Hello,
When I perform SVM using the following code and I have 26 features, when I change the number of feature from 2 to 4 for example, I get the following error in line related to sequentialfs?
Thanks for your guidance,
%%% The code
close all;
clear all;
%% When they are from different patients
X_train = xlsread('PM105_antibodies_uptake_intensity.xlsx');
[num,str] = xlsread('labels_PM105_intensity.xlsx');
y_train = grp2idx(str);
X_test = xlsread('PM80_antibodies_uptake_intensity.xlsx');
[num2,str2] = xlsread('labels_PM80_intensity.xlsx');
y_test = grp2idx(str2);
% features_antibodies_uptake
%
% labels
c = cvpartition(y_train,'k',5);
opts = statset('display','iter');
classf = @(train_data, train_labels, test_data, test_labels)...
sum(predict(fitcsvm(train_data, train_labels,'KernelFunction','rbf'), test_data) ~= test_labels);
[fs, history] = sequentialfs(classf, X_train, y_train, 'cv', c, 'options', opts,'nfeatures',2);
%% Best hyperparameter
X_train_w_best_feature = X_train(:,fs);
Md1 = fitcsvm(X_train_w_best_feature,y_train,'KernelFunction','rbf','OptimizeHyperparameters','auto',...
'HyperparameterOptimizationOptions',struct('AcquisitionFunctionName',...
'expected-improvement-plus','ShowPlots',true)); % Bayes' Optimization ??.
%% Final test with test set
X_test_w_best_feature = X_test(:,fs);
test_accuracy_for_iter = sum((predict(Md1,X_test_w_best_feature) == y_test))/length(y_test)*100
%% hyperplane ??
figure;
hgscatter = gscatter(X_train_w_best_feature(:,1),X_train_w_best_feature(:,2),y_train);
hold on;
h_sv=plot(Md1.SupportVectors(:,1),Md1.SupportVectors(:,2),'ko','markersize',8);
% test set? data? ?? ??? ????.
gscatter(X_test_w_best_feature(:,1),X_test_w_best_feature(:,2),y_test,'rb','xx')
% decision plane
XLIMs = get(gca,'xlim');
YLIMs = get(gca,'ylim');
[xi,yi] = meshgrid([XLIMs(1):0.01:XLIMs(2)],[YLIMs(1):0.01:YLIMs(2)]);
dd = [xi(:), yi(:)];
pred_mesh = predict(Md1, dd);
redcolor = [1, 0.8, 0.8];
bluecolor = [0.8, 0.8, 1];
pos = find(pred_mesh == 1);
h1 = plot(dd(pos,1), dd(pos,2),'s','color',redcolor,'Markersize',5,'MarkerEdgeColor',redcolor,'MarkerFaceColor',redcolor);
pos = find(pred_mesh == 2);
h2 = plot(dd(pos,1), dd(pos,2),'s','color',bluecolor,'Markersize',5,'MarkerEdgeColor',bluecolor,'MarkerFaceColor',bluecolor);
uistack(h1,'bottom');
uistack(h2,'bottom');
legend([hgscatter;h_sv],{'setosa','versicolor','support vectors'})
%%% The error
Error using classreg.learning.impl.CompactSVMImpl/score (line 62)
You must pass X as a matrix with 4 columns.
Error in classreg.learning.classif.CompactClassificationSVM/score (line 564)
f = score(this.Impl,X,true,varargin{:});
Error in classreg.learning.classif.ClassificationModel/predict (line 411)
scores = score(this,X,varargin{:});
Error in classreg.learning.classif.CompactClassificationSVM/predict (line 406)
predict@classreg.learning.classif.ClassificationModel(this,X,varargin{:});
Error in svm_test_MATLAB (line 88)
pred_mesh = predict(Md1, dd);
