From 580ba1db8e3185323bd03f430e4f0c9466a0c947 Mon Sep 17 00:00:00 2001
From: Tiago Freitas Pereira <tiagofrepereira@gmail.com>
Date: Tue, 17 Feb 2015 12:08:09 +0100
Subject: [PATCH] Documenting and changing the documentation stuff
---
bob/learn/em/ML_gmm_trainer.cpp | 32 +-
bob/learn/em/__MAP_gmm_trainer__.py | 53 +--
bob/learn/em/__ML_gmm_trainer__.py | 85 ----
bob/learn/em/__empca_trainer__.py | 77 ---
bob/learn/em/__init__.py | 8 +-
bob/learn/em/__isv_trainer__.py | 49 --
bob/learn/em/__ivector_trainer__.py | 51 --
bob/learn/em/__jfa_trainer__.py | 71 ---
bob/learn/em/__kmeans_trainer__.py | 81 ----
bob/learn/em/__plda_trainer__.py | 50 --
bob/learn/em/gmm_base_trainer.cpp | 437 ------------------
.../em/include/bob.learn.em/ISVTrainer.h | 12 +
bob/learn/em/isv_trainer.cpp | 55 ++-
bob/learn/em/ivector_trainer.cpp | 20 +-
bob/learn/em/jfa_trainer.cpp | 2 +-
bob/learn/em/kmeans_machine.cpp | 2 +-
bob/learn/em/kmeans_trainer.cpp | 15 +-
bob/learn/em/linear_scoring.cpp | 8 +-
bob/learn/em/main.cpp | 6 +-
bob/learn/em/main.h | 18 +-
bob/learn/em/plda_trainer.cpp | 54 ++-
bob/learn/em/test/test_em.py | 38 +-
bob/learn/em/test/test_jfa_trainer.py | 15 +-
bob/learn/em/test/test_kmeans_trainer.py | 21 +-
bob/learn/em/test/test_plda_trainer.py | 36 +-
bob/learn/em/train.py | 61 +++
bob/learn/em/ztnorm.cpp | 12 +-
doc/guide.rst | 24 +-
28 files changed, 299 insertions(+), 1094 deletions(-)
delete mode 100644 bob/learn/em/__ML_gmm_trainer__.py
delete mode 100644 bob/learn/em/__empca_trainer__.py
delete mode 100644 bob/learn/em/__isv_trainer__.py
delete mode 100644 bob/learn/em/__ivector_trainer__.py
delete mode 100644 bob/learn/em/__jfa_trainer__.py
delete mode 100644 bob/learn/em/__kmeans_trainer__.py
delete mode 100644 bob/learn/em/__plda_trainer__.py
delete mode 100644 bob/learn/em/gmm_base_trainer.cpp
create mode 100644 bob/learn/em/train.py
diff --git a/bob/learn/em/ML_gmm_trainer.cpp b/bob/learn/em/ML_gmm_trainer.cpp
index e9d9955..6b5b271 100644
--- a/bob/learn/em/ML_gmm_trainer.cpp
+++ b/bob/learn/em/ML_gmm_trainer.cpp
@@ -156,20 +156,21 @@ static auto initialize = bob::extension::FunctionDoc(
"",
true
)
-.add_prototype("gmm_machine")
-.add_parameter("gmm_machine", ":py:class:`bob.learn.em.GMMMachine`", "GMMMachine Object");
+.add_prototype("gmm_machine,data")
+.add_parameter("gmm_machine", ":py:class:`bob.learn.em.GMMMachine`", "GMMMachine Object")
+.add_parameter("data", "array_like <float, 2D>", "Ignored.");
static PyObject* PyBobLearnEMMLGMMTrainer_initialize(PyBobLearnEMMLGMMTrainerObject* self, PyObject* args, PyObject* kwargs) {
BOB_TRY
/* Parses input arguments in a single shot */
char** kwlist = initialize.kwlist(0);
-
PyBobLearnEMGMMMachineObject* gmm_machine = 0;
+ PyBlitzArrayObject* data = 0;
+
+ if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!|O&", kwlist, &PyBobLearnEMGMMMachine_Type, &gmm_machine,
+ &PyBlitzArray_Converter, &data)) return 0;
+ auto data_ = make_safe(data);
- if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!", kwlist, &PyBobLearnEMGMMMachine_Type, &gmm_machine)){
- PyErr_Format(PyExc_RuntimeError, "%s.%s. Was not possible to read :py:class:`bob.learn.em.GMMMachine`", Py_TYPE(self)->tp_name, initialize.name());
- return 0;
- }
self->cxx->initialize(*gmm_machine->cxx);
BOB_CATCH_MEMBER("cannot perform the initialize method", 0)
@@ -222,17 +223,22 @@ static auto mStep = bob::extension::FunctionDoc(
true
)
-.add_prototype("gmm_machine")
-.add_parameter("gmm_machine", ":py:class:`bob.learn.em.GMMMachine`", "GMMMachine Object");
+.add_prototype("gmm_machine,data")
+.add_parameter("gmm_machine", ":py:class:`bob.learn.em.GMMMachine`", "GMMMachine Object")
+.add_parameter("data", "array_like <float, 2D>", "Ignored.");
static PyObject* PyBobLearnEMMLGMMTrainer_mStep(PyBobLearnEMMLGMMTrainerObject* self, PyObject* args, PyObject* kwargs) {
BOB_TRY
/* Parses input arguments in a single shot */
char** kwlist = mStep.kwlist(0);
- PyBobLearnEMGMMMachineObject* gmm_machine;
-
- if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!", kwlist, &PyBobLearnEMGMMMachine_Type, &gmm_machine)) return 0;
+ PyBobLearnEMGMMMachineObject* gmm_machine = 0;
+ PyBlitzArrayObject* data = 0;
+
+ if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!|O&", kwlist, &PyBobLearnEMGMMMachine_Type, &gmm_machine,
+ &PyBlitzArray_Converter, &data)) return 0;
+ if(data != NULL)
+ auto data_ = make_safe(data);
self->cxx->mStep(*gmm_machine->cxx);
@@ -330,6 +336,6 @@ bool init_BobLearnEMMLGMMTrainer(PyObject* module)
// add the type to the module
Py_INCREF(&PyBobLearnEMMLGMMTrainer_Type);
- return PyModule_AddObject(module, "_ML_GMMTrainer", (PyObject*)&PyBobLearnEMMLGMMTrainer_Type) >= 0;
+ return PyModule_AddObject(module, "ML_GMMTrainer", (PyObject*)&PyBobLearnEMMLGMMTrainer_Type) >= 0;
}
diff --git a/bob/learn/em/__MAP_gmm_trainer__.py b/bob/learn/em/__MAP_gmm_trainer__.py
index 6f4af38..322d88f 100644
--- a/bob/learn/em/__MAP_gmm_trainer__.py
+++ b/bob/learn/em/__MAP_gmm_trainer__.py
@@ -11,7 +11,7 @@ import numpy
# define the class
class MAP_GMMTrainer(_MAP_GMMTrainer):
- def __init__(self, prior_gmm, update_means=True, update_variances=False, update_weights=False, convergence_threshold=0.001, max_iterations=10, converge_by_likelihood=True, **kwargs):
+ def __init__(self, prior_gmm, update_means=True, update_variances=False, update_weights=False, **kwargs):
"""
:py:class:`bob.learn.em.MAP_GMMTrainer` constructor
@@ -43,57 +43,6 @@ class MAP_GMMTrainer(_MAP_GMMTrainer):
relevance_factor = kwargs.get('relevance_factor')
_MAP_GMMTrainer.__init__(self, prior_gmm, relevance_factor=relevance_factor, update_means=update_means, update_variances=update_variances,update_weights=update_weights)
- self.convergence_threshold = convergence_threshold
- self.max_iterations = max_iterations
- self.converge_by_likelihood = converge_by_likelihood
-
-
-
-
-
- def train(self, gmm_machine, data):
- """
- Train the :py:class:bob.learn.em.GMMMachine using data
-
- Keyword Parameters:
- gmm_machine
- The :py:class:bob.learn.em.GMMMachine class
- data
- The data to be trained
- """
-
- #Initialization
- self.initialize(gmm_machine);
-
- #Do the Expectation-Maximization algorithm
- average_output_previous = 0
- average_output = -numpy.inf;
-
-
- #eStep
- self.eStep(gmm_machine, data);
-
- if(self.converge_by_likelihood):
- average_output = self.compute_likelihood(gmm_machine);
-
- for i in range(self.max_iterations):
- #saves average output from last iteration
- average_output_previous = average_output;
-
- #mStep
- self.mStep(gmm_machine);
-
- #eStep
- self.eStep(gmm_machine, data);
-
- #Computes log likelihood if required
- if(self.converge_by_likelihood):
- average_output = self.compute_likelihood(gmm_machine);
-
- #Terminates if converged (and likelihood computation is set)
- if abs((average_output_previous - average_output)/average_output_previous) <= self.convergence_threshold:
- break
-
# copy the documentation from the base class
__doc__ = _MAP_GMMTrainer.__doc__
diff --git a/bob/learn/em/__ML_gmm_trainer__.py b/bob/learn/em/__ML_gmm_trainer__.py
deleted file mode 100644
index 35ac30c..0000000
--- a/bob/learn/em/__ML_gmm_trainer__.py
+++ /dev/null
@@ -1,85 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-# Mon Jan 22 18:29:10 2015
-#
-# Copyright (C) 2011-2015 Idiap Research Institute, Martigny, Switzerland
-
-from ._library import _ML_GMMTrainer
-import numpy
-
-# define the class
-class ML_GMMTrainer(_ML_GMMTrainer):
-
- def __init__(self, update_means=True, update_variances=False, update_weights=False, convergence_threshold=0.001, max_iterations=10, converge_by_likelihood=True):
- """
- :py:class:bob.learn.em.ML_GMMTrainer constructor
-
- Keyword Parameters:
- update_means
-
- update_variances
-
- update_weights
-
- convergence_threshold
- Convergence threshold
- max_iterations
- Number of maximum iterations
- converge_by_likelihood
- Tells whether we compute log_likelihood as a convergence criteria, or not
-
- """
-
- _ML_GMMTrainer.__init__(self, update_means=update_means, update_variances=update_variances, update_weights=update_weights)
- self.convergence_threshold = convergence_threshold
- self.max_iterations = max_iterations
- self.converge_by_likelihood = converge_by_likelihood
-
-
- def train(self, gmm_machine, data):
- """
- Train the :py:class:bob.learn.em.GMMMachine using data
-
- Keyword Parameters:
- gmm_machine
- The :py:class:bob.learn.em.GMMMachine class
- data
- The data to be trained
- """
-
- #Initialization
- self.initialize(gmm_machine);
-
- #Do the Expectation-Maximization algorithm
- average_output_previous = 0
- average_output = -numpy.inf;
-
-
- #eStep
- self.eStep(gmm_machine, data);
-
- if(self.converge_by_likelihood):
- average_output = self.compute_likelihood(gmm_machine);
-
- for i in range(self.max_iterations):
- #saves average output from last iteration
- average_output_previous = average_output;
-
- #mStep
- self.mStep(gmm_machine);
-
- #eStep
- self.eStep(gmm_machine, data);
-
- #Computes log likelihood if required
- if(self.converge_by_likelihood):
- average_output = self.compute_likelihood(gmm_machine);
-
- #Terminates if converged (and likelihood computation is set)
- if abs((average_output_previous - average_output)/average_output_previous) <= self.convergence_threshold:
- break
-
-
-# copy the documentation from the base class
-__doc__ = _ML_GMMTrainer.__doc__
diff --git a/bob/learn/em/__empca_trainer__.py b/bob/learn/em/__empca_trainer__.py
deleted file mode 100644
index d710dc8..0000000
--- a/bob/learn/em/__empca_trainer__.py
+++ /dev/null
@@ -1,77 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-# Wed Fev 04 13:35:10 2015 +0200
-#
-# Copyright (C) 2011-2015 Idiap Research Institute, Martigny, Switzerland
-
-from ._library import _EMPCATrainer
-import numpy
-
-# define the class
-class EMPCATrainer (_EMPCATrainer):
-
- def __init__(self, convergence_threshold=0.001, max_iterations=10, compute_likelihood=True):
- """
- :py:class:`bob.learn.em.EMPCATrainer` constructor
-
- Keyword Parameters:
- convergence_threshold
- Convergence threshold
- max_iterations
- Number of maximum iterations
- compute_likelihood
-
- """
-
- _EMPCATrainer.__init__(self,convergence_threshold)
- self._max_iterations = max_iterations
- self._compute_likelihood = compute_likelihood
-
-
- def train(self, linear_machine, data):
- """
- Train the :py:class:bob.learn.em.LinearMachine using data
-
- Keyword Parameters:
- linear_machine
- The :py:class:bob.learn.em.LinearMachine class
- data
- The data to be trained
- """
-
- #Initialization
- self.initialize(linear_machine, data);
-
- #Do the Expectation-Maximization algorithm
- average_output_previous = 0
- average_output = -numpy.inf;
-
- #eStep
- self.eStep(linear_machine, data);
-
- if(self._compute_likelihood):
- average_output = self.compute_likelihood(linear_machine);
-
- for i in range(self._max_iterations):
-
- #saves average output from last iteration
- average_output_previous = average_output;
-
- #mStep
- self.mStep(linear_machine);
-
- #eStep
- self.eStep(linear_machine, data);
-
