h5轮播代码（移动端轮播图代码）

今天给各位分享支持向量机回归代码的知识，其中也会对支持向量机回归代码实现进行解释，如果能碰巧解决你现在面临的问题，别忘了关注本站，现在开始吧！

本文目录一览：

• 1、csv怎么支持向量机回归
• 2、支持向量机的matlab代码
• 3、如何通俗易懂地解释支持向量回归
• 4、非线性支持向量回归机的Matlab程序

csv怎么支持向量机回归

用crv对基本处理的数据进行特征分析来实现向量回归。

因为数据和特征决定了机器学习的上限，而模型和算法只是逼近这个上限而已。为了模型有更好的结果，特征分析和提取是非常重要的。导入所有的库数据进行特征分析，得到的图案进行向量研究从而实现向量机回归。

支持向量机和支持向量回归是目前机器学习领域用得较多的方法，不管是人脸识别，字符识别，行为识别，姿态识别等，都可以看到它们的影子。支持向量机回归SVR是支持向量机在回归问题上的应用模型。支持向量机回归模型基于不同的损失函数产生了很多变种。

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.

% Copyright 2004 The MathWorks, Inc.

% $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

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

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

end

end

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

如何通俗易懂地解释支持向量回归

超级通俗的解释：

支持向量机是用来解决分类问题的。

先考虑最简单的情况，豌豆和米粒，用晒子很快可以分开，小颗粒漏下去，大颗粒保留。

用一个函数来表示就是当直径d大于某个值D，就判定为豌豆，小于某个值就是米粒。

dD, 豌豆

d

在数轴上就是在d左边就是米粒，右边就是绿豆，这是一维的情况。

但是实际问题没这么简单，考虑的问题不单单是尺寸，一个花的两个品种，怎么分类？

假设决定他们分类的有两个属性，花瓣尺寸和颜色。单独用一个属性来分类，像刚才分米粒那样，就不行了。这个时候我们设置两个值 尺寸x和颜色y.

我们把所有的数据都丢到x-y平面上作为点，按道理如果只有这两个属性决定了两个品种，数据肯定会按两类聚集在这个二维平面上。

我们只要找到一条直线，把这两类划分开来，分类就很容易了，以后遇到一个数据，就丢进这个平面，看在直线的哪一边，就是哪一类。

比如x+y-2=0这条直线，我们把数据(x,y)代入，只要认为x+y-20的就是A类，x+y-20的就是B类。

以此类推，还有三维的，四维的，N维的 属性的分类，这样构造的也许就不是直线，而是平面，超平面。

一个三维的函数分类 ：x+y+z-2=0，这就是个分类的平面了。

有时候，分类的那条线不一定是直线，还有可能是曲线，我们通过某些函数来转换，就可以转化成刚才的哪种多维的分类问题，这个就是核函数的思想。

例如：分类的函数是个圆形x^2+y^2-4=0。这个时候令x^2=a; y^2=b,还不就变成了a+b-4=0 这种直线问题了。

这就是支持向量机的思想。

机的意思就是 算法，机器学习领域里面常常用“机”这个字表示算法

支持向量意思就是 数据集种的某些点，位置比较特殊，比如刚才提到的x+y-2=0这条直线，直线上面区域x+y-20的全是A类，下面的x+y-20的全是B类，我们找这条直线的时候，一般就看聚集在一起的两类数据，他们各自的最边缘位置的点，也就是最靠近划分直线的那几个点，而其他点对这条直线的最终位置的确定起不了作用，所以我姑且叫这些点叫“支持点”（意思就是有用的点），但是在数学上，没这种说法，数学里的点，又可以叫向量，比如二维点(x,y)就是二维向量，三维度的就是三维向量（ x,y,z)。所以 “支持点”改叫“支持向量”，听起来比较专业，NB。

所以就是 支持向量机 了。

非线性支持向量回归机的Matlab程序

function [Alpha1,Alpha2,Alpha,Flag,B]=SVMNR(X,Y,Epsilon,C,TKF,Para1,Para2)

