Bayesian decision theory: Bayesian decision theory considers how to select the optimal category markers based on known probabilities and misjudgment losses.
Prior probability refers to the probability obtained based on previous experience and analysis. That is, the probability is obtained by guessing the probability before obtaining data and evidence without considering the reason.
Likelihood function: Likelihood is used to describe the possible values of unknown parameters when the output result of random variable is known. Likelihood function focuses on the relationship between the known result and an inherent attribute, rather than the probability of the result or cause. Therefore, likelihood is called the fitting of inherent attribute, so it cannot be called probability.
Posterior probability is the conditional probability after relevant evidence or background is given and taken into consideration. It’s the probability of cause and effect.
Prior distribution: the distribution that random variables should satisfy according to general experience

Posterior distribution: The distribution of random variables modified by the current training data is more consistent with the current data than the prior distribution

Likelihood estimation: a method to estimate model parameters by maximizing likelihood given training data and given model

The posterior distribution is usually calculated based on the prior distribution and maximum likelihood estimation.
Summary of the distinction between prior, likelihood and posterior:

1) prior — the probability of rain in a place based on years of statistics (experience) or climate (common sense);

2) likelihood — the probability that there are clouds (due to/evidence/observed data) when it rains (if), that is, there is already an effect, a description of the possibility of evidence occurring;

3) posteriori — probability of rain (result) based on clouds in the sky (cause or evidence/observation);

Posterior ~ prior * likelihood: there is the possibility of rain (prior), there will be dark clouds before rain (likelihood) ~ Infer the probability of rain by the presence of dark clouds (posterior);

Or:

Setting background: When the wine is in the middle of its intoxicant, suddenly the clouds are cloudy and the shower is coming.

Scenario 1: “It will not rain, historically the probability of rain here is 20%” —- prior probability “but when the clouds are indifferent, the probability of rain is 80%” —- posterior probability

Situation two: “Don’t hurry to go, Feifei, the probability of death at the wine table is only 5% in history” —- prior probability “but he is Cao Cao, murder in dreams” —- posterior probability
Maximum Likelihood Estimation (MM) : Maximum Likelihood Estimation (MM) is a regression of the model parameters that are most likely to lead to such a result based on the results of known samples.
Bayes’ formula:
1. ```
 
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  In general, "the probability of event A under the condition that event B occurs" is different from "the probability of event B under the condition that event A occurs", but the relationship between the two is definite, which is what Bayes formula studies.
2. Formula:
3. Explanation:
  1. P (A | B) as the A posteriori probability, namely event B occurs under the condition of the probability of event A occurs, because from the B value and the probability is called the posterior probability of A.
  2. P(A) is A prior probability (edge probability), that is, the occurrence of A does not consider any factor of B.
  3. As the conditional probability P (B | A) (kind of conditional probability density), namely in the event A occurs under the condition of the probability of event B occurs, and 1 is called as the A posteriori probability of B. — > is called likelihood
  4. P(B) is “evidence for normalization”, which can be regarded as a known quantity. The value of P(B) in Bayesian classifier has nothing to do with classification.
Normal distribution (Gaussian distribution) :
Multi-source normal distribution:
1. Personal understanding: The problem of extending the Gaussian distribution of dichotomies into multiple classifications. Defined as: www.cnblogs.com/bingjianing…
2. It includes standardization, naturalization and other processes.

The figure on the left shows the naturalization process of the multi-source Gaussian distribution on the right#

(This is the lead probability knowledge)

Bayesian classifier

Bayesian decision theory:
1. Premise: All relevant probabilities are known
2. Concern: Loss of misjudgment
Expected loss (risk) : λij is the loss incurred when Cj is misclassified into Ci for N possible markers. Based on posterior probability: P (ci x |) is generated when the error is divided into ci expected loss, the loss is also called “risk”, when we make a standard h x risk for each sample the most hours (when the overall risk of the whole sample R * (h) (also minimum bayes risk)), called h bayesian optimal classifier.

Expected loss (risk) expression

Overall risk expression

Minimize the risk per sample
Posterior probability maximization and risk minimization: for dichotomous problems, λ is equal to either 0 or 1
- In this case, the conditional risk (the risk under this condition) is
- Therefore, when the classification error rate reaches the minimum, the posterior probability P needs to be minimum, and then the posterior probability maximization is to minimize the risk. That is:
According to 3 and Bayes’ formula, the minimum risk requires the maximum posteriori probability, while the maximum posteriori probability requires the maximum likelihood. This leads to — > maximum likelihood estimation.

Maximum Likelihood Estimation

First of all, our goal is: P (x | c) this YiShi likelihood probability, according to the frequency of school’s point of view:

Although the parameters are unknown, when there are objective fixed values.

We assume that the likelihood probability is controlled by a certain θc, so our goal is to determine the value of θc through the training set, and thus determine the likelihood probability.
Assuming that Dc represents the set of class C samples on training set D, and they meet the condition that the samples obey independent distribution, then the likelihood of parameter θc to data set Dc can be expressed as:

Like: P (A | B) = P (AB)/P (B)
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Take the logarithm of both sides (log likelihood) :

At this point, the maximum likelihood estimation expression for θc maximum is:

Disadvantage: This method relies heavily on the assumption that objective fixed values exist.
Bias and unbias of MLE estimation results:
1. For the mean: unbiased
2. For contrast: biased, 1/n should be changed to 1/(n-1)
There is a problem with the maximum likelihood estimation in the case of insufficient samples: suppose I flip a coin 10 times and get 7 heads, then my maximum likelihood estimation probability is 0.7, but according to common sense our probability should be closer to 0.5, so we need to consider the prior probability. — > Elicits the maximum posterior probability estimate.

