Input
Input tables are from DecisionTree Example 1:
- attribute_table: iris_attribute_test
- model_table: iris_attribute_output
SQL Call
CREATE MULTISET TABLE singletree_predict AS (
SELECT * FROM Single_Tree_Predict@coprocessor (
ON iris_attribute_test AS attribute_table PARTITION BY pid ORDER BY attribute
ON iris_attribute_output as model_table DIMENSION
USING
AttrTableGroupByColumns ('attribute')
AttrTablePIDColumns ('pid')
AttrTableValColumn ('attrvalue')
OutputResponseProbDist ('true')
Responses ('1','2','3')
) AS dt
) WITH DATA;
Output
This query returns the following table:
SELECT * FROM singletree_predict ORDER BY pid;
pid | pred_label | prob_for_label_1 | prob_for_label_2 | prob_for_label_3 -----+------------+------------------+------------------+------------------ 5 | 1 | 0.95348 | 0.02326 | 0.02326 10 | 1 | 0.95348 | 0.02326 | 0.02326 15 | 1 | 0.95348 | 0.02326 | 0.02326 20 | 1 | 0.95348 | 0.02326 | 0.02326 25 | 1 | 0.95348 | 0.02326 | 0.02326 30 | 1 | 0.95348 | 0.02326 | 0.02326 35 | 1 | 0.95348 | 0.02326 | 0.02326 40 | 1 | 0.95348 | 0.02326 | 0.02326 45 | 1 | 0.95348 | 0.02326 | 0.02326 50 | 1 | 0.95348 | 0.02326 | 0.02326 55 | 2 | 0.02632 | 0.94736 | 0.02632 60 | 2 | 0.02632 | 0.94736 | 0.02632 65 | 2 | 0.02632 | 0.94736 | 0.02632 70 | 2 | 0.02632 | 0.94736 | 0.02632 75 | 2 | 0.02632 | 0.94736 | 0.02632 80 | 2 | 0.02632 | 0.94736 | 0.02632 85 | 2 | 0.02632 | 0.94736 | 0.02632 90 | 2 | 0.02632 | 0.94736 | 0.02632 95 | 2 | 0.02632 | 0.94736 | 0.02632 100 | 2 | 0.02632 | 0.94736 | 0.02632 105 | 3 | 0.06250 | 0.12500 | 0.81250 110 | 3 | 0.07692 | 0.07692 | 0.84616 115 | 3 | 0.06250 | 0.06250 | 0.87500 120 | 2 | 0.14286 | 0.57143 | 0.28571 125 | 3 | 0.07692 | 0.07692 | 0.84616 130 | 2 | 0.14286 | 0.57143 | 0.28571 135 | 2 | 0.14286 | 0.57143 | 0.28571 140 | 3 | 0.06250 | 0.12500 | 0.81250 145 | 3 | 0.07692 | 0.07692 | 0.84616 150 | 3 | 0.25000 | 0.25000 | 0.50000 (30 rows)