updated files
This commit is contained in:
Mannohargs
commit
d6f8e56ae7
58
FINAL_DESIGN_RUN_HORSEHERD.m
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58
FINAL_DESIGN_RUN_HORSEHERD.m
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clc
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clear all
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close all
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%%
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[datanum,datatxt_final,dataraw]=xlsread('labeled_data.csv');
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datatxt=datatxt_final(2:end,6);
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no_of_data_to_process=1000;
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txtdataout=datatxt(1:no_of_data_to_process);
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classdata=datanum(1:no_of_data_to_process,6);
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neitherloc=find(classdata==2);
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offensiveloc=find(classdata==1);
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hateloc=find(classdata==0);
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txtdataout=string(txtdataout);
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traindata_doc=textpreprocessing(txtdataout);
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encode_word=wordEncoding(traindata_doc);
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seq_maxleen=10;
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traindata=doc2sequence(encode_word,traindata_doc,'Length',seq_maxleen);
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testdata=doc2sequence(encode_word,traindata_doc,'Length',seq_maxleen);
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trainclass=categorical(classdata+1);
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numofdata=length(trainclass);
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networkiter=5;
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datapass{1}=traindata;
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datapass{2}=trainclass;
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datapass{3}=testdata;
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datapass{5}=numofdata;
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datapass{6}=encode_word;
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datapass{21}=0;
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datapass{22}=networkiter;
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pop_size=2;
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no_of_iter=10;
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[conver_result,Final_result,all_result]=horseherd_process(datapass,pop_size,no_of_iter);
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tardata=all_result{3};resdata=all_result{4};
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figure,chadata=confusionchart(all_result{2},{'Hate speech','offensive' , 'neither'});
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chadata.Title='Horse Herd optimization Algorithm';
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chadata.RowSummary = 'row-normalized';
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chadata.ColumnSummary = 'column-normalized';
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figure,plot(conver_result,'r-s','linewidth',2);
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grid on;xlabel('iteration');ylabel('Accuracy');
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title('Convergence graph for Horse Herd optimization Algorithm');
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281
FINAL_DESIGN_RUN_IMAYFLY.m
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281
FINAL_DESIGN_RUN_IMAYFLY.m
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clc
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clear all
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close all
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%%
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[datanum,datatxt_final,dataraw]=xlsread('labeled_data.csv');
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datatxt=datatxt_final(2:end,6);
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no_of_data_to_process=1000;
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txtdataout=datatxt(1:no_of_data_to_process);
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classdata=datanum(1:no_of_data_to_process,6);
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neitherloc=find(classdata==2);
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offensiveloc=find(classdata==1);
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hateloc=find(classdata==0);
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txtdataout=string(txtdataout);
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traindata_doc=textpreprocessing(txtdataout);
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encode_word=wordEncoding(traindata_doc);
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seq_maxleen=10;
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traindata=doc2sequence(encode_word,traindata_doc,'Length',seq_maxleen);
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testdata=doc2sequence(encode_word,traindata_doc,'Length',seq_maxleen);
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trainclass=categorical(classdata+1);
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numofdata=length(trainclass);
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networkiter=5;
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datapass{1}=traindata;
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datapass{2}=trainclass;
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datapass{3}=testdata;
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datapass{5}=numofdata;
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datapass{6}=encode_word;
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datapass{21}=0;
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datapass{22}=networkiter;
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pop_size=2;
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no_of_iter=10;
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traindata=datapass{1};
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traindata_matrix=cell2mat(traindata);
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len_data=max(max(traindata_matrix));
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max_val1=1;
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min_val1=0;
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dim=len_data;
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max_range=[repmat(max_val1,[1 dim])];
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min_range=[repmat(min_val1,[1 dim])];
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len=length(max_range);
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int_pos_data=init_pop_data(pop_size,len,max_range,min_range);
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Positions=rand(pop_size,len).*(max_range-min_range)+min_range;
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a =rand(pop_size,len);
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b= (max_range-min_range)+min_range;
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data_pass_to{1}=[];
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for indr=1:pop_size
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dataele=(int_pos_data(indr,:));
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dataele=limit_chk_process(dataele,max_val1,min_val1,data_pass_to);
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elechoose=dataele;
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traindata=datapass{1};
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trainclass=datapass{2};
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testdata=datapass{3};
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lengthdata=datapass{5};
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encode_word=datapass{6};
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flag=datapass{21};
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networkiter=datapass{22};
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datanum1=find(elechoose==0);
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traindata_matrix=cell2mat(traindata);
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ckloc = ismember(traindata_matrix,datanum1);
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loc=find(ckloc);
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traindata_matrix(loc)=0;
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[rr,cc]=size(traindata_matrix);
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for kr=1:rr
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traindata1{kr}=traindata_matrix(kr,:);
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end
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traindata=traindata1;
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datain_size=1;
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dim_data=50;
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hidden_len=80;
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total_word=encode_word.NumWords;
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no_of_class=3;
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network_layer_infor=[ ...
