Source code for bob.fusion.base.algorithm.GMM

#!/usr/bin/env python

from __future__ import absolute_import, division

import logging

import numpy as np

from bob.learn.em import GMMMachine

from .AlgorithmBob import AlgorithmBob

logger = logging.getLogger("bob.fusion.base")

class GMM(AlgorithmBob):
    """GMM Score fusion"""

[docs] def __init__( self, # parameters for the GMM number_of_gaussians=None, # parameters of GMM training # Maximum number of iterations for ML GMM Training gmm_training_iterations=25, # Threshold to end the ML training training_threshold=5e-4, # Minimum value that a variance can reach variance_threshold=5e-4, update_weights=True, update_means=True, update_variances=True, init_seed=5489, **kwargs, ): super().__init__(classifier=self, **kwargs) self.str["number_of_gaussians"] = number_of_gaussians self.str["gmm_training_iterations"] = gmm_training_iterations self.str["training_threshold"] = training_threshold self.str["variance_threshold"] = variance_threshold self.str["update_weights"] = update_weights self.str["update_means"] = update_means self.str["update_variances"] = update_variances self.str["init_seed"] = init_seed # copy parameters self.n_gaussians = number_of_gaussians self.gmm_training_iterations = gmm_training_iterations self.training_threshold = training_threshold self.variance_threshold = variance_threshold self.update_weights = update_weights self.update_means = update_means self.update_variances = update_variances self.init_seed = init_seed # this is needed to be able to load the machine self.machine = GMMMachine(n_gaussians=1)
[docs] def train(self, train_neg, train_pos, devel_neg=None, devel_pos=None):"Using only positive samples for training") array = train_pos logger.debug("Training files have the shape of {}".format(array.shape)) if self.n_gaussians is None: self.n_gaussians = array.shape[1] + 1 logger.warning( "Number of Gaussians was None. " "Using {}.".format(self.n_gaussians) ) # Creates the machines (KMeans and GMM) logger.debug("Training GMM machine") self.machine = GMMMachine( n_gaussians=self.n_gaussians, convergence_threshold=self.training_threshold, max_fitting_steps=self.gmm_training_iterations, random_state=self.init_seed, update_means=self.update_means, update_variances=self.update_variances, update_weights=self.update_weights, )
[docs] def decision_function(self, scores): return np.fromiter( (self.machine.log_likelihood(s) for s in scores), np.float, scores.shape[0], )