The GMMPredict function applies the model created by the GMM function to the test input to cluster the test data.
Input
- InputTable: gmm_iris_test, as in GMM Examples Input
- Model: gmm_output_ex1, output by GMM Example: 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 Model DIMENSION ON gmm_iris_test AS InputTable 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 5.0 3.6 1.4 0.2 1 0 0.9999999999999989 5 5.0 3.6 1.4 0.2 2 2 1.0698003688064299E-15 5 5.0 3.6 1.4 0.2 3 1 8.212771939415291E-29 10 4.9 3.1 1.5 0.1 1 0 0.9999999999999949 10 4.9 3.1 1.5 0.1 2 2 5.215047489403572E-15 10 4.9 3.1 1.5 0.1 3 1 8.596087853138194E-24 15 5.8 4.0 1.2 0.2 1 0 0.9999999999998073 15 5.8 4.0 1.2 0.2 2 2 1.9283604401940617E-13 15 5.8 4.0 1.2 0.2 3 1 2.023083459936497E-35 20 5.1 3.8 1.5 0.3 1 0 0.9999999999999865 20 5.1 3.8 1.5 0.3 2 2 1.3581233688332843E-14 20 5.1 3.8 1.5 0.3 3 1 3.5015069719597627E-28 25 4.8 3.4 1.9 0.2 1 0 0.9999999999990372 25 4.8 3.4 1.9 0.2 2 2 9.628706763468497E-13 25 4.8 3.4 1.9 0.2 3 1 2.258612841490787E-19 30 4.7 3.2 1.6 0.2 1 0 0.9999999999999887 30 4.7 3.2 1.6 0.2 2 2 1.1240537970056623E-14 30 4.7 3.2 1.6 0.2 3 1 9.676416490528892E-23 35 4.9 3.1 1.5 0.2 1 0 0.9999999999999918 35 4.9 3.1 1.5 0.2 2 2 8.12474270733355E-15 35 4.9 3.1 1.5 0.2 3 1 4.1669847147126875E-23 40 5.1 3.4 1.5 0.2 1 0 0.9999999999999916 40 5.1 3.4 1.5 0.2 2 2 8.454813076593314E-15 40 5.1 3.4 1.5 0.2 3 1 3.446661810603676E-25 45 5.1 3.8 1.9 0.4 1 0 0.999999999988463 45 5.1 3.8 1.9 0.4 2 2 1.1536928120751568E-11 45 5.1 3.8 1.9 0.4 3 1 4.013220603920191E-21 50 5.0 3.3 1.4 0.2 1 0 0.9999999999999982 50 5.0 3.3 1.4 0.2 2 2 1.6654268270139521E-15 50 5.0 3.3 1.4 0.2 3 1 3.429446069942643E-26 55 6.5 2.8 4.6 1.5 1 2 0.9999999999990403 55 6.5 2.8 4.6 1.5 2 1 9.59569181252332E-13 55 6.5 2.8 4.6 1.5 3 0 5.569025600510225E-38 60 5.2 2.7 3.9 1.4 1 1 0.9442250899448984 60 5.2 2.7 3.9 1.4 2 2 0.055774910055101865 60 5.2 2.7 3.9 1.4 3 0 3.1599350623031505E-20 65 5.6 2.9 3.6 1.3 1 1 0.846860212056736 65 5.6 2.9 3.6 1.3 2 2 0.1531397879432626 65 5.6 2.9 3.6 1.3 3 0 1.2234934358485754E-15 70 5.6 2.5 3.9 1.1 1 1 0.9465447895583854 70 5.6 2.5 3.9 1.1 2 2 0.053455210441614366 70 5.6 2.5 3.9 1.1 3 0 1.1208865308842914E-20 75 6.4 2.9 4.3 1.3 1 2 0.9999999950777232 75 6.4 2.9 4.3 1.3 2 1 4.9222768050956666E-9 75 6.4 2.9 4.3 1.3 3 0 2.1848015795180576E-30 80 5.7 2.6 3.5 1.0 1 1 0.9917937569411431 80 5.7 2.6 3.5 1.0 2 2 0.008206243058854052 80 5.7 2.6 3.5 1.0 3 0 2.8538614115610594E-15 85 5.4 3.0 4.5 1.5 1 2 0.999987668251635 85 5.4 3.0 4.5 1.5 2 1 1.2331748365215967E-5 85 5.4 3.0 4.5 1.5 3 0 3.218903379735191E-29 90 5.5 2.5 4.0 1.3 1 1 0.8465631916397747 90 5.5 2.5 4.0 1.3 2 2 0.15343680836022525 90 5.5 2.5 4.0 1.3 3 0 1.6381671580627518E-22 95 5.6 2.7 4.2 1.3 1 2 0.9609392973181826 95 5.6 2.7 4.2 1.3 2 1 0.03906070268181772 95 5.6 2.7 4.2 1.3 3 0 1.5258770448488742E-24 100 5.7 2.8 4.1 1.3 1 2 0.9726572812010832 100 5.7 2.8 4.1 1.3 2 1 0.027342718798916598 100 5.7 2.8 4.1 1.3 3 0 4.573488798874464E-23 105 6.5 3.0 5.8 2.2 1 2 1.0 105 6.5 3.0 5.8 2.2 2 1 7.494570350886698E-34 105 6.5 3.0 5.8 2.2 3 0 1.6339290393405167E-67 110 7.2 3.6 6.1 2.5 1 2 1.0 110 7.2 3.6 6.1 2.5 2 1 5.6048659811737865E-55 110 7.2 3.6 6.1 2.5 3 0 2.4584327163583323E-83 115 5.8 2.8 5.1 2.4 1 2 1.0 115 5.8 2.8 5.1 2.4 2 1 6.91446024044145E-19 115 5.8 2.8 5.1 2.4 3 0 1.0620275638612122E-50 120 6.0 2.2 5.0 1.5 1 2 0.9999999999881917 120 6.0 2.2 5.0 1.5 2 1 1.1808581767564222E-11 120 6.0 2.2 5.0 1.5 3 0 1.736214062469676E-44 125 6.7 3.3 5.7 2.1 1 2 1.0 125 6.7 3.3 5.7 2.1 2 1 2.4157753132451544E-35 125 6.7 3.3 5.7 2.1 3 0 1.847134529156803E-65 130 7.2 3.0 5.8 1.6 1 2 1.0 130 7.2 3.0 5.8 1.6 2 1 1.309602745870143E-38 130 7.2 3.0 5.8 1.6 3 0 7.637564893228441E-69 135 6.1 2.6 5.6 1.4 1 2 1.0 135 6.1 2.6 5.6 1.4 2 1 6.916995349512563E-21 135 6.1 2.6 5.6 1.4 3 0 7.482737218326153E-55 140 6.9 3.1 5.4 2.1 1 2 1.0 140 6.9 3.1 5.4 2.1 2 1 8.867700057480836E-32 140 6.9 3.1 5.4 2.1 3 0 3.6438229003460426E-61 145 6.7 3.3 5.7 2.5 1 2 1.0 145 6.7 3.3 5.7 2.5 2 1 5.998454220109692E-39 145 6.7 3.3 5.7 2.5 3 0 1.5286168703203913E-69 150 5.9 3.0 5.1 1.8 1 2 0.9999999999999991 150 5.9 3.0 5.1 1.8 2 1 9.595729554874454E-16 150 5.9 3.0 5.1 1.8 3 0 4.913952998440134E-45
Download a zip file of all examples and a SQL script file that creates their input tables.