#include "xf_data_analytics/classification/naive_bayes.hpp"
template < int DT_WIDTH = 32, int WL = 3, typename DT = unsigned int > void naiveBayesTrain ( const int num_of_class, const int num_of_term, hls::stream <ap_uint <64>> i_data_strm [1<< WL], hls::stream <bool> i_e_strm [1<< WL], hls::stream <int>& o_terms_strm, hls::stream <ap_uint <64>> o_data0_strm [1<< WL], hls::stream <ap_uint <64>> o_data1_strm [1<< WL] )
naiveBayesTrain, top function of multinomial Naive Bayes Training.
This function will firstly load train dataset from the i_data_strm, then counte the frequency for each hit term. After scaning all sample, the likehood probability matrix and prior probability will be output from two independent stream
Parameters:
| DT_WIDTH | the width of type DT, in bits |
| WL | the width of bit to enable dispatcher, only 3 is supported so far |
| DT | the data type of internal counter for terms, can be 32/64-bit integer, float or double |
| num_of_class | the number of class in sample dataset, should be exactly same with real dataset |
| num_of_term | the number of terms, must be larger than the number of feature, and num_of_class * num_of_term <= (1 << (20-WL)) must be satisfied. |
| i_data_strm | input data stream of ap_uint<64> in multiple channel |
| i_e_strm | end flag stream for each input data channel |
| o_terms_strm | the output number of statistic feature |
| o_data0_strm | the output likehood matrix |
| o_data1_strm | the output prior probablity vector |