- #Computes log likelihood if required
- if(self._compute_likelihood):
- average_output = self.compute_likelihood(linear_machine);
-
- #Terminates if converged (and likelihood computation is set)
- if abs((average_output_previous - average_output)/average_output_previous) <= self._convergence_threshold:
- break
-
-
-# copy the documentation from the base class
-__doc__ = _EMPCATrainer.__doc__
diff --git a/bob/learn/em/__init__.py b/bob/learn/em/__init__.py
index dbe487b..4e9c455 100644
--- a/bob/learn/em/__init__.py
+++ b/bob/learn/em/__init__.py
@@ -11,14 +11,8 @@ bob.extension.load_bob_library('bob.learn.em', __file__)
from ._library import *
from . import version
from .version import module as __version__
-from .__kmeans_trainer__ import *
-from .__ML_gmm_trainer__ import *
from .__MAP_gmm_trainer__ import *
-from .__jfa_trainer__ import *
-from .__isv_trainer__ import *
-from .__ivector_trainer__ import *
-from .__plda_trainer__ import *
-
+from train import *
def ztnorm_same_value(vect_a, vect_b):
"""Computes the matrix of boolean D for the ZT-norm, which indicates where
diff --git a/bob/learn/em/__isv_trainer__.py b/bob/learn/em/__isv_trainer__.py
deleted file mode 100644
index 9f4b84f..0000000
--- a/bob/learn/em/__isv_trainer__.py
+++ /dev/null
@@ -1,49 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-# Mon Fev 02 21:40:10 2015 +0200
-#
-# Copyright (C) 2011-2015 Idiap Research Institute, Martigny, Switzerland
-
-from ._library import _ISVTrainer
-import numpy
-
-# define the class
-class ISVTrainer (_ISVTrainer):
-
- def __init__(self, max_iterations=10, relevance_factor=4.):
- """
- :py:class:`bob.learn.em.ISVTrainer` constructor
-
- Keyword Parameters:
- max_iterations
- Number of maximum iterations
- """
- _ISVTrainer.__init__(self, relevance_factor)
- self._max_iterations = max_iterations
-
-
- def train(self, isv_base, data):
- """
- Train the :py:class:`bob.learn.em.ISVBase` using data
-
- Keyword Parameters:
- jfa_base
- The `:py:class:bob.learn.em.ISVBase` class
- data
- The data to be trained
- """
-
- #Initialization
- self.initialize(isv_base, data);
-
- for i in range(self._max_iterations):
- #eStep
- self.eStep(isv_base, data);
- #mStep
- self.mStep(isv_base);
-
-
-
-# copy the documentation from the base class
-__doc__ = _ISVTrainer.__doc__
diff --git a/bob/learn/em/__ivector_trainer__.py b/bob/learn/em/__ivector_trainer__.py
deleted file mode 100644
index 579a1d8..0000000
--- a/bob/learn/em/__ivector_trainer__.py
+++ /dev/null
@@ -1,51 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-# Tue Fev 03 13:20:10 2015 +0200
-#
-# Copyright (C) 2011-2015 Idiap Research Institute, Martigny, Switzerland
-
-from ._library import _IVectorTrainer
-import numpy
-
-# define the class
-class IVectorTrainer (_IVectorTrainer):
-
- def __init__(self, max_iterations=10, update_sigma=False):
- """
- :py:class:`bob.learn.em.IVectorTrainer` constructor
-
- Keyword Parameters:
- max_iterations
- Number of maximum iterations
- update_sigma
-
- """
- _IVectorTrainer.__init__(self, update_sigma)
- self._max_iterations = max_iterations
-
-
- def train(self, ivector_machine, data):
- """
- Train the :py:class:`bob.learn.em.IVectorMachine` using data
-
- Keyword Parameters:
- ivector_machine
- The `:py:class:bob.learn.em.IVectorMachine` class
- data
- The data to be trained
- """
-
- #Initialization
- self.initialize(ivector_machine);
-
- for i in range(self._max_iterations):
- #eStep
- self.eStep(ivector_machine, data);
- #mStep
- self.mStep(ivector_machine);
-
-
-
-# copy the documentation from the base class
-__doc__ = _IVectorTrainer.__doc__
diff --git a/bob/learn/em/__jfa_trainer__.py b/bob/learn/em/__jfa_trainer__.py
deleted file mode 100644
index 2d8ccec..0000000
--- a/bob/learn/em/__jfa_trainer__.py
+++ /dev/null
@@ -1,71 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-# Sun Fev 01 21:10:10 2015 +0200
-#
-# Copyright (C) 2011-2015 Idiap Research Institute, Martigny, Switzerland
-
-from ._library import _JFATrainer
-import numpy
-
-# define the class
-class JFATrainer (_JFATrainer):
-
- def __init__(self, max_iterations=10):
- """
- :py:class:`bob.learn.em.JFATrainer` constructor
-
- Keyword Parameters:
- max_iterations
- Number of maximum iterations
- """
-
- _JFATrainer.__init__(self)
- self._max_iterations = max_iterations
-
-
- def train_loop(self, jfa_base, data):
- """
- Train the :py:class:`bob.learn.em.JFABase` using data
-
- Keyword Parameters:
- jfa_base
- The `:py:class:bob.learn.em.JFABase` class
- data
- The data to be trained
- """
- #V Subspace
- for i in range(self._max_iterations):
- self.e_step1(jfa_base, data)
- self.m_step1(jfa_base, data)
- self.finalize1(jfa_base, data)
-
- #U subspace
- for i in range(self._max_iterations):
- self.e_step2(jfa_base, data)
- self.m_step2(jfa_base, data)
- self.finalize2(jfa_base, data)
-
- # d subspace
- for i in range(self._max_iterations):
- self.e_step3(jfa_base, data)
- self.m_step3(jfa_base, data)
- self.finalize3(jfa_base, data)
-
-
- def train(self, jfa_base, data):
- """
- Train the :py:class:`bob.learn.em.JFABase` using data
-
- Keyword Parameters:
- jfa_base
- The `:py:class:bob.learn.em.JFABase` class
- data
- The data to be trained
- """
- self.initialize(jfa_base, data)
- self.train_loop(jfa_base, data)
-
-
-# copy the documentation from the base class
-__doc__ = _JFATrainer.__doc__
diff --git a/bob/learn/em/__kmeans_trainer__.py b/bob/learn/em/__kmeans_trainer__.py
deleted file mode 100644
index b7ef48e..0000000
--- a/bob/learn/em/__kmeans_trainer__.py
+++ /dev/null
@@ -1,81 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-# Mon Jan 19 11:35:10 2015 +0200
-#
-# Copyright (C) 2011-2015 Idiap Research Institute, Martigny, Switzerland
-
-from ._library import _KMeansTrainer
-import numpy
-
-# define the class
-class KMeansTrainer (_KMeansTrainer):
-
- def __init__(self, initialization_method="RANDOM", convergence_threshold=0.001, max_iterations=10, converge_by_average_min_distance=True):
- """
- :py:class:`bob.learn.em.KMeansTrainer` constructor
-
- Keyword Parameters:
- initialization_method
- The initialization method to generate the initial means
- convergence_threshold
- Convergence threshold
- max_iterations
- Number of maximum iterations
- converge_by_average_min_distance
- Tells whether we compute the average min (square Euclidean) distance, as a convergence criteria, or not
-
- """
-
- _KMeansTrainer.__init__(self, initialization_method="RANDOM", )
- self._convergence_threshold = convergence_threshold
- self._max_iterations = max_iterations
- self._converge_by_average_min_distance = converge_by_average_min_distance
-
-
- def train(self, kmeans_machine, data):
- """
- Train the :py:class:bob.learn.em.KMeansMachine using data
-
- Keyword Parameters:
- kmeans_machine
- The :py:class:bob.learn.em.KMeansMachine class
- data
- The data to be trained
- """
-
- #Initialization
- self.initialize(kmeans_machine, data);
-
- #Do the Expectation-Maximization algorithm
- average_output_previous = 0
- average_output = -numpy.inf;
-
- #eStep
- self.eStep(kmeans_machine, data);
-
- if(self._converge_by_average_min_distance):
- average_output = self.compute_likelihood(kmeans_machine);
-
- for i in range(self._max_iterations):
-
- #saves average output from last iteration
- average_output_previous = average_output;
-
- #mStep
- self.mStep(kmeans_machine);
-
- #eStep
- self.eStep(kmeans_machine, data);
-
- #Computes log likelihood if required
- if(self._converge_by_average_min_distance):
- average_output = self.compute_likelihood(kmeans_machine);
-
- #Terminates if converged (and likelihood computation is set)
- if abs((average_output_previous - average_output)/average_output_previous) <= self._convergence_threshold:
- break
-
-
-# copy the documentation from the base class
-__doc__ = _KMeansTrainer.__doc__
diff --git a/bob/learn/em/__plda_trainer__.py b/bob/learn/em/__plda_trainer__.py
deleted file mode 100644
index 3989b8d..0000000
--- a/bob/learn/em/__plda_trainer__.py
+++ /dev/null
@@ -1,50 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-# Mon Fev 02 21:40:10 2015 +0200
-#
-# Copyright (C) 2011-2015 Idiap Research Institute, Martigny, Switzerland
-
-from ._library import _PLDATrainer
-import numpy
-
-# define the class
-class PLDATrainer (_PLDATrainer):
-
- def __init__(self, max_iterations=10, use_sum_second_order=False):
- """
- :py:class:`bob.learn.em.PLDATrainer` constructor
-
- Keyword Parameters:
- max_iterations
- Number of maximum iterations
- """
- _PLDATrainer.__init__(self, use_sum_second_order)
- self._max_iterations = max_iterations
-
-
- def train(self, plda_base, data):
- """
- Train the :py:class:`bob.learn.em.PLDABase` using data
-
- Keyword Parameters:
- jfa_base
- The `:py:class:bob.learn.em.PLDABase` class
- data
- The data to be trained
- """
-
- #Initialization
- self.initialize(plda_base, data);
-
- for i in range(self._max_iterations):
- #eStep
- self.e_step(plda_base, data);
- #mStep
- self.m_step(plda_base, data);
- self.finalize(plda_base, data);
-
-
-
-# copy the documentation from the base class
-__doc__ = _PLDATrainer.__doc__
diff --git a/bob/learn/em/gmm_base_trainer.cpp b/bob/learn/em/gmm_base_trainer.cpp
deleted file mode 100644
index 96dfdc6..0000000
--- a/bob/learn/em/gmm_base_trainer.cpp
+++ /dev/null
@@ -1,437 +0,0 @@
-/**
- * @author Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
- * @date Web 21 Jan 12:30:00 2015
- *
- * @brief Python API for bob::learn::em
- *
- * Copyright (C) 2011-2014 Idiap Research Institute, Martigny, Switzerland
- */
-
-#include "main.h"
-#include <boost/make_shared.hpp>
-
-/******************************************************************/
-/************ Constructor Section *********************************/
-/******************************************************************/
-
-static inline bool f(PyObject* o){return o != 0 && PyObject_IsTrue(o) > 0;} /* converts PyObject to bool and returns false if object is NULL */
-
-static auto GMMBaseTrainer_doc = bob::extension::ClassDoc(
- BOB_EXT_MODULE_PREFIX ".GMMBaseTrainer",
- "This class implements the E-step of the expectation-maximisation"
- "algorithm for a :py:class:`bob.learn.em.GMMMachine`"
-).add_constructor(
- bob::extension::FunctionDoc(
- "__init__",
- "Creates a GMMBaseTrainer",
- "",
- true
- )
- .add_prototype("update_means, [update_variances], [update_weights], [mean_var_update_responsibilities_threshold]","")
- .add_prototype("other","")
- .add_prototype("","")
-
- .add_parameter("update_means", "bool", "Update means on each iteration")
- .add_parameter("update_variances", "bool", "Update variances on each iteration")
- .add_parameter("update_weights", "bool", "Update weights on each iteration")
- .add_parameter("mean_var_update_responsibilities_threshold", "float", "Threshold over the responsibilities of the Gaussians Equations 9.24, 9.25 of Bishop, `Pattern recognition and machine learning`, 2006 require a division by the responsibilities, which might be equal to zero because of numerical issue. This threshold is used to avoid such divisions.")
