支持向量机代码（支持向量机代码matlab）

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本文目录一览：

1、支持向量机的matlab代码
2、ENVI5.1不能运行支持向量机分类，并出现错误代码？
3、如何用MATLAB支持向量机将2组数据分类，跪求源代码（实例），邮箱1033558779@qq.com
4、求支持向量机的vc++原代码

支持向量机的matlab代码

如果是7.0以上版本

edit svmtrain

edit svmclassify

edit svmpredict

function [svm_struct, svIndex] = svmtrain(training, groupnames, varargin)

%SVMTRAIN trains a support vector machine classifier

%

% SVMStruct = SVMTRAIN(TRAINING,GROUP) trains a support vector machine

% classifier using data TRAINING taken from two groups given by GROUP.

% SVMStruct contains information about the trained classifier that is

% used by SVMCLASSIFY for classification. GROUP is a column vector of

% values of the same length as TRAINING that defines two groups. Each

% element of GROUP specifies the group the corresponding row of TRAINING

% belongs to. GROUP can be a numeric vector, a string array, or a cell

% array of strings. SVMTRAIN treats NaNs or empty strings in GROUP as

% missing values and ignores the corresponding rows of TRAINING.

%

% SVMTRAIN(...,'KERNEL_FUNCTION',KFUN) allows you to specify the kernel

% function KFUN used to map the training data into kernel space. The

% default kernel function is the dot product. KFUN can be one of the

% following strings or a function handle:

%

% 'linear' Linear kernel or dot product

% 'quadratic' Quadratic kernel

% 'polynomial' Polynomial kernel (default order 3)

% 'rbf' Gaussian Radial Basis Function kernel

% 'mlp' Multilayer Perceptron kernel (default scale 1)

% function A kernel function specified using @,

% for example @KFUN, or an anonymous function

%

% A kernel function must be of the form

%

% function K = KFUN(U, V)

%

% The returned value, K, is a matrix of size M-by-N, where U and V have M

% and N rows respectively. If KFUN is parameterized, you can use

% anonymous functions to capture the problem-dependent parameters. For

% example, suppose that your kernel function is

%

% function k = kfun(u,v,p1,p2)

% k = tanh(p1*(u*v')+p2);

%

% You can set values for p1 and p2 and then use an anonymous function:

% @(u,v) kfun(u,v,p1,p2).

%

% SVMTRAIN(...,'POLYORDER',ORDER) allows you to specify the order of a

% polynomial kernel. The default order is 3.

%

% SVMTRAIN(...,'MLP_PARAMS',[P1 P2]) allows you to specify the

% parameters of the Multilayer Perceptron (mlp) kernel. The mlp kernel

% requires two parameters, P1 and P2, where K = tanh(P1*U*V' + P2) and P1

% 0 and P2 0. Default values are P1 = 1 and P2 = -1.

%

% SVMTRAIN(...,'METHOD',METHOD) allows you to specify the method used

% to find the separating hyperplane. Options are

%

% 'QP' Use quadratic programming (requires the Optimization Toolbox)

% 'LS' Use least-squares method

%

% If you have the Optimization Toolbox, then the QP method is the default

% method. If not, the only available method is LS.

%

% SVMTRAIN(...,'QUADPROG_OPTS',OPTIONS) allows you to pass an OPTIONS

% structure created using OPTIMSET to the QUADPROG function when using

% the 'QP' method. See help optimset for more details.

%

% SVMTRAIN(...,'SHOWPLOT',true), when used with two-dimensional data,

% creates a plot of the grouped data and plots the separating line for

% the classifier.

%

% Example:

% % Load the data and select features for classification

% load fisheriris

% data = [meas(:,1), meas(:,2)];

% % Extract the Setosa class

% groups = ismember(species,'setosa');

% % Randomly select training and test sets

% [train, test] = crossvalind('holdOut',groups);

% cp = classperf(groups);

% % Use a linear support vector machine classifier

% svmStruct = svmtrain(data(train,:),groups(train),'showplot',true);

% classes = svmclassify(svmStruct,data(test,:),'showplot',true);

% % See how well the classifier performed

% classperf(cp,classes,test);

% cp.CorrectRate

%

% See also CLASSIFY, KNNCLASSIFY, QUADPROG, SVMCLASSIFY.

