资源简介
极限学习机ELM的最简单实用代码。黄广斌2004年论文代码。
代码片段和文件信息
function [TrainingTime TestingTime TrainingAccuracy TestingAccuracy] = elm(TrainingData_File TestingData_File Elm_Type NumberofHiddenNeurons ActivationFunction)
% Usage: elm(TrainingData_File TestingData_File Elm_Type NumberofHiddenNeurons ActivationFunction)
% OR: [TrainingTime TestingTime TrainingAccuracy TestingAccuracy] = elm(TrainingData_File TestingData_File Elm_Type NumberofHiddenNeurons ActivationFunction)
%
% Input:
% TrainingData_File - Filename of training data set
% TestingData_File - Filename of testing data set
% Elm_Type - 0 for regression; 1 for (both binary and multi-classes) classification
% NumberofHiddenNeurons - Number of hidden neurons assigned to the ELM
% ActivationFunction - Type of activation function:
% ‘sig‘ for Sigmoidal function
% ‘sin‘ for Sine function
% ‘hardlim‘ for Hardlim function
% ‘tribas‘ for Triangular basis function
% ‘radbas‘ for Radial basis function (for additive type of SLFNs instead of RBF type of SLFNs)
%
% Output:
% TrainingTime - Time (seconds) spent on training ELM
% TestingTime - Time (seconds) spent on predicting ALL testing data
% TrainingAccuracy - Training accuracy:
% RMSE for regression or correct classification rate for classification
% TestingAccuracy - Testing accuracy:
% RMSE for regression or correct classification rate for classification
%
% MULTI-CLASSE CLASSIFICATION: NUMBER OF OUTPUT NEURONS WILL BE AUTOMATICALLY SET EQUAL TO NUMBER OF CLASSES
% FOR EXAMPLE if there are 7 classes in all there will have 7 output
% neurons; neuron 5 has the highest output means input belongs to 5-th class
%
% Sample1 regression: [TrainingTime TestingTime TrainingAccuracy TestingAccuracy] = elm(‘sinc_train‘ ‘sinc_test‘ 0 20 ‘sig‘)
% Sample2 classification: elm(‘diabetes_train‘ ‘diabetes_test‘ 1 20 ‘sig‘)
%
%%%% Authors: MR QIN-YU ZHU AND DR GUANG-BIN HUANG
%%%% NANYANG TECHNOLOGICAL UNIVERSITY SINGAPORE
%%%% EMAIL: EGBHUANG@NTU.EDU.SG; GBHUANG@IEEE.ORG
%%%% WEBSITE: http://www.ntu.edu.sg/eee/icis/cv/egbhuang.htm
%%%% DATE: APRIL 2004
%% Macro definition
REGRESSION=0;
CLASSIFIER=1;
%% Loading Dataset
%%%%%%%%%%% Load training dataset
train_data=load(TrainingData_File);
T=train_data(:1)‘;
P=train_data(:2:size(train_data2))‘;
clear train_data; % Release raw training data array
%%%%%%%%%%% Load testing dataset
test_data=load(TestingData_File);
TV.T=test_data(:1)‘;
TV.P=test_data(:2:size(test_data2))‘;
clear test_data; % Release raw testing data array
NumberofTrainingData=size(P2);
NumberofTestingData=size(TV.P2);
NumberofInputNeurons=size(P1);
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