Maximum a posteriori Estimation

The difference between the MAP and MLE: parameter theta maximum likelihood estimation is, the likelihood function P (x | theta) is the largest. Maximum a posteriori probability estimation is wants to let theta P (x | theta) P (theta) is the largest. The obtained θ not only increases the likelihood function, but also increases the prior probability of θ itself.
The basic idea of MAP is still based on The Bayesian formula itself. The purpose of MLE is to obtain the maximum likelihood estimate, while the purpose of MAP is to obtain the maximum posterior probability itself. A prior probability is added on the basis of MLE, and its expression is as follows:
The main difference is between the Bayes school and the frequency school.

Naive Bayes classifier

Guide:
1. If I have d samples, and each sample has 2 states 0 or 1,The dimension is k？？？？ .So their combination is 2 to the dK minus 1, but when everything is independent, they get (2-1).Dk-1, which greatly reduces the parameters.
2. So naive Bayes on top of this condition is called naive because he needs to satisfy the condition that all conditions are independent.
The probabilistic models that naive Bayes classifier relies on are MAP and MLE.
Application: text sorting/spam filtering, etc.

function varargout = NaiveBayesClassifier(varargin)
% NAIVEBAYESCLASSIFIER M-file for NaiveBayesClassifier.fig
%      NAIVEBAYESCLASSIFIER, by itself, creates a new NAIVEBAYESCLASSIFIER or raises the existing
%      singleton*.
%
%      H = NAIVEBAYESCLASSIFIER returns the handle to a new NAIVEBAYESCLASSIFIER or the handle to
%      the existing singleton*.
%
%      NAIVEBAYESCLASSIFIER('CALLBACK',hObject,eventData,handles,...) calls the local
%      function named CALLBACK in NAIVEBAYESCLASSIFIER.M with the given input arguments.
%
%      NAIVEBAYESCLASSIFIER('Property','Value',...) creates a new NAIVEBAYESCLASSIFIER or raises the
%      existing singleton*.  Starting from the left, property value pairs are
%      applied to the GUI before NaiveBayesClassifier_OpeningFcn gets called.  An
%      unrecognized property name or invalid value makes property application
%      stop.  All inputs are passed to NaiveBayesClassifier_OpeningFcn via varargin.
%
%      *See GUI Options on GUIDE's Tools menu.  Choose "GUI allows only one
%      instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES

% Edit the above text to modify the response to help NaiveBayesClassifier

% Last Modified by GUIDE v2.5 09-May-2013 06:54:52

% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name',       mfilename, ...
                   'gui_Singleton',  gui_Singleton, ...
                   'gui_OpeningFcn', @NaiveBayesClassifier_OpeningFcn, ...
                   'gui_OutputFcn',  @NaiveBayesClassifier_OutputFcn, ...
                   'gui_LayoutFcn',  [] , ...
                   'gui_Callback',   []);
if nargin && ischar(varargin{1})
    gui_State.gui_Callback = str2func(varargin{1});
    clc;
    
    % set window potition (get_size_screen/gsl_)
    gsl_ = get(0,'ScreenSize');
    
end

if nargout
    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
    gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT


% --- Executes just before NaiveBayesClassifier is made visible.
function NaiveBayesClassifier_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject    handle to figure
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
% varargin   command line arguments to NaiveBayesClassifier (see VARARGIN)

% Choose default command line output for NaiveBayesClassifier
handles.output = hObject;

% Update handles structure
guidata(hObject, handles);

% UIWAIT makes NaiveBayesClassifier wait for user response (see UIRESUME)
% uiwait(handles.NaiveBayesClassifier);

% Set the figure icon by matlabfreecode.wordpress.com
warning('off','MATLAB:HandleGraphics:ObsoletedProperty:JavaFrame');
jframe=get(handles.NaiveBayesClassifier,'javaframe');
jIcon=javax.swing.ImageIcon('citrus-icon.png');
jframe.setFigureIcon(jIcon);

% --- Outputs from this function are returned to the command line.
function varargout = NaiveBayesClassifier_OutputFcn(hObject, eventdata, handles) 
% varargout  cell array for returning output args (see VARARGOUT);
% hObject    handle to figure
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Get default command line output from handles structure
varargout{1} = handles.output;


% --- Executes on button press in NaiveBayesClassifier.
function trainingdata_Callback(hObject, eventdata, handles)
% hObject    handle to NaiveBayesClassifier (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
NaiveBayesClassifierProject=guidata(gcbo);
GetImageTraining=get(NaiveBayesClassifierProject.ImageTraining,'Userdata');

% determine the training data path
path_data_train=strrep(cd,...
    'Matlab_Code_To_Classification_Citrus','CitrusImage\Data Training');

% data training of Citrus nipis (jn)
lots_of_data_train_jn=7;

% data training of Citrus lemon (jl)
lots_of_data_train_jl=3;

% data training of Citrus orange (jm)
lots_of_data_train_jm=5;

lots_of_feature=4;
lots_of_class=3;

