Input
- Input table: fspredict_input, as in FellegiSunterPredict Example: Unsupervised Learning Model
- Model: fg_supervised_model, output by FellegiSunter Example: Supervised Learning
SQL Call
SELECT * FROM FellegiSunterPredict ( ON fspredict_input PARTITION BY ANY ON fg_supervised_model AS Model DIMENSION USING Accumulate ('id', 'src_text2', 'tar_text', 'jaro1_sim', 'ld1_sim','ngram1_sim', 'jw1_sim') ) AS dt ORDER BY id;
Output
The final column, match_result, contains the model prediction—M for match, U for no match. The weight column contains the weight of the object pair.
id src_text2 tar_text jaro1_sim ld1_sim ngram1_sim jw1_sim weight match_result -- -------------- -------------- ------------------ ------------------ ------------------ ------------------ ------------------- ------------ 5 allen allies 0.8222222222222223 0.6666666666666666 0.4 0.8755555555555556 -0.7958592832197748 M 7 center centre 0.9444444444444445 0.6666666666666666 0.6 0.9666666666666667 23.042599737443407 M 3 acquire acquiesce 0.8412698412698413 0.6666666666666666 0.5 0.9047619047619048 -0.7958592832197748 M 12 bear bear 1.0 1.0 1.0 1.0 44.296096257385436 M 8 cheap chief 0.7333333333333334 0.4 0.25 0.7866666666666667 -43.98092422973521 U 4 cccgggaaccaacc ccagggaaacccac 0.8754578754578755 0.7142857142857143 0.6923076923076923 0.9003663003663004 23.042599737443407 M 2 fone phone 0.7833333333333333 0.6 0.5 0.7833333333333333 -43.98092422973521 U 6 angle angels 0.8777777777777779 0.6666666666666666 0.4 0.9144444444444445 -0.7958592832197748 M 11 dell lead 0.5 0.25 0.0 0.5 -43.98092422973521 U 9 circle circuit 0.746031746031746 0.5714285714285714 0.5 0.8476190476190476 -23.049355803921173 U 10 debut debris 0.7000000000000001 0.5 0.4 0.79 -43.98092422973521 U
Download a zip file of all examples and a SQL script file that creates their input tables.