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sequenceInputLayer(datain_size)
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wordEmbeddingLayer(dim_data,total_word)
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lstmLayer(hidden_len,'OutputMode','last')
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fullyConnectedLayer(no_of_class)
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softmaxLayer
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classificationLayer];
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if(flag==1)
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train_opt=trainingOptions('adam', ...
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'MiniBatchSize',16, ...
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'GradientThreshold',2, ...
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'Shuffle','every-epoch', ...
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'Plots','training-progress', ...
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'Verbose',false);
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else
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train_opt=trainingOptions('adam', ...
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'MiniBatchSize',16, ...
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'GradientThreshold',2, ...
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'Shuffle','every-epoch', ...
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'Plots','none', ...
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'Verbose',false);
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end
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train_opt.MaxEpochs=networkiter;
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net=trainNetwork(traindata,trainclass,network_layer_infor,train_opt);
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resultout=predict(net,testdata);
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[maxval,maxlc]=max(((round(resultout.'))));
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ypred1=categorical(maxlc).';
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sin=double(trainclass);
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sout=double(ypred1);
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tardata=[];
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resdata=[];
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for km=1:length(sin)
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tardata=[tardata double(ismember([1;2;3],sin(km)))];
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resdata=[resdata double(ismember([1;2;3],sout(km)))];
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end
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[~,confu_result]=confusion(tardata,resdata);
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%% find accuracy
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accuracy=(sum(diag(confu_result))/sum(confu_result(:)))*100;
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final_result{1}=accuracy;
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final_result{2}=confu_result;
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final_result{3}=tardata;
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final_result{4}=resdata;
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int_pos_data(indr,:)=dataele;
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fitness(indr)=final_result{1};
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finalall{indr}=final_result;
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end
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[maxval,maxloc]=max(fitness);
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bestdata=int_pos_data(maxloc(1),:);
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bestfit=maxval;
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xg=bestdata;
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gbestdata=int_pos_data(maxloc(1),:);
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gbestfit=maxval;
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xhi=gbestdata;
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[rr,cc]=size(int_pos_data);
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initvel=ones(rr,cc)*0.01;
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iter_inc=1;% Loop counter
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% Main loop
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data_pass_to{1}=0;
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while iter_inc<=no_of_iter
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gdata=linspace(1,0.1,pop_size);
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for kpop=1:pop_size
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xit=int_pos_data(kpop,:);
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if(fitness(kpop)>gbestfit)
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a1=rand;a2=rand;betaval=rand;
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rpdata=norm(bestdata-int_pos_data(kpop,:),2);
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rgdata=norm(gbestdata-int_pos_data(kpop,:),2);
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f1=gdata(kpop)*initvel(kpop,:);
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f2=a1*exp(-betaval/rpdata)*(xhi-xit);
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f3=a2*exp(-betaval/rgdata)*(xg-xit);
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newvel=f1+f2+f3;
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else
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dval=rand;rval=randn;
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newvel=gdata(kpop)*initvel(kpop,:)+dval*rval;
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end
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initvel(kpop,:)=newvel;
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end
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int_pos_data=int_pos_data+initvel;
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for indr=1:pop_size
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dataele=(int_pos_data(indr,:));
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dataele=limit_chk_process(dataele,...
