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。