The GMMPredict function applies the model created by the GMM function to the test input to cluster the test data.
Input
- testdata: gmm_iris_test, as in GMM Examples Input
- modeldata: gmm_output_ex1, output by GMM Example 1: Basic GMM, Spherical Covariance, Packed Output
| id | sepal_length | sepal_width | petal_length | petal_width |
|---|---|---|---|---|
| 5 | 5 | 3.6 | 1.4 | 0.2 |
| 10 | 4.9 | 3.1 | 1.5 | 0.1 |
| 15 | 5.8 | 4 | 1.2 | 0.2 |
| 20 | 5.1 | 3.8 | 1.5 | 0.3 |
| 25 | 4.8 | 3.4 | 1.9 | 0.2 |
| 30 | 4.7 | 3.2 | 1.6 | 0.2 |
| 35 | 4.9 | 3.1 | 1.5 | 0.2 |
| 40 | 5.1 | 3.4 | 1.5 | 0.2 |
| 45 | 5.1 | 3.8 | 1.9 | 0.4 |
| 50 | 5 | 3.3 | 1.4 | 0.2 |
| 55 | 6.5 | 2.8 | 4.6 | 1.5 |
| 60 | 5.2 | 2.7 | 3.9 | 1.4 |
| 65 | 5.6 | 2.9 | 3.6 | 1.3 |
| 70 | 5.6 | 2.5 | 3.9 | 1.1 |
| 75 | 6.4 | 2.9 | 4.3 | 1.3 |
| 80 | 5.7 | 2.6 | 3.5 | 1 |
| 85 | 5.4 | 3 | 4.5 | 1.5 |
| 90 | 5.5 | 2.5 | 4 | 1.3 |
| 95 | 5.6 | 2.7 | 4.2 | 1.3 |
| 100 | 5.7 | 2.8 | 4.1 | 1.3 |
| 105 | 6.5 | 3 | 5.8 | 2.2 |
| 110 | 7.2 | 3.6 | 6.1 | 2.5 |
| 115 | 5.8 | 2.8 | 5.1 | 2.4 |
| 120 | 6 | 2.2 | 5 | 1.5 |
| 125 | 6.7 | 3.3 | 5.7 | 2.1 |
| 130 | 7.2 | 3 | 5.8 | 1.6 |
| 135 | 6.1 | 2.6 | 5.6 | 1.4 |
| 140 | 6.9 | 3.1 | 5.4 | 2.1 |
| 145 | 6.7 | 3.3 | 5.7 | 2.5 |
| 150 | 5.9 | 3 | 5.1 | 1.8 |
SQL Call
SELECT * FROM GMMPredict ( ON gmm_output_ex1 AS modeldata DIMENSION ON gmm_iris_test AS testdata PARTITION BY id USING TopK (3) ) AS dt ORDER BY id, prob desc;
Output
The output table shows the dimensions and id of each sample, and the probability that it belongs to each of the three clusters.
| id | sepal_length | sepal_width | petal_length | petal_width | cluster_rank | cluster_id | prob |
|---|---|---|---|---|---|---|---|
| 5 | 5 | 3.59999990463257 | 1.39999997615814 | 0.200000002980232 | 1 | 0 | 0.999999996516264 |
| 5 | 5 | 3.59999990463257 | 1.39999997615814 | 0.200000002980232 | 2 | 2 | 3.48373546251981e-09 |
| 5 | 5 | 3.59999990463257 | 1.39999997615814 | 0.200000002980232 | 3 | 0 | 0 |
| 10 | 4.90000009536743 | 3.09999990463257 | 1.5 | 0.100000001490116 | 1 | 1 | 0.999999999978146 |
| 10 | 4.90000009536743 | 3.09999990463257 | 1.5 | 0.100000001490116 | 2 | 2 | 2.18542496451431e-11 |
| 10 | 4.90000009536743 | 3.09999990463257 | 1.5 | 0.100000001490116 | 3 | 0 | 0 |
| 15 | 5.80000019073486 | 4 | 1.20000004768372 | 0.200000002980232 | 1 | 1 | 0.999999998968956 |
| 15 | 5.80000019073486 | 4 | 1.20000004768372 | 0.200000002980232 | 2 | 2 | 1.03104372199014e-09 |
| 15 | 5.80000019073486 | 4 | 1.20000004768372 | 0.200000002980232 | 3 | 0 | 0 |
| 20 | 5.09999990463257 | 3.79999995231628 | 1.5 | 0.300000011920929 | 1 | 1 | 0.999999999970437 |
| 20 | 5.09999990463257 | 3.79999995231628 | 1.5 | 0.300000011920929 | 2 | 2 | 2.9562773726376e-11 |
| 20 | 5.09999990463257 | 3.79999995231628 | 1.5 | 0.300000011920929 | 3 | 0 | 0 |
| 25 | 4.80000019073486 | 3.40000009536743 | 1.89999997615814 | 0.200000002980232 | 1 | 1 | 0.999999998740311 |
| 25 | 4.80000019073486 | 3.40000009536743 | 1.89999997615814 | 0.200000002980232 | 2 | 2 | 1.25968923928218e-09 |
| 25 | 4.80000019073486 | 3.40000009536743 | 1.89999997615814 | 0.200000002980232 | 3 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... |