- .add_parameter("other", ":py:class:`bob.learn.em.GMMBaseTrainer`", "A GMMBaseTrainer object to be copied.")
-);
-
-
-
-static int PyBobLearnEMGMMBaseTrainer_init_copy(PyBobLearnEMGMMBaseTrainerObject* self, PyObject* args, PyObject* kwargs) {
-
- char** kwlist = GMMBaseTrainer_doc.kwlist(1);
- PyBobLearnEMGMMBaseTrainerObject* tt;
- if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!", kwlist, &PyBobLearnEMGMMBaseTrainer_Type, &tt)){
- GMMBaseTrainer_doc.print_usage();
- return -1;
- }
-
- self->cxx.reset(new bob::learn::em::GMMBaseTrainer(*tt->cxx));
- return 0;
-}
-
-
-static int PyBobLearnEMGMMBaseTrainer_init_bool(PyBobLearnEMGMMBaseTrainerObject* self, PyObject* args, PyObject* kwargs) {
-
- char** kwlist = GMMBaseTrainer_doc.kwlist(0);
- PyObject* update_means = 0;
- PyObject* update_variances = 0;
- PyObject* update_weights = 0;
- double mean_var_update_responsibilities_threshold = std::numeric_limits<double>::epsilon();
-
- if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!|O!O!d", kwlist, &PyBool_Type, &update_means, &PyBool_Type,
- &update_variances, &PyBool_Type, &update_weights, &mean_var_update_responsibilities_threshold)){
- GMMBaseTrainer_doc.print_usage();
- return -1;
- }
- self->cxx.reset(new bob::learn::em::GMMBaseTrainer(f(update_means), f(update_variances), f(update_weights), mean_var_update_responsibilities_threshold));
- return 0;
-}
-
-
-static int PyBobLearnEMGMMBaseTrainer_init(PyBobLearnEMGMMBaseTrainerObject* self, PyObject* args, PyObject* kwargs) {
- BOB_TRY
-
- int nargs = (args?PyTuple_Size(args):0) + (kwargs?PyDict_Size(kwargs):0);
-
- if (nargs==0){ //default initializer ()
- self->cxx.reset(new bob::learn::em::GMMBaseTrainer());
- return 0;
- }
- else{
- //Reading the input argument
- PyObject* arg = 0;
- if (PyTuple_Size(args))
- arg = PyTuple_GET_ITEM(args, 0);
- else {
- PyObject* tmp = PyDict_Values(kwargs);
- auto tmp_ = make_safe(tmp);
- arg = PyList_GET_ITEM(tmp, 0);
- }
-
- // If the constructor input is GMMBaseTrainer object
- if (PyBobLearnEMGMMBaseTrainer_Check(arg))
- return PyBobLearnEMGMMBaseTrainer_init_copy(self, args, kwargs);
- else
- return PyBobLearnEMGMMBaseTrainer_init_bool(self, args, kwargs);
- }
-
- BOB_CATCH_MEMBER("cannot create GMMBaseTrainer_init_bool", 0)
- return 0;
-}
-
-
-static void PyBobLearnEMGMMBaseTrainer_delete(PyBobLearnEMGMMBaseTrainerObject* self) {
- self->cxx.reset();
- Py_TYPE(self)->tp_free((PyObject*)self);
-}
-
-
-int PyBobLearnEMGMMBaseTrainer_Check(PyObject* o) {
- return PyObject_IsInstance(o, reinterpret_cast<PyObject*>(&PyBobLearnEMGMMBaseTrainer_Type));
-}
-
-
-static PyObject* PyBobLearnEMGMMBaseTrainer_RichCompare(PyBobLearnEMGMMBaseTrainerObject* self, PyObject* other, int op) {
- BOB_TRY
-
- if (!PyBobLearnEMGMMBaseTrainer_Check(other)) {
- PyErr_Format(PyExc_TypeError, "cannot compare `%s' with `%s'", Py_TYPE(self)->tp_name, Py_TYPE(other)->tp_name);
- return 0;
- }
- auto other_ = reinterpret_cast<PyBobLearnEMGMMBaseTrainerObject*>(other);
- switch (op) {
- case Py_EQ:
- if (*self->cxx==*other_->cxx) Py_RETURN_TRUE; else Py_RETURN_FALSE;
- case Py_NE:
- if (*self->cxx==*other_->cxx) Py_RETURN_FALSE; else Py_RETURN_TRUE;
- default:
- Py_INCREF(Py_NotImplemented);
- return Py_NotImplemented;
- }
- BOB_CATCH_MEMBER("cannot compare GMMBaseTrainer objects", 0)
-}
-
-
-/******************************************************************/
-/************ Variables Section ***********************************/
-/******************************************************************/
-
-
-/***** gmm_stats *****/
-static auto gmm_stats = bob::extension::VariableDoc(
- "gmm_stats",
- ":py:class:`bob.learn.em.GMMStats`",
- "Get/Set GMMStats",
- ""
-);
-PyObject* PyBobLearnEMGMMBaseTrainer_getGMMStats(PyBobLearnEMGMMBaseTrainerObject* self, void*){
- BOB_TRY
-
- bob::learn::em::GMMStats stats = self->cxx->getGMMStats();
- boost::shared_ptr<bob::learn::em::GMMStats> stats_shared = boost::make_shared<bob::learn::em::GMMStats>(stats);
-
- //Allocating the correspondent python object
- PyBobLearnEMGMMStatsObject* retval =
- (PyBobLearnEMGMMStatsObject*)PyBobLearnEMGMMStats_Type.tp_alloc(&PyBobLearnEMGMMStats_Type, 0);
-
- retval->cxx = stats_shared;
-
- return Py_BuildValue("O",retval);
- BOB_CATCH_MEMBER("GMMStats could not be read", 0)
-}
-/*
-int PyBobLearnEMGMMBaseTrainer_setGMMStats(PyBobLearnEMGMMBaseTrainerObject* self, PyObject* value, void*){
- BOB_TRY
-
- if (!PyBobLearnEMGMMStats_Check(value)){
- PyErr_Format(PyExc_RuntimeError, "%s %s expects a :py:class:`bob.learn.em.GMMStats`", Py_TYPE(self)->tp_name, gmm_stats.name());
- return -1;
- }
-
- PyBobLearnEMGMMStatsObject* stats = 0;
- PyArg_Parse(value, "O!", &PyBobLearnEMGMMStats_Type,&stats);
-
- self->cxx->setGMMStats(*stats->cxx);
-
- return 0;
- BOB_CATCH_MEMBER("gmm_stats could not be set", -1)
-}
-*/
-
-
-/***** update_means *****/
-static auto update_means = bob::extension::VariableDoc(
- "update_means",
- "bool",
- "Update means on each iteration",
- ""
-);
-PyObject* PyBobLearnEMGMMBaseTrainer_getUpdateMeans(PyBobLearnEMGMMBaseTrainerObject* self, void*){
- BOB_TRY
- return Py_BuildValue("O",self->cxx->getUpdateMeans()?Py_True:Py_False);
- BOB_CATCH_MEMBER("update_means could not be read", 0)
-}
-
-/***** update_variances *****/
-static auto update_variances = bob::extension::VariableDoc(
- "update_variances",
- "bool",
- "Update variances on each iteration",
- ""
-);
-PyObject* PyBobLearnEMGMMBaseTrainer_getUpdateVariances(PyBobLearnEMGMMBaseTrainerObject* self, void*){
- BOB_TRY
- return Py_BuildValue("O",self->cxx->getUpdateVariances()?Py_True:Py_False);
- BOB_CATCH_MEMBER("update_variances could not be read", 0)
-}
-
-
-/***** update_weights *****/
-static auto update_weights = bob::extension::VariableDoc(
- "update_weights",
- "bool",
- "Update weights on each iteration",
- ""
-);
-PyObject* PyBobLearnEMGMMBaseTrainer_getUpdateWeights(PyBobLearnEMGMMBaseTrainerObject* self, void*){
- BOB_TRY
- return Py_BuildValue("O",self->cxx->getUpdateWeights()?Py_True:Py_False);
- BOB_CATCH_MEMBER("update_weights could not be read", 0)
-}
-
-
-
-
-
-/***** mean_var_update_responsibilities_threshold *****/
-static auto mean_var_update_responsibilities_threshold = bob::extension::VariableDoc(
- "mean_var_update_responsibilities_threshold",
- "bool",
- "Threshold over the responsibilities of the Gaussians"
- "Equations 9.24, 9.25 of Bishop, \"Pattern recognition and machine learning\", 2006"
- "require a division by the responsibilities, which might be equal to zero"
- "because of numerical issue. This threshold is used to avoid such divisions.",
- ""
-);
-PyObject* PyBobLearnEMGMMBaseTrainer_getMeanVarUpdateResponsibilitiesThreshold(PyBobLearnEMGMMBaseTrainerObject* self, void*){
- BOB_TRY
- return Py_BuildValue("d",self->cxx->getMeanVarUpdateResponsibilitiesThreshold());
- BOB_CATCH_MEMBER("update_weights could not be read", 0)
-}
-
-
-static PyGetSetDef PyBobLearnEMGMMBaseTrainer_getseters[] = {
- {
- update_means.name(),
- (getter)PyBobLearnEMGMMBaseTrainer_getUpdateMeans,
- 0,
- update_means.doc(),
- 0
- },
- {
- update_variances.name(),
- (getter)PyBobLearnEMGMMBaseTrainer_getUpdateVariances,
- 0,
- update_variances.doc(),
- 0
- },
- {
- update_weights.name(),
- (getter)PyBobLearnEMGMMBaseTrainer_getUpdateWeights,
- 0,
- update_weights.doc(),
- 0
- },
- {
- mean_var_update_responsibilities_threshold.name(),
- (getter)PyBobLearnEMGMMBaseTrainer_getMeanVarUpdateResponsibilitiesThreshold,
- 0,
- mean_var_update_responsibilities_threshold.doc(),
- 0
- },
- {
- gmm_stats.name(),
- (getter)PyBobLearnEMGMMBaseTrainer_getGMMStats,
- 0, //(setter)PyBobLearnEMGMMBaseTrainer_setGMMStats,
- gmm_stats.doc(),
- 0
- },
- {0} // Sentinel
-};
-
-
-/******************************************************************/
-/************ Functions Section ***********************************/
-/******************************************************************/
-
-/*** initialize ***/
-static auto initialize = bob::extension::FunctionDoc(
- "initialize",
- "Initialization before the EM steps",
- "Instanciate :py:class:`bob.learn.em.GMMStats`",
- true
-)
-.add_prototype("gmm_machine")
-.add_parameter("gmm_machine", ":py:class:`bob.learn.em.GMMMachine`", "GMMMachine Object");
-static PyObject* PyBobLearnEMGMMBaseTrainer_initialize(PyBobLearnEMGMMBaseTrainerObject* self, PyObject* args, PyObject* kwargs) {
- BOB_TRY
-
- /* Parses input arguments in a single shot */
- char** kwlist = initialize.kwlist(0);
-
- PyBobLearnEMGMMMachineObject* gmm_machine = 0;
-
- if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!", kwlist, &PyBobLearnEMGMMMachine_Type, &gmm_machine)) Py_RETURN_NONE;
-
- self->cxx->initialize(*gmm_machine->cxx);
-
- BOB_CATCH_MEMBER("cannot perform the initialize method", 0)
-
- Py_RETURN_NONE;
-}
-
-
-
-/*** eStep ***/
-static auto eStep = bob::extension::FunctionDoc(
- "eStep",
- "Calculates and saves statistics across the dataset,"
- "and saves these as m_ss. ",
-
- "Calculates the average log likelihood of the observations given the GMM,"
- "and returns this in average_log_likelihood."
