1、ELM训练函数elmtrain
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function
[IW,B,LW,TF,TYPE] = elmtrain(P,T,N,TF,TYPE)
% ELMTRAIN Create and Train a Extreme Learning Machine
% Syntax
% [IW,B,LW,TF,TYPE] = elmtrain(P,T,N,TF,TYPE)
% Description
% Input
% P - Input Matrix of Training Set (R*Q)
% T - Output Matrix of Training Set (S*Q)
% N -
Number
of Hidden Neurons (
default
= Q)
% TF - Transfer Function:
%
'sig'
for
Sigmoidal
function
(
default
)
%
'sin'
for
Sine
function
%
'hardlim'
for
Hardlim
function
% TYPE - Regression (
0
,
default
) or Classification (
1
)
% Output
% IW - Input Weight Matrix (N*R)
% B - Bias Matrix (N*
1
)
% LW - Layer Weight Matrix (N*S)
% Example
% Regression:
% [IW,B,LW,TF,TYPE] = elmtrain(P,T,
20
,
'sig'
,
0
)
% Y = elmtrain(P,IW,B,LW,TF,TYPE)
% Classification
% [IW,B,LW,TF,TYPE] = elmtrain(P,T,
20
,
'sig'
,
1
)
% Y = elmtrain(P,IW,B,LW,TF,TYPE)
if
nargin <
2
error(
'ELM:Arguments'
,
'Not enough input arguments.'
);
end
if
nargin <
3
N = size(P,
2
);
end
if
nargin <
4
TF =
'sig'
;
end
if
nargin <
5
TYPE =
0
;
end
if
size(P,
2
) ~= size(T,
2
)
error(
'ELM:Arguments'
,
'The columns of P and T must be same.'
);
end
[R,Q] = size(P);
if
TYPE ==
1
T = ind2vec(T);
end
[S,Q] = size(T);
% Randomly Generate the Input Weight Matrix
IW = rand(N,R) *
2
-
1
;
% Randomly Generate the Bias Matrix
B = rand(N,
1
);
BiasMatrix = repmat(B,
1
,Q);
% Calculate the Layer Output Matrix H
tempH = IW * P + BiasMatrix;
switch
TF
case
'sig'
H =
1
./ (
1
+ exp(-tempH));
case
'sin'
H = sin(tempH);
case
'hardlim'
H = hardlim(tempH);
end
% Calculate the Output Weight Matrix
LW = pinv(H
') * T'
;
|
2、ELM测试函数elmtrain
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function
Y = elmpredict(P,IW,B,LW,TF,TYPE)
% ELMPREDICT Simulate a Extreme Learning Machine
% Syntax
% Y = elmtrain(P,IW,B,LW,TF,TYPE)
% Description
% Input
% P - Input Matrix of Training Set (R*Q)
% IW - Input Weight Matrix (N*R)
% B - Bias Matrix (N*
1
)
% LW - Layer Weight Matrix (N*S)
% TF - Transfer Function:
%
'sig'
for
Sigmoidal
function
(
default
)
%
'sin'
for
Sine
function
%
'hardlim'
for
Hardlim
function
% TYPE - Regression (
0
,
default
) or Classification (
1
)
% Output
% Y - Simulate Output Matrix (S*Q)
% Example
% Regression:
% [IW,B,LW,TF,TYPE] = elmtrain(P,T,
20
,
'sig'
,
0
)
% Y = elmtrain(P,IW,B,LW,TF,TYPE)
% Classification
% [IW,B,LW,TF,TYPE] = elmtrain(P,T,
20
,
'sig'
,
1
)
% Y = elmtrain(P,IW,B,LW,TF,TYPE)
% See also ELMTRAIN
% Yu Lei,
11
-
7
-
2010
% Copyright www.matlabsky.com
if
nargin <
6
error(
'ELM:Arguments'
,
'Not enough input arguments.'
