namespace internal

// classes

template <
    typename MType,
    int WAxi,
    int WData,
    int BurstLen,
    int D,
    int DDepth,
    RAMType RAMWeight,
    RAMType RAMIntercept,
    RAMType RAMAvgWeight,
    RAMType RAMAvgIntercept
    >
class LASSORegressionSGDTrainer

template <
    typename MType,
    int D
    >
class linearLeastSquareGradient

template <
    typename MType,
    int WAxi,
    int WData,
    int BurstLen,
    int D,
    int DDepth,
    RAMType RAMWeight,
    RAMType RAMIntercept,
    RAMType RAMAvgWeight,
    RAMType RAMAvgIntercept
    >
class linearLeastSquareGradientProcessor

template <
    typename MType,
    int WAxi,
    int WData,
    int BurstLen,
    int D,
    int DDepth,
    RAMType RAMWeight,
    RAMType RAMIntercept,
    RAMType RAMAvgWeight,
    RAMType RAMAvgIntercept
    >
class linearLeastSquareRegressionSGDTrainer

template <
    typename MType,
    int WAxi,
    int WData,
    int BurstLen,
    int D,
    int DDepth,
    RAMType RAMWeight,
    RAMType RAMIntercept,
    RAMType RAMAvgWeight,
    RAMType RAMAvgIntercept
    >
class ridgeRegressionSGDTrainer

predictFun

#include "xf_data_analytics/regression/decision_tree_predict.hpp"

template <
    typename MType,
    unsigned WD,
    unsigned MAX_FEA_NUM,
    unsigned MAX_TREE_DEPTH
    >
void predictFun (
    ap_uint <WD> onesample [MAX_FEA_NUM],
    struct NodeR <MType> nodes [(MAX_TREE_DEPTH+1)/2+1][MAX_NODES_NUM],
    MType& reg_value
    )

predictFun implements the detailed predict functions.

This function read one sample, and output a category id

Parameters:

MType	The data type of sample
WD	The width of data type MType, can get by sizeof(MType)
MAX_FEA_NUM	Max feature num function can support
MAX_TREE_DEPTH	Max tree depth function can support
onesample	One sample
nodes	Decision tree (Decision Tree node array)
cat_id	Category id

getPredictions

#include "xf_data_analytics/regression/decision_tree_predict.hpp"

template <
    typename MType,
    unsigned WD,
    unsigned MAX_FEA_NUM,
    unsigned MAX_TREE_DEPTH
    >
void getPredictions (
    hls::stream <ap_uint <WD>> dstrm_batch [MAX_FEA_NUM],
    hls::stream <bool>& estrm_batch,
    struct NodeR <MType> nodes [(MAX_TREE_DEPTH+1)/2+1][MAX_NODES_NUM],
    hls::stream <MType>& predictionsStrm,
    hls::stream <bool>& predictionsTag
    )

getPredictions wrap predictFun to implement batch samples predicion.

This function read sample streams, and output prediction result into a stream

Parameters:

MType	The data type of sample
WD	The width of data type MType, can get by sizeof(MType)
MAX_FEA_NUM	Max feature num function can support
MAX_TREE_DEPTH	Max tree depth function can support
dstrm_batch	Input data streams of ap_uint<WD>
estrm_batch	End flag stream for input data
nodes	Decision tree (Decision Tree node array)
predictionsStrm	Output data streams
predictionsTagStrm	End flag stream for output

getTree

#include "xf_data_analytics/regression/decision_tree_predict.hpp"

template <
    typename MType,
    unsigned MAX_TREE_DEPTH
    >
void getTree (
    hls::stream <ap_uint <512>>& tree_strm,
    hls::stream <bool>& eTag,
    struct NodeR <MType> nodes [(MAX_TREE_DEPTH+1)/2+1][MAX_NODES_NUM]
    )

getTree loads 512-bit decision tree into a list of NodeR

Note that nodes is duplicated (max_tree_depth+1)/2+1 times for unroll each tree layer in getPredictions.

Parameters:

MType	The data type of sample
MAX_TREE_DEPTH	Max tree depth function can support
tree_strm	Decision tree streams
eTag	End flag stream for decision tree nodes
nodes	Decision tree (Decision Tree node array)