## Developer Guide and Reference

• 2021.6
• 04/11/2022
• Public Content
Contents

# Correlation Distance Matrix

Given feature vectors of dimension , the problem is to compute the symmetric matrix of distances between feature vectors, where     ## Batch Processing

Algorithm Input
The correlation distance matrix 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.
Algorithm Input for Correlation Distance Matrix (Batch Processing)
Input ID
Input
data
Pointer to the numeric table for which the distance is computed.
The input can be an object of any class derived from
NumericTable
.
Algorithm Parameters
The correlation distance matrix algorithm has the following parameters:
Algorithm Parameters for Correlation Distance Matrix (Batch Processing)
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.
Algorithm Output
The correlation distance matrix 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.
Algorithm Output for Correlation Distance Matrix (Batch Processing)
Result ID
Result
correlationDistance
Pointer to the numeric table that represents the symmetric distance matrix .
By default, the result is an object of the
PackedSymmetricMatrix
class with the
lowerPackedSymmetricMatrix
layout. However, you can define the result as an object of any class derived from
NumericTable
except
PackedTriangularMatrix
and
CSRNumericTable
.

## Examples

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

## Performance Considerations

To get the best overall performance when computing the correlation distance matrix:
• If input data is homogeneous, provide the input data and store results in homogeneous numeric tables of the same type as specified in the
algorithmFPType
class template parameter.
• If input data is non-homogeneous, use AOS layout rather than SOA layout.
Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex​.
Notice revision #20201201

#### Product and Performance Information

1

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