function varargout = Algorithm_GUI(varargin)
% ALGORITHM_GUI MATLAB code for Algorithm_GUI.fig
% ALGORITHM_GUI, by itself, creates a new ALGORITHM_GUI or raises the existing
% singleton*.
%
% H = ALGORITHM_GUI returns the handle to a new ALGORITHM_GUI or the handle to
% the existing singleton*.
%
% ALGORITHM_GUI('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in ALGORITHM_GUI.M with the given input arguments.
%
% ALGORITHM_GUI('Property','Value',...) creates a new ALGORITHM_GUI or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before Algorithm_GUI_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to Algorithm_GUI_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 Algorithm_GUI
% Last Modified by GUIDE v2.5 12-Aug-2018 18:21:53
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @Algorithm_GUI_OpeningFcn, ...
'gui_OutputFcn', @Algorithm_GUI_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
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 Algorithm_GUI is made visible.
function Algorithm_GUI_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 Algorithm_GUI (see VARARGIN)
% Choose default command line output for Algorithm_GUI
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes Algorithm_GUI wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = Algorithm_GUI_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 checkbox1.
function popupmenu1_Callback(hObject, eventdata, handles)
% hObject handle to popupmenu1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: contents = cellstr(get(hObject,'String')) returns popupmenu1 contents as cell array
% contents{get(hObject,'Value')} returns selected item from popupmenu1
load wine.mat;
% 画出测试数据的box可视化图
figure;
boxplot(wine,'orientation','horizontal','labels',categories);
title('wine数据的box可视化图','FontSize',12);
xlabel('属性值','FontSize',12);
grid on;
% 画出测试数据的分维可视化图
figure
subplot(3,5,1);
hold on
for run = 1:178
plot(run,wine_labels(run),'*');
end
xlabel('样本','FontSize',10);
ylabel('类别标签','FontSize',10);
title('class','FontSize',10);
for run = 2:14
subplot(3,5,run);
hold on;
str = ['attrib ',num2str(run-1)];
for i = 1:178
plot(i,wine(i,run-1),'*');
end
xlabel('样本','FontSize',10);
ylabel('属性值','FontSize',10);
title(str,'FontSize',10);
end
% --- Executes during object creation, after setting all properties.
function popupmenu1_CreateFcn(hObject, eventdata, handles)
% hObject handle to popupmenu1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: popupmenu 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 on selection change in A.
function A_Callback(hObject, eventdata, handles)
% hObject handle to A (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: contents = cellstr(get(hObject,'String')) returns A contents as cell array
% contents{get(hObject,'Value')} returns selected item from A
global val
val=get(hObject,'Value');
% --- Executes during object creation, after setting all properties.
function A_CreateFcn(hObject, eventdata, handles)
% hObject handle to A (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: popupmenu 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 on button press in pushbutton1.
function pushbutton1_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global val
global train_wine
global test_wine_labels
global test_wine
global train_wine_labels
global bestc1
global bestg1
global bestc2
global bestg2
load wine.mat;
% 选定训练集和测试集
% 将第一类的1-30,第二类的60-95,第三类的131-153做为训练集
train_wine = [wine(1:30,:);wine(60:95,:);wine(131:153,:)];
% 相应的训练集的标签也要分离出来
train_wine_labels = [wine_labels(1:30);wine_labels(60:95);wine_labels(131:153)];
% 将第一类的31-59,第二类的96-130,第三类的154-178做为测试集
test_wine = [wine(31:59,:);wine(96:130,:);wine(154:178,:)];
% 相应的测试集的标签也要分离出来
test_wine_labels = [wine_labels(31:59);wine_labels(96:130);wine_labels(154:178)];
%% 数据预处理
% 数据预处理,将训练集和测试集归一化到[0,1]区间
[mtrain,ntrain] = size(train_wine);
[mtest,ntest] = size(test_wine);
dataset = [train_wine;test_wine];
% mapminmax为MATLAB自带的归一化函数
[dataset_scale,ps] = mapminmax(dataset',0,1);
dataset_scale = dataset_scale';
train_wine = dataset_scale(1:mtrain,:);
test_wine = dataset_scale( (mtrain+1):(mtrain+mtest),: );
if val==1
%% 选择最佳的SVM参数c&g
% 首先进行粗略选择: c&g 的变化范围是 2^(-10),2^(-9),...,2^(10)
[bestacc1,bestc1,bestg1] = SVMcgForClass(train_wine_labels,train_wine,-10,10,-10,10);
% 打印粗略选择结果
disp('打印粗略选择结果');
str = sprintf( 'Best Cross Validation Accuracy = %g%% Best c = %g Best g = %g',bestacc1,bestc1,bestg1);
disp(str);
% 根据粗略选择的结果图再进行精细选择: c 的变化范围是 2^(-2),2^(-1.5),...,2^(4), g 的变化范围是 2^(-4),2^(-3.5),...,2^(4),
[bestacc1,bestc1,bestg1] = SVMcgForClass(train_wine_labels,train_wine,-2,4,-4,4,3,0.5,0.5,0.9);
% 打印精细选择结果
disp('打印精细选择结果');
str = sprintf( 'Best Cross Validation Accuracy = %g%% Best c = %g Best g = %g',bestacc1,bestc1,bestg1);
disp(str);
end
if val==2
[bestacc2,bestc2,bestg2] = psoSVMcgForClass(train_wine_labels,train_wine);
% 打印选择结果
disp('打印选择结果');
str = sprintf( 'Best Cross Validation Accuracy = %g%% Best c = %g Best g = %g',bestacc2,bestc2,bestg2);
disp(str);
end
% --- Executes on button press in pushbutton2.
function pushbutton2_Callback(hObject, eventdata, handles)
% hObject