% $Revision: 1.1.12.1 $ $Date: 2004/12/24 20:43:35 $

% References:

% [1] Kecman, V, Learning and Soft Computing,

% MIT Press, Cambridge, MA. 2001.

% [2] Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B.,

% Vandewalle, J., Least Squares Support Vector Machines,

% World Scientific, Singapore, 2002.

% [3] Scholkopf, B., Smola, A.J., Learning with Kernels,

% MIT Press, Cambridge, MA. 2002.

%

% SVMTRAIN(...,'KFUNARGS',ARGS) allows you to pass additional

% arguments to kernel functions.

% set defaults

plotflag = false;

qp_opts = [];

kfunargs = {};

setPoly = false; usePoly = false;

setMLP = false; useMLP = false;

if ~isempty(which('quadprog'))

useQuadprog = true;

else

useQuadprog = false;

end

% set default kernel function

kfun = @linear_kernel;

% check inputs

if nargin 2

error(nargchk(2,Inf,nargin))

end

numoptargs = nargin -2;

optargs = varargin;

% grp2idx sorts a numeric grouping var ascending, and a string grouping

% var by order of first occurrence

[g,groupString] = grp2idx(groupnames);

% check group is a vector -- though char input is special...

if ~isvector(groupnames) ~ischar(groupnames)

error('Bioinfo:svmtrain:GroupNotVector',...

'Group must be a vector.');

end

% make sure that the data is correctly oriented.

if size(groupnames,1) == 1

groupnames = groupnames';

end

% make sure data is the right size

n = length(groupnames);

if size(training,1) ~= n

if size(training,2) == n

training = training';

else

error('Bioinfo:svmtrain:DataGroupSizeMismatch',...

'GROUP and TRAINING must have the same number of rows.')

end

% NaNs are treated as unknown classes and are removed from the training

% data

nans = find(isnan(g));

if length(nans) 0

training(nans,:) = [];

g(nans) = [];

end

ngroups = length(groupString);

if ngroups 2

error('Bioinfo:svmtrain:TooManyGroups',...

'SVMTRAIN only supports classification into two groups.\nGROUP contains %d different groups.',ngroups)

end

% convert to 1, -1.

g = 1 - (2* (g-1));

% handle optional arguments

if numoptargs = 1

if rem(numoptargs,2)== 1

error('Bioinfo:svmtrain:IncorrectNumberOfArguments',...

'Incorrect number of arguments to %s.',mfilename);

end

okargs = {'kernel_function','method','showplot','kfunargs','quadprog_opts','polyorder','mlp_params'};

for j=1:2:numoptargs

pname = optargs{j};

pval = optargs{j+1};

k = strmatch(lower(pname), okargs);%#ok

if isempty(k)

error('Bioinfo:svmtrain:UnknownParameterName',...

'Unknown parameter name: %s.',pname);

elseif length(k)1

error('Bioinfo:svmtrain:AmbiguousParameterName',...

'Ambiguous parameter name: %s.',pname);

else

switch(k)

case 1 % kernel_function

if ischar(pval)

okfuns = {'linear','quadratic',...

'radial','rbf','polynomial','mlp'};

funNum = strmatch(lower(pval), okfuns);%#ok

if isempty(funNum)

funNum = 0;

end

switch funNum %maybe make this less strict in the future

case 1

kfun = @linear_kernel;

case 2

kfun = @quadratic_kernel;

case {3,4}

kfun = @rbf_kernel;

case 5

kfun = @poly_kernel;

usePoly = true;

case 6

kfun = @mlp_kernel;

useMLP = true;

otherwise

error('Bioinfo:svmtrain:UnknownKernelFunction',...

'Unknown Kernel Function %s.',kfun);

end

elseif isa (pval, 'function_handle')

kfun = pval;

else

error('Bioinfo:svmtrain:BadKernelFunction',...

