Please tell me the error in this code.

function [TrainingTime, TestingTime, TrainingAccuracy, TestingAccuracy] = ELM(sinc_train.txt,sinc_test.txt,0,1,radbas);

% Usage: elm(TrainingData_File, TestingData_File, Elm_Type, NumberofHiddenNeurons, ActivationFunction)
%function    [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:      [e-mail]; [e-mail]
    %%%%   WEBSITE:    http://www.ntu.edu.sg/eee/icis/cv/egbhuang.htm
    %%%%   DATE:       APRIL 2004

%%%%%%%%%%%Macro definition
REGRESSION=0;
CLASSIFIER=1;

%%%%%%%%%%%Load training dataset
train_data=load(TrainingData_File);
T=train_data(:,1)';
P=train_data(:,2:size(train_data,2))';
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_data,2))';
clear test_data;                                    %   Release raw testing data array

NumberofTrainingData=size(P,2);
NumberofTestingData=size(TV.P,2);
NumberofInputNeurons=size(P,1);

if Elm_Type~=REGRESSION
    %%%%%%%%%%%%Preprocessing the data of classification
    sorted_target=sort(cat(2,T,TV.T),2);
    label=zeros(1,1);                               %   Find and save in 'label' class label from training and testing data sets
    label(1,1)=sorted_target(1,1);
    j=1;
    for i = 2:(NumberofTrainingData+NumberofTestingData)
        if sorted_target(1,i) ~= label(1,j)
            j=j+1;
            label(1,j) = sorted_target(1,i);
        end
    end
    number_class=j;
    NumberofOutputNeurons=number_class;

      %%%%%%%%%%Processing the targets of training
      temp_T=zeros(NumberofOutputNeurons, NumberofTrainingData);
      for i = 1:NumberofTrainingData
          for j = 1:number_class
              if label(1,j) == T(1,i)
                  break; 
              end
          end
          temp_T(j,i)=1;
      end
      T=temp_T*2-1;

      %%%%%%%%%%Processing the targets of testing
      temp_TV_T=zeros(NumberofOutputNeurons, NumberofTestingData);
      for i = 1:NumberofTestingData
          for j = 1:number_class
              if label(1,j) == TV.T(1,i)
                  break; 
              end
          end
          temp_TV_T(j,i)=1;
      end
      TV.T=temp_TV_T*2-1;

end                                                 %   end if of Elm_Type

%%%%%%%%%%%Calculate weights & biases
start_time_train=cputime;

%%%%%%%%%%%Random generate input weights InputWeight (w_i) and biases BiasofHiddenNeurons (b_i) of hidden neurons
InputWeight=rand(NumberofHiddenNeurons,NumberofInputNeurons)*2-1;
BiasofHiddenNeurons=rand(NumberofHiddenNeurons,1);
tempH=InputWeight*P;
clear P;                                            %   Release input of training data 
ind=ones(1,NumberofTrainingData);
BiasMatrix=BiasofHiddenNeurons(:,ind);              %   Extend the bias matrix BiasofHiddenNeurons to match the demention of H
tempH=tempH+BiasMatrix;

%%%%%%%%%%%Calculate hidden neuron output matrix H
switch lower(ActivationFunction)
    case {'sig','sigmoid'}
        %%%%%%%%Sigmoid 
        H = 1 ./ (1 + exp(-tempH));
    case {'sin','sine'}
        %%%%%%%%Sine
        H = sin(tempH);    
    case {'hardlim'}
        %%%%%%%%Hard Limit
        H = double(hardlim(tempH));
    case {'tribas'}
        %%%%%%%%Triangular basis function
        H = tribas(tempH);
    case {'radbas'}
        %%%%%%%%Radial basis function
        H = radbas(tempH);
        %%%%%%%%More activation functions can be added here                
end
clear tempH;                                        %   Release the temparary array for calculation of hidden neuron output matrix H

