SAP HANA Predictive Analysis Library (PAL)

INSERT INTO PAL_CM_DATA_TBL VALUES(4,1,2);
INSERT INTO PAL_CM_DATA_TBL VALUES(5,1,1);
INSERT INTO PAL_CM_DATA_TBL VALUES(6,2,2);
INSERT INTO PAL_CM_DATA_TBL VALUES(7,2,1);
INSERT INTO PAL_CM_DATA_TBL VALUES(8,2,2);
INSERT INTO PAL_CM_DATA_TBL VALUES(9,2,2);
INSERT INTO PAL_CM_DATA_TBL VALUES(10,2,2);
DROP TABLE #PAL_CONTROL_TBL;
CREATE LOCAL TEMPORARY COLUMN TABLE #PAL_CONTROL_TBL LIKE PAL_CONTROL_T;
INSERT INTO #PAL_CONTROL_TBL VALUES('BETA',NULL,1,null);
DROP TABLE PAL_CM_CONFUSIONMATRIX_TBL;
CREATE COLUMN TABLE PAL_CM_CONFUSIONMATRIX_TBL LIKE PAL_CM_CONFUSIONMATRIX_T;
DROP TABLE PAL_CM_CLASSIFICATIONREPORT_TBL;
CREATE COLUMN TABLE PAL_CM_CLASSIFICATIONREPORT_TBL LIKE
PAL_CM_CLASSIFICATIONREPORT_T;
CALL DM_PAL.PAL_CM(PAL_CM_DATA_TBL, #PAL_CONTROL_TBL,
PAL_CM_CONFUSIONMATRIX_TBL,PAL_CM_CLASSIFICATIONREPORT_TBL) with OVERVIEW;
SELECT * FROM PAL_CM_CONFUSIONMATRIX_TBL;
SELECT * FROM PAL_CM_CLASSIFICATIONREPORT_TBL;
Expected Results
PAL_CM_CONFUSIONMATRIX_TBL:
PAL_CM_CLASSIFICATIONREPORT_TBL:
3.2.7 KNN
K-Nearest Neighbor (KNN) is a memory based classification method with no explicit training phase. In the
testing phase, given a query sample x, its top K nearest samples is found in the training set first, then the label
of x is assigned as the most frequent label of the K nearest neighbors. In this release of PAL, the description of
each sample should be real numbers. In order to speed up the search, the KD-tree searching method is
provided.
Prerequisites
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The first column of the training data and input data is an ID column. The second column of the training
data is of class type. The class type column is of integer type. Other data columns are of integer or double
type.
The input data does not contain null value.
SAP HANA Predictive Analysis Library (PAL)
PAL Functions P U B L I C 161