bp_lv.m

本人用matlab内部函数编写的BP神经网络算法实现

function [TrainingTime, TestingTime, TrainingAccuracy, TestingAccuracy] = bp_lv(TrainingData_File, TestingData_File, Problem_Type, NumberofHiddenNeurons, Max_Epochs, Goal)
% Usage: bp_lm(TrainingData_File, TestingData_File, Problem_Type, NumberofHiddenNeurons, Max_Epochs)
% OR:    [TrainingTime, TestingTime, TrainingAccuracy, TestingAccuracy] = bp_lm(TrainingData_File, TestingData_File, Problem_Type, NumberofHiddenNeurons, Max_Epochs)

%
% Input:
% TrainingData_File     - Filename of training data set
% TestingData_File      - Filename of testing data set
% Problem_Type           - 0 for regression; 1 for (both binary and multi-classes) classification
% NumberofHiddenNeurons - Number of hidden neurons assigned to the network
% Max_Epochs            - Max number of training epochs
% Goal                  - The performance target represented in mean
%                           squared error (MSE)
%
% Output: 
% TrainingTime          - Time (seconds) spent on training 
% TestingTime           - Time (seconds) spent on predicting ALL testing data
% TrainingAccuracy      - Training accuracy: 
%                           RMSE for regression or correct classification rate for classifcation
% TestingAccuracy       - Testing accuracy: 
%                           RMSE for regression or correct classification rate for classifcation
%
% 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] = bp_lm('sinc_train', 'sinc_test', 0, 20, 100)
% Sample2 classification: bp_lm('diabetes_train', 'diabetes_test', 1, 20, 100)


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

% fhandle = str2func(PTGenerator);
% [I,T,TestMatrix] = feval(fhandle);
% %%%%%%%%%%% Load training dataset
% 
% T = T;
% P = I;
%                                %   Release raw training data array
% %%%%%%%%%%% Load testing dataset
% test_data=load(TestingData_File);
% TV.T = TestMatrix.T;
% TV.P = TestMatrix.P;
%                                          %   Copy the testing set to the validation set
                                         
                                         
%%%%%%%%%%% 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);
NumberofOutputNeurons=size(T,1);
NumberofValidationData = round(NumberofTestingData / 2);

if Problem_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  
clear temp_T;
VV.P = TV.P(:,1:NumberofValidationData);
VV.T = TV.T(:,1:NumberofValidationData);
TV.P(:,1:NumberofValidationData)=[];
TV.T(:,1:NumberofValidationData)=[];
NumberofTestingData = NumberofTestingData - NumberofValidationData;

net=newff(minmax(P),[NumberofHiddenNeurons NumberofOutputNeurons],{'logsig','purelin'},'trainlm');
net.trainParam.show = 50;
net.trainParam.goal = Goal;
net.trainParam.lr = 0.2;
net.trainParam.epochs = Max_Epochs;
reduc = 1;
net.trainParam.mem_reduc=reduc;
startTime=cputime;
[trainedNet,trainingRecord]=train(net,P,T,[],[],VV);
OutputWeight = trainedNet.lw(2,1);
Beta = mean(abs((OutputWeight{1})'));
% [trainedNet,trainingRecord]=train(net,P*Gain,T);
Y=sim(trainedNet,P);
endTime=cputime;
% trainedNet.layers{1}.transferFcn = 'hardlim'; %Change the activation function to hardlim
start_test_time=cputime;
TY=sim(trainedNet,TV.P);
end_test_time=cputime;
TrainingTime=endTime-startTime;
TestingTime=end_test_time-start_test_time;

if Problem_Type == REGRESSION
    TrainingAccuracy=sqrt(mse(Y-T));
    TestingAccuracy=sqrt(mse(TY-TV.T));
end

if Problem_Type == CLASSIFIER
    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

% figure(2);
% hold on; plot(TV.P, TV.T,'-.k');
% hold on; plot(P,T,'.r');
% plot(TV.P, TY);