- "The statistics, m_ss, will be used in the mStep() that follows.",
-
- true
-)
-.add_prototype("gmm_machine,data")
-.add_parameter("gmm_machine", ":py:class:`bob.learn.em.GMMMachine`", "GMMMachine Object")
-.add_parameter("data", "array_like <float, 2D>", "Input data");
-static PyObject* PyBobLearnEMGMMBaseTrainer_eStep(PyBobLearnEMGMMBaseTrainerObject* self, PyObject* args, PyObject* kwargs) {
- BOB_TRY
-
- /* Parses input arguments in a single shot */
- char** kwlist = eStep.kwlist(0);
-
- PyBobLearnEMGMMMachineObject* gmm_machine;
- PyBlitzArrayObject* data = 0;
- if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!O&", kwlist, &PyBobLearnEMGMMMachine_Type, &gmm_machine,
- &PyBlitzArray_Converter, &data)) Py_RETURN_NONE;
- auto data_ = make_safe(data);
-
- self->cxx->eStep(*gmm_machine->cxx, *PyBlitzArrayCxx_AsBlitz<double,2>(data));
-
- BOB_CATCH_MEMBER("cannot perform the eStep method", 0)
-
- Py_RETURN_NONE;
-}
-
-
-/*** computeLikelihood ***/
-static auto compute_likelihood = bob::extension::FunctionDoc(
- "compute_likelihood",
- "This functions returns the average min (Square Euclidean) distance (average distance to the closest mean)",
- 0,
- true
-)
-.add_prototype("gmm_machine")
-.add_parameter("gmm_machine", ":py:class:`bob.learn.em.GMMMachine`", "GMMMachine Object");
-static PyObject* PyBobLearnEMGMMBaseTrainer_compute_likelihood(PyBobLearnEMGMMBaseTrainerObject* self, PyObject* args, PyObject* kwargs) {
- BOB_TRY
-
- /* Parses input arguments in a single shot */
- char** kwlist = compute_likelihood.kwlist(0);
-
- PyBobLearnEMGMMMachineObject* gmm_machine;
- if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!", kwlist, &PyBobLearnEMGMMMachine_Type, &gmm_machine)) Py_RETURN_NONE;
-
- double value = self->cxx->computeLikelihood(*gmm_machine->cxx);
- return Py_BuildValue("d", value);
-
- BOB_CATCH_MEMBER("cannot perform the computeLikelihood method", 0)
-}
-
-
-static PyMethodDef PyBobLearnEMGMMBaseTrainer_methods[] = {
- {
- initialize.name(),
- (PyCFunction)PyBobLearnEMGMMBaseTrainer_initialize,
- METH_VARARGS|METH_KEYWORDS,
- initialize.doc()
- },
- {
- eStep.name(),
- (PyCFunction)PyBobLearnEMGMMBaseTrainer_eStep,
- METH_VARARGS|METH_KEYWORDS,
- eStep.doc()
- },
- {
- compute_likelihood.name(),
- (PyCFunction)PyBobLearnEMGMMBaseTrainer_compute_likelihood,
- METH_VARARGS|METH_KEYWORDS,
- compute_likelihood.doc()
- },
- {0} /* Sentinel */
-};
-
-
-/******************************************************************/
-/************ Module Section **************************************/
-/******************************************************************/
-
-// Define the Gaussian type struct; will be initialized later
-PyTypeObject PyBobLearnEMGMMBaseTrainer_Type = {
- PyVarObject_HEAD_INIT(0,0)
- 0
-};
-
-bool init_BobLearnEMGMMBaseTrainer(PyObject* module)
-{
- // initialize the type struct
- PyBobLearnEMGMMBaseTrainer_Type.tp_name = GMMBaseTrainer_doc.name();
- PyBobLearnEMGMMBaseTrainer_Type.tp_basicsize = sizeof(PyBobLearnEMGMMBaseTrainerObject);
- PyBobLearnEMGMMBaseTrainer_Type.tp_flags = Py_TPFLAGS_DEFAULT;
- PyBobLearnEMGMMBaseTrainer_Type.tp_doc = GMMBaseTrainer_doc.doc();
-
- // set the functions
- PyBobLearnEMGMMBaseTrainer_Type.tp_new = PyType_GenericNew;
- PyBobLearnEMGMMBaseTrainer_Type.tp_init = reinterpret_cast<initproc>(PyBobLearnEMGMMBaseTrainer_init);
- PyBobLearnEMGMMBaseTrainer_Type.tp_dealloc = reinterpret_cast<destructor>(PyBobLearnEMGMMBaseTrainer_delete);
- PyBobLearnEMGMMBaseTrainer_Type.tp_richcompare = reinterpret_cast<richcmpfunc>(PyBobLearnEMGMMBaseTrainer_RichCompare);
- PyBobLearnEMGMMBaseTrainer_Type.tp_methods = PyBobLearnEMGMMBaseTrainer_methods;
- PyBobLearnEMGMMBaseTrainer_Type.tp_getset = PyBobLearnEMGMMBaseTrainer_getseters;
- PyBobLearnEMGMMBaseTrainer_Type.tp_call = reinterpret_cast<ternaryfunc>(PyBobLearnEMGMMBaseTrainer_compute_likelihood);
-
-
- // check that everything is fine
- if (PyType_Ready(&PyBobLearnEMGMMBaseTrainer_Type) < 0) return false;
-
- // add the type to the module
- Py_INCREF(&PyBobLearnEMGMMBaseTrainer_Type);
- return PyModule_AddObject(module, "GMMBaseTrainer", (PyObject*)&PyBobLearnEMGMMBaseTrainer_Type) >= 0;
-}
-
diff --git a/bob/learn/em/include/bob.learn.em/ISVTrainer.h b/bob/learn/em/include/bob.learn.em/ISVTrainer.h
index e314ad6..f0177ec 100644
--- a/bob/learn/em/include/bob.learn.em/ISVTrainer.h
+++ b/bob/learn/em/include/bob.learn.em/ISVTrainer.h
@@ -133,6 +133,18 @@ class ISVTrainer
{ m_base_trainer.setAccUA1(acc); }
void setAccUA2(const blitz::Array<double,2>& acc)
{ m_base_trainer.setAccUA2(acc); }
+
+ /**
+ * @brief Sets the Random Number Generator
+ */
+ void setRng(const boost::shared_ptr<boost::mt19937> rng)
+ { m_rng = rng; }
+
+ /**
+ * @brief Gets the Random Number Generator
+ */
+ const boost::shared_ptr<boost::mt19937> getRng() const
+ { return m_rng; }
private:
diff --git a/bob/learn/em/isv_trainer.cpp b/bob/learn/em/isv_trainer.cpp
index 44f294b..66f99e1 100644
--- a/bob/learn/em/isv_trainer.cpp
+++ b/bob/learn/em/isv_trainer.cpp
@@ -99,7 +99,6 @@ static auto ISVTrainer_doc = bob::extension::ClassDoc(
.add_prototype("","")
.add_parameter("other", ":py:class:`bob.learn.em.ISVTrainer`", "A ISVTrainer object to be copied.")
