Input
See GMM Examples Input.
Dirichlet Process GMM (DP-GMM) estimates the number of clusters in the data using an algorithm based on variational Bayesian methods.
SQL Call
DROP TABLE dpgmm_output_ex3;
SELECT * FROM GMM (
ON (SELECT 1) AS InitialValues PARTITION BY 1
ON gmm_iris_train AS InputTable
OUT TABLE OutputTable (dpgmm_output_ex3)
USING
MaxClusterNum (3)
CovarianceType ('full')
MaxIterNum (10)
PackOutput (0)
) AS dt ;
Output
property value Output Table Table Name Specified in OutputTable argument Algorithm Used Dirichlet Process GMM Stopping Criterion Algorithm converged with tolerance 0.001 Delta Log Likelihood 0.000494 Number of Iterations 9 Number of Clusters Found 1 Covariance Type full 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 1550.435 Akaike Information Criterion -3012.870 on 44 parameters Bayesian Information Criterion -2890.220 on 44 parameters
SELECT * FROM dpgmm_output_ex3;
cluster_id points_assigned covariance_type weight sepal_length sepal_width petal_length petal_width cov_11 cov_12 cov_13 cov_14 cov_22 cov_23 cov_24 cov_33 cov_34 cov_44 log_determinant prec
----------- --------------- --------------- ---------------------- ---------------------- ---------------------- ---------------------- ---------------------- ---------------------- ---------------------- ---------------------- ---------------------- ---------------------- ---------------------- ---------------------- ---------------------- ---------------------- ---------------------- ---------------------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
0 120 full 9.99991735613739E-001 5.73419321840163E 000 3.01843398222490E 000 3.61166171552218E 000 1.14486231793446E 000 1.66229691309346E 000 -2.14185076572784E-001 1.69427437178447E 000 6.93859028192593E-001 1.14410673681517E 000 2.62470215164859E-002 1.96024408024593E-002 4.00032159904576E 000 1.27929542158923E 000 1.50738876901989E 000 1.88394523840622E 000 [[0.7824065708713632, -0.04166720450521028, -0.2990852151033951, -0.10577545815433718], [-0.09328115290486803, 0.875436053888177, 0.03258314726425615, 0.0039006629777845513], [-0.169999622325278, 0.13304206449205377, 0.40094413849390415, -0.26375291186527555], [-0.04690499623137513, 0.04519483374938999, -0.25244627156275695, 0.898648652091002]]
1 0 full 8.25613837088043E-006 0.00000000000000E 000 0.00000000000000E 000 0.00000000000000E 000 0.00000000000000E 000 1.66666666666667E-001 0.00000000000000E 000 0.00000000000000E 000 0.00000000000000E 000 1.66666666666667E-001 0.00000000000000E 000 0.00000000000000E 000 1.66666666666667E-001 0.00000000000000E 000 1.66666666666667E-001 -7.16703787691222E 000 [[6.0, 0.0, 0.0, 0.0], [0.0, 6.0, 0.0, 0.0], [0.0, 0.0, 6.0, 0.0], [0.0, 0.0, 0.0, 6.0]]
2 0 full 8.24789043818501E-009 0.00000000000000E 000 0.00000000000000E 000 0.00000000000000E 000 0.00000000000000E 000 1.66666666666667E-001 0.00000000000000E 000 0.00000000000000E 000 0.00000000000000E 000 1.66666666666667E-001 0.00000000000000E 000 0.00000000000000E 000 1.66666666666667E-001 0.00000000000000E 000 1.66666666666667E-001 -7.16703787691222E 000 [[6.0, 0.0, 0.0, 0.0], [0.0, 6.0, 0.0, 0.0], [0.0, 0.0, 6.0, 0.0], [0.0, 0.0, 0.0, 6.0]]
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