Classes and functions for modelling multivariate data as a Mixture of Gaussians. More...
Classes | |
class | itpp::MOG_diag |
Diagonal Mixture of Gaussians (MOG) class. More... | |
class | itpp::MOG_generic |
Generic Mixture of Gaussians (MOG) class. Used as a base for other MOG classes. More... | |
Functions | |
void | itpp::MOG_diag_ML (MOG_diag &model_in, Array< vec > &X_in, int max_iter_in, double var_floor_in, double weight_floor_in, bool verbose_in) |
void | itpp::MOG_diag_kmeans (MOG_diag &model_in, Array< vec > &X_in, int max_iter_in, double trust_in, bool normalise_in, bool verbose_in) |
Classes and functions for modelling multivariate data as a Mixture of Gaussians.
The following example shows how to model data:
See also the tutorial section for a more elaborate example.
void itpp::MOG_diag_ML | ( | MOG_diag & | model_in, |
Array< vec > & | X_in, | ||
int | max_iter_in = 10 , |
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double | var_floor_in = 0.0 , |
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double | weight_floor_in = 0.0 , |
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bool | verbose_in = false |
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) |
Maximum Likelihood Expectation Maximisation based optimisation of the parameters of an instance of the MOG_diag class. The seed values (starting points) are typically first obtained via MOG_diag_kmeans(). See [CSB06] and the references therein for detailed mathematical descriptions.
model_in | The model to optimise (MOG_diag) |
X_in | The training data (array of vectors) |
max_iter_in | Maximum number of iterations. Default is 10. |
var_floor_in | Variance floor (lowest allowable variance). Default is 0.0 (but see the note below) |
weight_floor_in | Weight floor (lowest allowable weight). Default is 0.0 (but see the note below) |
verbose_in | Whether progress in printed. Default is false. |
Definition at line 316 of file mog_diag_em.cpp.
References itpp::MOG_diag_EM_sup::ml().
void itpp::MOG_diag_kmeans | ( | MOG_diag & | model_in, |
Array< vec > & | X_in, | ||
int | max_iter_in = 10 , |
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double | trust_in = 0.5 , |
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bool | normalise_in = true , |
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bool | verbose_in = false |
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) |
K-means based optimisation (training) of the parameters of an instance of the MOG_diag class. The obtained parameters are typically used as a seed by MOG_diag_ML().
model_in | The model to optimise |
X_in | The training data |
max_iter_in | Maximum number of iterations. Default is 10. |
trust_in | The trust factor, where 0 <= trust_in <= 1. Default is 0.5. |
normalise_in | Use normalised distance measure (in effect). Default is true. |
verbose_in | Whether to print progress. Default is false. |
normalise_in
to true causes the the training data to be normalised to zero mean and unit variance prior to running the k-means algorithm. The data is unnormalised before returning. The normalisation helps clustering when the range of values varies greatly between dimensions. e.g. dimension 1 may have values in the [-1,+1] interval, while dimension 2 may have values in the [-100,+100] interval. Without normalisation, the distance between vectors is dominated by dimension 2. Definition at line 347 of file mog_diag_kmeans.cpp.
References itpp::MOG_diag_kmeans_sup::run().
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