'The kernel function input does not appear to be a function handle\nor valid function name.')

end

case 2 % method

if strncmpi(pval,'qp',2)

useQuadprog = true;

if isempty(which('quadprog'))

warning('Bioinfo:svmtrain:NoOptim',...

'The Optimization Toolbox is required to use the quadratic programming method.')

useQuadprog = false;

end

elseif strncmpi(pval,'ls',2)

useQuadprog = false;

else

error('Bioinfo:svmtrain:UnknownMethod',...

'Unknown method option %s. Valid methods are ''QP'' and ''LS''',pval);

end

case 3 % display

if pval ~= 0

if size(training,2) == 2

plotflag = true;

else

warning('Bioinfo:svmtrain:OnlyPlot2D',...

'The display option can only plot 2D training data.')

end

case 4 % kfunargs

if iscell(pval)

kfunargs = pval;

else

kfunargs = {pval};

end

case 5 % quadprog_opts

if isstruct(pval)

qp_opts = pval;

elseif iscell(pval)

qp_opts = optimset(pval{:});

else

error('Bioinfo:svmtrain:BadQuadprogOpts',...

'QUADPROG_OPTS must be an opts structure.');

end

case 6 % polyorder

if ~isscalar(pval) || ~isnumeric(pval)

error('Bioinfo:svmtrain:BadPolyOrder',...

'POLYORDER must be a scalar value.');

end

if pval ~=floor(pval) || pval 1

error('Bioinfo:svmtrain:PolyOrderNotInt',...

'The order of the polynomial kernel must be a positive integer.')

end

kfunargs = {pval};

setPoly = true;

case 7 % mlpparams

if numel(pval)~=2

error('Bioinfo:svmtrain:BadMLPParams',...

'MLP_PARAMS must be a two element array.');

end

if ~isscalar(pval(1)) || ~isscalar(pval(2))

error('Bioinfo:svmtrain:MLPParamsNotScalar',...

'The parameters of the multi-layer perceptron kernel must be scalar.');

end

kfunargs = {pval(1),pval(2)};

setMLP = true;

end

if setPoly ~usePoly

warning('Bioinfo:svmtrain:PolyOrderNotPolyKernel',...

'You specified a polynomial order but not a polynomial kernel');

end

if setMLP ~useMLP

warning('Bioinfo:svmtrain:MLPParamNotMLPKernel',...

'You specified MLP parameters but not an MLP kernel');

end

% plot the data if requested

if plotflag

[hAxis,hLines] = svmplotdata(training,g);

legend(hLines,cellstr(groupString));

end

% calculate kernel function

try

kx = feval(kfun,training,training,kfunargs{:});

% ensure function is symmetric

kx = (kx+kx')/2;

catch

error('Bioinfo:svmtrain:UnknownKernelFunction',...

'Error calculating the kernel function:\n%s\n', lasterr);

end

% create Hessian

% add small constant eye to force stability

H =((g*g').*kx) + sqrt(eps(class(training)))*eye(n);

if useQuadprog

% The large scale solver cannot handle this type of problem, so turn it

% off.

qp_opts = optimset(qp_opts,'LargeScale','Off');

% X=QUADPROG(H,f,A,b,Aeq,beq,LB,UB,X0,opts)

alpha = quadprog(H,-ones(n,1),[],[],...

g',0,zeros(n,1),inf *ones(n,1),zeros(n,1),qp_opts);

% The support vectors are the non-zeros of alpha

svIndex = find(alpha sqrt(eps));

sv = training(svIndex,:);

% calculate the parameters of the separating line from the support

% vectors.

alphaHat = g(svIndex).*alpha(svIndex);

% Calculate the bias by applying the indicator function to the support

% vector with largest alpha.

[maxAlpha,maxPos] = max(alpha); %#ok

bias = g(maxPos) - sum(alphaHat.*kx(svIndex,maxPos));

% an alternative method is to average the values over all support vectors

% bias = mean(g(sv)' - sum(alphaHat(:,ones(1,numSVs)).*kx(sv,sv)));

% An alternative way to calculate support vectors is to look for zeros of

% the Lagrangians (fifth output from QUADPROG).

%

% [alpha,fval,output,exitflag,t] = quadprog(H,-ones(n,1),[],[],...