% initialization of matrix dataset
dataset=zeros(lots_of_data_train_jn,lots_of_feature);

for i=1:(lots_of_data_train_jn+lots_of_data_train_jl+lots_of_data_train_jm)
    
    if(i<=lots_of_data_train_jn)
        % membaca setiap file citra jeruk nipis
        filename=strcat(path_data_train,'\','CitrusNipis',...
        num2str(i),'.jpg');
        class{i}='Nipis';
    elseif(i<=(lots_of_data_train_jn+lots_of_data_train_jl))
        % membaca setiap file citra jeruk lemon
        filename=strcat(path_data_train,'\','CitrusLemon',...
            num2str(i-lots_of_data_train_jn),'.jpg');
        class{i}='Lemon';
    else
        % membaca setiap file citra jeruk manis
        filename=strcat(path_data_train,'\','CitrusOrange',...
            num2str(i-(lots_of_data_train_jn+lots_of_data_train_jl)),'.jpg');
        class{i}='Orange';
    end
    
    I = imread (filename);
    
    % merisize image size
    % I = imresize(I,[256 256]);

    if(size(I,3)==4)
        I(:,:,1)=[]; % convert to I = [MxNx3]
    end
    
    % count mean Red, Green, Blue
    mean_red=mean(mean(I(:,:,1)));
    mean_green=mean(mean(I(:,:,2)));
    mean_blue=mean(mean(I(:,:,3)));
       
    axes(handles.ImageTraining);
    imshow(I);    
    
    set(NaiveBayesClassifierProject.ImageTraining,'Userdata',I);   
    
    SizeImageTraining=size(I);
    StringSizeImageTraining=sprintf(strcat(num2str(SizeImageTraining(1)),'x',num2str(SizeImageTraining(2)),'x',num2str(SizeImageTraining(3))));    
    StringSizeImageTraining = strrep(StringSizeImageTraining,'x',' x ');
    
    set(handles.SizeImageTraining,'String',StringSizeImageTraining);
    
    % set(handles.SizeImageTraining,'String',StringSizeImageTraining);
    
    set(handles.NameImageTraining,'String',sprintf(strcat('Image Training (Color) ->',num2str(i))));
    
    %% create gray-scale image
    I_gray=Function_ColorToGray(I);
    
    axes(handles.ImageGray);
    imshow(I_gray);  
    
    %%
    
    %% create binary image
%     level=graythresh(I);
%     I_biner=im2bw(I_gray,level);
    
    I_biner=zeros(size(I_gray,1),size(I_gray,2));
    I_biner(find(I_gray<255))=1;

    axes(handles.ImageBinary);
    imshow(I_biner);  
    
    %% create max filter image from binary image
       %windowing_size must be valued an odd number >=3
       windowing_size=5;
       max_filter_I_biner=Function_MaxFilterBiner_(I_biner,windowing_size); 
       
       axes(handles.ImageMaxFilter);
       imshow(max_filter_I_biner);
       
       
    %% count diameter with unit length horizontal each pixel 
       % determine index which contains the value 1
         [idx_,idy_]=find(max_filter_I_biner==1);
         diameter=idy_(numel(idy_))-idy_(1)+1;
    
            
    %% create boundary line in diameter horizontal
        
        %numel((XY2Index(1,idy_(1),size(I_gray,1)):XY2Index(size(I_gray,1),idy_(numel(idy_)),size(I_gray,1)))')
        
        
        % replace pixel value
        I_red=I(:,:,1);
        I_red(:,idy_(1):(idy_(1)+10))=105;
        I_red(:,(idy_(numel(idy_))-10):idy_(numel(idy_)))=105;
        I(:,:,1)=I_red;
        
        I_green=I(:,:,2);
        I_green(:,idy_(1):(idy_(1)+10))=75;
        I_green(:,(idy_(numel(idy_))-10):idy_(numel(idy_)))=75;
        I(:,:,2)=I_green;
        
        I_blue=I(:,:,3);
        I_blue(:,idy_(1):(idy_(1)+10))=245; 
        I_blue(:,(idy_(numel(idy_))-10):idy_(numel(idy_)))=245;
        I(:,:,3)=I_blue;
        
        
        axes(handles.ImageTraining);
        imshow(I); 
        
%     %% count diameter with unit length vertical each pixel
%        % determine index which contains the value 1
%          [idx_v,idy_v]=find(max_filter_I_biner==1);         
%          [value_max,idx_v_max]=max(idx_v);
%          [value_min,idx_v_min]=min(idx_v);         
%          diameter_v=value_max-value_min+1;
%          
%     %% create boundary line in diameter vertical    
%         
%         % replace pixel value
%         I_red=I(:,:,1);
%         I_red(idx_v(idx_v_min):(idx_v(idx_v_min)+10),:)=105;
%         I_red((idx_v(idx_v_max)-10):idx_v(idx_v_max),:)=105;
%         I(:,:,1)=I_red;
%         
%         I_green=I(:,:,2);
%         I_green(idx_v(idx_v_min):(idx_v(idx_v_min)+10),:)=75;
%         I_green((idx_v(idx_v_max)-10):idx_v(idx_v_max),:)=75;
%         I(:,:,2)=I_green;
%         
%         I_blue=I(:,:,3);
%         I_blue(idx_v(idx_v_min):(idx_v(idx_v_min)+10),:)=245; 
%         I_blue((idx_v(idx_v_max)-10):idx_v(idx_v_max),:)=245;
%         I(:,:,3)=I_blue;        
%         
%         axes(handles.ImageTraining);
%         imshow(I); 
        
    % collect the feature value
    dataset(i,:)=[mean_red mean_green mean_blue diameter];
    
end

class';
% 
% dataset_class{1}={mat2cell(dataset) class{1}'}
% 
% dataset_class{1}

dataset;

set(handles.NameImageTraining,'String',sprintf(strcat('Image Training (Color) DONE !')));

merge_data={dataset,class'};

% lots of data multiply with lots of feature
data_multiply_feature=(lots_of_data_train_jn+lots_of_data_train_jl+lots_of_data_train_jm)*lots_of_feature;

