按照要求配置了libsvm-3.1-[FarutoUltimate3.1]，然後出現這個錯誤，求解救。

function Chapter_CharacterRecognitionUsingLibsvm
%% Matlab神經網路43個案例分析

% 基於SVM的手寫字型識別
% by 李洋(faruto)
% http://www.matlabsky.com
% Email:[email protected]
% http://weibo.com/faruto 
% http://blog.sina.com.cn/faruto
% 2013.01.01
%% A Little Clean Work
close all;
clear;
clc;
format compact;
%% 載入訓練資料

% 利用uigetfile函式互動式選取訓練樣本
[FileName,PathName,FilterIndex] = uigetfile( ...
    {'*.jpg';'*.bmp'},'請匯入訓練圖片','*.jpg','MultiSelect','on');
if ~FilterIndex
    return;
end
num_train = length(FileName);
TrainData = zeros(num_train,16*16);
TrainLabel = zeros(num_train,1);
for k = 1:num_train
    pic = imread([PathName,FileName{k}]);
    pic = pic_preprocess(pic);
    
    % 將標準化影象按列拉成一個向量並轉置，生成50*256的訓練樣本矩陣
    TrainData(k,:) = double(pic(:)');
    % 樣本標籤為樣本所對應的數字
    TrainLabel(k) = str2double(FileName{k}(4));
end
%% 建立支援向量機
% [bestCVaccuracy,bestc,bestg] = ...
%     SVMcgForClass(TrainLabel,TrainData,-8,8,-8,8,10,0.8,0.8,4.5)

% 設定GA相關引數
    ga_option.maxgen = 100;
    ga_option.sizepop = 20;
    ga_option.cbound = [0,100];
    ga_option.gbound = [0,100];
    ga_option.v = 10;
    ga_option.ggap = 0.9;
    [bestCVaccuracy,bestc,bestg] = ...
    gaSVMcgForClass(TrainLabel,TrainData,ga_option);
disp('列印選擇結果');
str = sprintf( 'Best Cross Validation MSE = %g Best c = %g Best g = %g',bestCVaccuracy,bestc,bestg);
% 訓練
cmd = ['-c ',num2str(bestc),' -g ',num2str(bestg)];
model = svmtrain(TrainLabel, TrainData, cmd);
% 在訓練集上檢視識別能力
preTrainLabel = svmpredict(TrainLabel, TrainData, model);
%% 載入測試樣本
[FileName,PathName,FilterIndex] = uigetfile( ...
    {'*.jpg';'*.bmp'},'請匯入測試圖片','*.bmp','MultiSelect','on');
if ~FilterIndex
    return;
end
num_train = length(FileName);
TestData = zeros(num_train,16*16);
TestLabel = zeros(num_train,1);
for k = 1:num_train
    pic = imread([PathName,FileName{k}]);
    pic = pic_preprocess(pic);
    
    TestData(k,:) = double(pic(:)');
    TestLabel(k) = str2double(FileName{k}(4));
end
%% 對測試樣本進行分類
preTestLabel = svmpredict(TestLabel, TestData, model);
assignin('base','TestLabel',TestLabel);
assignin('base','preTestLabel',preTestLabel);
TestLabel'
preTestLabel'
%% sub function of pre-processing pic
function pic_preprocess = pic_preprocess(pic)
% 圖片預處理子函式
% 影象反色處理
pic = 255-pic;
% 設定閾值，將反色影象轉成二值影象
pic = im2bw(pic,0.4);
% 查詢數字上所有畫素點的行標y和列標x
[y,x] = find(pic == 1);
% 擷取包含完整數字的最小區域
pic_preprocess = pic(min(y):max(y), min(x):max(x));
% 將擷取的包含完整數字的最小區域影象轉成16*16的標準化影象
pic_preprocess = imresize(pic_preprocess,[16,16]);