template class xf::data_analytics::classification::logisticRegressionPredict

#include "logisticRegression.hpp"

Overview

linear least square regression predict

Parameters:

MType	datatype of regression, support double and float
D	Number of features that processed each cycle
DDepth	DDepth * D is max feature numbers supported.
K	Number of weight vectors that processed each cycle
KDepth	KDepth * K is max weight vectors supported.
RAMWeight	Use which kind of RAM to store weight, could be LUTRAM, BRAM or URAM.
RAMIntercept	Use which kind of RAM to store intercept, could be LUTRAM, BRAM or URAM.

template <
    typename MType,
    int D,
    int DDepth,
    int K,
    int KDepth,
    RAMType RAMWeight,
    RAMType RAMIntercept
    >
class logisticRegressionPredict

// fields

static const int marginDepth
sl2 <MType, D, DDepth, K, KDepth,&funcMul <MType>,&funcSum <MType>,&funcAssign <MType>, AdditionLatency <MType>::value, RAMWeight, RAMIntercept> marginProcessor
pickMaxProcess <MType, K> pickProcessor

Methods

pickFromK

void pickFromK (
    MType margin [K],
    ap_uint <32> counter,
    ap_uint <32> ws,
    MType& maxMargin,
    ap_uint <32>& maxIndex
    )

pick best weight vector for classification from K vectors

Parameters:

margin	K margins generate by K weight vectors.
counter	start index of this K margins in all margins.
ws	number of margins
maxMargin	max of K margins.
maxIndex	which index does max margin sits.

pick

void pick (
    hls::stream <MType> marginStrm [K],
    hls::stream <bool>& eMarginStrm,
    hls::stream <ap_uint <32>>& retStrm,
    hls::stream <bool>& eRetStrm,
    ap_uint <32> ws
    )

pick best weight vector for classification

Parameters:

marginStrm	margin stream. To get a vector of L margins, marginStrm will be read (L + K - 1) / D times. Margin 0 to K-1 will be read from marginStrm[0] to marginStrm[D-1] at the first time. Then margin D to 2*D - 1. The last round will readin fake data if L is not divisiable by K. These data won’t be used, just to allign K streams.
eMarginStrm	Endflag of marginStrm.
retStrm	result stream of classification.
eRetStrm	Endflag of retStrm.
ws	number of weight vectors used.

predict

void predict (
    hls::stream <MType> opStrm [D],
    hls::stream <bool>& eOpStrm,
    ap_uint <32> cols,
    ap_uint <32> classNum,
    hls::stream <ap_uint <32>>& retStrm,
    hls::stream <bool>& eRetStrm
    )

classification function of logistic regression

Parameters:

opStrm	feature input streams. To get a vector of L features, opStrm will be read (L + D - 1) / D times. Feature 0 to D-1 will be read from opStrm[0] to opStrm[D-1] at the first time. Then feature D to 2*D - 1. The last round will readin fake data if L is not divisiable by D. These data won’t be used, just to allign D streams.
eOpStrm	End flag of opStrm.
cols	Feature numbers
classNum	Number of classes.
retStrm	result stream of classification.
eRetStrm	Endflag of retStrm.

setWeight

void setWeight (
    MType inputW [K][D][KDepth *DDepth],
    ap_uint <32> cols,
    ap_uint <32> classNum
    )

set up weight parameters for prediction

Parameters:

inputW	weight
cols	Effective weight numbers
classNum	number of classes.

setIntercept

void setIntercept (
    MType inputI [K][KDepth],
    ap_uint <32> classNum
    )

set up intercept parameters for prediction

Parameters:

inputI	intercept, should be set to zero if don’t needed.
classNum	number of classes.