% collect data feature R, G, B and Diameter
for i=1:data_multiply_feature
   dat_init{i}=num2str(merge_data{1}(i),'%.3f');
end

% collect data class Citrus : Nipis, Lemon and Orange
for j=1:(lots_of_data_train_jn+lots_of_data_train_jl+lots_of_data_train_jm)
   dat_init{data_multiply_feature+j}=char(merge_data{2}(j));
end

dat=reshape(dat_init,[(lots_of_data_train_jn+lots_of_data_train_jl+lots_of_data_train_jm) (lots_of_feature+1)]);

set(NaiveBayesClassifierProject.DataTraining,'Userdata',dat);

%% insert data into dataset
t=uitable('Data', dat, 'ColumnName',...
        {'R (Red)', 'G (Green)', 'B (Blue)', 'D (Diameter)','Class (Citrus)'},...
        'Position', [20 20 430 150]);
    
   
    
    set(NaiveBayesClassifierProject.dataset_all_feature_class,'Userdata',dataset);
    
% --- Executes during object creation, after setting all properties.
function SizeImageTraining_CreateFcn(hObject, eventdata, handles)
% hObject    handle to SizeImageTraining (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called


% --- Executes during object creation, after setting all properties.
function SizeImageTesting_CreateFcn(hObject, eventdata, handles)
% hObject    handle to SizeImageTesting (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called


% --- Executes during object creation, after setting all properties.
function NameImageTraining_CreateFcn(hObject, eventdata, handles)
% hObject    handle to NameImageTraining (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called


% --- Executes during object creation, after setting all properties.
function uitabledataset_CreateFcn(hObject, eventdata, handles)
% hObject    handle to uitabledataset (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called


% --- Executes when entered data in editable cell(s) in uitabledataset.
function uitabledataset_CellEditCallback(hObject, eventdata, handles)
% hObject    handle to uitabledataset (see GCBO)
% eventdata  structure with the following fields (see UITABLE)
%	Indices: row and column indices of the cell(s) edited
%	PreviousData: previous data for the cell(s) edited
%	EditData: string(s) entered by the user
%	NewData: EditData or its converted form set on the Data property. Empty if Data was not changed
%	Error: error string when failed to convert EditData to appropriate value for Data
% handles    structure with handles and user data (see GUIDATA)


% --- Executes when selected cell(s) is changed in uitabledataset.
function uitabledataset_CellSelectionCallback(hObject, eventdata, handles)
% hObject    handle to uitabledataset (see GCBO)
% eventdata  structure with the following fields (see UITABLE)
%	Indices: row and column indices of the cell(s) currently selecteds
% handles    structure with handles and user data (see GUIDATA)


% --- Executes on button press in testingdata.
function testingdata_Callback(hObject, eventdata, handles)
% hObject    handle to testingdata (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
NaiveBayesClassifierProject=guidata(gcbo);

% data training of Citrus nipis (jn)
lots_of_data_train_jn=7;

% data training of Citrus lemon (jl)
lots_of_data_train_jl=3;

% data training of Citrus orange (jm)
lots_of_data_train_jm=5;

lots_of_feature=4;
lots_of_class=3;

[basefilename,path]= uigetfile({'*.*'},'Open All Image File');
filename= fullfile(path, basefilename);

if sum(strfind(lower(basefilename), '.'))==0
else
    
    I_testing = imread (filename);

    % if I = [MxNx4]
    if(size(I_testing,3)==4)
        I_testing(:,:,1)=[]; % convert to I = [MxNx3]
    end
        
    %set(NaiveBayesClassifierProject.NaiveBayesClassifier,'CurrentAxes',NaiveBayesClassifierProject.ImageTraining);
    %set (imshow(I));
    
    axes(handles.ImageTesting);
    imshow(I_testing);
    
    % count mean Red, Green, Blue
    mean_red_testing=mean(mean(I_testing(:,:,1)));
    mean_green_testing=mean(mean(I_testing(:,:,2)));
    mean_blue_testing=mean(mean(I_testing(:,:,3)));
    
    set(NaiveBayesClassifierProject.var_mean_red_testing,...
        'String',num2str(mean_red_testing,'%.3f'));
    
    set(NaiveBayesClassifierProject.var_mean_green_testing,...
        'String',num2str(mean_green_testing,'%.3f'));
    
    set(NaiveBayesClassifierProject.var_mean_blue_testing,...
        'String',num2str(mean_blue_testing,'%.3f'));
    
    set(NaiveBayesClassifierProject.ImageTesting,'Userdata',I_testing);   
    
    SizeImageTesting=size(I_testing);
    StringSizeImageTesting=sprintf(strcat(num2str(SizeImageTesting(1)),'x',num2str(SizeImageTesting(2)),'x',num2str(SizeImageTesting(3))));    
    StringSizeImageTesting = strrep(StringSizeImageTesting,'x',' x ');
    
    set(handles.SizeImageTesting,'String',StringSizeImageTesting);
    
    %% create gray-scale image
    I_gray_testing=Function_ColorToGray(I_testing);
    
    axes(handles.ImageGrayTesting);
    imshow(I_gray_testing);  
    
    %%
    
    %% create binary image _testing
%     level=graythresh(I_testing);
%     I_biner_testing=im2bw(I_gray_testing,level);
    
    I_biner_testing=zeros(size(I_gray_testing,1),size(I_gray_testing,2));
    I_biner_testing(find(I_gray_testing<255))=1;

    axes(handles.ImageBinaryTesting);
    imshow(I_biner_testing);  
    
