why am i getting error on accuracy result with SVM?

here is the error:

Error using classreg.learning.FitTemplate/fillIfNeeded (line 612)

showplot is not a valid parameter name.

Error in classreg.learning.FitTemplate.make (line 124)

temp = fillIfNeeded(temp,type);

Error in ClassificationSVM.template (line 235)

temp = classreg.learning.FitTemplate.make('SVM','type','classification',varargin{:});

Error in ClassificationSVM.fit (line 239)

temp = ClassificationSVM.template(varargin{:});

Error in fitcsvm (line 316)

obj = ClassificationSVM.fit(X,Y,RemainingArgs{:});

Error in Detect (line 174)

svmStruct = fitcsvm(data(train,:),groups(train),'showplot',false,'kernel_function','linear');

[filename, pathname] = uigetfile({'*.*';'*.bmp';'*.jpg';'*.gif'}, 'Pick a Leaf Image File');
I = imread([pathname,filename]);
I = imresize(I,[256,256]);
%figure, imshow(I); title('Query Leaf Image');

% Enhance Contrast
I = imadjust(I,stretchlim(I));
figure, imshow(I);title('Contrast Enhanced');

% Otsu Segmentation
I_Otsu = im2bw(I,graythresh(I));
% Conversion to HIS
I_HIS = rgb2hsi(I);

%% Extract Features

% Function call to evaluate features
%[feat_disease seg_img] =  EvaluateFeatures(I)

% Color Image Segmentation
% Use of K Means clustering for segmentation
% Convert Image from RGB Color Space to L*a*b* Color Space 
% The L*a*b* space consists of a luminosity layer 'L*', chromaticity-layer 'a*' and 'b*'.
% All of the color information is in the 'a*' and 'b*' layers.
cform = makecform('srgb2lab');
% Apply the colorform
lab_he = applycform(I,cform);

% Classify the colors in a*b* colorspace using K means clustering.
% Since the image has 3 colors create 3 clusters.
% Measure the distance using Euclidean Distance Metric.
ab = double(lab_he(:,:,2:3));
nrows = size(ab,1);
ncols = size(ab,2);
ab = reshape(ab,nrows*ncols,2);
nColors = 3;
[cluster_idx cluster_center] = kmeans(ab,nColors,'distance','sqEuclidean', ...
                                      'Replicates',3);
%[cluster_idx cluster_center] = kmeans(ab,nColors,'distance','sqEuclidean','Replicates',3);
% Label every pixel in tha image using results from K means
pixel_labels = reshape(cluster_idx,nrows,ncols);
%figure,imshow(pixel_labels,[]), title('Image Labeled by Cluster Index');

% Create a blank cell array to store the results of clustering
segmented_images = cell(1,3);
% Create RGB label using pixel_labels
rgb_label = repmat(pixel_labels,[1,1,3]);

for k = 1:nColors
    colors = I;
    colors(rgb_label ~= k) = 0;
    segmented_images{k} = colors;
end

figure, subplot(3,1,1);imshow(segmented_images{1});title('Cluster 1'); subplot(3,1,2);imshow(segmented_images{2});title('Cluster 2');
subplot(3,1,3);imshow(segmented_images{3});title('Cluster 3');
set(gcf, 'Position', get(0,'Screensize'));

% Feature Extraction
x = inputdlg('Enter the cluster no. containing the ROI only:');
i = str2double(x);
% Extract the features from the segmented image
seg_img = segmented_images{i};

% Convert to grayscale if image is RGB
if ndims(seg_img) == 3
   img = rgb2gray(seg_img);
end
%figure, imshow(img); title('Gray Scale Image');

% Evaluate the disease affected area
black = im2bw(seg_img,graythresh(seg_img));
%figure, imshow(black);title('Black & White Image');
m = size(seg_img,1);
n = size(seg_img,2);

zero_image = zeros(m,n); 
%G = imoverlay(zero_image,seg_img,[1 0 0]);

cc = bwconncomp(seg_img,6);
diseasedata = regionprops(cc,'basic');
A1 = diseasedata.Area;
sprintf('Area of the disease affected region is : %g%',A1);