.add_parameter("relevance_factor", "double", "")
- .add_parameter("convergence_threshold", "double", "")
);
@@ -266,9 +265,6 @@ int PyBobLearnEMISVTrainer_set_acc_u_a2(PyBobLearnEMISVTrainerObject* self, PyOb
}
-
-
-
static auto __X__ = bob::extension::VariableDoc(
"__X__",
"list",
@@ -329,6 +325,39 @@ int PyBobLearnEMISVTrainer_set_Z(PyBobLearnEMISVTrainerObject* self, PyObject* v
}
+/***** rng *****/
+static auto rng = bob::extension::VariableDoc(
+ "rng",
+ "str",
+ "The Mersenne Twister mt19937 random generator used for the initialization of subspaces/arrays before the EM loop.",
+ ""
+);
+PyObject* PyBobLearnEMISVTrainer_getRng(PyBobLearnEMISVTrainerObject* self, void*) {
+ BOB_TRY
+ //Allocating the correspondent python object
+
+ PyBoostMt19937Object* retval =
+ (PyBoostMt19937Object*)PyBoostMt19937_Type.tp_alloc(&PyBoostMt19937_Type, 0);
+
+ retval->rng = self->cxx->getRng().get();
+ return Py_BuildValue("O", retval);
+ BOB_CATCH_MEMBER("Rng method could not be read", 0)
+}
+int PyBobLearnEMISVTrainer_setRng(PyBobLearnEMISVTrainerObject* self, PyObject* value, void*) {
+ BOB_TRY
+
+ if (!PyBoostMt19937_Check(value)){
+ PyErr_Format(PyExc_RuntimeError, "%s %s expects an PyBoostMt19937_Check", Py_TYPE(self)->tp_name, rng.name());
+ return -1;
+ }
+
+ PyBoostMt19937Object* boostObject = 0;
+ PyBoostMt19937_Converter(value, &boostObject);
+ self->cxx->setRng((boost::shared_ptr<boost::mt19937>)boostObject->rng);
+
+ return 0;
+ BOB_CATCH_MEMBER("Rng could not be set", 0)
+}
static PyGetSetDef PyBobLearnEMISVTrainer_getseters[] = {
@@ -361,6 +390,13 @@ static PyGetSetDef PyBobLearnEMISVTrainer_getseters[] = {
0
},
+ {
+ rng.name(),
+ (getter)PyBobLearnEMISVTrainer_getRng,
+ (setter)PyBobLearnEMISVTrainer_setRng,
+ rng.doc(),
+ 0
+ },
{0} // Sentinel
@@ -442,8 +478,9 @@ static auto m_step = bob::extension::FunctionDoc(
"",
true
)
-.add_prototype("isv_base")
-.add_parameter("isv_base", ":py:class:`bob.learn.em.ISVBase`", "ISVBase Object");
+.add_prototype("isv_base, stats")
+.add_parameter("isv_base", ":py:class:`bob.learn.em.ISVBase`", "ISVBase Object")
+.add_parameter("stats", ":py:class:`bob.learn.em.GMMStats`", "Ignored");
static PyObject* PyBobLearnEMISVTrainer_m_step(PyBobLearnEMISVTrainerObject* self, PyObject* args, PyObject* kwargs) {
BOB_TRY
@@ -451,8 +488,10 @@ static PyObject* PyBobLearnEMISVTrainer_m_step(PyBobLearnEMISVTrainerObject* sel
char** kwlist = m_step.kwlist(0);
PyBobLearnEMISVBaseObject* isv_base = 0;
+ PyObject* stats = 0;
- if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!", kwlist, &PyBobLearnEMISVBase_Type, &isv_base)) return 0;
+ if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!|O!", kwlist, &PyBobLearnEMISVBase_Type, &isv_base,
+ &PyList_Type, &stats)) return 0;
self->cxx->mStep(*isv_base->cxx);
@@ -561,6 +600,6 @@ bool init_BobLearnEMISVTrainer(PyObject* module)
// add the type to the module
Py_INCREF(&PyBobLearnEMISVTrainer_Type);
- return PyModule_AddObject(module, "_ISVTrainer", (PyObject*)&PyBobLearnEMISVTrainer_Type) >= 0;
+ return PyModule_AddObject(module, "ISVTrainer", (PyObject*)&PyBobLearnEMISVTrainer_Type) >= 0;
}
diff --git a/bob/learn/em/ivector_trainer.cpp b/bob/learn/em/ivector_trainer.cpp
index 72bced8..2d639ac 100644
--- a/bob/learn/em/ivector_trainer.cpp
+++ b/bob/learn/em/ivector_trainer.cpp
@@ -313,8 +313,9 @@ static auto initialize = bob::extension::FunctionDoc(
"",
true
)
-.add_prototype("ivector_machine")
-.add_parameter("ivector_machine", ":py:class:`bob.learn.em.ISVBase`", "IVectorMachine Object");
+.add_prototype("ivector_machine, stats")
+.add_parameter("ivector_machine", ":py:class:`bob.learn.em.ISVBase`", "IVectorMachine Object")
+.add_parameter("stats", ":py:class:`bob.learn.em.GMMStats`", "Ignored");
static PyObject* PyBobLearnEMIVectorTrainer_initialize(PyBobLearnEMIVectorTrainerObject* self, PyObject* args, PyObject* kwargs) {
BOB_TRY
@@ -322,8 +323,10 @@ static PyObject* PyBobLearnEMIVectorTrainer_initialize(PyBobLearnEMIVectorTraine
char** kwlist = initialize.kwlist(0);
PyBobLearnEMIVectorMachineObject* ivector_machine = 0;
+ PyObject* stats = 0;
- if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!", kwlist, &PyBobLearnEMIVectorMachine_Type, &ivector_machine)) return 0;
+ if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!|O!", kwlist, &PyBobLearnEMIVectorMachine_Type, &ivector_machine,
+ &PyList_Type, &stats)) return 0;
self->cxx->initialize(*ivector_machine->cxx);
@@ -371,8 +374,9 @@ static auto m_step = bob::extension::FunctionDoc(
"",
true
)
-.add_prototype("ivector_machine")
-.add_parameter("ivector_machine", ":py:class:`bob.learn.em.ISVBase`", "IVectorMachine Object");
+.add_prototype("ivector_machine, stats")
+.add_parameter("ivector_machine", ":py:class:`bob.learn.em.ISVBase`", "IVectorMachine Object")
+.add_parameter("stats", ":py:class:`bob.learn.em.GMMStats`", "Ignored");
static PyObject* PyBobLearnEMIVectorTrainer_m_step(PyBobLearnEMIVectorTrainerObject* self, PyObject* args, PyObject* kwargs) {
BOB_TRY
@@ -380,8 +384,10 @@ static PyObject* PyBobLearnEMIVectorTrainer_m_step(PyBobLearnEMIVectorTrainerObj
char** kwlist = m_step.kwlist(0);
PyBobLearnEMIVectorMachineObject* ivector_machine = 0;
+ PyObject* stats = 0;
- if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!", kwlist, &PyBobLearnEMIVectorMachine_Type, &ivector_machine)) return 0;
+ if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!|O!", kwlist, &PyBobLearnEMIVectorMachine_Type, &ivector_machine,
+ &PyList_Type, &stats)) return 0;
self->cxx->mStep(*ivector_machine->cxx);
@@ -448,6 +454,6 @@ bool init_BobLearnEMIVectorTrainer(PyObject* module)
// add the type to the module
Py_INCREF(&PyBobLearnEMIVectorTrainer_Type);
- return PyModule_AddObject(module, "_IVectorTrainer", (PyObject*)&PyBobLearnEMIVectorTrainer_Type) >= 0;
+ return PyModule_AddObject(module, "IVectorTrainer", (PyObject*)&PyBobLearnEMIVectorTrainer_Type) >= 0;
}
diff --git a/bob/learn/em/jfa_trainer.cpp b/bob/learn/em/jfa_trainer.cpp
index edcc8ec..5a1734c 100644
--- a/bob/learn/em/jfa_trainer.cpp
+++ b/bob/learn/em/jfa_trainer.cpp
@@ -1008,6 +1008,6 @@ bool init_BobLearnEMJFATrainer(PyObject* module)
// add the type to the module
Py_INCREF(&PyBobLearnEMJFATrainer_Type);
- return PyModule_AddObject(module, "_JFATrainer", (PyObject*)&PyBobLearnEMJFATrainer_Type) >= 0;
+ return PyModule_AddObject(module, "JFATrainer", (PyObject*)&PyBobLearnEMJFATrainer_Type) >= 0;
}
diff --git a/bob/learn/em/kmeans_machine.cpp b/bob/learn/em/kmeans_machine.cpp
index 8f3f55f..352ccde 100644
--- a/bob/learn/em/kmeans_machine.cpp
+++ b/bob/learn/em/kmeans_machine.cpp
@@ -366,7 +366,7 @@ static auto get_mean = bob::extension::FunctionDoc(
".. note:: An exception is thrown if i is out of range.",
true
)
-.add_prototype("i")
+.add_prototype("i","mean")
.add_parameter("i", "int", "Index of the mean")
.add_return("mean","array_like <float, 1D>","Mean array");
static PyObject* PyBobLearnEMKMeansMachine_get_mean(PyBobLearnEMKMeansMachineObject* self, PyObject* args, PyObject* kwargs) {
diff --git a/bob/learn/em/kmeans_trainer.cpp b/bob/learn/em/kmeans_trainer.cpp
index d80339c..d346272 100644
--- a/bob/learn/em/kmeans_trainer.cpp
+++ b/bob/learn/em/kmeans_trainer.cpp
@@ -27,7 +27,7 @@ static inline const std::string& IM2string(bob::learn::em::KMeansTrainer::Initia
static auto KMeansTrainer_doc = bob::extension::ClassDoc(
- BOB_EXT_MODULE_PREFIX "._KMeansTrainer",
+ BOB_EXT_MODULE_PREFIX ".KMeansTrainer",
"Trains a KMeans machine."
"This class implements the expectation-maximization algorithm for a k-means machine."
"See Section 9.1 of Bishop, \"Pattern recognition and machine learning\", 2006"
@@ -411,8 +411,9 @@ static auto mStep = bob::extension::FunctionDoc(
0,
true
)
-.add_prototype("kmeans_machine")
-.add_parameter("kmeans_machine", ":py:class:`bob.learn.em.KMeansMachine`", "KMeansMachine Object");
+.add_prototype("kmeans_machine,data")
+.add_parameter("kmeans_machine", ":py:class:`bob.learn.em.KMeansMachine`", "KMeansMachine Object")
+.add_parameter("data", "array_like <float, 2D>", "Ignored.");
static PyObject* PyBobLearnEMKMeansTrainer_mStep(PyBobLearnEMKMeansTrainerObject* self, PyObject* args, PyObject* kwargs) {
BOB_TRY
@@ -420,7 +421,11 @@ static PyObject* PyBobLearnEMKMeansTrainer_mStep(PyBobLearnEMKMeansTrainerObject
char** kwlist = mStep.kwlist(0);
PyBobLearnEMKMeansMachineObject* kmeans_machine;
- if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!", kwlist, &PyBobLearnEMKMeansMachine_Type, &kmeans_machine)) return 0;
+ PyBlitzArrayObject* data = 0;
+ if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!O&", kwlist, &PyBobLearnEMKMeansMachine_Type, &kmeans_machine,
+ &PyBlitzArray_Converter, &data)) return 0;
+ if(data!=NULL)
+ auto data_ = make_safe(data);
self->cxx->mStep(*kmeans_machine->cxx);
@@ -548,6 +553,6 @@ bool init_BobLearnEMKMeansTrainer(PyObject* module)
// add the type to the module
Py_INCREF(&PyBobLearnEMKMeansTrainer_Type);
- return PyModule_AddObject(module, "_KMeansTrainer", (PyObject*)&PyBobLearnEMKMeansTrainer_Type) >= 0;
+ return PyModule_AddObject(module, "KMeansTrainer", (PyObject*)&PyBobLearnEMKMeansTrainer_Type) >= 0;
}
diff --git a/bob/learn/em/linear_scoring.cpp b/bob/learn/em/linear_scoring.cpp
index e2cc215..091b3f7 100644
--- a/bob/learn/em/linear_scoring.cpp
+++ b/bob/learn/em/linear_scoring.cpp
