59 lines
1.4 KiB
Mathematica
59 lines
1.4 KiB
Mathematica
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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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