%%

% SVMNR.m

% Support Vector Machine for Nonlinear Regression

% All rights reserved

%%

% 支持向量机非线性回归通用程序

% GreenSim团队原创作品，转载请注明

% Email:greensim@163.com

% GreenSim团队主页：

% 欢迎访问GreenSim——算法仿真团队→

% 程序功能：

% 使用支持向量机进行非线性回归，得到非线性函数y=f(x1,x2,…,xn)的支持向量解析式，

% 求解二次规划时调用了优化工具箱的quadprog函数。本函数在程序入口处对数据进行了

% [-1,1]的归一化处理，所以计算得到的回归解析式的系数是针对归一化数据的，仿真测

% 试需使用与本函数配套的Regression函数。

% 主要参考文献:

% 朱国强,刘士荣等.支持向量机及其在函数逼近中的应用.华东理工大学学报

% 输入参数列表

% X 输入样本原始数据，n×l的矩阵，n为变量个数，l为样本个数

% Y 输出样本原始数据，1×l的矩阵，l为样本个数

% Epsilon ε不敏感损失函数的参数，Epsilon越大，支持向量越少

% C 惩罚系数，C过大或过小，泛化能力变差

% TKF Type of Kernel Function 核函数类型

% TKF=1 线性核函数，注意：使用线性核函数，将进行支持向量机的线性回归

% TKF=2 多项式核函数

% TKF=3 径向基核函数

% TKF=4 指数核函数

% TKF=5 Sigmoid核函数

% TKF=任意其它值，自定义核函数

% Para1 核函数中的第一个参数

% Para2 核函数中的第二个参数

% 注：关于核函数参数的定义请见Regression.m和SVMNR.m内部的定义

% 输出参数列表

% Alpha1 α系数

% Alpha2 α*系数

% Alpha 支持向量的加权系数（α－α*）向量

% Flag 1×l标记，0对应非支持向量，1对应边界支持向量，2对应标准支持向量

% B 回归方程中的常数项

%--------------------------------------------------------------------------

%%

%-----------------------数据归一化处理--------------------------------------

nntwarn off

X=premnmx(X);

Y=premnmx(Y);

%%

%%

%-----------------------核函数参数初始化------------------------------------

switch TKF

case 1

%线性核函数 K=sum(x.*y)

%没有需要定义的参数

case 2

%多项式核函数 K=(sum(x.*y)+c)^p

c=Para1;%c=0.1;

p=Para2;%p=2;

case 3

%径向基核函数 K=exp(-(norm(x-y))^2/(2*sigma^2))

sigma=Para1;%sigma=6;

case 4

%指数核函数 K=exp(-norm(x-y)/(2*sigma^2))

sigma=Para1;%sigma=3;

case 5

%Sigmoid核函数 K=1/(1+exp(-v*sum(x.*y)+c))

v=Para1;%v=0.5;

c=Para2;%c=0;

otherwise

%自定义核函数，需由用户自行在函数内部修改，注意要同时修改好几处！

%暂时定义为 K=exp(-(sum((x-y).^2)/(2*sigma^2)))

sigma=Para1;%sigma=8;

end

%%

%%

%-----------------------构造K矩阵-------------------------------------------

l=size(X,2);

K=zeros(l,l);%K矩阵初始化

for i=1:l

for j=1:l

x=X(:,i);

y=X(:,j);

switch TKF%根据核函数的类型，使用相应的核函数构造K矩阵

case 1

K(i,j)=sum(x.*y);

case 2

K(i,j)=(sum(x.*y)+c)^p;

case 3

K(i,j)=exp(-(norm(x-y))^2/(2*sigma^2));

case 4

K(i,j)=exp(-norm(x-y)/(2*sigma^2));

case 5

K(i,j)=1/(1+exp(-v*sum(x.*y)+c));

otherwise

K(i,j)=exp(-(sum((x-y).^2)/(2*sigma^2)));

end

end

end

%%

%%

%------------构造二次规划模型的参数H,Ft,Aeq,Beq,lb,ub------------------------

%支持向量机非线性回归，回归函数的系数，要通过求解一个二次规划模型得以确定

Ft=[Epsilon*ones(1,l)-Y,Epsilon*ones(1,l)+Y];

