SAP HANA Predictive Analysis Library (PAL)

BPNN performs three steps as follows:
1. Build the network structure and initialize the network.
2. Feed the training data repeatedly to train the network.
3. Make prediction with the trained network.
Training
During the training, BPNN adjusts the connection weights by comparing its output and expected target to
make the output more accurate. The following training styles are available:
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Batch training: weights updating is based on the error of the entire package of training patterns. Thus, in
one round the weights are updated once.
Stochastic training: weights updating is based on the error of a single training pattern. Thus, in one round
the weights are updated for each pattern.
Support for Categorical Attributes
If an attribute is of category type, it will be converted to a binary vector and then be used as numerical
attributes. For example, in the below table, "Gender" is of category type.
Table 87:
Customer ID Age Income Gender
T1 31 10,000 Female
T2 27 8,000 Male
Because "Gender" has two distinct values, it will be converted into a binary vector with two dimensions:
Table 88:
Customer ID Age Income Gender_1 Gender_2
T1 31 10,000 0 1
T2 27 8,000 1 0
Prerequisites
For training data:
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The data are of integer, varchar, nvarchar, or double data type and do not contain null value. Otherwise the
algorithm will issue errors.
If it is for classification, then the last column is considered as the label column and is of integer, varchar, or
nvarchar type.
If it is for regression, then you should specify how many last columns are considered as target values, and
they are of integer or double type.
For predicted data:
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The data are of integer, varchar, nvarchar, or double data type and does not contain null value. Otherwise
the algorithm will issue errors.
The first column is ID column and should be of integer type.
The column order and column number of the predicted data are the same as the order and number used in
model training.
126 P U B L I C
SAP HANA Predictive Analysis Library (PAL)
PAL Functions