clients),
- label='Impostors', normed=True,
- color='C1', alpha=0.5, bins=50)
-plt.hist(genuine_scores.reshape(n_scores_per_client * n_clients),
- label='Genuine', normed=True,
- color='C0', alpha=0.5, bins=50)
-plt.legend(fontsize=8)
-plt.yticks([], [])
-
-
-figure = plt.subplot(2, 1, 2)
-ax = figure.axes
-plt.title("Z-norm scores", fontsize=8)
-plt.hist(z_norm_impostors.reshape(n_scores_per_client * n_clients),
- label='Z-Norm Impostors', normed=True,
- color='C1', alpha=0.5, bins=50)
-plt.hist(z_norm_genuine.reshape(n_scores_per_client * n_clients),
- label='Z-Norm Genuine', normed=True,
- color='C0', alpha=0.5, bins=50)
-plt.yticks([], [])
-plt.legend(fontsize=8)
-
-plt.tight_layout()
-plt.show()
diff --git a/doc/plot/plot_iVector.py b/doc/plot/plot_iVector.py
deleted file mode 100755
index 95953d3a2d40c64ca7a08d0dd6eff0d7346524fb..0000000000000000000000000000000000000000
--- a/doc/plot/plot_iVector.py
+++ /dev/null
@@ -1,201 +0,0 @@
-import bob.db.iris
-import bob.learn.em
-import bob.learn.linear
-import matplotlib.pyplot as plt
-import numpy
-numpy.random.seed(2) # FIXING A SEED
-
-
-def train_ubm(features, n_gaussians):
- """
- Train UBM
-
- **Parameters**
- features: 2D numpy array with the features
-
- n_gaussians: Number of Gaussians
-
- """
-
- input_size = features.shape[1]
-
- kmeans_machine = bob.learn.em.KMeansMachine(int(n_gaussians), input_size)
- ubm = bob.learn.em.GMMMachine(int(n_gaussians), input_size)
-
- # The K-means clustering is firstly used to used to estimate the initial
- # means, the final variances and the final weights for each gaussian
- # component
- kmeans_trainer = bob.learn.em.KMeansTrainer('RANDOM_NO_DUPLICATE')
- bob.learn.em.train(kmeans_trainer, kmeans_machine, features)
-
- # Getting the means, weights and the variances for each cluster. This is a
- # very good estimator for the ML
- (variances, weights) = kmeans_machine.get_variances_and_weights_for_each_cluster(features)
- means = kmeans_machine.means
-
- # initialize the UBM with the output of kmeans
- ubm.means = means
- ubm.variances = variances
- ubm.weights = weights
-
- # Creating the ML Trainer. We will adapt only the means
- trainer = bob.learn.em.ML_GMMTrainer(
- update_means=True, update_variances=False, update_weights=False)
- bob.learn.em.train(trainer, ubm, features)
-
- return ubm
-
-
-def ivector_train(features, ubm):
- """
- Trains T matrix
-
- **Parameters**
- features: List of :py:class:`bob.learn.em.GMMStats`
-
- n_gaussians: UBM (:py:class:`bob.learn.em.GMMMachine`)
-
- """
-
- stats = []
- for user in features:
- s = bob.learn.em.GMMStats(ubm.shape[0], ubm.shape[1])
- for f in user:
- ubm.acc_statistics(f, s)
- stats.append(s)
-
- subspace_dimension_of_t = 2
-
- ivector_trainer = bob.learn.em.IVectorTrainer(update_sigma=True)
- ivector_machine = bob.learn.em.IVectorMachine(
- ubm, subspace_dimension_of_t, 10e-5)
-
- # train IVector model
- bob.learn.em.train(ivector_trainer, ivector_machine, stats, 500)
-
- return ivector_machine
-
-
-def acc_stats(data, gmm):
- gmm_stats = []
- for d in data:
- s = bob.learn.em.GMMStats(gmm.shape[0], gmm.shape[1])
- gmm.acc_statistics(d, s)
- gmm_stats.append(s)
-
- return gmm_stats
-
-
-def compute_ivectors(gmm_stats, ivector_machine):
- """
- Given :py:class:`bob.learn.em.GMMStats` and an T matrix, get the iVectors.
