Matlab 支持向量机示例

Question

我正在尝试实施 SVM 进行分类。目标是输出功率信号（.wav 文件）的正确原点网格。网格标题为 A-I，训练集总共有 93 个信号，练习信号有 49 个。我有一个 93x10x36 的特征向量矩阵。有谁知道为什么我会显示错误？ TrainCorrectGrid 和 Training_Cepstrum1 都有 93 行，所以我不明白问题出在哪里。非常感谢任何帮助。

我的代码显示在这里：

clc; clear; close all;

load('avg_fft_feature (4).mat'); %training feature vectors
load('practice_fft_Mag_all (2).mat'); %practice feauture vectors
load('practice_GridOrigin.mat'); %correct grids of origin for practice data
load PracticeCorrectGrid.mat;
load Training_Cepstrum1;
load Practice_Cepstrum1a;
load fSet1.mat %load in correct practice grids

TrainCorrectGrid=['A';'A';'A';'A';'A';'A';'A';'A';'A';'B';'B';'B';'B';'B';'B';'B';'B';'B';'B';'C';'C';'C';'C';'C';'C';'C';'C';'C';'C';'C';'D';'D';'D';'D';'D';'D';'D';'D';'D';'D';'D';'E';'E';'E';'E';'E';'E';'E';'E';'E';'E';'E';'F';'F';'F';'F';'F';'F';'F';'F';'G';'G';'G';'G';'G';'G';'G';'G';'G';'G';'G';'H';'H';'H';'H';'H';'H';'H';'H';'H';'H';'H';'I';'I';'I';'I';'I';'I';'I';'I';'I';'I';'I']; 
%[results,u] = multisvm(avg_fft_feature, TrainCorrectGrid, avg_fft_feature_practice);%avg_fft_feature); 
[results,u] = multisvm(Training_Cepstrum1(93,:,1), TrainCorrectGrid, Practice_Cepstrum1a(49,:,1));  
disp('Grids of Origin (SVM)'); 

%Display SVM Results
for i = 1:numel(u)
    str = sprintf('%d: %s', i, u(i));
    disp(str);
end

%Display Percent Correct
numCorrect = 0;
for i = 1:numel(u)
    %if (strcmp(TrainCorrectGrid(i,1), u(i))==1); %compare training to
    %training
    if (strcmp(PracticeCorrectGrid(i,1), u(i))==1); %compare practice data to training
        numCorrect = numCorrect + 1; 
    end
end 
numberOfElements = numel(u);
percentCorrect =  numCorrect / numberOfElements * 100;
% percentCorrect = round(percentCorrect, 2);
dispPercent = sprintf('Percent Correct = %0.3f%%', percentCorrect);
disp(dispPercent);

error shown here

此处显示 multisvm 函数：

function [result, u] = multisvm(TrainingSet,GroupTrain,TestSet)
%Models a given training set with a corresponding group vector and 
%classifies a given test set using an SVM classifier according to a 
%one vs. all relation. 
%
%This code was written by Cody Neuburger cneuburg@fau.edu
%Florida Atlantic University, Florida USA and slightly modified by Renny Varghese
%This code was adapted and cleaned from Anand Mishra's multisvm function
%found at http://www.mathworks.com/matlabcentral/fileexchange/33170-multi-class-support-vector-machine/

u=unique(GroupTrain);
numClasses=length(u);
result = zeros(length(TestSet(:,1)),1);

%build models
for k=1:numClasses
    %Vectorized statement that binarizes Group
    %where 1 is the current class and 0 is all other classes
    G1vAll=(GroupTrain==u(k));
    models(k) = svmtrain(TrainingSet,G1vAll);
end

%classify test cases
for j=1:size(TestSet,1)
    for k=1:numClasses
        if(svmclassify(models(k),TestSet(j,:))) 
            break;
        end
    end
    result(j) = k;
end

mapValues = 'ABCDEFGHI';
u = mapValues(result);

Answer 1

您声明 Training_Cepstrum1 的大小为 [93,10,36]。但是当你调用 multisvm 时，你只是传递了大小为 [1,10] 的 Training_Cepstrum1(93,:,1)。由于 TrainCorrectGrid 的大小为 [93,1]，因此行数不匹配。

看起来你在传入 Practice_Cepstrum1a 时犯了同样的错误。

尝试用

替换对multisvm的调用

[results,u] = multisvm(Training_Cepstrum1(:,:,1), TrainCorrectGrid, Practice_Cepstrum1a(:,:,1));

这样 Training_Cepstrum1(:,:,1) 的大小为 [93,10]，行数与 TrainCorrectGrid 相同。

Matlab 支持向量机示例

Matlab SVM example

matlab

svm