    %% create max filter image from biner_testing image
       %windowing_size must be valued an odd number >=3
       windowing_size=5;
       max_filter_I_biner_testing=Function_MaxFilterBiner_(I_biner_testing,windowing_size); 
       
       axes(handles.ImageMaxFilterTesting);
       imshow(max_filter_I_biner_testing);
       
       
    %% count diameter with unit length each pixel
       % determine index which contains the value 1 (white color)
         [idx_,idy_]=find(max_filter_I_biner_testing==1);
         diameter_testing=idy_(numel(idy_))-idy_(1)+1;
         
         set(NaiveBayesClassifierProject.var_diameter_testing,...
        'String',num2str(diameter_testing,'%.2f'));
         
    % collect feature value
    dataset(1,:)=[mean_red_testing mean_green_testing mean_blue_testing diameter_testing];
    
    if(isempty(strfind(basefilename, 'Lemon'))==0)
        class_testing{1}='Lemon';
    elseif(isempty(strfind(basefilename, 'Orange'))==0)
        class_testing{1}='Orange';
    elseif(isempty(strfind(basefilename, 'Nipis'))==0)
        class_testing{1}='Nipis'; 
    else
        class_testing{1}='UnKnown';
    end
    
    class_testing{1}
    
    set(NaiveBayesClassifierProject.var_class_testing,...
        'String',char(class_testing{1}));
    
    % replace pixel value
        I_red=I_testing(:,:,1);
        I_red(:,idy_(1):(idy_(1)+10))=105;
        I_red(:,(idy_(numel(idy_))-10):idy_(numel(idy_)))=105;
        I_testing(:,:,1)=I_red;
        
        I_green=I_testing(:,:,2);
        I_green(:,idy_(1):(idy_(1)+10))=75;
        I_green(:,(idy_(numel(idy_))-10):idy_(numel(idy_)))=75;
        I_testing(:,:,2)=I_green;
        
        I_blue=I_testing(:,:,3);
        I_blue(:,idy_(1):(idy_(1)+10))=245; 
        I_blue(:,(idy_(numel(idy_))-10):idy_(numel(idy_)))=245;
        I_testing(:,:,3)=I_blue;
        
        
        axes(handles.ImageTesting);
        imshow(I_testing); 
        
        
      %% count Probability of Posterior
        %GetDataTraining=get(NaiveBayesClassifierProject.DataTraining,'User
        %data');
        %Getmean_varian=get(NaiveBayesClassifierProject.dataset_all_feature_class,'Userdata');      
        dataset=get(NaiveBayesClassifierProject.dataset_all_feature_class,'Userdata');      
        
        Xrgbd=[mean_red_testing;mean_green_testing;mean_blue_testing;...
            diameter_testing];
        
%         % normalization dataset    
%         dataset
%         Xrgbd=Xrgbd'
%         dataset_Xrgbd=[dataset;Xrgbd];
%         dataset_Xrgbd_norm=dataset_Xrgbd./(padarray((sum(dataset_Xrgbd.*dataset_Xrgbd).^0.5),[(size(dataset_Xrgbd,1)-1) 0],'replicate','post'))
%         
%         % replace dataset and Xrgbd with normalization
%         dataset = dataset_Xrgbd_norm(1:(size(dataset_Xrgbd_norm,1)-1),:)
%         Xrgbd = dataset_Xrgbd_norm(size(dataset_Xrgbd_norm,1),:)
        
        %% count & save result of mean and varian from every feature & class
            % Initialization
            Getmean_varian=zeros(2*lots_of_class,lots_of_feature);

            for i=1:lots_of_feature
                % set feature 1 for R, 2 for G, 3 for B, 4 for D
                feature_rgbd=dataset(:,i);

                % count mean_class_citrus_nipis(jn),_lemon(jl),_orange(jm)
                mean_feature_rgbd_jn=mean(feature_rgbd(1:lots_of_data_train_jn));
                mean_feature_rgbd_jl=mean(feature_rgbd(...
                    (lots_of_data_train_jn+1):(lots_of_data_train_jn+lots_of_data_train_jl)));
                mean_feature_rgbd_jm=mean(feature_rgbd(...
                    (lots_of_data_train_jn+lots_of_data_train_jl+1):...
                    (lots_of_data_train_jn+lots_of_data_train_jl+lots_of_data_train_jm)));

                % count varian_class_citrus_nipis(jn),_lemon(jl),_orange(jm
                varian_feature_rgbd_jn=var(feature_rgbd(1:lots_of_data_train_jn));
                varian_feature_rgbd_jl=var(feature_rgbd(...
                    (lots_of_data_train_jn+1):(lots_of_data_train_jn+lots_of_data_train_jl)));
                varian_feature_rgbd_jm=var(feature_rgbd(...
                    (lots_of_data_train_jn+lots_of_data_train_jl+1):...
                    (lots_of_data_train_jn+lots_of_data_train_jl+lots_of_data_train_jm)));

                Getmean_varian(:,i)=[mean_feature_rgbd_jn,mean_feature_rgbd_jl,...
                    mean_feature_rgbd_jm,varian_feature_rgbd_jn,varian_feature_rgbd_jl,...
                    varian_feature_rgbd_jm];
            end

        %% note of format mean_varian matrix :
           % size : 
             % rows = 2*lots_of_class
             % column = lots_of_feature