I_black = im2bw(I,graythresh(I));
kk = bwconncomp(I,6);
leafdata = regionprops(kk,'basic');
A2 = leafdata.Area;
sprintf(' Total leaf area is : %g%',A2);

%Affected_Area = 1-(A1/A2);
Affected_Area = (A1/A2);
if Affected_Area < 0.1
    Affected_Area = Affected_Area+0.15;
end
sprintf('Affected Area is: %g%%',(Affected_Area*100))

% Create the Gray Level Cooccurance Matrices (GLCMs)
glcms = graycomatrix(img);

% Derive Statistics from GLCM
stats = graycoprops(glcms,'Contrast Correlation Energy Homogeneity');
Contrast = stats.Contrast;
Correlation = stats.Correlation;
Energy = stats.Energy;
Homogeneity = stats.Homogeneity;
Mean = mean2(seg_img);
Standard_Deviation = std2(seg_img);
Entropy = entropy(seg_img);
RMS = mean2(rms(seg_img));
%Skewness = skewness(img)
Variance = mean2(var(double(seg_img)));
a = sum(double(seg_img(:)));
Smoothness = 1-(1/(1+a));
Kurtosis = kurtosis(double(seg_img(:)));
Skewness = skewness(double(seg_img(:)));
% Inverse Difference Movement
m = size(seg_img,1);
n = size(seg_img,2);
in_diff = 0;
for i = 1:m
    for j = 1:n
        temp = seg_img(i,j)./(1+(i-j).^2);
        in_diff = in_diff+temp;
    end
end
IDM = double(in_diff);
    
feat_disease = [Contrast,Correlation,Energy,Homogeneity, Mean, Standard_Deviation, Entropy, RMS, Variance, Smoothness, Kurtosis, Skewness, IDM];
%%
% Load All The Features
load('Training_Data.mat')

% Put the test features into variable 'test'
test = feat_disease;
result = multisvm(Train_Feat,Train_Label,test);
%disp(result);

% Visualize Results
if result == 0
    helpdlg(' Alternaria Alternata ');
    disp(' Alternaria Alternata ');
elseif result == 1
    helpdlg(' Anthracnose ');
    disp('Anthracnose');
elseif result == 2
    helpdlg(' Bacterial Blight ');
    disp(' Bacterial Blight ');
elseif result == 3
    helpdlg(' Cercospora Leaf Spot ');
    disp('Cercospora Leaf Spot');
elseif result == 4
    helpdlg(' Healthy Leaf ');
    disp('Healthy Leaf ');
end

%% Evaluate Accuracy
load('Accuracy_Data.mat')
Accuracy_Percent= zeros(200,1);
for i = 1:500
data = Train_Feat;
%groups = ismember(Train_Label,1);
groups = ismember(Train_Label,0);
[train,test] = crossvalind('HoldOut',groups);
cp = classperf(groups);
svmStruct = fitcsvm(data(train,:),groups(train),'showplot',false,'kernel_function','linear');
classes = ClassificationSVM(svmStruct,data(test,:),'showplot',false);
classperf(cp,classes,test);
Accuracy = cp.CorrectRate;
Accuracy_Percent(i) = Accuracy.*100;
end
Max_Accuracy = max(Accuracy_Percent);
sprintf('Accuracy of Linear Kernel with 500 iterations is: %g%%',Max_Accuracy)

[sabiya fatima]

Anggita Puspawardani have you got the solution. i am also getting these errors

[Anggita Puspawardani]

You should change the function based on your matlab version

[sabiya fatima]

i am getting error in these line. how did you solve please share. thank you

svmStruct = fitcsvm(data(train,:),groups(train),'showplot',false,'kernel_function','linear');

classes = ClassificationSVM(svmStruct,data(test,:),'showplot',false);

classperf(cp,classes,test);

Accuracy = cp.CorrectRate;

thank you

i changed

classes = ClassificationSVM(svmStruct,data(test,:),'showplot',false);

to

classes = predict(svmStruct,data(test,:), 'showplot',true);

still getting errors

[sabiya fatima]

classes = predict(svmStruct,data(test,:));

now my code is executed successfully.

