K-Means (Predict)

This document describes the structure and execution of kMeansPredict.

k-means prediction Structure
kMeansPredict provides prediction the cluster index for each sample, in which the centers are stored in an array
whose 1st dimension should partition in its definition.

In order to achieve to accelertion prediction, DV elements in a sample are input at the same time and used for computing distance with KU centers. The static configures are set by template parameters and dynamic by arguments of the API in which dynamic ones should not greater than static ones.

There are Applicable conditions:

1.All centers are stored in local buffer, so Dim*Kcluster should less than a fixed value. For example, Dim*Kcluster<=1024*1024 for centers with float stored in URAM and 1024*512 for double on U250.

2.KU and DV should be configured properly due to limitation to local memory. For example,KU*DV=128 when centers are stored in URAMon U250.

3.The dynamic confugures should close to static ones in order to void unuseful computing inside.