kMeansPredict - 2023.2 English

#include "xf_data_analytics/clustering/kmeansPredict.hpp"

template <
    typename DT,
    int Dim,
    int Kcluster,
    int uramDepth,
    int KU,
    int DV
    >
void kMeansPredict (
    hls::stream <ap_uint <sizeof (DT)*8>> sampleStrm [DV],
    hls::stream <bool>& endSampleStrm,
    ap_uint <sizeof (DT)*8*DV> centers [KU][uramDepth],
    const int dims,
    const int kcluster,
    hls::stream <ap_uint <32>>& tagStrm,
    hls::stream <bool>& endTagStrm
    )

kMeansPredict predicts cluster index for each sample. In order to achive to acceleration, please make sure partition 1-dim of centers.

Parameters:

DT	data type, supporting float and double
Dim	the maximum number of dimensions,dynamic number of dimension should be not greater than the maximum.
Kcluster	the maximum number of cluster,dynamic number of cluster should be not greater than the maximum.
uramDepth	the depth of uram where centers are stored. uramDepth should be not less than ceiling(Kcluster/KU) ceil(Dim/DV)
KU	unroll factor of Kcluster, KU centers are took part in calculating distances concurrently with one sample. After Kcluster/KU+1 times at most, ouput the minimum distance of a sample and Kcluster centers.
DV	unroll factor of Dim, DV elements in a center are took part in calculating distances concurrently with one sample.
sampleStrm	input sample streams, a sample needs ceiling(dims/DV) times to read.
endSampleStrm	the end flag of sample stream.
centers	an array stored centers, user should partition dim=1 in its defination.
dims	the number of dimensions.
kcluster	the number of clusters.
tagStrm	tag stream, label a cluster ID for each sample.
endTagStrm	end flag of tag stream.