模式识别 matlab函数,MATLAB GUI模式识别 线性判别器实现（含源码）详细直观

好的，我可以为您提供MATLAB GUI模式识别线性判别器的实现方法和源码。 首先，我们需要了解什么是线性判别器。线性判别器是一种常用的模式识别方法，它可以将数据分成不同的类别。对于二分类问题，线性判别器可以使用一条直线将两个类别分开，对于多分类问题，可以使用多条直线或平面将不同的类别分开。 接下来，我们需要创建一个MATLAB GUI界面，用于输入和展示数据。我们可以使用MATLAB的GUIDE工具来创建GUI界面。在GUI界面中，我们需要添加以下组件： 1. 一个axes组件，用于展示数据和分隔线； 2. 两个edit组件，用于输入数据； 3. 一个pushbutton组件，用于添加数据； 4. 一个popupmenu组件，用于选择不同的分类器。 接下来，我们需要编写代码实现线性判别器。我们可以使用MATLAB的分类器工具箱中的线性判别器函数fitcdiscr来训练模型，并使用predict函数来预测新的数据。具体实现过程如下： 1. 在GUI界面中添加一个pushbutton按钮，用于添加数据。当用户点击该按钮时，将读取两个edit组件中的数据，并将其添加到一个矩阵中。 2. 在GUI界面中添加一个popupmenu组件，用于选择不同的分类器。当用户选择不同的分类器时，将使用fitcdiscr函数训练模型，并使用predict函数预测新的数据。 3. 在GUI界面中添加一个axes组件，用于展示数据和分隔线。当用户添加新的数据时，将在该axes组件中绘制数据点。使用fitcdiscr函数训练模型后，将绘制分隔线。 下面是MATLAB GUI模式识别线性判别器的源码示例： ``` function varargout = LinearDiscriminantAnalysis_GUI(varargin) % LINEARDISCRIMINANTANALYSIS_GUI MATLAB code for LinearDiscriminantAnalysis_GUI.fig % LINEARDISCRIMINANTANALYSIS_GUI, by itself, creates a new LINEARDISCRIMINANTANALYSIS_GUI or raises the existing % singleton*. % % H = LINEARDISCRIMINANTANALYSIS_GUI returns the handle to a new LINEARDISCRIMINANTANALYSIS_GUI or the handle to % the existing singleton*. % % LINEARDISCRIMINANTANALYSIS_GUI('CALLBACK',hObject,eventData,handles,...) calls the local % function named CALLBACK in LINEARDISCRIMINANTANALYSIS_GUI.M with the given input arguments. % % LINEARDISCRIMINANTANALYSIS_GUI('Property','Value',...) creates a new LINEARDISCRIMINANTANALYSIS_GUI or raises the % existing singleton*. Starting from the left, property value pairs are % applied to the GUI before LinearDiscriminantAnalysis_GUI_OpeningFcn gets called. An % unrecognized property name or invalid value makes property application % stop. All inputs are passed to LinearDiscriminantAnalysis_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 LinearDiscriminantAnalysis_GUI % Last Modified by GUIDE v2.5 30-Nov-2021 20:49:31 % Begin initialization code - DO NOT EDIT gui_Singleton = 1; gui_State = struct('gui_Name', mfilename, ... 'gui_Singleton', gui_Singleton, ... 'gui_OpeningFcn', @LinearDiscriminantAnalysis_GUI_OpeningFcn, ... 'gui_OutputFcn', @LinearDiscriminantAnalysis_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 LinearDiscriminantAnalysis_GUI is made visible. function LinearDiscriminantAnalysis_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 LinearDiscriminantAnalysis_GUI (see VARARGIN) % Choose default command line output for LinearDiscriminantAnalysis_GUI handles.output = hObject; % Update handles structure guidata(hObject, handles); % UIWAIT makes LinearDiscriminantAnalysis_GUI wait for user response (see UIRESUME) % uiwait(handles.figure1); % initialize data matrix handles.data = []; % plot initial figure axes(handles.axes1); plot(0, 0, 'o', 'MarkerFaceColor', 'k'); xlim([-1 1]); ylim([-1 1]); hold on; handles.hline = plot([0 0], [-1 1], 'r-', 'LineWidth', 2); hold off; % Update handles structure guidata(hObject, handles); % --- Outputs from this function are returned to the command line. function varargout = LinearDiscriminantAnalysis_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 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) % read data from edit boxes x = str2double(get(handles.edit1, 'String')); y = str2double(get(handles.edit2, 'String')); % add data to data matrix handles.data = [handles.data; x y]; % plot data points axes(handles.axes1); plot(x, y, 'o', 'MarkerFaceColor', 'b'); % update handles structure guidata(hObject, handles); % --- Executes on selection change in popupmenu1. 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) % get selected classifier classifier = get(handles.popupmenu1, 'Value'); % train linear discriminant analysis model if classifier == 1 % train model using all data model = fitcdiscr(handles.data(:, 1:2), handles.data(:, 3)); else % train model using first 80% of data idx = randperm(size(handles.data, 1)); train_idx = idx(1:round(0.8*size(handles.data, 1))); test_idx = idx(round(0.8*size(handles.data, 1))+1:end); model = fitcdiscr(handles.data(train_idx, 1:2), handles.data(train_idx, 3)); end % get decision boundary k = model.Coeffs(1, 2).Const; b = model.Coeffs(1, 2).Linear; x = -1:0.01:1; y = k*x+b; % plot decision boundary axes(handles.axes1); delete(handles.hline); handles.hline = plot(x, y, 'r-', 'LineWidth', 2); % update handles structure guidata(hObject, handles); % --- 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 % add items to popupmenu set(hObject, 'String', {'Linear Discriminant Analysis (All Data)', 'Linear Discriminant Analysis (80% Training Data)'}); % --- Executes during object creation, after setting all properties. function axes1_CreateFcn(hObject, eventdata, handles) % hObject handle to axes1 (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 axes1 ``` 您可以将以上代码复制到MATLAB中，然后使用GUIDE工具创建GUI界面。在创建GUI界面时，需要将代码中的组件与GUIDE中的组件对应起来。完成后，您可以运行该程序并测试其功能。 希望这个代码示例能帮助到您。如果您有任何问题，请随时向我提问。