%%%%%%%%%%%Calculate output weights OutputWeight (beta_i)
OutputWeight=pinv(H') * T';                        % implementation without regularization factor //refer to 2006 Neurocomputing paper
%OutputWeight=inv(eye(size(H,1))/C+H * H') * H * T';   % faster method 1 //refer to 2012 IEEE TSMC-B paper
%implementation; one can set regularizaiton factor C properly in classification applications 
%OutputWeight=(eye(size(H,1))/C+H * H') \ H * T';      % faster method 2 //refer to 2012 IEEE TSMC-B paper
%implementation; one can set regularizaiton factor C properly in classification applications

%If you use faster methods or kernel method, PLEASE CITE in your paper properly: 

%Guang-Bin Huang, Hongming Zhou, Xiaojian Ding, and Rui Zhang, "Extreme Learning Machine for Regression and Multi-Class Classification," submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence, October 2010. 

end_time_train=cputime;
TrainingTime=end_time_train-start_time_train;        %   Calculate CPU time (seconds) spent for training ELM

%%%%%%%%%%%Calculate the training accuracy
Y=(H' * OutputWeight)';                             %   Y: the actual output of the training data
if Elm_Type == REGRESSION
    TrainingAccuracy=sqrt(mse(T - Y));               %   Calculate training accuracy (RMSE) for regression case
end
clear H;

%%%%%%%%%%%Calculate the output of testing input
start_time_test=cputime;
tempH_test=InputWeight*TV.P;
clear TV.P;             %   Release input of testing data             
ind=ones(1,NumberofTestingData);
BiasMatrix=BiasofHiddenNeurons(:,ind);              %   Extend the bias matrix BiasofHiddenNeurons to match the demention of H
tempH_test=tempH_test + BiasMatrix;
switch lower(ActivationFunction)
    case {'sig','sigmoid'}
        %%%%%%%%Sigmoid 
        H_test = 1 ./ (1 + exp(-tempH_test));
    case {'sin','sine'}
        %%%%%%%%Sine
        H_test = sin(tempH_test);        
    case {'hardlim'}
        %%%%%%%%Hard Limit
        H_test = hardlim(tempH_test);        
    case {'tribas'}
        %%%%%%%%Triangular basis function
        H_test = tribas(tempH_test);        
    case {'radbas'}
        %%%%%%%%Radial basis function
        H_test = radbas(tempH_test);        
        %%%%%%%%More activation functions can be added here        
end
TY=(H_test' * OutputWeight)';                       %   TY: the actual output of the testing data
end_time_test=cputime;
TestingTime=end_time_test-start_time_test;           %   Calculate CPU time (seconds) spent by ELM predicting the whole testing data

if Elm_Type == REGRESSION
    TestingAccuracy=sqrt(mse(TV.T - TY));            %   Calculate testing accuracy (RMSE) for regression case
end

if Elm_Type == CLASSIFIER
%%%%%%%%%%Calculate training & testing classification accuracy
    MissClassificationRate_Training=0;
    MissClassificationRate_Testing=0;

      for i = 1 : size(T, 2)
          [~, label_index_expected]=max(T(:,i));
          [~, label_index_actual]=max(Y(:,i));
          if label_index_actual~=label_index_expected
              MissClassificationRate_Training=MissClassificationRate_Training+1;
          end
      end
      TrainingAccuracy=1-MissClassificationRate_Training/size(T,2);
      for i = 1 : size(TV.T, 2)
          [~, label_index_expected]=max(TV.T(:,i));
          [~, label_index_actual]=max(TY(:,i));
          if label_index_actual~=label_index_expected
              MissClassificationRate_Testing=MissClassificationRate_Testing+1;
          end
      end
      TestingAccuracy=1-MissClassificationRate_Testing/size(TV.T,2);  
  end

When i run there is error , i can't understand. plz tell me

[Adam]

Please use the { } Code block to format the full code rather than just bits of it, although people are unlikely to want to plough through such a vast block of code. You don't even tell us anything about the error so how do you expect us to help you?!

Include the full error message and point to which line it occurs on.

Few people are that brilliant at coding that they can glance at hundreds of lines of unfamiliar code and spot a bug without being able to run it or being given any hint as to where or what the bug is!

Answer 1

[Geoff Hayes]

tejasvee - please format your code so that it is readable. Better yet (given the number of lines) just attach it the m-file to this question.

