SAP HANA Predictive Analysis Library (PAL)

INSERT INTO PAL_TRAIN_NN_DATA_TBL VALUES( 12, 3.15, 'AA', 2, 'C');
INSERT INTO PAL_TRAIN_NN_DATA_TBL VALUES( 7, 1.05, 'CA', 3, 'AB');
INSERT INTO PAL_TRAIN_NN_DATA_TBL VALUES( 6, 1.50, 'CA', 2, 'AB');
INSERT INTO PAL_TRAIN_NN_DATA_TBL VALUES( 9, 1.97, 'CA', 6, 'C');
INSERT INTO PAL_TRAIN_NN_DATA_TBL VALUES( 5, 1.26, 'AA', 1, 'AA');
INSERT INTO PAL_TRAIN_NN_DATA_TBL VALUES( 12, 2.13, 'AC', 4, 'C');
INSERT INTO PAL_TRAIN_NN_DATA_TBL VALUES( 18, 1.87, 'AC', 6, 'AA');
--CALL PROCEDURE
CALL DM_PAL.PAL_NN_TRAIN(PAL_TRAIN_NN_DATA_TBL, "#PAL_CONTROL_TBL",
PAL_TRAIN_NN_RESULT_TBL, PAL_CLASSIFICATION_NN_MODEL_TBL) WITH OVERVIEW;
SELECT * FROM PAL_TRAIN_NN_RESULT_TBL;
SELECT * FROM PAL_CLASSIFICATION_NN_MODEL_TBL;
Expected Results
Note: Your result may look slightly different from the following results.
PAL_TRAIN_NN_RESULT_TBL:
PAL_CLASSIFICATION_NN_MODEL_TBL:
Regression example:
SET SCHEMA DM_PAL;
--TRAINING PART
--CREATE TABLE TYPE
DROP TYPE PAL_TRAIN_NN_DATA_T;
CREATE TYPE PAL_TRAIN_NN_DATA_T AS TABLE(
"V000" INTEGER,
"V001" DOUBLE,
"V002" VARCHAR(10),
"V003" INTEGER,
"T001" DOUBLE,
"T002" DOUBLE,
"T003" DOUBLE
);
DROP TYPE PAL_TRAIN_NN_RESULT_T;
CREATE TYPE PAL_TRAIN_NN_RESULT_T AS TABLE
(
"NAME" VARCHAR(100),
"VALUE" DOUBLE
);
DROP TYPE PAL_NN_MODEL_T;
CREATE TYPE PAL_NN_MODEL_T AS TABLE(
"NAME" VARCHAR(100),
"MODEL" CLOB
);
DROP TYPE PAL_CONTROL_T;
CREATE TYPE PAL_CONTROL_T AS TABLE(
"NAME" VARCHAR(100),
"INTARGS" INTEGER,
"DOUBLEARGS" DOUBLE,
"STRINGARGS" VARCHAR(100)
);
--CREATE PROCEDURE
DROP TABLE PAL_NN_PDATA_TBL;
SAP HANA Predictive Analysis Library (PAL)
PAL Functions P U B L I C 131