);
end
% Calculate the Layer Output Matrix H
Q = size(P,
2
);
BiasMatrix = repmat(B,
1
,Q);
tempH = IW * P + BiasMatrix;
switch
TF
case
'sig'
H =
1
./ (
1
+ exp(-tempH));
case
'sin'
H = sin(tempH);
case
'hardlim'
H = hardlim(tempH);
end
% Calculate the Simulate Output
Y = (H
' * LW)'
;
if
TYPE ==
1
temp_Y = zeros(size(Y));
for
i =
1
:size(Y,
2
)
[~,index] = max(Y(:,i));
temp_Y(index,i) =
1
;
end
Y = vec2ind(temp_Y);
end
|
3、使用oil-dataset.mat数据集测试石油辛烷值（回归）
说明：oil_dataset.mat数据集由60个样本，401维属性组成，包含sample属性为60*401矩阵和输出label标签为60*1矩阵。
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%% I. 清空环境变量
clear all
clc
%% II. 训练集/测试集产生
%%
%
1
. 导入数据
load oil_dataset.mat
%%
%
2
. 随机产生训练集和测试集
temp = randperm(size(sample,
1
));
% 训练集——
50
个样本
P_train = sample(temp(
1
:
50
),:)';
T_train =
label
(temp(
1
:
50
),:)';
% 测试集——
10
个样本
P_test = sample(temp(
51
:end),:)';
T_test =
label
(temp(
51
:end),:)';
N = size(P_test,
2
);
%% III. 数据归一化
%%
%
1
. 训练集
[p_train,inputps] = mapminmax(P_train);
p_test = mapminmax(
'apply'
,P_test,inputps);
%%
%
2
. 测试集
[t_train,outputps] = mapminmax(T_train);
t_test = mapminmax(
'apply'
,T_test,outputps);
%% IV. ELM创建/训练
[IW,B,LW,TF,TYPE] = elmtrain(p_train,t_train,
30
,
'sig'
,
0
);
%% V. ELM仿真测试
t_sim = elmpredict(p_test,IW,B,LW,TF,TYPE);
%%
%
1
. 反归一化
T_sim = mapminmax(
'reverse'
,t_sim,outputps);
%% VI. 结果对比
result = [T_test
' T_sim'
];
%%
%
1
. 均方误差
E = mse(T_sim - T_test);
%%
%
2
. 决定系数
N = length(T_test);
R2=(N*sum(T_sim.*T_test)-sum(T_sim)*sum(T_test))^
2
/((N*sum((T_sim).^
2
)-(sum(T_sim))^
2
)*(N*sum((T_test).^
2
)-(sum(T_test))^
2
));
%% VII. 绘图
figure(
1
)
plot(
1
:N,T_test,
'r-*'
,
1
:N,T_sim,
'b:o'
)
grid on
legend(
'真实值'
,
'预测值'
)
xlabel(
'样本编号'
)
ylabel(
'辛烷值'
)
string = {
'ELM算法测试集辛烷值含量预测结果对比(ELM)'
;[
'(mse = '
num2str(E)
' R^2 = '
num2str(R2)
')'
]};
title(string)
|
4、使用cement_dataset.mat数据集测试石油辛烷值（回归）
说明：数据集为混凝土抗压强度预测数据集cement_dataset.mat,包含103个样本，属性sample为：103*7矩阵，输出label为：103*1矩阵。
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%数据集为混凝土抗压强度预测数据集cement_dataset.mat,将整个数据集中的
103
个样本随机划分为训练集与测试集，其中训练集包含
80
个样本，测试集包含
23
个样本；
%建立极限学习机模型，并训练；利用训练好的极限学习机模型对测试集中的
23
个样本进行预测；输出结果并绘图（真实值与预测值对比图）
%% I. 清空环境变量
clear all
clc
%% II. 训练集/测试集产生
%%
%
1
. 导入水泥样本数据，sample由
103
个样本个体组成，单个样本为
9
维属性组成，输出值在out变量中
load cement_dataset.mat
%%
%
2
. 随机产生训练集和测试集
temp = randperm(size(sample,
1
));
% 训练集——
80
个样本
P_train = sample(temp(
1
:
80
),:)';
T_train =
label
(temp(
1
:
80
),:)';
% 测试集——
23
个样本
P_test = sample(temp(
81
:end),:)';
T_test =
label
(temp(
81
:end),:)';
N = size(P_test,
2
);
%% III. 数据归一化
%%
%
1
. 训练集
[p_train,inputps] = mapminmax(P_train);
p_test = mapminmax(
'apply'
,P_test,inputps);
%%
%
2
. 测试集
[t_train,outputps] = mapminmax(T_train);
t_test = mapminmax(
'apply'
,T_test,outputps);
%% IV. ELM创建/训练
[IW,B,LW,TF,TYPE] = elmtrain(p_train,t_train,
7
,
'sig'
,
0
);
%% V. ELM仿真测试
t_sim = elmpredict(p_test,IW,B,LW,TF,TYPE);
%%
%
1
. 反归一化
T_sim = mapminmax(
'reverse'
,t_sim,outputps);
%% VI. 结果对比
result = [T_test
' T_sim'
];
%%
%
1
. 均方误差
E = mse(T_sim - T_test);
%%
%
2
. 决定系数
N = length(T_test);
R2=(N*sum(T_sim.*T_test)-sum(T_sim)*sum(T_test))^
2
/((N*sum((T_sim).^
2
)-(sum(T_sim))^
2
)*(N*sum((T_test).^
2
)-(sum(T_test))^
2
));
%% VII. 绘图
figure(
1
)
plot(
1
:N,T_test,
'r-*'
,
1
:N,T_sim,
'b:o'
)
grid on
legend(
'真实值'
,
'预测值'
)
xlabel(
'样本编号'
)
ylabel(
'水泥强度值'
)
string = {
'ELM算法测试集水泥强度值含量预测结果对比(ELM)'
;[
'(mse = '
num2str(E)
' R^2 = '
num2str(R2)
')'
]};
title(string)
|
5、使用iris_dataset.mat实现分类
说明：iris以鸢尾花的特征作为数据来源，数据集包含150个数据集，分为3类，每类50个数据，每个数据包含4个属性。三类分别为:setosa, versicolor, virginica。
