SAP HANA Predictive Analysis Library (PAL)

DROP TYPE PAL_VARIMPL_TYPE;
DROP TYPE PAL_ERROR_TYPE;
DROP TYPE PAL_CONFUSION_TYPE;
DROP TABLE PAL_RFPS_PDATA_TBL;
DROP TABLE PAL_FUNC_TBL;
DROP TABLE PAL_DATA_TBL;
DROP TABLE PAL_PARA_TBL;
DROP TABLE PAL_EVAL_TBL;
DROP TABLE PAL_PARASEL_TBL;
DROP TABLE PAL_MODEL_TBL;
DROP TABLE PAL_VARIMPL_TBL;
DROP TABLE PAL_ERROR_TBL;
DROP TABLE PAL_CONFUSION_TBL;
CREATE TYPE PAL_FUNC_TYPE AS TABLE("ID" INTEGER, "FUNCNAME" VARCHAR(5000));
CREATE TYPE PAL_DATA_TYPE AS TABLE("ID" INTEGER, "C0" VARCHAR(5000), "C1"
INTEGER, "C2" INTEGER, "C3" VARCHAR(5000), "C8" VARCHAR(5000));
CREATE TYPE PAL_PARA_TYPE AS TABLE("NAME" VARCHAR(5000), "INTARGS" INTEGER,
"DOUBLEARGS" DOUBLE, "STRINGARGS" VARCHAR(5000));
CREATE TYPE PAL_EVAL_TYPE AS TABLE( "ID" INTEGER, "NAME" VARCHAR(5000),
"CONTENT" VARCHAR(5000) );
CREATE TYPE PAL_PARASEL_TYPE AS TABLE( "ID" INTEGER, "NAME" VARCHAR(5000),
"CONTENT" VARCHAR(5000) );
CREATE TYPE PAL_MODEL_TYPE AS TABLE("ID" INTEGER, "TREEID" INTEGER,
"JSON_STRING" VARCHAR(5000));
CREATE TYPE PAL_VARIMPL_TYPE AS TABLE("ID" VARCHAR(5000), "IMPVAL" DOUBLE);
CREATE TYPE PAL_ERROR_TYPE AS TABLE("ID" INTEGER, "ERRORRATES" DOUBLE);
CREATE TYPE PAL_CONFUSION_TYPE AS TABLE("ID" INTEGER, "JSON_STRING"
VARCHAR(500));
CREATE COLUMN TABLE PAL_RFPS_PDATA_TBL("POSITION" INT, "SCHEMA_NAME"
NVARCHAR(256), "TYPE_NAME" NVARCHAR(256), "PARAMETER_TYPE" VARCHAR(7));
INSERT INTO PAL_RFPS_PDATA_TBL VALUES (1, 'DM_PAL', 'PAL_FUNC_TYPE', 'IN');
INSERT INTO PAL_RFPS_PDATA_TBL VALUES (2, 'DM_PAL', 'PAL_DATA_TYPE', 'IN');
INSERT INTO PAL_RFPS_PDATA_TBL VALUES (3, 'DM_PAL', 'PAL_PARA_TYPE', 'IN');
INSERT INTO PAL_RFPS_PDATA_TBL VALUES (4, 'DM_PAL', 'PAL_EVAL_TYPE', 'OUT');
INSERT INTO PAL_RFPS_PDATA_TBL VALUES (5, 'DM_PAL', 'PAL_PARASEL_TYPE', 'OUT');
INSERT INTO PAL_RFPS_PDATA_TBL VALUES (6, 'DM_PAL', 'PAL_MODEL_TYPE', 'OUT');
INSERT INTO PAL_RFPS_PDATA_TBL VALUES (7, 'DM_PAL', 'PAL_VARIMPL_TYPE', 'OUT');
INSERT INTO PAL_RFPS_PDATA_TBL VALUES (8, 'DM_PAL', 'PAL_ERROR_TYPE', 'OUT');
INSERT INTO PAL_RFPS_PDATA_TBL VALUES (9, 'DM_PAL', 'PAL_CONFUSION_TYPE', 'OUT');
CALL SYS.AFLLANG_WRAPPER_PROCEDURE_DROP('DM_PAL', 'PAL_RFPS_PROC');
CALL SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE('AFLPAL','PSME', 'DM_PAL',
'PAL_RFPS_PROC', PAL_RFPS_PDATA_TBL);
CREATE COLUMN TABLE PAL_FUNC_TBL LIKE PAL_FUNC_TYPE;
CREATE COLUMN TABLE PAL_DATA_TBL LIKE PAL_DATA_TYPE;
CREATE COLUMN TABLE PAL_PARA_TBL LIKE PAL_PARA_TYPE;
CREATE COLUMN TABLE PAL_EVAL_TBL LIKE PAL_EVAL_TYPE;
CREATE COLUMN TABLE PAL_PARASEL_TBL LIKE PAL_PARASEL_TYPE;
CREATE COLUMN TABLE PAL_MODEL_TBL LIKE PAL_MODEL_TYPE;
CREATE COLUMN TABLE PAL_VARIMPL_TBL LIKE PAL_VARIMPL_TYPE;
CREATE COLUMN TABLE PAL_ERROR_TBL LIKE PAL_ERROR_TYPE;
CREATE COLUMN TABLE PAL_CONFUSION_TBL LIKE PAL_CONFUSION_TYPE;
INSERT INTO PAL_FUNC_TBL VALUES (1, 'RANDOMFOREST');
INSERT INTO PAL_DATA_TBL VALUES (1, 'sunny', 75, 70, 'YES', 'PLAY');
INSERT INTO PAL_DATA_TBL VALUES (2, 'sunny', 76, 90, 'YES', 'NOT, PLAY');
INSERT INTO PAL_DATA_TBL VALUES (3, 'sunny', 83, 85, 'NO', 'NOT, PLAY');
INSERT INTO PAL_DATA_TBL VALUES (4, 'sunny', 79, 95, 'NO', 'NOT, PLAY');
INSERT INTO PAL_DATA_TBL VALUES (5, 'sunny', 68, 70, 'NO', 'PLAY');
INSERT INTO PAL_DATA_TBL VALUES (6, 'overcast', 78, 90, 'YES', 'PLAY');
INSERT INTO PAL_DATA_TBL VALUES (7, 'overcast', 70, 78, 'NO', 'PLAY');
INSERT INTO PAL_DATA_TBL VALUES (8, 'overcast', 69, 65, 'YES', 'PLAY');
INSERT INTO PAL_DATA_TBL VALUES (9, 'overcast', 99, 75, 'NO', 'PLAY');
INSERT INTO PAL_DATA_TBL VALUES (10, 'rain', 100, 80, 'YES', 'NOT, PLAY');
INSERT INTO PAL_DATA_TBL VALUES (11, 'rain', 98, 70, 'YES', 'NOT, PLAY');
INSERT INTO PAL_DATA_TBL VALUES (12, 'rain', 97, 80, 'NO', 'PLAY');
INSERT INTO PAL_DATA_TBL VALUES (13, 'rain', 96, 80, 'NO', 'PLAY');
INSERT INTO PAL_DATA_TBL VALUES (14, 'rain', 95, 96, 'NO', 'PLAY');
INSERT INTO PAL_PARA_TBL VALUES('TRY_NUM_MIN', 1, null, null);
INSERT INTO PAL_PARA_TBL VALUES('TRY_NUM_STEP', 1, null, null);
214 P U B L I C
SAP HANA Predictive Analysis Library (PAL)
PAL Functions