        % content mean_varian matrix :
         % -----------------------------------------------------------------------------------
         % |       Red        |     Green         |      Blue         |      Diameter        | 
         % -----------------------------------------------------------------------------------
         % |   mean_jn_red    |   mean_jn_green   |  mean_jn_blue     |   mean_jn_diameter   |
         % |   mean_jl_red    |   mean_jl_green   |  mean_jl_blue     |   mean_jl_diameter   |
         % |   mean_jm_red    |   mean_jm_green   |  mean_jm_blue     |   mean_jm_diameter   |
         % |  varian_jn_red   |  varian_jn_green  |  varian_jn_blue   |  varian_jn_diameter  |
         % |  varian_jl_red   |  varian_jl_green  |  varian_jl_blue   |  varian_jl_diameter  |
         % |  varian_jm_red   |  varian_jm_green  |  varian_jm_blue   |  varian_jm_diameter  |
         % -----------------------------------------------------------------------------------
        %%
        
        
        
        % count Probability of Prior
        P_Prior_jn=lots_of_data_train_jn/(lots_of_data_train_jn+...
        lots_of_data_train_jl+lots_of_data_train_jm);
        P_Prior_jl=lots_of_data_train_jl/(lots_of_data_train_jn+...
            lots_of_data_train_jl+lots_of_data_train_jm);
        P_Prior_jm=lots_of_data_train_jm/(lots_of_data_train_jn+...
            lots_of_data_train_jl+lots_of_data_train_jm);
        
        % initialization Probability value of Posterior
        P_Posterior_jn=1*P_Prior_jn
        P_Posterior_jl=1*P_Prior_jl
        P_Posterior_jm=1*P_Prior_jm
        
%% note format Getmean_varian matrix :
   % size : 
     % rows = 2*lots_of_class
     % column = lots_of_feature

% content of Getmean_varian matrix :
 % -----------------------------------------------------------------------------------
 % |       Red        |     Green         |      Blue         |      Diameter        | 
 % -----------------------------------------------------------------------------------
 % |   mean_jn_red    |   mean_jn_green   |  mean_jn_blue     |   mean_jn_diameter   |
 % |   mean_jl_red    |   mean_jl_green   |  mean_jl_blue     |   mean_jl_diameter   |
 % |   mean_jm_red    |   mean_jm_green   |  mean_jm_blue     |   mean_jm_diameter   |
 % |  varian_jn_red   |  varian_jn_green  |  varian_jn_blue   |  varian_jn_diameter  |
 % |  varian_jl_red   |  varian_jl_green  |  varian_jl_blue   |  varian_jl_diameter  |
 % |  varian_jm_red   |  varian_jm_green  |  varian_jm_blue   |  varian_jm_diameter  |
 % -----------------------------------------------------------------------------------
%%
  
        % count Probability of Likelihood from RBGD feature
        for i=1:lots_of_feature      
            mean_varian_RGBD=Getmean_varian(:,i);
            P_Likelihood_x_RGB_to_jn=...
                (1/sqrt(2*(22/7)*mean_varian_RGBD(4)))...
                *exp(-1*(((Xrgbd(i)-mean_varian_RGBD(1))^2)/(2*mean_varian_RGBD(4))))
            P_Posterior_jn=P_Posterior_jn*P_Likelihood_x_RGB_to_jn
            
            mean_varian_RGBD(5)
            mean_varian_RGBD(2)
            Xrgbd(i)
            P_Likelihood_x_RGB_to_jl=...
                (1/sqrt(2*(22/7)*mean_varian_RGBD(5)))...
                *exp(-1*(((Xrgbd(i)-mean_varian_RGBD(2))^2)/(2*mean_varian_RGBD(5))))
            P_Posterior_jl=P_Posterior_jl*P_Likelihood_x_RGB_to_jl
            
            P_Likelihood_x_RGB_to_jm=...
                (1/sqrt(2*(22/7)*mean_varian_RGBD(6)))...
                *exp(-1*(((Xrgbd(i)-mean_varian_RGBD(3))^2)/(2*mean_varian_RGBD(6))))
            P_Posterior_jm=P_Posterior_jm*P_Likelihood_x_RGB_to_jm
        end
        
        set(NaiveBayesClassifierProject.posterior_class_jn,...
        'String',strcat(num2str(P_Posterior_jn,'%.20f'),{'  ('},num2str(P_Posterior_jn),')'));
        set(NaiveBayesClassifierProject.posterior_class_jl,...
        'String',strcat(num2str(P_Posterior_jl,'%.20f'),{'  ('},num2str(P_Posterior_jl),')'));
        set(NaiveBayesClassifierProject.posterior_class_jm,...
        'String',strcat(num2str(P_Posterior_jm,'%.20f'),{'  ('},num2str(P_Posterior_jm),')'));
        
        All_P_Posterior=[P_Posterior_jn;P_Posterior_jl;P_Posterior_jm];
      
        [vmax_Posterior,idxmax_Posterior]=max(All_P_Posterior);
        
        Decision_Of_Classification='';
        if(idxmax_Posterior==1)
            Decision_Of_Classification='Nipis';
        elseif(idxmax_Posterior==2)
            Decision_Of_Classification='Lemon';
        else
            Decision_Of_Classification='Orange';
        end
        
        set(NaiveBayesClassifierProject.classification_result,...
        'String',Decision_Of_Classification);
        

end



function var_mean_red_testing_Callback(hObject, eventdata, handles)
% hObject    handle to var_mean_red_testing (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of var_mean_red_testing as text
%        str2double(get(hObject,'String')) returns contents of var_mean_red_testing as a double
NaiveBayesClassifierProject = guidata(gcbo);
var_mean_red_testing = str2double(get(hObject, 'String'));
NaiveBayesClassifierProject.var_mean_red_testing = var_mean_red_testing;