Aeq=[ones(1,l),-ones(1,l)];

Beq=0;

ub=C*ones(2*l,1);

%%

%%

%--------------调用优化工具箱quadprog函数求解二次规划------------------------

OPT=optimset;

OPT.LargeScale='off';

OPT.Display='off';

%%

%%

%------------------------整理输出回归方程的系数------------------------------

Alpha1=(Gamma(1:l,1))';

Alpha2=(Gamma((l+1):end,1))';

Alpha=Alpha1-Alpha2;

Flag=2*ones(1,l);

%%

%%

%---------------------------支持向量的分类----------------------------------

Err=0.000000000001;

for i=1:l

AA=Alpha1(i);

BB=Alpha2(i);

if (abs(AA-0)=Err)(abs(BB-0)=Err)

Flag(i)=0;%非支持向量

end

if (AAErr)(AAC-Err)(abs(BB-0)=Err)

Flag(i)=2;%标准支持向量

end

if (abs(AA-0)=Err)(BBErr)(BBC-Err)

Flag(i)=2;%标准支持向量

end

if (abs(AA-C)=Err)(abs(BB-0)=Err)

Flag(i)=1;%边界支持向量

end

if (abs(AA-0)=Err)(abs(BB-C)=Err)

Flag(i)=1;%边界支持向量

end

end

%%

%%

%--------------------计算回归方程中的常数项B---------------------------------

B=0;

counter=0;

for i=1:l

AA=Alpha1(i);

BB=Alpha2(i);

if (AAErr)(AAC-Err)(abs(BB-0)=Err)

%计算支持向量加权值

SUM=0;

for j=1:l

if Flag(j)0

switch TKF

case 1

SUM=SUM+Alpha(j)*sum(X(:,j).*X(:,i));

case 2

SUM=SUM+Alpha(j)*(sum(X(:,j).*X(:,i))+c)^p;

case 3

SUM=SUM+Alpha(j)*exp(-(norm(X(:,j)-X(:,i)))^2/(2*sigma^2));

case 4

SUM=SUM+Alpha(j)*exp(-norm(X(:,j)-X(:,i))/(2*sigma^2));

case 5

SUM=SUM+Alpha(j)*1/(1+exp(-v*sum(X(:,j).*X(:,i))+c));

otherwise

SUM=SUM+Alpha(j)*exp(-(sum((X(:,j)-X(:,i)).^2)/(2*sigma^2)));

end

end

end

b=Y(i)-SUM-Epsilon;

B=B+b;

counter=counter+1;

end

if (abs(AA-0)=Err)(BBErr)(BBC-Err)

SUM=0;

for j=1:l

if Flag(j)0

switch TKF

case 1

SUM=SUM+Alpha(j)*sum(X(:,j).*X(:,i));

case 2

SUM=SUM+Alpha(j)*(sum(X(:,j).*X(:,i))+c)^p;

case 3

SUM=SUM+Alpha(j)*exp(-(norm(X(:,j)-X(:,i)))^2/(2*sigma^2));

case 4

SUM=SUM+Alpha(j)*exp(-norm(X(:,j)-X(:,i))/(2*sigma^2));

case 5

SUM=SUM+Alpha(j)*1/(1+exp(-v*sum(X(:,j).*X(:,i))+c));

otherwise

SUM=SUM+Alpha(j)*exp(-(sum((X(:,j)-X(:,i)).^2)/(2*sigma^2)));

end

end

end

b=Y(i)-SUM+Epsilon;

B=B+b;

counter=counter+1;

end

end

if counter==0

B=0;

else

B=B/counter;

end

给分吧！

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