@@ -69,7 +69,7 @@ static inline bool f(PyObject* o){return o != 0 && PyObject_IsTrue(o) > 0;}
/*** linear_scoring ***/
-static auto linear_scoring1 = bob::extension::FunctionDoc(
+bob::extension::FunctionDoc linear_scoring1 = bob::extension::FunctionDoc(
"linear_scoring",
"",
0,
@@ -84,7 +84,7 @@ static auto linear_scoring1 = bob::extension::FunctionDoc(
.add_return("output","array_like<float,1>","Score");
-static auto linear_scoring2 = bob::extension::FunctionDoc(
+bob::extension::FunctionDoc linear_scoring2 = bob::extension::FunctionDoc(
"linear_scoring",
"",
0,
@@ -101,7 +101,7 @@ static auto linear_scoring2 = bob::extension::FunctionDoc(
-static auto linear_scoring3 = bob::extension::FunctionDoc(
+bob::extension::FunctionDoc linear_scoring3 = bob::extension::FunctionDoc(
"linear_scoring",
"",
0,
@@ -116,7 +116,7 @@ static auto linear_scoring3 = bob::extension::FunctionDoc(
.add_parameter("frame_length_normalisation", "bool", "")
.add_return("output","array_like<float,1>","Score");
-static PyObject* PyBobLearnEM_linear_scoring(PyObject*, PyObject* args, PyObject* kwargs) {
+PyObject* PyBobLearnEM_linear_scoring(PyObject*, PyObject* args, PyObject* kwargs) {
//Cheking the number of arguments
int nargs = (args?PyTuple_Size(args):0) + (kwargs?PyDict_Size(kwargs):0);
diff --git a/bob/learn/em/main.cpp b/bob/learn/em/main.cpp
index c166cff..6444c99 100644
--- a/bob/learn/em/main.cpp
+++ b/bob/learn/em/main.cpp
@@ -9,9 +9,6 @@
#undef NO_IMPORT_ARRAY
#endif
#include "main.h"
-#include "ztnorm.cpp"
-#include "linear_scoring.cpp"
-
static PyMethodDef module_methods[] = {
{
@@ -75,7 +72,6 @@ static PyObject* create_module (void) {
if (!init_BobLearnEMGMMMachine(module)) return 0;
if (!init_BobLearnEMKMeansMachine(module)) return 0;
if (!init_BobLearnEMKMeansTrainer(module)) return 0;
- //if (!init_BobLearnEMGMMBaseTrainer(module)) return 0;
if (!init_BobLearnEMMLGMMTrainer(module)) return 0;
if (!init_BobLearnEMMAPGMMTrainer(module)) return 0;
@@ -130,7 +126,7 @@ static PyObject* create_module (void) {
if (import_bob_blitz() < 0) return 0;
if (import_bob_core_random() < 0) return 0;
if (import_bob_io_base() < 0) return 0;
- //if (import_bob_learn_linear() < 0) return 0;
+ if (import_bob_learn_linear() < 0) return 0;
Py_INCREF(module);
return module;
diff --git a/bob/learn/em/main.h b/bob/learn/em/main.h
index 4afa17d..a9750a2 100644
--- a/bob/learn/em/main.h
+++ b/bob/learn/em/main.h
@@ -80,8 +80,6 @@
return ret;\
}
-static inline char* c(const char* o){return const_cast<char*>(o);} /* converts const char* to char* */
-
/// inserts the given key, value pair into the given dictionaries
static inline int insert_item_string(PyObject* dict, PyObject* entries, const char* key, Py_ssize_t value){
auto v = make_safe(Py_BuildValue("n", value));
@@ -308,5 +306,21 @@ bool init_BobLearnEMEMPCATrainer(PyObject* module);
int PyBobLearnEMEMPCATrainer_Check(PyObject* o);
+//ZT Normalization
+PyObject* PyBobLearnEM_ztNorm(PyObject*, PyObject* args, PyObject* kwargs);
+extern bob::extension::FunctionDoc zt_norm;
+
+PyObject* PyBobLearnEM_tNorm(PyObject*, PyObject* args, PyObject* kwargs);
+extern bob::extension::FunctionDoc t_norm;
+
+PyObject* PyBobLearnEM_zNorm(PyObject*, PyObject* args, PyObject* kwargs);
+extern bob::extension::FunctionDoc z_norm;
+
+
+//Linear scoring
+PyObject* PyBobLearnEM_linear_scoring(PyObject*, PyObject* args, PyObject* kwargs);
+extern bob::extension::FunctionDoc linear_scoring1;
+extern bob::extension::FunctionDoc linear_scoring2;
+extern bob::extension::FunctionDoc linear_scoring3;
#endif // BOB_LEARN_EM_MAIN_H
diff --git a/bob/learn/em/plda_trainer.cpp b/bob/learn/em/plda_trainer.cpp
index 70e09f3..1a47d3c 100644
--- a/bob/learn/em/plda_trainer.cpp
+++ b/bob/learn/em/plda_trainer.cpp
@@ -132,10 +132,10 @@ static int PyBobLearnEMPLDATrainer_init_copy(PyBobLearnEMPLDATrainerObject* self
static int PyBobLearnEMPLDATrainer_init_bool(PyBobLearnEMPLDATrainerObject* self, PyObject* args, PyObject* kwargs) {
char** kwlist = PLDATrainer_doc.kwlist(0);
- PyObject* use_sum_second_order;
+ PyObject* use_sum_second_order = Py_False;
//Parsing the input argments
- if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!", kwlist, &PyBool_Type, &use_sum_second_order))
+ if (!PyArg_ParseTupleAndKeywords(args, kwargs, "|O!", kwlist, &PyBool_Type, &use_sum_second_order))
return -1;
self->cxx.reset(new bob::learn::em::PLDATrainer(f(use_sum_second_order)));
@@ -149,7 +149,9 @@ static int PyBobLearnEMPLDATrainer_init(PyBobLearnEMPLDATrainerObject* self, PyO
// get the number of command line arguments
int nargs = (args?PyTuple_Size(args):0) + (kwargs?PyDict_Size(kwargs):0);
- if(nargs==1){
+ if(nargs==0)
+ return PyBobLearnEMPLDATrainer_init_bool(self, args, kwargs);
+ else if(nargs==1){
//Reading the input argument
PyObject* arg = 0;
if (PyTuple_Size(args))
@@ -367,6 +369,32 @@ int PyBobLearnEMPLDATrainer_setSigmaMethod(PyBobLearnEMPLDATrainerObject* self,
}
+static auto use_sum_second_order = bob::extension::VariableDoc(
+ "use_sum_second_order",
+ "bool",
+ "Tells whether the second order statistics are stored during the training procedure, or only their sum.",
+ ""
+);
+PyObject* PyBobLearnEMPLDATrainer_getUseSumSecondOrder(PyBobLearnEMPLDATrainerObject* self, void*){
+ BOB_TRY
+ return Py_BuildValue("O",self->cxx->getUseSumSecondOrder()?Py_True:Py_False);
+ BOB_CATCH_MEMBER("use_sum_second_order could not be read", 0)
+}
+int PyBobLearnEMPLDATrainer_setUseSumSecondOrder(PyBobLearnEMPLDATrainerObject* self, PyObject* value, void*) {
+ BOB_TRY
+
+ if (!PyBool_Check(value)){
+ PyErr_Format(PyExc_RuntimeError, "%s %s expects an str", Py_TYPE(self)->tp_name, use_sum_second_order.name());
+ return -1;
+ }
+ self->cxx->setUseSumSecondOrder(f(value));
+
+ return 0;
+ BOB_CATCH_MEMBER("use_sum_second_order method could not be set", 0)
+}
+
+
+
static PyGetSetDef PyBobLearnEMPLDATrainer_getseters[] = {
{
z_first_order.name(),
@@ -416,7 +444,14 @@ static PyGetSetDef PyBobLearnEMPLDATrainer_getseters[] = {
(setter)PyBobLearnEMPLDATrainer_setSigmaMethod,
init_sigma_method.doc(),
0
- },
+ },
+ {
+ use_sum_second_order.name(),
+ (getter)PyBobLearnEMPLDATrainer_getUseSumSecondOrder,
+ (setter)PyBobLearnEMPLDATrainer_setUseSumSecondOrder,
+ use_sum_second_order.doc(),
+ 0
+ },
{0} // Sentinel
};
@@ -459,8 +494,8 @@ static PyObject* PyBobLearnEMPLDATrainer_initialize(PyBobLearnEMPLDATrainerObjec
/*** e_step ***/
static auto e_step = bob::extension::FunctionDoc(
- "e_step",
- "e_step before the EM steps",
+ "eStep",
+ "Expectation step before the EM steps",
"",
true
)
@@ -491,8 +526,8 @@ static PyObject* PyBobLearnEMPLDATrainer_e_step(PyBobLearnEMPLDATrainerObject* s
/*** m_step ***/
static auto m_step = bob::extension::FunctionDoc(
- "m_step",
- "m_step before the EM steps",
+ "mStep",
+ "Maximization step ",
"",
true
)
@@ -699,6 +734,5 @@ bool init_BobLearnEMPLDATrainer(PyObject* module)
// add the type to the module
Py_INCREF(&PyBobLearnEMPLDATrainer_Type);
- return PyModule_AddObject(module, "_PLDATrainer", (PyObject*)&PyBobLearnEMPLDATrainer_Type) >= 0;
+ return PyModule_AddObject(module, "PLDATrainer", (PyObject*)&PyBobLearnEMPLDATrainer_Type) >= 0;
}
-
diff --git a/bob/learn/em/test/test_em.py b/bob/learn/em/test/test_em.py
index 85cac67..bd7ffc4 100644
--- a/bob/learn/em/test/test_em.py
+++ b/bob/learn/em/test/test_em.py
@@ -15,6 +15,8 @@ from bob.io.base.test_utils import datafile
from bob.learn.em import KMeansMachine, GMMMachine, KMeansTrainer, \
ML_GMMTrainer, MAP_GMMTrainer
+
+import bob.learn.em
#, MAP_GMMTrainer
@@ -51,7 +53,8 @@ def test_gmm_ML_1():
gmm = loadGMM()
ml_gmmtrainer = ML_GMMTrainer(True, True, True)
- ml_gmmtrainer.train(gmm, ar)
+ #ml_gmmtrainer.train(gmm, ar)
+ bob.learn.em.train(ml_gmmtrainer, gmm, ar, convergence_threshold=0.001)
#config = bob.io.base.HDF5File(datafile('gmm_ML.hdf5", __name__), 'w')
#gmm.save(config)
@@ -82,14 +85,12 @@ def test_gmm_ML_2():
prior = 0.001
max_iter_gmm = 25
accuracy = 0.00001
- ml_gmmtrainer = ML_GMMTrainer(True, True, True, prior, converge_by_likelihood=True)
- ml_gmmtrainer.max_iterations = max_iter_gmm
- ml_gmmtrainer.convergence_threshold = accuracy
+ ml_gmmtrainer = ML_GMMTrainer(True, True, True, prior)
# Run ML
- ml_gmmtrainer.train(gmm, ar)
-
-
+ #ml_gmmtrainer.train(gmm, ar)
+ bob.learn.em.train(ml_gmmtrainer, gmm, ar, max_iterations = max_iter_gmm, convergence_threshold=accuracy)
+
# Test results
# Load torch3vision reference
meansML_ref = bob.io.base.load(datafile('meansAfterML.hdf5', __name__, path="../data/"))
@@ -114,11 +115,9 @@ def test_gmm_MAP_1():
gmmprior = GMMMachine(bob.io.base.HDF5File(datafile("gmm_ML.hdf5", __name__, path="../data/")))
map_gmmtrainer = MAP_GMMTrainer(update_means=True, update_variances=False, update_weights=False, prior_gmm=gmmprior, relevance_factor=4.)
- #map_gmmtrainer.set_prior_gmm(gmmprior)
- map_gmmtrainer.train(gmm, ar)
-
- #config = bob.io.base.HDF5File(datafile('gmm_MAP.hdf5", 'w', __name__))
- #gmm.save(config)
+
+ #map_gmmtrainer.train(gmm, ar)
+ bob.learn.em.train(map_gmmtrainer, gmm, ar)
gmm_ref = GMMMachine(bob.io.base.HDF5File(datafile('gmm_MAP.hdf5', __name__, path="../data/")))
@@ -141,15 +140,15 @@ def test_gmm_MAP_2():
gmm.weights = weights
map_adapt = MAP_GMMTrainer(update_means=True, update_variances=False, update_weights=False, mean_var_update_responsibilities_threshold=0.,prior_gmm=gmm, relevance_factor=4.)