- """
-
- ivectors = []
- for g in gmm_stats:
- ivectors.append(ivector_machine(g))
-
- return numpy.array(ivectors)
-
-
-# GENERATING DATA
-data_per_class = bob.db.iris.data()
-setosa = numpy.column_stack(
- (data_per_class['setosa'][:, 0], data_per_class['setosa'][:, 3]))
-versicolor = numpy.column_stack(
- (data_per_class['versicolor'][:, 0], data_per_class['versicolor'][:, 3]))
-virginica = numpy.column_stack(
- (data_per_class['virginica'][:, 0], data_per_class['virginica'][:, 3]))
-data = numpy.vstack((setosa, versicolor, virginica))
-
-# TRAINING THE PRIOR
-ubm = train_ubm(data, 3)
-ivector_machine = ivector_train([setosa, versicolor, virginica], ubm)
-
-# Variability direction U
-# t0 = T[0:2, 0] / numpy.linalg.norm(T[0:2, 0])
-# t1 = T[2:4, 0] / numpy.linalg.norm(T[2:4, 0])
-# t2 = T[4:6, 0] / numpy.linalg.norm(T[4:6, 0])
-
-
-# figure, ax = plt.subplots()
-figure = plt.subplot(2, 1, 1)
-ax = figure.axes
-plt.title("Raw fetures")
-plt.scatter(setosa[:, 0], setosa[:, 1], c="darkcyan", label="setosa")
-plt.scatter(versicolor[:, 0], versicolor[:, 1],
- c="goldenrod", label="versicolor")
-plt.scatter(virginica[:, 0], virginica[:, 1], c="dimgrey", label="virginica")
-# plt.grid(True)
-# plt.xlabel('Sepal length')
-plt.ylabel('Petal width')
-plt.legend(loc=2)
-plt.ylim([-1, 3.5])
-plt.xticks([], [])
-plt.yticks([], [])
-
-
-figure = plt.subplot(2, 1, 2)
-ax = figure.axes
-ivector_setosa = compute_ivectors(acc_stats(setosa, ubm), ivector_machine)
-ivector_versicolor = compute_ivectors(
- acc_stats(versicolor, ubm), ivector_machine)
-ivector_virginica = compute_ivectors(
- acc_stats(virginica, ubm), ivector_machine)
-
-
-# Whitening iVectors
-whitening_trainer = bob.learn.linear.WhiteningTrainer()
-whitener_machine = bob.learn.linear.Machine(
- ivector_setosa.shape[1], ivector_setosa.shape[1])
-whitening_trainer.train(numpy.vstack(
- (ivector_setosa, ivector_versicolor, ivector_virginica)), whitener_machine)
-ivector_setosa = whitener_machine(ivector_setosa)
-ivector_versicolor = whitener_machine(ivector_versicolor)
-ivector_virginica = whitener_machine(ivector_virginica)
-
-
-# LDA ivectors
-lda_trainer = bob.learn.linear.FisherLDATrainer()
-lda_machine = bob.learn.linear.Machine(
- ivector_setosa.shape[1], ivector_setosa.shape[1])
-lda_trainer.train([ivector_setosa, ivector_versicolor,
- ivector_virginica], lda_machine)
-ivector_setosa = lda_machine(ivector_setosa)
-ivector_versicolor = lda_machine(ivector_versicolor)
-ivector_virginica = lda_machine(ivector_virginica)
-
-
-# WCCN ivectors
-# wccn_trainer = bob.learn.linear.WCCNTrainer()
-# wccn_machine = bob.learn.linear.Machine(
-# ivector_setosa.shape[1], ivector_setosa.shape[1])
-# wccn_trainer.train([ivector_setosa, ivector_versicolor,
-# ivector_virginica], wccn_machine)
-# ivector_setosa = wccn_machine(ivector_setosa)
-# ivector_versicolor = wccn_machine(ivector_versicolor)
-# ivector_virginica = wccn_machine(ivector_virginica)
-
-
-plt.title("First two ivectors")
-plt.scatter(ivector_setosa[:, 0],
- ivector_setosa[:, 1], c="darkcyan", label="setosa",
- marker="x")
-plt.scatter(ivector_versicolor[:, 0],
- ivector_versicolor[:, 1], c="goldenrod", label="versicolor",
- marker="x")
-plt.scatter(ivector_virginica[:, 0],
- ivector_virginica[:, 1], c="dimgrey", label="virginica",
- marker="x")
-
-plt.xticks([], [])
-plt.yticks([], [])
-
-# plt.grid(True)
-# plt.xlabel('Sepal length')
-# plt.ylabel('Petal width')
-plt.legend(loc=2)
-plt.ylim([-1, 3.5])
-
-plt.tight_layout()
-plt.show()
diff --git a/doc/py_api.rst b/doc/py_api.rst
index ba31cb5074149fc23d7696d0fd44e4d1caebac58..420373802d21e23fec0ad6bcefbe9776439b7b7f 100644
--- a/doc/py_api.rst
+++ b/doc/py_api.rst
@@ -18,7 +18,10 @@ Trainers
.. autosummary::
- bob.learn.em.KMeansTrainer
+ bob.learn.em.cluster.KMeansTrainer
+
+..
+ TODO uncomment when implemented
bob.learn.em.ML_GMMTrainer
bob.learn.em.MAP_GMMTrainer
bob.learn.em.ISVTrainer
@@ -32,7 +35,10 @@ Machines
.. autosummary::
- bob.learn.em.KMeansMachine
+ bob.learn.em.cluster.KMeansMachine
+
+..
+ TODO uncomment when implemented
bob.learn.em.Gaussian
bob.learn.em.GMMStats
bob.learn.em.GMMMachine
@@ -46,11 +52,11 @@ Machines
Functions
---------
-.. autosummary::
+..
+ TODO uncomment when implemented
+ .. autosummary::
- bob.learn.em.linear_scoring
- bob.learn.em.train
- bob.learn.em.train_jfa
+ bob.learn.em.linear_scoring
Detailed Information
--------------------