genSigmaGPCM

genSigmaGPCM generates covariance matrix for the 14 Gaussian Parsimonious Clustering Models

Syntax

  • S=genSigmaGPCM(v, k, modeltype)example

Description

example

S =genSigmaGPCM(v, k, modeltype) Covariance matrices contours for the 14 models.

Examples

expand all

  • Covariance matrices contours for the 14 models.
  • Two dimensions

    v=2;
    % 3 groups
    k=3;
    models={'VVE','EVE','VVV','EVV','VEE','EEE','VEV','EEV','VVI',...
    'EVI','VEI','EEI','VII','EII'};
    % Specify the colors for the ellipses
    col='rbk';
    % if withseed is true the same plot is always obtained otherwise every time
    % a different plot is obtained
    withseed=true;
    close all
    % These numbers are those which better exemplify the caractheristics of the
    % 14 specifications.
    seeds=[100 20 12 209 51 6 76 8 9 22 11 12 130 14];
    for j=1:length(models)
    if withseed==true
    rng(seeds(j))
    end
    modeltype=models{j};
    S=genSigmaGPCM(v, k, modeltype);
    subplot(4,4,j)
    hold('on')
    for i=1:k
    ellipse(zeros(v,1), S(:,:,i),0.95,col(i));
    end
    axis equal
    legend('off')
    title(modeltype)
    end
    Click here for the graphical output of this example (link to Ro.S.A. website). Graphical output could not be included in the installation file because toolboxes cannot be greater than 20MB. To load locally the image files, download zip file http://rosa.unipr.it/fsda/images.zip and unzip it to <tt>(docroot)/FSDA/images</tt> or simply run routine <tt>downloadGraphicalOutput.m</tt>

    Input Arguments

    expand all

    v — number of dimensions (variables). Scalar.

    Desired number of variables.

    Data Types: int16|int32|int64|single|double

    k — number of groups (components). Scalar.

    Desired number of groups.

    Data Types: int16|int32|int64|single|double

    modeltype — type of Gaussian Parsimonious Clustering Model. Character.

    A 3 letter word in the set:

    'VVE','EVE','VVV','EVV','VEE','EEE','VEV','EEV','VVI', 'EVI','VEI','EEI','VII','EII'

    Data Types: Character

    Output Arguments

    More About

    expand all

    Additional Details

    Generate covariance matrices from the 14 parsimonious Gaussian clustering models (GPCM).

    References

    Celeux, G., Govaert, G. (1995), Gaussian parsimonious clustering models, "Pattern Recognition", 28, pp. 781-793.

    This page has been automatically generated by our routine publishFS