- #map_adapt.set_prior_gmm(gmm)
gmm_adapted = GMMMachine(2,50)
gmm_adapted.means = means
gmm_adapted.variances = variances
gmm_adapted.weights = weights
- map_adapt.max_iterations = 1
- map_adapt.train(gmm_adapted, data)
+ #map_adapt.max_iterations = 1
+ #map_adapt.train(gmm_adapted, data)
+ bob.learn.em.train(map_adapt, gmm_adapted, data, max_iterations = 1)
new_means = bob.io.base.load(datafile('new_adapted_mean.hdf5', __name__, path="../data/"))
@@ -184,15 +183,16 @@ def test_gmm_MAP_3():
max_iter_gmm = 1
accuracy = 0.00001
map_factor = 0.5
- map_gmmtrainer = MAP_GMMTrainer(update_means=True, update_variances=False, update_weights=False, convergence_threshold=prior, prior_gmm=prior_gmm, alpha=map_factor)
- map_gmmtrainer.max_iterations = max_iter_gmm
- map_gmmtrainer.convergence_threshold = accuracy
+ map_gmmtrainer = MAP_GMMTrainer(prior_gmm, alpha=map_factor, update_means=True, update_variances=False, update_weights=False, convergence_threshold=prior)
+ #map_gmmtrainer.max_iterations = max_iter_gmm
+ #map_gmmtrainer.convergence_threshold = accuracy
gmm = GMMMachine(n_gaussians, n_inputs)
gmm.set_variance_thresholds(threshold)
# Train
- map_gmmtrainer.train(gmm, ar)
+ #map_gmmtrainer.train(gmm, ar)
+ bob.learn.em.train(map_gmmtrainer, gmm, ar, max_iterations = max_iter_gmm, convergence_threshold=accuracy)
# Test results
# Load torch3vision reference
diff --git a/bob/learn/em/test/test_jfa_trainer.py b/bob/learn/em/test/test_jfa_trainer.py
index cc6e055..1bd520e 100644
--- a/bob/learn/em/test/test_jfa_trainer.py
+++ b/bob/learn/em/test/test_jfa_trainer.py
@@ -81,7 +81,7 @@ def test_JFATrainer_updateYandV():
ubm.mean_supervector = UBM_MEAN
ubm.variance_supervector = UBM_VAR
m = JFABase(ubm,2,2)
- t = JFATrainer(10)
+ t = JFATrainer()
t.initialize(m, TRAINING_STATS)
m.u = M_u
m.v = M_v
@@ -115,7 +115,7 @@ def test_JFATrainer_updateXandU():
ubm.mean_supervector = UBM_MEAN
ubm.variance_supervector = UBM_VAR
m = JFABase(ubm,2,2)
- t = JFATrainer(10)
+ t = JFATrainer()
t.initialize(m, TRAINING_STATS)
m.u = M_u
m.v = M_v
@@ -148,7 +148,7 @@ def test_JFATrainer_updateZandD():
ubm.mean_supervector = UBM_MEAN
ubm.variance_supervector = UBM_VAR
m = JFABase(ubm,2,2)
- t = JFATrainer(10)
+ t = JFATrainer()
t.initialize(m, TRAINING_STATS)
m.u = M_u
m.v = M_v
@@ -173,12 +173,12 @@ def test_JFATrainAndEnrol():
ubm.mean_supervector = UBM_MEAN
ubm.variance_supervector = UBM_VAR
mb = JFABase(ubm, 2, 2)
- t = JFATrainer(10)
+ t = JFATrainer()
t.initialize(mb, TRAINING_STATS)
mb.u = M_u
mb.v = M_v
mb.d = M_d
- t.train_loop(mb, TRAINING_STATS)
+ bob.learn.em.train_jfa(t,mb, TRAINING_STATS, initialize=False)
v_ref = numpy.array([[0.245364911936476, 0.978133261775424], [0.769646805052223, 0.940070736856596], [0.310779202800089, 1.456332053893072],
[0.184760934399551, 2.265139705602147], [0.701987784039800, 0.081632150899400], [0.074344030229297, 1.090248340917255]], 'float64')
@@ -225,7 +225,7 @@ def test_ISVTrainAndEnrol():
ubm.mean_supervector = UBM_MEAN
ubm.variance_supervector = UBM_VAR
mb = ISVBase(ubm,2)
- t = ISVTrainer(10, 4.)
+ t = ISVTrainer(4.)
#t.train(mb, TRAINING_STATS)
t.initialize(mb, TRAINING_STATS)
mb.u = M_u
@@ -252,6 +252,7 @@ def test_ISVTrainAndEnrol():
t.enrol(m, gse, 5)
assert numpy.allclose(m.z, z_ref, eps)
+
def test_JFATrainInitialize():
# Check that the initialization is consistent and using the rng (cf. issue #118)
@@ -266,7 +267,7 @@ def test_JFATrainInitialize():
jb = JFABase(ubm, 2, 2)
# first round
rng = bob.core.random.mt19937(0)
- jt = JFATrainer(10)
+ jt = JFATrainer()
jt.rng = rng
jt.initialize(jb, TRAINING_STATS)
u1 = jb.u
diff --git a/bob/learn/em/test/test_kmeans_trainer.py b/bob/learn/em/test/test_kmeans_trainer.py
index b62e992..8794a92 100644
--- a/bob/learn/em/test/test_kmeans_trainer.py
+++ b/bob/learn/em/test/test_kmeans_trainer.py
@@ -108,7 +108,8 @@ def test_kmeans_a():
machine = KMeansMachine(2, 1)
trainer = KMeansTrainer()
- trainer.train(machine, data)
+ #trainer.train(machine, data)
+ bob.learn.em.train(trainer,machine,data)
[variances, weights] = machine.get_variances_and_weights_for_each_cluster(data)
variances_b = numpy.ndarray(shape=(2,1), dtype=numpy.float64)
@@ -140,9 +141,10 @@ def test_kmeans_b():
trainer = KMeansTrainer()
#trainer.seed = 1337
- trainer.train(machine, arStd)
+ bob.learn.em.train(trainer,machine, arStd, convergence_threshold=0.001)
[variances, weights] = machine.get_variances_and_weights_for_each_cluster(arStd)
+
means = machine.means
multiplyVectorsByFactors(means, std)
@@ -160,22 +162,11 @@ def test_kmeans_b():
assert equals(means, gmmMeans, 1e-3)
assert equals(weights, gmmWeights, 1e-3)
assert equals(variances, gmmVariances, 1e-3)
-
- # Check comparison operators
- trainer1 = KMeansTrainer()
- trainer2 = KMeansTrainer()
- #trainer1.rng = trainer2.rng
-
- #assert trainer1 == trainer2
- #assert (trainer1 != trainer2) is False
- trainer1.max_iterations = 1337
- #assert (trainer1 == trainer2) is False
- #assert trainer1 != trainer2
# Check that there is no duplicate means during initialization
machine = KMeansMachine(2, 1)
trainer = KMeansTrainer()
trainer.initialization_method = 'RANDOM_NO_DUPLICATE'
data = numpy.array([[1.], [1.], [1.], [1.], [1.], [1.], [2.], [3.]])
- trainer.train(machine, data)
- assert (numpy.isnan(machine.means).any()) == False
\ No newline at end of file
+ bob.learn.em.train(trainer, machine, data)
+ assert (numpy.isnan(machine.means).any()) == False
diff --git a/bob/learn/em/test/test_plda_trainer.py b/bob/learn/em/test/test_plda_trainer.py
index f72ab6f..02ede06 100644
--- a/bob/learn/em/test/test_plda_trainer.py
+++ b/bob/learn/em/test/test_plda_trainer.py
@@ -13,6 +13,7 @@ import numpy
import numpy.linalg
from bob.learn.em import PLDATrainer, PLDABase, PLDAMachine
+import bob.learn.em
class PythonPLDATrainer():
"""A simplified (and slower) version of the PLDATrainer"""
@@ -362,7 +363,7 @@ def test_plda_EM_vs_Python():
# Runs the PLDA trainer EM-steps (2 steps)
# Defines base trainer and machine
- t = PLDATrainer(10)
+ t = PLDATrainer()
t_py = PythonPLDATrainer(max_iterations=10)
m = PLDABase(D,nf,ng)
m_py = PLDABase(D,nf,ng)
@@ -372,8 +373,10 @@ def test_plda_EM_vs_Python():
t.init_g_method = 'WITHIN_SCATTER'
t.init_sigma_method = 'VARIANCE_DATA'
- t.train(m, l)
+ #t.train(m, l)
+ bob.learn.em.train(t, m, l, max_iterations=10)
t_py.train(m_py, l)
+
assert numpy.allclose(m.mu, m_py.mu)
assert numpy.allclose(m.f, m_py.f)
assert numpy.allclose(m.g, m_py.g)
@@ -533,7 +536,7 @@ def test_plda_EM_vs_Prince():
m_py.f = F_init
# E-step 1
- t.e_step(m,l)
+ t.eStep(m,l)
t_py.e_step(m_py,l)
# Compares statistics to Prince matlab reference
assert numpy.allclose(t.z_first_order[0], z_first_order_a_1, 1e-10)
@@ -545,7 +548,7 @@ def test_plda_EM_vs_Prince():
assert numpy.allclose(t.z_second_order_sum, t_py.m_sum_z_second_order, 1e-10)
# M-step 1
- t.m_step(m,l)
+ t.mStep(m,l)
t_py.m_step(m_py,l)
# Compares F, G and sigma to Prince matlab reference
assert numpy.allclose(m.f, F_1, 1e-10)
@@ -557,7 +560,7 @@ def test_plda_EM_vs_Prince():
assert numpy.allclose(m.sigma, m_py.sigma, 1e-10)
# E-step 2
- t.e_step(m,l)
+ t.eStep(m,l)
t_py.e_step(m_py,l)
# Compares statistics to Prince matlab reference
assert numpy.allclose(t.z_first_order[0], z_first_order_a_2, 1e-10)
@@ -569,7 +572,7 @@ def test_plda_EM_vs_Prince():
assert numpy.allclose(t.z_second_order_sum, t_py.m_sum_z_second_order, 1e-10)
# M-step 2
- t.m_step(m,l)
+ t.mStep(m,l)
t_py.m_step(m_py,l)
# Compares F, G and sigma to Prince matlab reference
assert numpy.allclose(m.f, F_2, 1e-10)
@@ -595,7 +598,7 @@ def test_plda_EM_vs_Prince():
m_py.f = F_init
# E-step 1
- t.e_step(m,l)
+ t.eStep(m,l)
t_py.e_step(m_py,l)
# Compares statistics to Prince matlab reference
assert numpy.allclose(t.z_first_order[0], z_first_order_a_1, 1e-10)
@@ -608,7 +611,7 @@ def test_plda_EM_vs_Prince():
assert numpy.allclose(t.z_second_order_sum, t_py.m_sum_z_second_order, 1e-10)
# M-step 1
- t.m_step(m,l)
+ t.mStep(m,l)
t_py.m_step(m_py,l)
# Compares F, G and sigma to the ones of the python implementation
assert numpy.allclose(m.f, m_py.f, 1e-10)
@@ -616,7 +619,7 @@ def test_plda_EM_vs_Prince():
assert numpy.allclose(m.sigma, m_py.sigma, 1e-10)
# E-step 2
- t.e_step(m,l)
+ t.eStep(m,l)
t_py.e_step(m_py,l)
# Compares statistics to Prince matlab reference
assert numpy.allclose(t.z_first_order[0], z_first_order_a_2, 1e-10)
@@ -629,7 +632,7 @@ def test_plda_EM_vs_Prince():
assert numpy.allclose(t.z_second_order_sum, t_py.m_sum_z_second_order, 1e-10)
# M-step 2
- t.m_step(m,l)
+ t.mStep(m,l)
t_py.m_step(m_py,l)
# Compares F, G and sigma to the ones of the python implementation
assert numpy.allclose(m.f, m_py.f, 1e-10)
@@ -718,24 +721,23 @@ def test_plda_comparisons():
m = PLDABase(4,1,1,1e-8)
t1.rng.seed(37)
t1.initialize(m, training_set)
- t1.e_step(m, training_set)
- t1.m_step(m, training_set)
