Code covered by the BSD License
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...
Handle arguments to function
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[idx,netsim,i,unconverged,dps...
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ind2cluster(labels)
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matrix_transform(M,ngiven,nma...
transform to full matrix
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silhouette2(X, clust, distanc...
SILHOUETTE Silhouette plot for clustered data.
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simatrix_make(data,type,nrow)
data: a matrix with each column representing a variable.
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similarity_euclid(data,vararg...
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similarity_pearson(data)
pearson coefficients between every two columns
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similarity_pearsonC(data, C)
pearson coefficients between every column and the center
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solution_evaluation(data,M,la...
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solution_positive(refseq_exon...
load GeneFindingProblem.mat;
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valid_errorate(labels, truela...
computing error rates for every clusters if true labels are given
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valid_external(index1,c2)
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valid_sumpearson(data,labels,...
within-, between-cluster and total sum of squares
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valid_sumsqures(data,labels,k...
data: a matrix with each column representing a variable.
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Main_adaptAP_demo.m
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data_load.m
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solution_findK.m
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View all files
from
Adaptive Affinity Propagation clustering
by Kaijun Wang
advantage of speed & performance appears under large number of clusters & large dataset
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| solution_positive(refseq_exon,refseq_intron,labelid,classend,Sid)
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%load GeneFindingProblem.mat;
% offset=0; % Change this prefence offset to change sensitivity
% [idx,netsim,dpsim,expref]=apcluster(s,p+offset,'maxits',15,'sparse');
% ap_exon=(idx(1:end-1)~=75067); % remove non-exon exemplar and identify exons
% true_positive_rate=sum(ap_exon.*refseq_exon)/sum(refseq_exon)
% false_positive_rate=sum(ap_exon.*refseq_intron)/sum(refseq_intron)
function [true_positive,false_positive] = solution_positive(refseq_exon,refseq_intron,labelid,classend,Sid)
% remove non-exon exemplar and identify exons
ap_exon = (labelid(1:end-1,Sid)~=classend);
true_positive=sum(ap_exon.*refseq_exon)/sum(refseq_exon);
false_positive=sum(ap_exon.*refseq_intron)/sum(refseq_intron);
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