template class xf::data_analytics::regression::internal::linearLeastSquareRegressionSGDTrainer

#include "linearRegression.hpp"

Overview

linear least square regression training using SGD framework

Parameters:

MType	datatype of regression, support double and float
WAxi	AXI interface width to load training data.
WData	Data width of feature data type.
BurstLen	Length of burst read.
D	Number of features that processed each cycle
DDepth	DDepth * D is max feature numbers 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.
RAMAvgWeight	Use which kind of RAM to store Avg of Weigth, could be LUTRAM, BRAM or URAM.
RAMAvgIntercept	Use which kind of RAM to store Avg of intercept, could be LUTRAM, BRAM or URAM.

template <
    typename MType,
    int WAxi,
    int WData,
    int BurstLen,
    int D,
    int DDepth,
    RAMType RAMWeight,
    RAMType RAMIntercept,
    RAMType RAMAvgWeight,
    RAMType RAMAvgIntercept
    >
class linearLeastSquareRegressionSGDTrainer: public xf::data_analytics::common::SGDFramework

Inherited Members

// typedefs

typedef Gradient::DataType MType

// fields

static const int WAxi
static const int D
static const int Depth
ap_uint <32> offset
ap_uint <32> rows
ap_uint <32> cols
ap_uint <32> bucketSize
float fraction
bool ifJump
MType stepSize
MType tolerance
bool withIntercept
ap_uint <32> maxIter
Gradient gradProcessor