Input
- InputTable: svm_iris_test (see SVMDense Examples Input)
- Model: densesvm_iris_sigmoid_model (see SVMDense Example: Sigmoid Model)
SQL Call
SELECT * FROM SVMDensePredict (
ON svm_iris_test AS InputTable PARTITION BY ANY
ON densesvm_iris_sigmoid_model AS Model DIMENSION
USING
IdColumn ('id')
TargetColumns ('[1:4]')
Accumulate ('id','species')
Responses ('virginica')
) AS dt ORDER BY 3 DESC;
Output
id predict_value prob_virginica species --- ------------- ---------------------- ---------- 110 virginica 7.87263965377601E-001 virginica 130 virginica 7.86459354916230E-001 virginica 145 virginica 7.82388613539467E-001 virginica 125 virginica 7.82388613290050E-001 virginica 140 virginica 7.82388612917917E-001 virginica 105 virginica 7.78537903303850E-001 virginica 55 virginica 6.39488974749763E-001 versicolor 75 virginica 5.85074341915205E-001 versicolor 135 virginica 5.21407157310471E-001 virginica 150 virginica 4.49736675000437E-001 virginica 115 versicolor 2.94569153762077E-001 virginica 120 versicolor 2.16878307508021E-001 virginica 95 versicolor 2.05474156301691E-001 versicolor 85 versicolor 2.05262614322069E-001 versicolor 15 versicolor 2.05248872428009E-001 setosa 100 versicolor 2.04257407723918E-001 versicolor 65 setosa 2.02248396041444E-001 versicolor 80 setosa 1.78812236946778E-001 versicolor 70 setosa 1.71744777702477E-001 versicolor 90 setosa 1.59496669721241E-001 versicolor 60 setosa 1.27621898606818E-001 versicolor 45 setosa 1.23291470282880E-001 setosa 50 setosa 1.07753417483852E-001 setosa 25 setosa 1.07705713554535E-001 setosa 10 setosa 1.07645134934353E-001 setosa 35 setosa 1.07644927941564E-001 setosa 20 setosa 1.07034860480663E-001 setosa 30 setosa 1.06060230361458E-001 setosa 40 setosa 1.06000413521546E-001 setosa 5 setosa 1.06000103878239E-001 setosa
Download a zip file of all examples and a SQL script file that creates their input tables.