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max_val1,min_val1,data_pass_to);
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elechoose=dataele;
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obj_result=objective_process(datapass,elechoose);
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int_pos_data1(indr,:)=dataele;
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fitnessl(indr)=obj_result{1};
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finalall{indr}=obj_result;
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end
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for kpop=1:pop_size
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xit=int_pos_data(kpop,:);
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yit=int_pos_data1(kpop,:);
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if(fitness(kpop)<fitnessl(kpop))
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a3=rand;betaval=rand;
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rmfdata=norm(xit-yit,2);
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f1=gdata(kpop)*initvel(kpop,:);
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f3=a3*exp(-betaval/rmfdata)*(xit-yit);
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newvel=f1+f3;
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else
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flval=rand;r2val=randn;
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newvel=gdata(kpop)*initvel(kpop,:)+flval*r2val;
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end
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initvel(kpop,:)=newvel;
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end
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int_pos_data1=int_pos_data1+initvel;
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L=rand;
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int_pos_data11=L*int_pos_data1+(1-L)*int_pos_data;
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for indr=1:pop_size
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dataele=(int_pos_data11(indr,:));
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dataele=limit_chk_process(dataele,...
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max_val1,min_val1,data_pass_to);
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elechoose=dataele;
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if(iter_inc==no_of_iter && indr==pop_size)
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datapass{21}=1;
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else
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datapass{21}=0;
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end
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obj_result=objective_process(datapass,elechoose);
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int_pos_data11(indr,:)=dataele;
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fitnessl1(indr)=obj_result{1};
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finalall{indr}=obj_result;
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end
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[maxval,maxloc]=max(fitnessl1);
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bestdata=int_pos_data11(maxloc(1),:);
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bestdatafinal=finalall{maxloc(1)};
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xg=bestdata;
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[best_conver_data(iter_inc),best_location(iter_inc)]=max(fitnessl1);
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final_data{iter_inc}=bestdata;
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final_alldata{iter_inc}=bestdatafinal;
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if iter_inc>=2 && best_conver_data(iter_inc)<best_conver_data(iter_inc-1)
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best_conver_data(iter_inc)=best_conver_data(iter_inc-1);
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final_data{iter_inc}=final_data{iter_inc-1};
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final_alldata{iter_inc}=final_alldata{iter_inc-1};
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end
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iter_inc=iter_inc+1;
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end
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conver_result=best_conver_data;
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Final_result=final_data{end};
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all_result=final_alldata{end};
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tardata=all_result{3};resdata=all_result{4};
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figure,chadata=confusionchart(all_result{2},{'Hate speech','offensive' , 'neither'});
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chadata.Title = 'Improved Mayfly Algorithm';
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chadata.RowSummary = 'row-normalized';
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chadata.ColumnSummary = 'column-normalized';
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figure,plot(conver_result,'r-s','linewidth',2);
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grid on;xlabel('iteration');ylabel('Accuracy');
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title('Convergence graph for Improved Mayfly Algorithm');
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BIN
horseherd.pdf
Normal file
BIN
horseherd.pdf
Normal file
Binary file not shown.
124
horseherd_process.m
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124
horseherd_process.m
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function [conver_result,Final_result,all_result]=horseherd_process(datapass,pop_size,no_of_iter)
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traindata=datapass{1};
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traindata_matrix=cell2mat(traindata);
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len_data=max(max(traindata_matrix));
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max_val1=1;
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min_val1=0;
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dim=len_data;
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max_range=[repmat(max_val1,[1 dim])];
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min_range=[repmat(min_val1,[1 dim])];
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len=length(max_range);
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int_pos_data=init_pop_data(pop_size,len,max_range,min_range);
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data_pass_to{1}=[];
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for indr=1:pop_size
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dataele=(int_pos_data(indr,:));
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dataele=limit_chk_process(dataele,...