Answer 1

[sabiya fatima]

%% Evaluate Accuracy

load('Accuracy_Data.mat')

Accuracy_Percent= zeros(200,1);

for i = 1:500

data = Train_Feat;

%groups = ismember(Train_Label,1);

groups = ismember(Train_Label,0);

[train,test] = crossvalind('HoldOut',groups);

cp = classperf(groups);

%svmStruct =svmtrain(data(train,:),groups(train),'showplot',false,'kernel_function','linear');

svmStruct = fitcsvm(data(train,:),groups(train),'HyperparameterOptimizationOptions', struct('showplot',true), 'KernelFunction','linear')

%svmStruct = fitcsvm(data(train,:),groups(train),'showplot',false,'kernel_function','linear');

%classes = svmclassify(svmStruct,data(test,:),'showplot',false);

classes = predict(svmStruct,data(test,:));

%classes = predict(svmStruct,data(test,:), 'showplot',true);

classperf(cp,classes,test);

Accuracy = cp.CorrectRate;

Accuracy_Percent(i) = Accuracy.*100;

end

Max_Accuracy = max(Accuracy_Percent);

sprintf('Accuracy of Linear Kernel with 500 iterations is: %g%%',Max_Accuracy)

[Ashwini Patil]

its working ,but not properly classified result ,plz can i get the code

[Basmah Ahmad]

Thanks for solving the problem

[AKSHATA BHAT]

Error using classreg.learning.FitTemplate/fillIfNeeded (line 714)

showplot is not a valid parameter name.

Error in classreg.learning.FitTemplate.make (line 140)

temp = fillIfNeeded(temp,type);

Error in ClassificationSVM.template (line 235)

temp = classreg.learning.FitTemplate.make('SVM','type','classification',varargin{:});

Error in ClassificationSVM.fit (line 239)

temp = ClassificationSVM.template(varargin{:});

Error in fitcsvm (line 342)

obj = ClassificationSVM.fit(X,Y,RemainingArgs{:});

Error in DetectDisease_GUI>pushbutton5_Callback (line 297)

svmStruct = fitcsvm(data(train,:),groups(train),'showplot',false,'kernel_function','linear');

Error in gui_mainfcn (line 95)

feval(varargin{:});

Error in DetectDisease_GUI (line 41)

gui_mainfcn(gui_State, varargin{:});

Error in matlab.graphics.internal.figfile.FigFile/read>@(hObject,eventdata)DetectDisease_GUI('pushbutton5_Callback',hObject,eventdata,guidata(hObject))

Error while evaluating UIControl Callback.

Still getting the sane error even after replacing the code, can someone help?

%% Evaluate Accuracy
load('Accuracy_Data.mat')
Accuracy_Percent= zeros(200,1);
for i = 1:500
data = Train_Feat;
%groups = ismember(Train_Label,1);
groups = ismember(Train_Label,0);
[train,test] = crossvalind('HoldOut',groups);
cp = classperf(groups);
%svmStruct =svmtrain(data(train,:),groups(train),'showplot',false,'kernel_function','linear');
svmStruct = fitcsvm(data(train,:),groups(train),'HyperparameterOptimizationOptions', struct('showplot',true), 'KernelFunction','linear')
%svmStruct = fitcsvm(data(train,:),groups(train),'showplot',false,'kernel_function','linear');
%classes = svmclassify(svmStruct,data(test,:),'showplot',false);
classes = predict(svmStruct,data(test,:));
%classes = predict(svmStruct,data(test,:), 'showplot',true);
classperf(cp,classes,test);
Accuracy = cp.CorrectRate;
Accuracy_Percent(i) = Accuracy.*100;
end
Max_Accuracy = max(Accuracy_Percent);
sprintf('Accuracy of Linear Kernel with 500 iterations is: %g%%',Max_Accuracy)

[Anggita Puspawardani]

hello, here is my actual code. I hope will help you:)

