## Developer Guide and Reference

• 2021.4
• 09/27/2021
• Public Content
Contents

# Min-max

Min-max normalization is an algorithm to linearly scale the observations by each feature (column) into the range .

## Problem Statement

Given a set
X
of
n
feature vectors of dimension
p
, the problem is to compute the matrix where the
j
-th column is obtained as a result of normalizing the column of the original matrix as: where:  a
and
b
are the parameters of the algorithm.

## Batch Processing

Algorithm Input
The min-max normalization algorithm accepts the input described below. Pass the
Input ID
as a parameter to the methods that provide input for your algorithm. For more details, see Algorithms.
Input ID
Input
data
Pointer to the numeric table of size .
This table can be an object of any class derived from
NumericTable
.
Algorithm Parameters
The min-max normalization algorithm has the following parameters:
Parameter
Default Value
Description
algorithmFPType
float
The floating-point type that the algorithm uses for intermediate computations. Can be
float
or
double
.
method
defaultDense
Performance-oriented computation method, the only method supported by the algorithm.
lowerBound
0.0
The lower bound of the range to which the normalization scales values of the features.
upperBound
1.0
The upper bound of the range to which the normalization scales values of the features.
moments
SharedPtr<low_order_moments::Batch<algorithmFPType, low_order_moments::defaultDense> >
Pointer to the low order moments algorithm that computes minimums and maximums to be used for min-max normalization with the defaultDense method. For more details, see Batch Processing for Moments of Low Order.
Algorithm Output
The min-max normalization algorithm calculates the result described below. Pass the
Result ID
as a parameter to the methods that access the results of your algorithm. For more details, see
Algorithms
.
Result ID
Result
normalizedData
Pointer to the numeric table that stores the result of normalization.
By default, the result is an object of the
HomogenNumericTable
class, but you can define the result as an object of any class derived from
NumericTable
except
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.

## Examples

C++ (CPU)
Batch Processing:
Java*
There is no support for Java on GPU.
Batch Processing:
Python*
Batch Processing:

#### Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.