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%% I. 清空环境变量
clear all
clc
%% II. 训练集/测试集产生
%%
%
1
. 导入数据
load iris_dataset.mat
%%
%
2
. 随机产生训练集和测试集
P_train = [];
T_train = [];
P_test = [];
T_test = [];
for
i =
1
:
3
temp_input = features((i-
1
)*
50
+
1
:i*
50
,:);
temp_output = classes((i-
1
)*
50
+
1
:i*
50
,:);
n = randperm(
50
);
% 训练集——
120
个样本
P_train = [P_train temp_input(n(
1
:
40
),:)'];
T_train = [T_train temp_output(n(
1
:
40
),:)'];
% 测试集——
30
个样本
P_test = [P_test temp_input(n(
41
:
50
),:)'];
T_test = [T_test temp_output(n(
41
:
50
),:)'];
end
%% III. ELM创建/训练
[IW,B,LW,TF,TYPE] = elmtrain(P_train,T_train,
20
,
'sig'
,
1
);
%% IV. ELM仿真测试
T_sim_1 = elmpredict(P_train,IW,B,LW,TF,TYPE);
T_sim_2 = elmpredict(P_test,IW,B,LW,TF,TYPE);
%% V. 结果对比
result_1 = [T_train
' T_sim_1'
];
result_2 = [T_test
' T_sim_2'
];
%%
%
1
. 训练集正确率
k1 = length(find(T_train == T_sim_1));
n1 = length(T_train);
Accuracy_1 = k1 / n1 *
100
;
disp([
'训练集正确率Accuracy = '
num2str(Accuracy_1)
'%('
num2str(k1)
'/'
num2str(n1)
')'
])
%%
%
2
. 测试集正确率
k2 = length(find(T_test == T_sim_2));
n2 = length(T_test);
Accuracy_2 = k2 / n2 *
100
;
disp([
'测试集正确率Accuracy = '
num2str(Accuracy_2)
'%('
num2str(k2)
'/'
num2str(n2)
')'
])
%% VI. 绘图
figure(
1
)
plot(
1
:
30
,T_test,
'bo'
,
1
:
30
,T_sim_2,
'r-*'
)
grid on
xlabel(
'测试集样本编号'
)
ylabel(
'测试集样本类别'
)
string = {
'测试集预测结果对比(ELM)'
;[
'(正确率Accuracy = '
num2str(Accuracy_2)
'%)'
]};
title(string)
legend(
'真实值'
,
'ELM预测值'
)
|
训练集正确率Accuracy = 99.1667%(119/120)
测试集正确率Accuracy = 96.6667%(29/30)
附录：官方代码（参考理解，有点啰嗦）
训练函数：
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function
[TrainingTime,TrainingAccuracy] = elm_train(TrainingData_File, Elm_Type, NumberofHiddenNeurons, ActivationFunction)
% Usage: elm_train(TrainingData_File, Elm_Type, NumberofHiddenNeurons, ActivationFunction)
% OR: [TrainingTime, TrainingAccuracy] = elm_train(TrainingData_File, Elm_Type, NumberofHiddenNeurons, ActivationFunction)
%
% Input:
% TrainingData_File - Filename of training 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
%
% Output:
% TrainingTime - Time (seconds) spent on training ELM
% TrainingAccuracy - Training 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, TrainingAccuracy, TestingAccuracy] = elm_train(
'sinc_train'
,
0
,
20
,
'sig'
)
% Sample2 classification: elm_train(
'diabetes_train'
,
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
;
%%%%%%%%%%% Load training dataset
train_data=load(TrainingData_File);
%训练集输出
T=train_data(:,
1
)';
%训练集输入
P=train_data(:,
2
:size(train_data,
2
))';
clear train_data; % Release raw training data array
%训练样本个数
NumberofTrainingData=size(P,
2
);
%输入神经元个数
NumberofInputNeurons=size(P,
1
);
%若是分类，进行处理，否则跳出
if
Elm_Type~=REGRESSION
%统计分类类别个数，并将分类结果的不同类别值存储在
label
中，
label
中元素个数即为分类的类别数
sorted_target=sort(T,
2
);
label
=zeros(
1
,
1
);
label
(
1
,
1
)=sorted_target(
1
,
1
);
j=
1
;
for
i =
2
:NumberofTrainingData
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;
%将所有训练样本单一输出值转换为矩阵输出，例如有两类输出，则神经元输出结果为[
1
;
0
]或[
0
;
1
]
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
;
end
% 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 = hardlim(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'
;
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
output=Y;
end
clear H;
if
Elm_Type == CLASSIFIER
%%%%%%%%%% Calculate training & testing classification accuracy
MissClassificationRate_Training=
0
;
for
i =
1
: size(T,
2
)
[x, label_index_expected]=max(T(:,i));
[x, label_index_actual]=max(Y(:,i));
