SAP HANA Predictive Analysis Library (PAL)

Example 1: Fitting multiple linear regression model without penalties
SET SCHEMA DM_PAL;
DROP TYPE PAL_MLR_DATA_T;
CREATE TYPE PAL_MLR_DATA_T AS TABLE( "ID" INT,"Y" DOUBLE,"V1" DOUBLE, "V2"
DOUBLE);
DROP TYPE PAL_MLR_RESULT_T;
CREATE TYPE PAL_MLR_RESULT_T AS TABLE("Coefficient"
varchar(50),"CoefficientValue" DOUBLE);
DROP TYPE PAL_MLR_FITTED_T;
CREATE TYPE PAL_MLR_FITTED_T AS TABLE("ID" INT,"Fitted" DOUBLE);
DROP TYPE PAL_MLR_SIGNIFICANCE_T;
CREATE TYPE PAL_MLR_SIGNIFICANCE_T AS TABLE("NAME" varchar(50),"VALUE" DOUBLE);
DROP TYPE PAL_MLR_PMMLMODEL_T;
CREATE TYPE PAL_MLR_PMMLMODEL_T AS TABLE("ID" INT,"Model" varchar(5000));
DROP TYPE PAL_CONTROL_T;
CREATE TYPE PAL_CONTROL_T AS TABLE("NAME" VARCHAR(100), "INTARGS" INT,
"DOUBLEARGS" DOUBLE,"STRINGARGS" VARCHAR(100));
DROP TABLE PAL_MLR_PDATA_TBL;
CREATE COLUMN TABLE PAL_MLR_PDATA_TBL("POSITION" INT, "SCHEMA_NAME"
NVARCHAR(256), "TYPE_NAME" NVARCHAR(256), "PARAMETER_TYPE" VARCHAR(7));
INSERT INTO PAL_MLR_PDATA_TBL values (1,'DM_PAL','PAL_MLR_DATA_T','IN');
INSERT INTO PAL_MLR_PDATA_TBL values (2,'DM_PAL','PAL_CONTROL_T','IN');
INSERT INTO PAL_MLR_PDATA_TBL values (3,'DM_PAL','PAL_MLR_RESULT_T','OUT');
INSERT INTO PAL_MLR_PDATA_TBL values (4,'DM_PAL','PAL_MLR_FITTED_T','OUT');
INSERT INTO PAL_MLR_PDATA_TBL values (5,'DM_PAL','PAL_MLR_SIGNIFICANCE_T','OUT');
INSERT INTO PAL_MLR_PDATA_TBL values (6,'DM_PAL','PAL_MLR_PMMLMODEL_T','OUT');
CALL SYS.AFLLANG_WRAPPER_PROCEDURE_DROP('DM_PAL','PAL_LR_PROC');
CALL
SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE('AFLPAL','LRREGRESSION','DM_PAL','PAL_LR_PRO
C',PAL_MLR_PDATA_TBL);
DROP TABLE #PAL_CONTROL_TBL;
CREATE LOCAL TEMPORARY COLUMN TABLE #PAL_CONTROL_TBL ("NAME" VARCHAR(100),
"INTARGS" INT, "DOUBLEARGS" DOUBLE,"STRINGARGS" VARCHAR(100));
INSERT INTO #PAL_CONTROL_TBL VALUES ('THREAD_NUMBER',8,null,null);
INSERT INTO #PAL_CONTROL_TBL VALUES ('PMML_EXPORT',0,null,null);
DROP TABLE PAL_MLR_DATA_TBL;
CREATE COLUMN TABLE PAL_MLR_DATA_TBL ( "ID" INT,"Y" DOUBLE,"V1" DOUBLE, "V2"
DOUBLE);
INSERT INTO PAL_MLR_DATA_TBL VALUES (0,0.5,0.13,0.33);
INSERT INTO PAL_MLR_DATA_TBL VALUES (1,0.15,0.14,0.34);
INSERT INTO PAL_MLR_DATA_TBL VALUES (2,0.25,0.15,0.36);
INSERT INTO PAL_MLR_DATA_TBL VALUES (3,0.35,0.16,0.35);
INSERT INTO PAL_MLR_DATA_TBL VALUES (4,0.45,0.17,0.37);
INSERT INTO PAL_MLR_DATA_TBL VALUES (5,0.55,0.18,0.38);
INSERT INTO PAL_MLR_DATA_TBL VALUES (6,0.65,0.19,0.39);
INSERT INTO PAL_MLR_DATA_TBL VALUES (7,0.75,0.19,0.31);
INSERT INTO PAL_MLR_DATA_TBL VALUES (8,0.85,0.11,0.32);
INSERT INTO PAL_MLR_DATA_TBL VALUES (9,0.95,0.12,0.33);
DROP TABLE PAL_MLR_RESULTS_TBL;
CREATE COLUMN TABLE PAL_MLR_RESULTS_TBL ("Coefficient"
varchar(50),"CoefficientValue" DOUBLE);
DROP TABLE PAL_MLR_FITTED_TBL;
CREATE COLUMN TABLE PAL_MLR_FITTED_TBL ("ID" INT, "Fitted" DOUBLE);
DROP TABLE PAL_MLR_SIGNIFICANCE_TBL;
CREATE COLUMN TABLE PAL_MLR_SIGNIFICANCE_TBL ("NAME" varchar(50),"VALUE" DOUBLE);
DROP TABLE PAL_MLR_PMMLMODEL_TBL;
CREATE COLUMN TABLE PAL_MLR_PMMLMODEL_TBL ("ID" INT, "PMMLMODEL" VARCHAR(5000));
CALL DM_PAL.PAL_LR_PROC(PAL_MLR_DATA_TBL,"#PAL_CONTROL_TBL",
PAL_MLR_RESULTS_TBL, PAL_MLR_FITTED_TBL, PAL_MLR_SIGNIFICANCE_TBL,
PAL_MLR_PMMLMODEL_TBL) WITH OVERVIEW;
SELECT * FROM PAL_MLR_RESULTS_TBL;
SELECT * FROM PAL_MLR_FITTED_TBL;
SELECT * FROM PAL_MLR_SIGNIFICANCE_TBL;
Expected Results
SAP HANA Predictive Analysis Library (PAL)
PAL Functions P U B L I C 271