Also, please show how you are calling your code and copy and past the full error message (in to this question) so that we don't have to guess what the error is or which line it corresponds to.

Your function signature looks a little bizarre

 function [TrainingTime, TestingTime, TrainingAccuracy, TestingAccuracy] = ...
    ELM(sinc_train.txt,sinc_test.txt,0,1,radios);

The input parameters appear to be defined as actual values rather than named ones. The function signature should remain as

 function [TrainingTime, TestingTime, TrainingAccuracy, TestingAccuracy] = ...
    ELM(TrainingData_File, TestingData_File, Elm_Type, NumberofHiddenNeurons, ActivationFunction)

and you would call your function (from the command line or from within another script) as

 >> [TrainingTime, TestingTime, TrainingAccuracy, TestingAccuracy] = ...
        ELM(sinc_train.txt,sinc_test.txt,0,1,radios);

Note how one line of code defines the signature for the function and the other shows how you would call it.

[tejasvee]

function [TrainingTime, TestingTime, TrainingAccuracy, TestingAccuracy,TY] = elm_kernel(TrainingData_File, TestingData_File, Elm_Type, Regularization_coefficient, Kernel_type, Kernel_para) REGRESSION=0; CLASSIFIER=1;

%%%%%%%%%%% Load training dataset train_data=load(TrainingData_File); T=train_data(:,1)'; P=train_data(:,2:size(train_data,2))'; 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_data,2))'; clear test_data; % Release raw testing data array

C = Regularization_coefficient; NumberofTrainingData=size(P,2); NumberofTestingData=size(TV.P,2);

if Elm_Type~=REGRESSION %%%%%%%%%%%% Preprocessing the data of classification sorted_target=sort(cat(2,T,TV.T),2); label=zeros(1,1); % Find and save in 'label' class label from training and testing data sets label(1,1)=sorted_target(1,1); j=1; for i = 2:(NumberofTrainingData+NumberofTestingData) if sorted_target(1,i) ~= label(1,j) j=j+1; label(1,j) = sorted_target(1,i); end end number_class=j; NumberofOutputNeurons=number_class;

    %%%%%%%%%%Processing the targets of training
    temp_T=zeros(NumberofOutputNeurons, NumberofTrainingData);
    for i = 1:NumberofTrainingData
        for j = 1:number_class
            if label(1,j) == T(1,i)
                break; 
            end
        end
        temp_T(j,i)=1;
    end
    T=temp_T*2-1;

    %%%%%%%%%%Processing the targets of testing
    temp_TV_T=zeros(NumberofOutputNeurons, NumberofTestingData);
    for i = 1:NumberofTestingData
        for j = 1:number_class
            if label(1,j) == TV.T(1,i)
                break; 
            end
        end
        temp_TV_T(j,i)=1;
    end
    TV.T=temp_TV_T*2-1;
                                              %   end if of Elm_Type
end

%%%%%%%%%%% Training Phase %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% tic; n = size(T,2); Omega_train = kernel_matrix(P',Kernel_type, Kernel_para); OutputWeight=((Omega_train+speye(n)/C)\(T')); TrainingTime=toc;

%%%%%%%%%%% Calculate the training output Y=(Omega_train * OutputWeight)'; % Y: the actual output of the training data

%%%%%%%%%%% Calculate the output of testing input tic; Omega_test = kernel_matrix(P',Kernel_type, Kernel_para,TV.P'); TY=(Omega_test' * OutputWeight)'; % TY: the actual output of the testing data TestingTime=toc;

