Input
See GMM Examples Input.
SQL Call
SELECT * FROM GMM (
ON (SELECT 1) AS InitialValues PARTITION BY 1
ON gmm_iris_train AS InputTable
OUT TABLE OutputTable (gmm_output_ex1)
USING
ClusterNum (3)
CovarianceType ('spherical')
MaxIterNum (10)
PackOutput (1)
) AS dt ;
Output
Because the SQL call specifies PackOutput (1), a single mean column displays a vector containing the mean value for each dimension.
property value ----------------------------- ------------------------------------------------------------------------------------------------------------- Output Table Table Name Specified in OutputTable argument Algorithm Used Basic GMM Stopping Criterion Algorithm converged with tolerance 0.001 Delta Log Likelihood 0 Number of Iterations 5 Number of Clusters 3 Covariance Type spherical Number of Data Points 120 Global Mean [5.866, 3.055, 3.770, 1.205] Global Covariance [[0.7197, -0.04204, 1.326, 0.5265], [-0.04204, 0.1916, -0.3241, -0.1213], [1.326, -0.3241, 3.167, 1.298], [0.5265, -0.1213, 1.298, 0.5708]] Log Likelihood -332.722 Akaike Information Criterion 699.444 on 17 parameters Bayesian Information Criterion 746.831 on 17 parameters
SELECT * FROM gmm_output_ex1;
cluster_id points_assigned covariance_type weight mean covariance log_determinant prec 0 1 spherical 8.33E-03 [4.5, 2.3, 1.3, 0.3] 1.00E-12 -1.10524084463714E 002 1.00E+12 1 39 spherical 3.25E-01 [5.01025640674348, 3.4461538411338686, 1.446153846741701, 0.25128205118815566] 0.072353714 -1.05047539703453E 001 13.82099057 2 80 spherical 6.67E-01 [6.299999998810369, 2.873750003735264, 4.933749991865624, 1.6812499968280563] 0.351423837 -4.18304908500248E 000 2.845566791
Download a zip file of all examples and a SQL script file that creates their input tables.