+ t1.eStep(m, training_set)
+ t1.mStep(m, training_set)
assert (t1 == t2 ) is False
assert t1 != t2
assert (t1.is_similar_to(t2) ) is False
t2.rng.seed(37)
t2.initialize(m, training_set)
- t2.e_step(m, training_set)
- t2.m_step(m, training_set)
+ t2.eStep(m, training_set)
+ t2.mStep(m, training_set)
assert t1 == t2
assert (t1 != t2 ) is False
assert t1.is_similar_to(t2)
t2.rng.seed(77)
t2.initialize(m, training_set)
- t2.e_step(m, training_set)
- t2.m_step(m, training_set)
+ t2.eStep(m, training_set)
+ t2.mStep(m, training_set)
assert (t1 == t2 ) is False
assert t1 != t2
assert (t1.is_similar_to(t2) ) is False
-
diff --git a/bob/learn/em/train.py b/bob/learn/em/train.py
new file mode 100644
index 0000000..fe8b46c
--- /dev/null
+++ b/bob/learn/em/train.py
@@ -0,0 +1,61 @@
+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+# Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
+# Fri Feb 13 13:18:10 2015 +0200
+#
+# Copyright (C) 2011-2015 Idiap Research Institute, Martigny, Switzerland
+import numpy
+import bob.learn.em
+
+def train(trainer, machine, data, max_iterations = 50, convergence_threshold=None, initialize=True):
+
+ #Initialization
+ if initialize:
+ trainer.initialize(machine, data)
+
+ trainer.eStep(machine, data)
+ average_output = 0
+ average_output_previous = 0
+
+ if convergence_threshold!=None and hasattr(trainer,"compute_likelihood"):
+ average_output = trainer.compute_likelihood(machine)
+
+ for i in range(max_iterations):
+ average_output_previous = average_output
+ trainer.mStep(machine, data)
+ trainer.eStep(machine, data)
+
+ if convergence_threshold!=None and hasattr(trainer,"compute_likelihood"):
+ average_output = trainer.compute_likelihood(machine)
+
+ #Terminates if converged (and likelihood computation is set)
+ if convergence_threshold!=None and abs((average_output_previous - average_output)/average_output_previous) <= convergence_threshold:
+ break
+
+ if hasattr(trainer,"finalize"):
+ trainer.finalize(machine, data)
+
+
+def train_jfa(trainer, jfa_base, data, max_iterations=10, initialize=True):
+
+ if initialize:
+ trainer.initialize(jfa_base, data)
+
+ #V Subspace
+ for i in range(max_iterations):
+ trainer.e_step1(jfa_base, data)
+ trainer.m_step1(jfa_base, data)
+ trainer.finalize1(jfa_base, data)
+
+ #U subspace
+ for i in range(max_iterations):
+ trainer.e_step2(jfa_base, data)
+ trainer.m_step2(jfa_base, data)
+ trainer.finalize2(jfa_base, data)
+
+ # d subspace
+ for i in range(max_iterations):
+ trainer.e_step3(jfa_base, data)
+ trainer.m_step3(jfa_base, data)
+ trainer.finalize3(jfa_base, data)
+
diff --git a/bob/learn/em/ztnorm.cpp b/bob/learn/em/ztnorm.cpp
index b000b5f..a2dc672 100644
--- a/bob/learn/em/ztnorm.cpp
+++ b/bob/learn/em/ztnorm.cpp
@@ -10,7 +10,7 @@
#include "main.h"
/*** zt_norm ***/
-static auto zt_norm = bob::extension::FunctionDoc(
+bob::extension::FunctionDoc zt_norm = bob::extension::FunctionDoc(
"ztnorm",
"",
0,
@@ -23,7 +23,7 @@ static auto zt_norm = bob::extension::FunctionDoc(
.add_parameter("rawscores_zprobes_vs_tmodels", "array_like <float, 2D>", "")
.add_parameter("mask_zprobes_vs_tmodels_istruetrial", "array_like <float, 2D>", "")
.add_return("output","array_like <float, 2D>","");
-static PyObject* PyBobLearnEM_ztNorm(PyObject*, PyObject* args, PyObject* kwargs) {
+PyObject* PyBobLearnEM_ztNorm(PyObject*, PyObject* args, PyObject* kwargs) {
char** kwlist = zt_norm.kwlist(0);
@@ -71,7 +71,7 @@ static PyObject* PyBobLearnEM_ztNorm(PyObject*, PyObject* args, PyObject* kwargs
/*** t_norm ***/
-static auto t_norm = bob::extension::FunctionDoc(
+bob::extension::FunctionDoc t_norm = bob::extension::FunctionDoc(
"tnorm",
"",
0,
@@ -81,7 +81,7 @@ static auto t_norm = bob::extension::FunctionDoc(
.add_parameter("rawscores_probes_vs_models", "array_like <float, 2D>", "")
.add_parameter("rawscores_probes_vs_tmodels", "array_like <float, 2D>", "")
.add_return("output","array_like <float, 2D>","");
-static PyObject* PyBobLearnEM_tNorm(PyObject*, PyObject* args, PyObject* kwargs) {
+PyObject* PyBobLearnEM_tNorm(PyObject*, PyObject* args, PyObject* kwargs) {
char** kwlist = zt_norm.kwlist(0);
@@ -108,7 +108,7 @@ static PyObject* PyBobLearnEM_tNorm(PyObject*, PyObject* args, PyObject* kwargs)
/*** z_norm ***/
-static auto z_norm = bob::extension::FunctionDoc(
+bob::extension::FunctionDoc z_norm = bob::extension::FunctionDoc(
"znorm",
"",
0,
@@ -118,7 +118,7 @@ static auto z_norm = bob::extension::FunctionDoc(
.add_parameter("rawscores_probes_vs_models", "array_like <float, 2D>", "")
.add_parameter("rawscores_zprobes_vs_models", "array_like <float, 2D>", "")
.add_return("output","array_like <float, 2D>","");
-static PyObject* PyBobLearnEM_zNorm(PyObject*, PyObject* args, PyObject* kwargs) {
+PyObject* PyBobLearnEM_zNorm(PyObject*, PyObject* args, PyObject* kwargs) {
char** kwlist = zt_norm.kwlist(0);
diff --git a/doc/guide.rst b/doc/guide.rst
index d7895aa..0c11a33 100644
--- a/doc/guide.rst
+++ b/doc/guide.rst
@@ -391,10 +391,8 @@ be called.
:options: +NORMALIZE_WHITESPACE
>>> kmeansTrainer = bob.learn.em.KMeansTrainer()
- >>> kmeansTrainer.max_iterations = 200
- >>> kmeansTrainer.convergence_threshold = 1e-5
- >>> kmeansTrainer.train(kmeans, data) # Train the KMeansMachine
+ >>> bob.learn.em.train(kmeansTrainer, kmeans, data, max_iterations = 200, convergence_threshold = 1e-5) # Train the KMeansMachine
>>> print(kmeans.means)
[[ -6. 6. -100.5]
[ 3.5 -3.5 99. ]]
@@ -428,9 +426,7 @@ and the criterion used to determine if the parameters have converged.
:options: +NORMALIZE_WHITESPACE
>>> trainer = bob.learn.em.ML_GMMTrainer(True, True, True) # update means/variances/weights at each iteration
- >>> trainer.convergence_threshold = 1e-5
- >>> trainer.max_iterations = 200
- >>> trainer.train(gmm, data)
+ >>> bob.learn.em.train(trainer, gmm, data, max_iterations = 200, convergence_threshold = 1e-5)
>>> print(gmm) # doctest: +SKIP
@@ -476,7 +472,7 @@ set.
>>> trainer.convergence_threshold = 1e-5
>>> trainer.max_iterations = 200
>>> gmmAdapted = bob.learn.em.GMMMachine(2,3) # Create a new machine for the MAP estimate
- >>> trainer.train(gmmAdapted, dataMAP)
+ >>> bob.learn.em.train(trainer, gmmAdapted, dataMAP)
>>> print(gmmAdapted) # doctest: +SKIP
@@ -529,7 +525,7 @@ Next, we initialize a trainer, which is an instance of
.. doctest::
:options: +NORMALIZE_WHITESPACE
- >>> jfa_trainer = bob.learn.em.JFATrainer(10) # 10 is the number of iterations
+ >>> jfa_trainer = bob.learn.em.JFATrainer()
The training process is started by calling the
:py:meth:`bob.learn.em.JFATrainer.train`.
@@ -537,7 +533,7 @@ The training process is started by calling the
.. doctest::
:options: +NORMALIZE_WHITESPACE
- >>> jfa_trainer.train(jfa_base, TRAINING_STATS)
+ >>> bob.learn.em.train_jfa(jfa_trainer, jfa_base, TRAINING_STATS, max_iterations=10)
Once the training is finished (i.e. the subspaces :math:`U`, :math:`V` and
:math:`D` are estimated), the JFA model can be shared and used by several
@@ -590,7 +586,7 @@ Next, we initialize a trainer, which is an instance of
.. doctest::
:options: +NORMALIZE_WHITESPACE
- >>> isv_trainer = bob.learn.em.ISVTrainer(10, 4.) # 10 is the number of iterations, and 4 is the relevance factor
+ >>> isv_trainer = bob.learn.em.ISVTrainer(relevance_factor=4.) # 4 is the relevance factor
The training process is started by calling the
:py:meth:`bob.learn.em.ISVTrainer.train`.
@@ -598,7 +594,7 @@ The training process is started by calling the
.. doctest::
:options: +NORMALIZE_WHITESPACE
- >>> isv_trainer.train(isv_base, TRAINING_STATS)
+ >>> bob.learn.em.train(isv_trainer, isv_base, TRAINING_STATS, max_iterations=10)
Once the training is finished (i.e. the subspaces :math:`V` and :math:`D` are
estimated), the ISV model can be shared and used by several class-specific
@@ -638,7 +634,7 @@ Next, we initialize a trainer, which is an instance of
.. doctest::
:options: +NORMALIZE_WHITESPACE
- >>> ivec_trainer = bob.learn.em.IVectorTrainer(update_sigma=True, max_iterations=10)
+ >>> ivec_trainer = bob.learn.em.IVectorTrainer(update_sigma=True)
>>> TRAINING_STATS_flatten = [gs11, gs12, gs21, gs22]
The training process is started by calling the
@@ -647,7 +643,7 @@ The training process is started by calling the
.. doctest::
:options: +NORMALIZE_WHITESPACE
- >>> ivec_trainer.train(m, TRAINING_STATS_flatten)
+ >>> bob.learn.em.train(ivec_trainer, m, TRAINING_STATS_flatten, max_iterations=10)
More information about the training process can be found in [15]_.
@@ -693,7 +689,7 @@ Learning a PLDA model can be performed by instantiating the class
>>> pldabase = bob.learn.em.PLDABase(3,1,2)
>>> trainer = bob.learn.em.PLDATrainer()
- >>> trainer.train(pldabase, data)
+ >>> bob.learn.em.train(trainer, pldabase, data, max_iterations=10)
Once trained, this PLDA model can be used to compute the log-likelihood of a
set of samples given some hypothesis. For this purpose, a
--
GitLab