#!/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
import logging
logger = logging.getLogger('bob.learn.em')
[docs]def train(trainer, machine, data, max_iterations=50, convergence_threshold=None, initialize=True, rng=None,
check_inputs=True):
"""
Trains a machine given a trainer and the proper data
**Parameters**:
trainer : one of :py:class:`KMeansTrainer`, :py:class:`MAP_GMMTrainer`, :py:class:`ML_GMMTrainer`, :py:class:`ISVTrainer`, :py:class:`IVectorTrainer`, :py:class:`PLDATrainer`, :py:class:`EMPCATrainer`
A trainer mechanism
machine : one of :py:class:`KMeansMachine`, :py:class:`GMMMachine`, :py:class:`ISVBase`, :py:class:`IVectorMachine`, :py:class:`PLDAMachine`, :py:class:`bob.learn.linear.Machine`
A container machine
data : array_like <float, 2D>
The data to be trained
max_iterations : int
The maximum number of iterations to train a machine
convergence_threshold : float
The convergence threshold to train a machine. If None, the training procedure will stop with the iterations criteria
initialize : bool
If True, runs the initialization procedure
rng : :py:class:`bob.core.random.mt19937`
The Mersenne Twister mt19937 random generator used for the initialization of subspaces/arrays before the EM loop
check_inputs:
Shallow checks in the inputs. Check for inf and NaN
"""
if check_inputs and type(data) is numpy.ndarray:
if numpy.isinf(numpy.sum(data)):
raise ValueError("Please, check your inputs; numpy.inf detected in `data` ")
if numpy.isnan(numpy.sum(data)):
raise ValueError("Please, check your inputs; numpy.nan detected in `data` ")
# Initialization
if initialize:
if rng is not None:
trainer.initialize(machine, data, rng)
else:
trainer.initialize(machine, data)
trainer.e_step(machine, data)
average_output = 0
average_output_previous = 0
if hasattr(trainer, "compute_likelihood"):
average_output = trainer.compute_likelihood(machine)
for i in range(max_iterations):
logger.info("Iteration = %d/%d", i, max_iterations)
average_output_previous = average_output
trainer.m_step(machine, data)
trainer.e_step(machine, data)
if hasattr(trainer, "compute_likelihood"):
average_output = trainer.compute_likelihood(machine)
if type(machine) is bob.learn.em.KMeansMachine:
logger.info("average euclidean distance = %f", average_output)
else:
logger.info("log likelihood = %f", average_output)
convergence_value = abs((average_output_previous - average_output) / average_output_previous)
logger.info("convergence value = %f", convergence_value)
# Terminates if converged (and likelihood computation is set)
if convergence_threshold != None and convergence_value <= convergence_threshold:
break
if hasattr(trainer, "finalize"):
trainer.finalize(machine, data)
[docs]def train_jfa(trainer, jfa_base, data, max_iterations=10, initialize=True, rng=None):
"""
Trains a :py:class:`bob.learn.em.JFABase` given a :py:class:`bob.learn.em.JFATrainer` and the proper data
**Parameters**:
trainer : :py:class:`bob.learn.em.JFATrainer`
A JFA trainer mechanism
jfa_base : :py:class:`bob.learn.em.JFABase`
A container machine
data : [[:py:class:`bob.learn.em.GMMStats`]]
The data to be trained
max_iterations : int
The maximum number of iterations to train a machine
initialize : bool
If True, runs the initialization procedure
rng : :py:class:`bob.core.random.mt19937`
The Mersenne Twister mt19937 random generator used for the initialization of subspaces/arrays before the EM loops
"""
if initialize:
if rng is not None:
trainer.initialize(jfa_base, data, rng)
else:
trainer.initialize(jfa_base, data)
# V Subspace
logger.info("V subspace estimation...")
for i in range(max_iterations):
logger.info("Iteration = %d/%d", i, max_iterations)
trainer.e_step_v(jfa_base, data)
trainer.m_step_v(jfa_base, data)
trainer.finalize_v(jfa_base, data)
# U subspace
logger.info("U subspace estimation...")
for i in range(max_iterations):
logger.info("Iteration = %d/%d", i, max_iterations)
trainer.e_step_u(jfa_base, data)
trainer.m_step_u(jfa_base, data)
trainer.finalize_u(jfa_base, data)
# D subspace
logger.info("D subspace estimation...")
for i in range(max_iterations):
logger.info("Iteration = %d/%d", i, max_iterations)
trainer.e_step_d(jfa_base, data)
trainer.m_step_d(jfa_base, data)
trainer.finalize_d(jfa_base, data)