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max_val1,min_val1,data_pass_to);
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elechoose=dataele;
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obj_result=objective_process(datapass,elechoose);
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int_pos_data(indr,:)=dataele;
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fitness(indr)=obj_result{1};
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finalall{indr}=obj_result;
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end
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[maxval,maxloc]=max(fitness);
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bestdata=int_pos_data(maxloc(1),:);
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bestfit=maxval;
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xg=bestdata;
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gbestdata=int_pos_data(maxloc(1),:);
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gbestfit=maxval;
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xhi=gbestdata;
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[rr,cc]=size(int_pos_data);
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initvel=ones(rr,cc)*0.01;
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iter_inc=1;% Loop counter
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% Main loop
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data_pass_to{1}=0;
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percentagehorse=[10 20 30 40]/100;
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agedata=randsrc(1,pop_size,[1 2 3 4;percentagehorse]);
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wg=0.95;wh=0.9;wsoc=0.9;wim=0.8;wdefmec=0.9;wro=0.9;
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giter=1;hmiter=1;sociter=1;imiter=1;roiter=1;defmeciter=1;
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while iter_inc<=no_of_iter
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low=0.95;upp=1.05;
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giter=wg*giter;
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hmiter=hmiter*wh;
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sociter=sociter*wsoc;
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imiter=imiter*wim;
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defmeciter=defmeciter*wdefmec;
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roiter=roiter*wro;
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newpos=randsrc(1,5,1:pop_size);
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newpos2=randsrc(1,5,1:pop_size);
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for kpop=1:pop_size
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pit=int_pos_data(kpop,:);
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r=rand;
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gram=giter*(low+r*upp)*pit;
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hm=hmiter*(bestdata-pit);
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socm=sociter*(mean(int_pos_data)-pit);
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imm=imiter*(mean(int_pos_data(newpos,:))-pit);
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defmec=defmeciter*(mean(int_pos_data(newpos2,:))-pit);
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ro=roiter*(pit);
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velmalpha=gram+defmec;
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velmbeta=gram+hm+socm+defmec;
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velmgamma=gram+hm+socm+defmec+imm+ro;
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velmdel=gram+imm+ro;
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if(agedata(kpop)==1)
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initvel(kpop,:)=velmalpha;
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end
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if(agedata(kpop)==2)
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initvel(kpop,:)=velmbeta;
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end
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if(agedata(kpop)==3)
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initvel(kpop,:)=velmgamma;
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end
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if(agedata(kpop)==4)
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initvel(kpop,:)=velmdel;
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end
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end
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int_pos_data=int_pos_data+initvel;
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for indr=1:pop_size
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dataele=(int_pos_data(indr,:));
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dataele=limit_chk_process(dataele,...
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max_val1,min_val1,data_pass_to);
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elechoose=dataele;
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if(iter_inc==no_of_iter && indr==pop_size)
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datapass{21}=1;
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else
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datapass{21}=0;
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end
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obj_result=objective_process(datapass,elechoose);
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int_pos_data11(indr,:)=dataele;
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fitnessl1(indr)=obj_result{1};
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finalall{indr}=obj_result;
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end
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[maxval,maxloc]=max(fitnessl1);
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bestdata=int_pos_data11(maxloc(1),:);
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bestdatafinal=finalall{maxloc(1)};
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[best_conver_data(iter_inc),best_location(iter_inc)]=max(fitnessl1);
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final_data{iter_inc}=bestdata;
|
||||
final_alldata{iter_inc}=bestdatafinal;
|
||||
if iter_inc>=2 && best_conver_data(iter_inc)<best_conver_data(iter_inc-1)
|
||||
best_conver_data(iter_inc)=best_conver_data(iter_inc-1);
|
||||
final_data{iter_inc}=final_data{iter_inc-1};
|
||||
final_alldata{iter_inc}=final_alldata{iter_inc-1};
|
||||
end
|
||||
iter_inc=iter_inc+1;
|
||||
end
|
||||
conver_result=best_conver_data;
|
||||
Final_result=final_data{end};
|
||||
all_result=final_alldata{end};
|
||||
|
||||
|
||||
|
||||
134
imayfly_process.m
Normal file
134
imayfly_process.m
Normal file
@ -0,0 +1,134 @@
|
||||
function [conver_result,Final_result,all_result]=imayfly_process(datapass,pop_size,no_of_iter)
|
||||
|
||||
traindata=datapass{1};
|
||||
traindata_matrix=cell2mat(traindata);
|
||||
len_data=max(max(traindata_matrix));
|
||||
max_val1=1;
|
||||
min_val1=0;
|
||||
dim=len_data;
|
||||
max_range=[repmat(max_val1,[1 dim])];
|
||||
min_range=[repmat(min_val1,[1 dim])];
|
||||
len=length(max_range);
|
||||
int_pos_data=init_pop_data(pop_size,len,max_range,min_range);
|
||||
data_pass_to{1}=[];
|
||||
for indr=1:pop_size
|
||||
dataele=(int_pos_data(indr,:));
|
||||
dataele=limit_chk_process(dataele,...