% --- Executes during object creation, after setting all properties.
function var_mean_red_testing_CreateFcn(hObject, eventdata, handles)
% hObject    handle to var_mean_red_testing (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end



function var_mean_green_testing_Callback(hObject, eventdata, handles)
% hObject    handle to var_mean_green_testing (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of var_mean_green_testing as text
%        str2double(get(hObject,'String')) returns contents of var_mean_green_testing as a double
NaiveBayesClassifierProject = guidata(gcbo);
var_mean_green_testing = str2double(get(hObject, 'String'));
NaiveBayesClassifierProject.var_mean_green_testing = var_mean_green_testing;


% --- Executes during object creation, after setting all properties.
function var_mean_green_testing_CreateFcn(hObject, eventdata, handles)
% hObject    handle to var_mean_green_testing (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end



function var_mean_blue_testing_Callback(hObject, eventdata, handles)
% hObject    handle to var_mean_blue_testing (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of var_mean_blue_testing as text
%        str2double(get(hObject,'String')) returns contents of var_mean_blue_testing as a double
NaiveBayesClassifierProject = guidata(gcbo);
var_mean_blur_testing = str2double(get(hObject, 'String'));
NaiveBayesClassifierProject.var_mean_blue_testing = var_mean_blue_testing;


% --- Executes during object creation, after setting all properties.
function var_mean_blue_testing_CreateFcn(hObject, eventdata, handles)
% hObject    handle to var_mean_blue_testing (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end



function var_diameter_testing_Callback(hObject, eventdata, handles)
% hObject    handle to var_diameter_testing (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of var_diameter_testing as text
%        str2double(get(hObject,'String')) returns contents of var_diameter_testing as a double
NaiveBayesClassifierProject = guidata(gcbo);
var_diameter_testing = str2double(get(hObject, 'String'));
NaiveBayesClassifierProject.var_diameter_testing = var_diameter_testing;


% --- Executes during object creation, after setting all properties.
function var_diameter_testing_CreateFcn(hObject, eventdata, handles)
% hObject    handle to var_diameter_testing (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end



function var_class_testing_Callback(hObject, eventdata, handles)
% hObject    handle to var_class_testing (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of var_class_testing as text
%        str2double(get(hObject,'String')) returns contents of var_class_testing as a double
NaiveBayesClassifierProject = guidata(gcbo);
var_class_testing = str2double(get(hObject, 'String'));
NaiveBayesClassifierProject.var_class_testing = var_class_testing;



% --- Executes during object creation, after setting all properties.
function var_class_testing_CreateFcn(hObject, eventdata, handles)
% hObject    handle to var_class_testing (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end



function posterior_class_jn_Callback(hObject, eventdata, handles)
% hObject    handle to posterior_class_jn (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of posterior_class_jn as text
%        str2double(get(hObject,'String')) returns contents of posterior_class_jn as a double
NaiveBayesClassifierProject = guidata(gcbo);
var_class_jn = str2double(get(hObject, 'String'));
NaiveBayesClassifierProject.var_class_jn = var_class_jn;

% --- Executes during object creation, after setting all properties.
function posterior_class_jn_CreateFcn(hObject, eventdata, handles)
% hObject    handle to posterior_class_jn (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end



function posterior_class_jl_Callback(hObject, eventdata, handles)
% hObject    handle to posterior_class_jl (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of posterior_class_jl as text
%        str2double(get(hObject,'String')) returns contents of posterior_class_jl as a double
NaiveBayesClassifierProject = guidata(gcbo);
var_class_jl = str2double(get(hObject, 'String'));
NaiveBayesClassifierProject.var_class_jl = var_class_jl;

% --- Executes during object creation, after setting all properties.
function posterior_class_jl_CreateFcn(hObject, eventdata, handles)
% hObject    handle to posterior_class_jl (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end



function posterior_class_jm_Callback(hObject, eventdata, handles)
% hObject    handle to posterior_class_jm (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of posterior_class_jm as text
%        str2double(get(hObject,'String')) returns contents of posterior_class_jm as a double
NaiveBayesClassifierProject = guidata(gcbo);
var_class_jm = str2double(get(hObject, 'String'));
NaiveBayesClassifierProject.var_class_jm = var_class_jm;


% --- Executes during object creation, after setting all properties.
function posterior_class_jm_CreateFcn(hObject, eventdata, handles)
% hObject    handle to posterior_class_jm (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end



function mean_class_jn_Callback(hObject, eventdata, handles)
% hObject    handle to txt_mean_var (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of txt_mean_var as text
%        str2double(get(hObject,'String')) returns contents of txt_mean_var as a double
NaiveBayesClassifierProject = guidata(gcbo);
mean_class_jn = str2double(get(hObject, 'String'));
NaiveBayesClassifierProject.mean_class_jn = mean_class_jn;

% --- Executes during object creation, after setting all properties.
function mean_class_jn_CreateFcn(hObject, eventdata, handles)
% hObject    handle to txt_mean_var (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end



function varian_class_jn_Callback(hObject, eventdata, handles)
% hObject    handle to varian_class_jn (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of varian_class_jn as text
%        str2double(get(hObject,'String')) returns contents of varian_class_jn as a double
NaiveBayesClassifierProject = guidata(gcbo);
varian_class_jn = str2double(get(hObject, 'String'));
NaiveBayesClassifierProject.varian_class_jn = varian_class_jn;

