SAP HANA Predictive Analysis Library (PAL)

CREATE COLUMN TABLE PAL_NN_PDATA_TBL(
"POSITION" INT,
"SCHEMA_NAME" NVARCHAR(256),
"TYPE_NAME" NVARCHAR(256),
"PARAMETER_TYPE" VARCHAR(7)
);
DELETE FROM PAL_NN_PDATA_TBL;
INSERT INTO PAL_NN_PDATA_TBL VALUES (1, 'DM_PAL', 'PAL_TRAIN_NN_DATA_T', 'IN');
INSERT INTO PAL_NN_PDATA_TBL VALUES (2, 'DM_PAL', 'PAL_CONTROL_T', 'IN');
INSERT INTO PAL_NN_PDATA_TBL VALUES (3, 'DM_PAL', 'PAL_TRAIN_NN_RESULT_T',
'OUT');
INSERT INTO PAL_NN_PDATA_TBL VALUES (4, 'DM_PAL', 'PAL_NN_MODEL_T', 'OUT');
CALL SYS.AFLLANG_WRAPPER_PROCEDURE_DROP('DM_PAL', 'PAL_NN_TRAIN');
CALL SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE('AFLPAL', 'CREATEBPNN', 'DM_PAL',
'PAL_NN_TRAIN', PAL_NN_PDATA_TBL);
--RUN
DROP TABLE #PAL_CONTROL_TBL;
CREATE LOCAL TEMPORARY COLUMN TABLE #PAL_CONTROL_TBL(
"NAME" VARCHAR(100),
"INTARGS" INTEGER,
"DOUBLEARGS" DOUBLE,
"STRINGARGS" VARCHAR(100)
);
INSERT INTO #PAL_CONTROL_TBL VALUES ('THREAD_NUMBER', 3, NULL, NULL);
INSERT INTO #PAL_CONTROL_TBL VALUES ('HIDDEN_LAYER_SIZE', NULL, NULL, '10, 5');
INSERT INTO #PAL_CONTROL_TBL VALUES ('HIDDEN_LAYER_ACTIVE_FUNC', 9, NULL, NULL);
INSERT INTO #PAL_CONTROL_TBL VALUES ('OUTPUT_LAYER_ACTIVE_FUNC', 9, NULL, NULL);
INSERT INTO #PAL_CONTROL_TBL VALUES ('LEARNING_RATE', NULL, 0.001, NULL);
INSERT INTO #PAL_CONTROL_TBL VALUES ('MOMENTUM_FACTOR', NULL, 0.00001, NULL);
INSERT INTO #PAL_CONTROL_TBL VALUES ('FUNCTIONALITY', 1, NULL, NULL);
INSERT INTO #PAL_CONTROL_TBL VALUES ('TRAINING_STYLE', 0, NULL, NULL);
INSERT INTO #PAL_CONTROL_TBL VALUES ('TARGET_COLUMN_NUM', 3, NULL, NULL);
INSERT INTO #PAL_CONTROL_TBL VALUES ('MAX_ITERATION', 100, NULL, NULL);
INSERT INTO #PAL_CONTROL_TBL VALUES ('NORMALIZATION', 1, NULL, NULL);
INSERT INTO #PAL_CONTROL_TBL VALUES ('WEIGHT_INIT', 1, NULL, NULL);
DROP TABLE PAL_TRAIN_NN_RESULT_TBL;
CREATE COLUMN TABLE PAL_TRAIN_NN_RESULT_TBL LIKE PAL_TRAIN_NN_RESULT_T;
DROP TABLE PAL_REGRESSION_NN_MODEL_TBL;
CREATE COLUMN TABLE PAL_REGRESSION_NN_MODEL_TBL LIKE PAL_NN_MODEL_T;
DROP TABLE PAL_TRAIN_NN_DATA_TBL;
CREATE COLUMN TABLE PAL_TRAIN_NN_DATA_TBL LIKE PAL_TRAIN_NN_DATA_T;
INSERT INTO PAL_TRAIN_NN_DATA_TBL VALUES( 1, 1.71, 'AC', 0, 12.7, 2.8, 3.06);
INSERT INTO PAL_TRAIN_NN_DATA_TBL VALUES( 10, 1.78, 'CA', 5, 12.1, 8.0, 2.65);
INSERT INTO PAL_TRAIN_NN_DATA_TBL VALUES( 17, 2.36, 'AA', 6, 10.1, 2.8, 3.24);
INSERT INTO PAL_TRAIN_NN_DATA_TBL VALUES( 12, 3.15, 'AA', 2, 28.1, 5.6, 2.24);
INSERT INTO PAL_TRAIN_NN_DATA_TBL VALUES( 7, 1.05, 'CA', 3, 19.8, 7.1, 1.98);
INSERT INTO PAL_TRAIN_NN_DATA_TBL VALUES( 6, 1.50, 'CA', 2, 23.2, 4.9, 2.12);
INSERT INTO PAL_TRAIN_NN_DATA_TBL VALUES( 9, 1.97, 'CA', 6, 24.5, 4.2, 1.05);
INSERT INTO PAL_TRAIN_NN_DATA_TBL VALUES( 5, 1.26, 'AA', 1, 13.6, 5.1, 2.78);
INSERT INTO PAL_TRAIN_NN_DATA_TBL VALUES( 12, 2.13, 'AC', 4, 13.2, 1.9, 1.34);
INSERT INTO PAL_TRAIN_NN_DATA_TBL VALUES( 18, 1.87, 'AC', 6, 25.5, 3.6, 2.14);
--CALL PROCEDURE
CALL DM_PAL.PAL_NN_TRAIN(PAL_TRAIN_NN_DATA_TBL, "#PAL_CONTROL_TBL",
PAL_TRAIN_NN_RESULT_TBL, PAL_REGRESSION_NN_MODEL_TBL) WITH OVERVIEW;
SELECT * FROM PAL_TRAIN_NN_RESULT_TBL;
SELECT * FROM PAL_REGRESSION_NN_MODEL_TBL;
Expected Results
Note: Your result may look slightly different from the following results.
132 P U B L I C
SAP HANA Predictive Analysis Library (PAL)
PAL Functions