C++ Template of Extreme Learning Machine (ELM)

Introduction

This is a simple C++ template library of Extreme Learning Machine implemented by Shuo Jin.

In current version, basic ELM method is implemented in the library.

Date Structure:

• elm_sample - a pair of input and target data
• basic_elm - basic ELM method

Basic ELM: three types of activation functions are defined - Gaussian, sigmoid, and multi-quadric. More can be specified if necessary by following the template definition of activation function. Please refer to the comments in basic_elm.h for more information.

Example

#include "elm.h"

#include <random>

#define SAMPLENUMBER 20
#define INPUTDIM 3
#define TARGETDIM 2

int main()
{
    elm::basic_elm<double, INPUTDIM, TARGETDIM> belm;

    std::uniform_real_distribution<double> rand_generator(0.0, 1.0);

    std::default_random_engine dre;

    elm::elm_sample<double, INPUTDIM, TARGETDIM> samples[SAMPLENUMBER];

    // generate random samples for testing
    for (size_t i = 0; i < SAMPLENUMBER; ++i)
    {
        for (size_t j = 0; j < INPUTDIM; ++j)
        {
            samples[i].i_data[j] = rand_generator(dre);
        }
        for (size_t j = 0; j < TARGETDIM; ++j)
        {
            samples[i].t_data[j] = rand_generator(dre);
        }

        belm.add_sample(samples[i]);

        samples[i].output_on_console();
    }

    // set the number of hidden nodes
    belm.set_hidden_nodes_num(2 * SAMPLENUMBER);

    // basic elm training
    belm.train(0.0);    

    // output
    for (size_t i = 0; i < SAMPLENUMBER; ++i)
    {
        elm::elm_sample<double, INPUTDIM, TARGETDIM> sample(samples[i]);

        belm.predict(sample);

        sample.output_on_console();
    }

    return 0;
}

Dependency

Eigen Library should be configured properly to use this template. You can specify the path at the beginning of elm_base.h

Download

The code is available here.

Reference

1. Extreme Learning Machines: a survey. [Link]
2. Extreme Learning Machine for Regression and Multiclass Classification. [Link]
3. Regularized Extreme Learning Machine. [Link]