% --- Executes during object creation, after setting all properties.
function varian_class_jn_CreateFcn(hObject, eventdata, handles)
% hObject    handle to varian_class_jn (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end



function mean_class_jl_Callback(hObject, eventdata, handles)
% hObject    handle to mean_class_jl (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of mean_class_jl as text
%        str2double(get(hObject,'String')) returns contents of mean_class_jl as a double
NaiveBayesClassifierProject = guidata(gcbo);
mean_class_jl = str2double(get(hObject, 'String'));
NaiveBayesClassifierProject.mean_class_jl = mean_class_jl;

% --- Executes during object creation, after setting all properties.
function mean_class_jl_CreateFcn(hObject, eventdata, handles)
% hObject    handle to mean_class_jl (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end



function varian_class_jl_Callback(hObject, eventdata, handles)
% hObject    handle to varian_class_jl (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of varian_class_jl as text
%        str2double(get(hObject,'String')) returns contents of varian_class_jl as a double
NaiveBayesClassifierProject = guidata(gcbo);
varian_class_jl = str2double(get(hObject, 'String'));
NaiveBayesClassifierProject.varian_class_jl = varian_class_jl;

% --- Executes during object creation, after setting all properties.
function varian_class_jl_CreateFcn(hObject, eventdata, handles)
% hObject    handle to varian_class_jl (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end



function mean_class_jm_Callback(hObject, eventdata, handles)
% hObject    handle to mean_class_jm (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of mean_class_jm as text
%        str2double(get(hObject,'String')) returns contents of mean_class_jm as a double
NaiveBayesClassifierProject = guidata(gcbo);
mean_class_jm = str2double(get(hObject, 'String'));
NaiveBayesClassifierProject.mean_class_jm = mean_class_jm;

% --- Executes during object creation, after setting all properties.
function mean_class_jm_CreateFcn(hObject, eventdata, handles)
% hObject    handle to mean_class_jm (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end



function varian_class_jm_Callback(hObject, eventdata, handles)
% hObject    handle to varian_class_jm (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of varian_class_jm as text
%        str2double(get(hObject,'String')) returns contents of varian_class_jm as a double
NaiveBayesClassifierProject = guidata(gcbo);
varian_class_jm = str2double(get(hObject, 'String'));
NaiveBayesClassifierProject.varian_class_jm = varian_class_jm;

% --- Executes during object creation, after setting all properties.
function varian_class_jm_CreateFcn(hObject, eventdata, handles)
% hObject    handle to varian_class_jm (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end



function classification_result_Callback(hObject, eventdata, handles)
% hObject    handle to classification_result (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of classification_result as text
%        str2double(get(hObject,'String')) returns contents of classification_result as a double


% --- Executes during object creation, after setting all properties.
function classification_result_CreateFcn(hObject, eventdata, handles)
% hObject    handle to classification_result (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end


% --- Executes during object creation, after setting all properties.
function ImageTraining_CreateFcn(hObject, eventdata, handles)
% hObject    handle to ImageTraining (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: place code in OpeningFcn to populate ImageTraining


% --- Executes during object creation, after setting all properties.
function ImageTraining_CreateFcn(hObject, eventdata, handles)
% hObject    handle to ImageTraining (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: place code in OpeningFcn to populate ImageTraining


% --- Executes during object creation, after setting all properties.
function SizeImageTraining_CreateFcn(hObject, eventdata, handles)
% hObject    handle to SizeImageTraining (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called


% --- Executes during object creation, after setting all properties.
function NameImageTraining_CreateFcn(hObject, eventdata, handles)
% hObject    handle to NameImageTraining (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called



function var_class_testing_Callback(hObject, eventdata, handles)
% hObject    handle to var_class_testing (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of var_class_testing as text
%        str2double(get(hObject,'String')) returns contents of var_class_testing as a double


% --- Executes during object creation, after setting all properties.
function var_class_testing_CreateFcn(hObject, eventdata, handles)
% hObject    handle to var_class_testing (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end



function posterior_class_jn_Callback(hObject, eventdata, handles)
% hObject    handle to posterior_class_jn (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of posterior_class_jn as text
%        str2double(get(hObject,'String')) returns contents of posterior_class_jn as a double


% --- Executes during object creation, after setting all properties.
function posterior_class_jn_CreateFcn(hObject, eventdata, handles)
% hObject    handle to posterior_class_jn (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end



function posterior_class_jl_Callback(hObject, eventdata, handles)
% hObject    handle to posterior_class_jl (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of posterior_class_jl as text
%        str2double(get(hObject,'String')) returns contents of posterior_class_jl as a double


% --- Executes during object creation, after setting all properties.
function posterior_class_jl_CreateFcn(hObject, eventdata, handles)
% hObject    handle to posterior_class_jl (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end



function posterior_class_jm_Callback(hObject, eventdata, handles)
% hObject    handle to posterior_class_jm (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of posterior_class_jm as text
%        str2double(get(hObject,'String')) returns contents of posterior_class_jm as a double


% --- Executes during object creation, after setting all properties.
function posterior_class_jm_CreateFcn(hObject, eventdata, handles)
% hObject    handle to posterior_class_jm (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end
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[image recognition] Based on Bayesian classifier to achieve citrus recognition MATLAB source code

The figure on the left shows the naturalization process of the multi-source Gaussian distribution on the right#

Bayesian classifier

Maximum Likelihood Estimation

Maximum a posteriori Estimation

Naive Bayes classifier

[image recognition] Based on Bayesian classifier to achieve citrus recognition MATLAB source code

The figure on the left shows the naturalization process of the multi-source Gaussian distribution on the right#

Bayesian classifier

Maximum Likelihood Estimation

Maximum a posteriori Estimation

Naive Bayes classifier

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