SAP HANA Predictive Analysis Library (PAL)

In the training set, let N be the number of samples, X∈R N×P be the features where X i,p is the p-th feature of the
i-th sample, and y∈R N be the labels where y i ∈{1, 2, ..., K} is the label of the i-th sample. The output of
the training phase is denoted as W * ∈R (P+1)×K , where
(p ≤ P) corresponds the weight of the p-th feature
for the k-th class, and
following optimization problem:
corresponds the constant for the k-th class. W * is obtained by solving the
Testing Phase
In the testing set, let be the number of samples, ∈R N×P be the features where is the p-th feature of
the i-th sample. Let be the unknown labels where is the label of the i-th
sample, and be the prediction confidences of the prediction where is the confidence (likelihood) of the i-
th sample. , are computed as follows,
LRMCTR
This is the algorithm for the training phase.
Procedure Generation
CALL SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE (‘AFLPAL’, ‘LRMCTR’, ‘’,
'', );
180 P U B L I C
SAP HANA Predictive Analysis Library (PAL)
PAL Functions