output(i)=
label
(label_index_actual);
if
label_index_actual~=label_index_expected
MissClassificationRate_Training=MissClassificationRate_Training+
1
;
end
end
TrainingAccuracy=
1
-MissClassificationRate_Training/NumberofTrainingData
end
if
Elm_Type~=REGRESSION
save(
'elm_model'
,
'NumberofInputNeurons'
,
'NumberofOutputNeurons'
,
'InputWeight'
,
'BiasofHiddenNeurons'
,
'OutputWeight'
,
'ActivationFunction'
,
'label'
,
'Elm_Type'
);
else
save(
'elm_model'
,
'InputWeight'
,
'BiasofHiddenNeurons'
,
'OutputWeight'
,
'ActivationFunction'
,
'Elm_Type'
);
end
|
测试函数：
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function
[TestingTime, TestingAccuracy] = elm_predict(TestingData_File)
% Usage: elm_predict(TestingData_File)
% OR: [TestingTime, TestingAccuracy] = elm_predict(TestingData_File)
%
% Input:
% TestingData_File - Filename of testing data
set
%
% Output:
% TestingTime - Time (seconds) spent on predicting ALL testing data
% 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: [TestingTime, TestingAccuracy] = elm_predict(
'sinc_test'
)
% Sample2 classification: elm_predict(
'diabetes_test'
)
%
%%%% 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
;
%%%%%%%%%%% 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
NumberofTestingData=size(TV.P,
2
);
load elm_model.mat;
if
Elm_Type~=REGRESSION
%%%%%%%%%% Processing the targets of testing
temp_TV_T=zeros(NumberofOutputNeurons, NumberofTestingData);
for
i =
1
:NumberofTestingData
for
j =
1
:size(
label
,
2
)
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 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);
%%%%%%%% 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
output=TY;
end
if
Elm_Type == CLASSIFIER
%%%%%%%%%% Calculate training & testing classification accuracy
MissClassificationRate_Testing=
0
;
for
i =
1
: size(TV.T,
2
)
[x, label_index_expected]=max(TV.T(:,i));
[x, label_index_actual]=max(TY(:,i));
output(i)=
label
(label_index_actual);
if
label_index_actual~=label_index_expected
MissClassificationRate_Testing=MissClassificationRate_Testing+
1
;
end
end
TestingAccuracy=
1
-MissClassificationRate_Testing/NumberofTestingData
end
save(
'elm_output'
,
'output'
);
|
训练和测试在一起：
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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
;
%%%%%%%%%%% Load training dataset
train_data=load(TrainingData_File);
%训练集输出
T=train_data(:,
1
)';
%训练集输入
P=train_data(:,
2
:size(train_data,
2
))';
clear train_data; % Release raw training data array
%%%%%%%%%%% Load testing dataset
test_data=load(TestingData_File);
%测试集输出
TV.T=test_data(:,
1
)';
%测试集输入
TV.P=test_data(:,
2
:size(test_data,
2
))';
clear test_data; % Release raw testing data array
%训练样本个数
NumberofTrainingData=size(P,
2
);
%测试样本个数
NumberofTestingData=size(TV.P,
2
);
%输入神经元个数
NumberofInputNeurons=size(P,
1
);
%若是分类，进行处理，否则跳出
if
Elm_Type~=REGRESSION
%统计分类类别个数，并将分类结果的不同类别值存储在
label
中，
label
中元素个数即为分类的类别数
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;
%将所有训练样本单一输出值转换为矩阵输出，例如有两类输出，则神经元输出结果为[
1
;
0
]或[
0
;
1
]
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
%将神经元输出值转换为（-
1
,
1
）之间
T=temp_T*
2
-
1
;
%T=temp_T;
%将所有测试样本单一输出值转换为矩阵输出，例如有两类输出，则神经元输出结果为[
1
;
0
]或[
0
;
1
]
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
;
%TV.T=temp_TV_T;
end
%结束分类
%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
)
[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
|
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本文转自stock0991 51CTO博客，原文链接：http://blog.51cto.com/qing0991/1769248，如需转载请自行联系原作者