%%%%%%%%%% Calculate training & testing classification accuracy

if Elm_Type == REGRESSION %%%%%%%%%% Calculate training & testing accuracy (RMSE) for regression case TrainingAccuracy=sqrt(mse(T - Y)); TestingAccuracy=sqrt(mse(TV.T - TY)); end

if Elm_Type == CLASSIFIER %%%%%%%%%% Calculate training & testing classification accuracy MissClassificationRate_Training=0; MissClassificationRate_Testing=0;

    for i = 1 : size(T, 2)
        [x, label_index_expected]=max(T(:,i));
        [x, label_index_actual]=max(Y(:,i));
        if label_index_actual~=label_index_expected
            MissClassificationRate_Training=MissClassificationRate_Training+1;
        end
    end
    TrainingAccuracy=1-MissClassificationRate_Training/size(T,2)  
    for i = 1 : size(TV.T, 2)
        [x, label_index_expected]=max(TV.T(:,i));
        [x, label_index_actual]=max(TY(:,i));
        if label_index_actual~=label_index_expected
            MissClassificationRate_Testing=MissClassificationRate_Testing+1;
        end
    end
    TestingAccuracy=1-MissClassificationRate_Testing/size(TV.T,2);  
end

%%%%%%%%%%%%%%%%%% Kernel Matrix %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

function omega = kernel_matrix(Xtrain,kernel_type, kernel_pars,Xt)

nb_data = size(Xtrain,1);

if strcmp(kernel_type,'RBF_kernel'), if nargin<4, XXh = sum(Xtrain.^2,2)*ones(1,nb_data); omega = XXh+XXh'-2*(Xtrain*Xtrain'); omega = exp(-omega./kernel_pars(1)); else XXh1 = sum(Xtrain.^2,2)*ones(1,size(Xt,1)); XXh2 = sum(Xt.^2,2)*ones(1,nb_data); omega = XXh1+XXh2' - 2*Xtrain*Xt'; omega = exp(-omega./kernel_pars(1)); end

elseif strcmp(kernel_type,'lin_kernel') if nargin<4, omega = Xtrain*Xtrain'; else omega = Xtrain*Xt'; end

elseif strcmp(kernel_type,'poly_kernel') if nargin<4, omega = (Xtrain*Xtrain'+kernel_pars(1)).^kernel_pars(2); else omega = (Xtrain*Xt'+kernel_pars(1)).^kernel_pars(2); end

elseif strcmp(kernel_type,'wav_kernel') if nargin<4, XXh = sum(Xtrain.^2,2)*ones(1,nb_data); omega = XXh+XXh'-2*(Xtrain*Xtrain');

        XXh1 = sum(Xtrain,2)*ones(1,nb_data);
        omega1 = XXh1-XXh1';
        omega = cos(kernel_pars(3)*omega1./kernel_pars(2)).*exp(-omega./kernel_pars(1));

    else
        XXh1 = sum(Xtrain.^2,2)*ones(1,size(Xt,1));
        XXh2 = sum(Xt.^2,2)*ones(1,nb_data);
        omega = XXh1+XXh2' - 2*(Xtrain*Xt');

        XXh11 = sum(Xtrain,2)*ones(1,size(Xt,1));
        XXh22 = sum(Xt,2)*ones(1,nb_data);
        omega1 = XXh11-XXh22';

        omega = cos(kernel_pars(3)*omega1./kernel_pars(2)).*exp(-omega./kernel_pars(1));
    end
end

The error is Not enough input arguments.

Error in elm_kernel (line 43) train_data=load(TrainingData_File); plz run this code once.

[Geoff Hayes]

tejasvee - how can I run your code if I don't know the input parameters to elm_kernel?

Also, please format your code so that it is readable. Or (like I suggested before) just attach the code (that would be the m file) to this question using the paperclip button.

I can get this same error if I incorrectly run the code from the command line as

 >> elm_kernel

Error using elm_kernel (line 7)
Not enough input arguments.

where line 7 is

 train_data=load(TrainingData_File);

The function signature for elm_kernel tells you what the input parameters for this function need to be. You must supply a training data file name (including a path if needed), a testing data file name, a regularization coefficient, a kernel type and kernel parameters (?).

If you are unsure what you should be passing into this function, then I suggest that you contact the author or authors of the code (see http://www.ntu.edu.sg/home/egbhuang/elm_kernel.html for details).

[tejasvee]

Thankyou.

[preksha pareek]

You can first save your traindata by using save TrainingData_File.mat and test data save TestingData_File.mat This code should be included in ELM code as whenever loading is there, i

Answer 2

[Chhaya Gupta]

can this same code be found in oython please, urgently need the above code in python