|
||||
max_val1,min_val1,data_pass_to);
|
||||
elechoose=dataele;
|
||||
obj_result=objective_process(datapass,elechoose);
|
||||
int_pos_data(indr,:)=dataele;
|
||||
fitness(indr)=obj_result{1};
|
||||
finalall{indr}=obj_result;
|
||||
end
|
||||
[maxval,maxloc]=max(fitness);
|
||||
bestdata=int_pos_data(maxloc(1),:);
|
||||
bestfit=maxval;
|
||||
xg=bestdata;
|
||||
gbestdata=int_pos_data(maxloc(1),:);
|
||||
gbestfit=maxval;
|
||||
xhi=gbestdata;
|
||||
[rr,cc]=size(int_pos_data);
|
||||
initvel=ones(rr,cc)*0.01;
|
||||
iter_inc=1;% Loop counter
|
||||
% Main loop
|
||||
data_pass_to{1}=0;
|
||||
while iter_inc<=no_of_iter
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
gdata=linspace(1,0.1,pop_size);
|
||||
for kpop=1:pop_size
|
||||
xit=int_pos_data(kpop,:);
|
||||
if(fitness(kpop)>gbestfit)
|
||||
a1=rand;a2=rand;betaval=rand;
|
||||
rpdata=norm(bestdata-int_pos_data(kpop,:),2);
|
||||
rgdata=norm(gbestdata-int_pos_data(kpop,:),2);
|
||||
f1=gdata(kpop)*initvel(kpop,:);
|
||||
f2=a1*exp(-betaval/rpdata)*(xhi-xit);
|
||||
f3=a2*exp(-betaval/rgdata)*(xg-xit);
|
||||
newvel=f1+f2+f3;
|
||||
|
||||
else
|
||||
dval=rand;rval=randn;
|
||||
newvel=gdata(kpop)*initvel(kpop,:)+dval*rval;
|
||||
end
|
||||
initvel(kpop,:)=newvel;
|
||||
end
|
||||
int_pos_data=int_pos_data+initvel;
|
||||
%Male
|
||||
for indr=1:pop_size
|
||||
dataele=(int_pos_data(indr,:));
|
||||
dataele=limit_chk_process(dataele,...
|
||||
max_val1,min_val1,data_pass_to);
|
||||
elechoose=dataele;
|
||||
obj_result=objective_process(datapass,elechoose);
|
||||
int_pos_data1(indr,:)=dataele;
|
||||
fitnessl(indr)=obj_result{1};
|
||||
finalall{indr}=obj_result;
|
||||
end
|
||||
|
||||
for kpop=1:pop_size
|
||||
xit=int_pos_data(kpop,:);
|
||||
yit=int_pos_data1(kpop,:);
|
||||
if(fitness(kpop)<fitnessl(kpop))
|
||||
a3=rand;betaval=rand;
|
||||
rmfdata=norm(xit-yit,2);
|
||||
f1=gdata(kpop)*initvel(kpop,:);
|
||||
f3=a3*exp(-betaval/rmfdata)*(xit-yit);
|
||||
newvel=f1+f3;
|
||||
|
||||
else
|
||||
flval=rand;r2val=randn;
|
||||
newvel=gdata(kpop)*initvel(kpop,:)+flval*r2val;
|
||||
end
|
||||
initvel(kpop,:)=newvel;
|
||||
end
|
||||
int_pos_data1=int_pos_data1+initvel;
|
||||
L=rand;
|
||||
int_pos_data11=L*int_pos_data1+(1-L)*int_pos_data;
|
||||
%Female
|
||||
for indr=1:pop_size
|
||||
dataele=(int_pos_data11(indr,:));
|
||||
dataele=limit_chk_process(dataele,...