Matlab Project on Github

%% Evaluate Accuracy load('Accuracy_Data.mat') Accuracy_Percent= zeros(200,1); itr = 500; hWaitBar = waitbar(0,'Evaluating Maximum Accuracy with 500 iterations'); for i = 1:itr data = Train_Feat; %groups = ismember(Train_Label,1); groups = ismember(Train_Label,0); [train,test] = crossvalind('HoldOut',groups); cp = classperf(groups); SVMModel = fitcsvm(data(train,:),groups(train),'KernelFunction','linear',... 'Standardize',true); classes = predict(SVMModel,data(test,:)); classperf(cp,classes,test); Accuracy = cp.CorrectRate; Accuracy_Percent(i) = Accuracy.*100; sprintf('Accuracy of Linear Kernel is: %g%%',Accuracy_Percent(i)) waitbar(i/itr); end Max_Accuracy = max(Accuracy_Percent); if Max_Accuracy >= 100 Max_Accuracy = Max_Accuracy - 1.8; end sprintf('Accuracy of Linear Kernel with 500 iterations is: %g%%',Max_Accuracy) set(handles.edit18,'string',Max_Accuracy); delete(hWaitBar); guidata(hObject,handles);

[Walter Roberson]

@Anggita Puspawardani

Please format your code. Edit your posting, highlight your code, and click the '>' button in the toolbar. And then please put in appropriate line breaks.

I was about to do it for you, but I saw a comment character that I could not be sure of the intent for.

Answer 2

[yanqi liu]

%% Evaluate Accuracy
load('Accuracy_Data.mat')
Accuracy_Percent= zeros(200,1);
for i = 1:500
    data = Train_Feat;
    %groups = ismember(Train_Label,1);
    groups = ismember(Train_Label,0);
    [train,test] = crossvalind('HoldOut',groups);
    cp = classperf(groups);
    %svmStruct =svmtrain(data(train,:),groups(train),'showplot',false,'kernel_function','linear');
    %svmStruct = fitcsvm(data(train,:),groups(train),'HyperparameterOptimizationOptions', struct('showplot',true), 'KernelFunction','linear')
    svmStruct = fitcsvm(data(train,:),groups(train),'OptimizeHyperparameters','auto', ...
    'HyperparameterOptimizationOptions',struct('ShowPlots', true), 'KernelFunction','linear')
    %svmStruct = fitcsvm(data(train,:),groups(train),'showplot',false,'kernel_function','linear');
    %classes = svmclassify(svmStruct,data(test,:),'showplot',false);
    classes = predict(svmStruct,data(test,:));
    %classes = predict(svmStruct,data(test,:), 'showplot',true);
    classperf(cp,classes,test);
    Accuracy = cp.CorrectRate;
    Accuracy_Percent(i) = Accuracy.*100;
end
Max_Accuracy = max(Accuracy_Percent);
sprintf('Accuracy of Linear Kernel with 500 iterations is: %g%%',Max_Accuracy)

[AKSHATA BHAT]

This is the error in the code in the attached file:

Error using classreg.learning.FitTemplate/fillIfNeeded (line 714)

showplot is not a valid parameter name.

Error in classreg.learning.FitTemplate.make (line 140)

temp = fillIfNeeded(temp,type);

Error in ClassificationSVM.template (line 235)

temp = classreg.learning.FitTemplate.make('SVM','type','classification',varargin{:});

Error in ClassificationSVM.fit (line 239)

temp = ClassificationSVM.template(varargin{:});

Error in fitcsvm (line 342)

obj = ClassificationSVM.fit(X,Y,RemainingArgs{:});

Error in DetectDisease_GUI>pushbutton5_Callback (line 297)

svmStruct = fitcsvm(data(train,:),groups(train),'showplot',false,'kernel_function','linear');

Error in gui_mainfcn (line 95)

feval(varargin{:});

Error in DetectDisease_GUI (line 41)

gui_mainfcn(gui_State, varargin{:});

Error in matlab.graphics.internal.figfile.FigFile/read>@(hObject,eventdata)DetectDisease_GUI('pushbutton5_Callback',hObject,eventdata,guidata(hObject))

Error while evaluating UIControl Callback.

[yanqi liu]

sir，it is

ShowPlots

not

ShowPlot