% g',0,zeros(n,1),inf *ones(n,1),zeros(n,1),opts);

%

% sv = t.lower sqrt(eps) t.upper sqrt(eps);

else % Least-Squares

% now build up compound matrix for solver

A = [0 g';g,H];

b = [0;ones(size(g))];

x = A\b;

% calculate the parameters of the separating line from the support

% vectors.

sv = training;

bias = x(1);

alphaHat = g.*x(2:end);

end

svm_struct.SupportVectors = sv;

svm_struct.Alpha = alphaHat;

svm_struct.Bias = bias;

svm_struct.KernelFunction = kfun;

svm_struct.KernelFunctionArgs = kfunargs;

svm_struct.GroupNames = groupnames;

svm_struct.FigureHandles = [];

if plotflag

hSV = svmplotsvs(hAxis,svm_struct);

svm_struct.FigureHandles = {hAxis,hLines,hSV};

end

支持向量机代码（支持向量机代码matlab）

ENVI5.1不能运行支持向量机分类，并出现错误代码？

错误代码出现原因有很多种:

1.在装系统时没有装好;

2.电脑的有些硬件驱动没有装好;

3.系统的缓存不够,太小了;

4.某些应用程序在安装有些文件会与系统文件相互共用,一但你删除或损坏这个程序的话,也就等于损坏了系统.计算机出现错误代码大部分是以上四个情况导致的,建议你根据不同的情况进行修复!

其实，无论是什么软件、什么程序，都是由程序员进行编写的

每个程序员都希望能编写出完美的应用程序代码

但难免会有疏忽之处.原因有多种:

一.编译错误,是由于不正确编写代码而产生.如非法实用或丢失关键字,遗漏必要的标点符号,函数调用缺参数或括号不匹配等.其它如变量未采用强制显式声明等而引起的错误.VB通常会在代码写时或运行时报错,按报错提示查找解决.

二.运行时出错,是指应用程序在运行其间执行非法操作或某些操作失败,如要打开的文件没找到,磁盘空间不够,除发运算中除数为0等.数组下标越界是一种典型的运行时错误,只有在运行时才会发现.三.逻辑错误,语法上找不出错误,应用程序也能运行,但得不到到预期结果,需认真分析并借助调试工具才能查出错误原因并改正.

如何用MATLAB支持向量机将2组数据分类，跪求源代码（实例），邮箱1033558779@qq.com

p1=[…………];

t1=[…………];

[aa,bb]=size(p1);

n1=bb;

for i=1:bb

p(:,i)=(p1(:,i)-min(p1(:,i)))/(max(p1(:,i))-min(p1(:,i)));

end

[c1,c2]=size(t1);

for i=1:c2

test(:,i)=(t1(:,i)-min(t1(:,i)))/(max(t1(:,i))-min(t1(:,i)));

end

n=aa;

sigma=2;

T=[ones(19,1);-ones(19,1)];

for i=1:n

for j=1:n

K(i,j)=T(i)*p(i,:)*p(j,:)'*T(j);

end

f=-ones(n,1)

aeq=T';

beq=0;

la=zeros(1,n);

lb=ones(1,n);

[x,fval]=quadprog(K,f,[],[],aeq,beq,la,lb);

w=0;

for j=1:n

w=w+x(j)*p(j,:)*T(j);

end

cc=find(x0.5);

zb=cc(1);

if T(zb)0

b1=-1-p(zb,:)*w';

else if T(zb)0

b1=1-p(zb,:)*w';

end

t_test=p;

for j=1:n

f1(j)=t_test(j,:)*w'+b1;

end

for j=1:n

if f1(j)0

class(j)=1;

else if f1(j)0

class(j)=-1;

else class(j)=0;

end

t_test1=test;

for j=1:c1

f2(j)=t_test1(j,:)*w'+b1;

end

for j=1:c1

if f2(j)0

class1(j)=1;

else if f2(j)0

class1(j)=-1;

else class1(j)=0;

end

给p1和t1附上相应的矩阵即可算，支持向量机只能将数据分为两类

求支持向量机的vc++原代码