|
||||
max_val1,min_val1,data_pass_to);
|
||||
elechoose=dataele;
|
||||
|
||||
if(iter_inc==no_of_iter && indr==pop_size)
|
||||
datapass{21}=1;
|
||||
else
|
||||
datapass{21}=0;
|
||||
end
|
||||
|
||||
obj_result=objective_process(datapass,elechoose);
|
||||
int_pos_data11(indr,:)=dataele;
|
||||
fitnessl1(indr)=obj_result{1};
|
||||
finalall{indr}=obj_result;
|
||||
end
|
||||
|
||||
[maxval,maxloc]=max(fitnessl1);
|
||||
bestdata=int_pos_data11(maxloc(1),:);
|
||||
bestdatafinal=finalall{maxloc(1)};
|
||||
xg=bestdata;
|
||||
|
||||
|
||||
[best_conver_data(iter_inc),best_location(iter_inc)]=max(fitnessl1);
|
||||
|
||||
final_data{iter_inc}=bestdata;
|
||||
final_alldata{iter_inc}=bestdatafinal;
|
||||
|
||||
if iter_inc>=2 && best_conver_data(iter_inc)<best_conver_data(iter_inc-1)
|
||||
best_conver_data(iter_inc)=best_conver_data(iter_inc-1);
|
||||
final_data{iter_inc}=final_data{iter_inc-1};
|
||||
final_alldata{iter_inc}=final_alldata{iter_inc-1};
|
||||
end
|
||||
iter_inc=iter_inc+1;
|
||||
end
|
||||
conver_result=best_conver_data;
|
||||
Final_result=final_data{end};
|
||||
all_result=final_alldata{end};
|
||||
|
||||
|
||||
|
||||
BIN
improvedmayflyoptimizationalgorithm.pdf
Normal file
BIN
improvedmayflyoptimizationalgorithm.pdf
Normal file
Binary file not shown.
16
init_pop_data.m
Normal file
16
init_pop_data.m
Normal file
@ -0,0 +1,16 @@
|
||||
function Positions=init_pop_data(SearchAgents_no,dim,upper_lmt,lower_lmt)
|
||||
|
||||
Boundary_no= size(upper_lmt,2);
|
||||
if Boundary_no==1
|
||||
Positions=rand(SearchAgents_no,dim).*(upper_lmt-lower_lmt)+lower_lmt;
|
||||
end
|
||||
if Boundary_no>1
|
||||
for i=1:dim
|
||||
ub_i=upper_lmt(i);
|
||||
lb_i=lower_lmt(i);
|
||||
v1 = rand(SearchAgents_no,1);
|
||||
v2 =(ub_i-lb_i)+lb_i;
|
||||
Values = round(rand(SearchAgents_no,1)*(ub_i-lb_i)+lb_i);
|
||||
Positions(:,i)= Values;
|
||||
end
|
||||
end
|
||||
26954
labeled_data.csv
Normal file
26954
labeled_data.csv
Normal file
File diff suppressed because it is too large
Load Diff
22
limit_chk_process.asv
Normal file
22
limit_chk_process.asv
Normal file
@ -0,0 +1,22 @@
|
||||
function dataout2=limit_chk_process(datain,upper_lmt,lower_lmt,data_pass_to_loadflow)
|
||||
|
||||
|
||||
datain=round(datain);
|
||||
upper_cond=datain>upper_lmt;
|
||||
lower_cond=datain<lower_lmt;
|
||||
dataout=(datain.*(~(upper_cond+lower_cond)))+...
|
||||
upper_lmt.*upper_cond+lower_lmt.*lower_cond;
|
||||
|
||||
if(~isempty(find(upper_cond)) | ~isempty(find(lower_cond)) )
|
||||
dataout=randsrc(1,length(datain),[lower_lmt(1) upper_lmt(1)]);
|
||||
dataout1=dataout;
|
||||
else
|
||||
dataout1=dataout;
|
||||
end
|
||||
if(length(unique(dataout1))==1)
|
||||
dataout=randsrc(1,length(datain),[lower_lmt(1) upper_lmt(1)]);
|
||||
dataout1=dataout;
|
||||
|
||||
end
|
||||
dataout2=dataout1;
|
||||
|
||||
22
limit_chk_process.m
Normal file
22
limit_chk_process.m
Normal file
@ -0,0 +1,22 @@
|
||||
function dataout2=limit_chk_process(datain,upper_lmt,lower_lmt,data_pass_to_loadflow)
|
||||
|
||||
|
||||
datain=round(datain);
|
||||
upper_cond=datain>upper_lmt;
|
||||
lower_cond=datain<lower_lmt;
|
||||
dataout=(datain.*(~(upper_cond+lower_cond)))+...
|
||||
upper_lmt.*upper_cond+lower_lmt.*lower_cond;
|
||||
|
||||
if(~isempty(find(upper_cond)) | ~isempty(find(lower_cond)) )
|
||||
dataout=randsrc(1,length(datain),[lower_lmt(1) upper_lmt(1)]);
|
||||
dataout1=dataout;
|
||||
else
|
||||
dataout1=dataout;
|
||||
end
|
||||
if(length(unique(dataout1))==1)
|
||||
dataout=randsrc(1,length(datain),[lower_lmt(1) upper_lmt(1)]);
|
||||
dataout1=dataout;
|
||||
|
||||
end
|
||||
dataout2=dataout1;
|
||||
|
||||
75
objective_process.m
Normal file
75
objective_process.m
Normal file
@ -0,0 +1,75 @@
|
||||
function final_result=objective_process(datapass,elechoose)
|
||||
|
||||
traindata=datapass{1};
|
||||
trainclass=datapass{2};
|
||||
testdata=datapass{3};
|
||||
lengthdata=datapass{5};
|
||||
encode_word=datapass{6};
|
||||
flag=datapass{21};
|
||||
networkiter=datapass{22};
|
||||
datanum=find(elechoose==0);
|
||||
traindata_matrix=cell2mat(traindata);
|
||||
loc=find(ismember(traindata_matrix,datanum));
|
||||
traindata_matrix(loc)=0;
|
||||
[rr,cc]=size(traindata_matrix);
|
||||
for kr=1:rr
|
||||
traindata1{kr}=traindata_matrix(kr,:);
|
||||
end
|
||||
traindata=traindata1;
|
||||
datain_size=1;
|
||||
dim_data=50;
|
||||
hidden_len=80;
|
||||
total_word=encode_word.NumWords;
|
||||
no_of_class=3;
|
||||
network_layer_infor=[ ...
|
||||
sequenceInputLayer(datain_size)
|
||||
wordEmbeddingLayer(dim_data,total_word)
|
||||
lstmLayer(hidden_len,'OutputMode','last')
|
||||
fullyConnectedLayer(no_of_class)
|
||||
softmaxLayer
|
||||
classificationLayer];
|
||||
|
||||
if(flag==1)
|
||||
train_opt=trainingOptions('adam', ...
|
||||
'MiniBatchSize',16, ...
|
||||
'GradientThreshold',2, ...
|
||||
'Shuffle','every-epoch', ...
|
||||
'Plots','training-progress', ...
|
||||
'Verbose',false);
|
||||
|
||||
else
|
||||
|
||||
train_opt=trainingOptions('adam', ...
|
||||
'MiniBatchSize',16, ...
|
||||
'GradientThreshold',2, ...
|
||||
'Shuffle','every-epoch', ...
|
||||
'Plots','none', ...
|
||||
'Verbose',false);
|
||||
|
||||
end
|
||||
train_opt.MaxEpochs=networkiter;
|
||||
net=trainNetwork(traindata,trainclass,network_layer_infor,train_opt);
|
||||
resultout=predict(net,testdata);
|
||||
[maxval,maxlc]=max(((round(resultout.'))));
|
||||
ypred1=categorical(maxlc).';
|
||||
sin=double(trainclass);
|
||||
sout=double(ypred1);
|
||||
tardata=[];
|
||||
resdata=[];
|
||||
for km=1:length(sin)
|
||||
tardata=[tardata double(ismember([1;2;3],sin(km)))];
|
||||
resdata=[resdata double(ismember([1;2;3],sout(km)))];
|
||||
end
|
||||
[~,confu_result]=confusion(tardata,resdata);
|
||||
%% find accuracy
|
||||
accuracy=(sum(diag(confu_result))/sum(confu_result(:)))*100;
|
||||
final_result{1}=accuracy;
|
||||
final_result{2}=confu_result;
|
||||
final_result{3}=tardata;
|
||||
final_result{4}=resdata;
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
8
textpreprocessing.m
Normal file
8
textpreprocessing.m
Normal file
@ -0,0 +1,8 @@
|
||||
function documents=textpreprocessing(textData)
|
||||
|
||||
textData=eraseURLs(textData);
|
||||
documents = tokenizedDocument(textData,'DetectPatterns','at-mention');
|
||||
documents=removeStopWords(documents);
|
||||
documents = lower(documents);
|
||||
documents = erasePunctuation(documents);
|
||||
end
|
||||
Loading…
Reference in New Issue
Block a user