用Matlab 2015a svmtrain函数训练的SVM model在2021b无法使用的解决方法
背景
- 与r2015a版本的Matlab相比，r2021b版本中包含更多集成好的算法模块（尤其是深度学习的模块），想把原来r2015a版本的代码升级到r2021b
- 高版本的Matlab已经采用fitcsvm函数和predict函数替代了旧版本中svmtrain函数和svmclassify函数。在r2021b中运行原来的代码时提示
未定义与 'struct' 类型的输入参数相对应的函数 'svmclassify'
- 当直接把svmclassify换成predict函数时，提示
错误使用 predict (第 124 行)
No valid system or dataset was specified.
- 原先用于训练svm model的数据已经丢失，无法用新版本的fitcsvm函数重新训练svm model，想直接在r2021b中调用原先训练好的svm model
解决方法
把下面这4个函数保存到原来的代码文件夹中，再在Matlab r2021b中运行原来的代码即可，注意运行原来的代码前，把 svmclassify 改成 svmclassify_r2015a。（这4个函数是从matlab r2015a中复制过来并修改了函数名的）
function outclass = svmclassify_r2015a(svmStruct,sample, varargin)
%SVMCLASSIFY Classify data using a support vector machine
% SVMCLASSIFY will be removed in a future release. Use the PREDICT method of
% an object returned by FITCSVM instead.
%
% GROUP = SVMCLASSIFY(SVMSTRUCT, TEST) classifies each row in TEST using
% the support vector machine classifier structure SVMSTRUCT created
% using SVMTRAIN, and returns the predicted class level GROUP. TEST must
% have the same number of columns as the data used to train the
% classifier in SVMTRAIN. GROUP indicates the group to which each row of
% TEST is assigned.
%
% GROUP = SVMCLASSIFY(...,'SHOWPLOT',true) plots the test data TEST on
% the figure created using the SHOWPLOT option in SVMTRAIN.
%
% Example:
% % Load the data and select features for classification
% load fisheriris
% X = [meas(:,1), meas(:,2)];
% % Extract the Setosa class
% Y = nominal(ismember(species,'setosa'));
% % Randomly partitions observations into a training set and a test
% % set using stratified holdout
% P = cvpartition(Y,'Holdout',0.20);
% % Use a linear support vector machine classifier
% svmStruct = svmtrain(X(P.training,:),Y(P.training),'showplot',true);
% C = svmclassify(svmStruct,X(P.test,:),'showplot',true);
% err_rate = sum(Y(P.test)~= C)/P.TestSize % mis-classification rate
% conMat = confusionmat(Y(P.test),C) % the confusion matrix
%
% See also SVMTRAIN, CLASSIFY, TREEBAGGER, fitcsvm.
% Copyright 2004-2014 The MathWorks, Inc.
% References:
%
% [1] Cristianini, N., Shawe-Taylor, J An Introduction to Support
% Vector Machines, Cambridge University Press, Cambridge, UK. 2000.
% http://www.support-vector.net
% [2] Kecman, V, Learning and Soft Computing,
% MIT Press, Cambridge, MA. 2001.
% [3] Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B.,
% Vandewalle, J., Least Squares Support Vector Machines,
% World Scientific, Singapore, 2002.
% set defaults
plotflag = false;
% check inputs
narginchk(2, Inf);
% deal with struct input case
if ~isstruct(svmStruct)
error(message('stats:svmclassify:TwoInputsNoStruct'));
end
if ~isnumeric(sample) || ~ismatrix(sample)
error(message('stats:svmclassify:BadSample'));
end
if size(sample,2)~=size(svmStruct.SupportVectors,2)
error(message('stats:svmclassify:TestSizeMismatch'));
end
% deal with the various inputs
if nargin > 2
if rem(nargin,2) == 1
error(message('stats:svmclassify:IncorrectNumberOfArguments'));
end
okargs = {'showplot','-compilerhelper'};
for j=1:2:nargin-2
pname = varargin{j};
pval = varargin{j+1};
k = find(strncmpi(pname, okargs,numel(pname)));
if isempty(k)
error(message('stats:svmclassify:UnknownParameterName', pname));
elseif length(k)>1
error(message('stats:svmclassify:AmbiguousParameterName', pname));
else
switch(k)
case 1 % plotflag ('SHOWPLOT')
plotflag = opttf(pval,okargs{k});
case 2 % help the compiler find required function handles by including svmtrain
svmtrain_r2015a(eye(2),[1 0]);
end
end
end
end
groupnames = svmStruct.GroupNames;
% check group is a vector -- though char input is special...
if ~isvector(groupnames) && ~ischar(groupnames)
error(message('stats:svmclassify:GroupNotVector'));
end
% grp2idx sorts a numeric grouping var ascending, and a string grouping
% var by order of first occurrence
[~,groupString,glevels] = grp2idx(groupnames);
% do the classification
if ~isempty(sample)
% shift and scale the data if necessary:
sampleOrig = sample;
if ~isempty(svmStruct.ScaleData)
for c = 1:size(sample, 2)
sample(:,c) = svmStruct.ScaleData.scaleFactor(c) * ...
(sample(:,c) + svmStruct.ScaleData.shift(c));
end
end
try
outclass = svmdecision_r2015a(sample,svmStruct);
catch ME
error(message('stats:svmclassify:ClassifyFailed', ME.message));
end
if plotflag
if isempty(svmStruct.FigureHandles)
warning(message('stats:svmclassify:NoTrainingFigure'));
else
try
hAxis = svmStruct.FigureHandles{1};
hLines = svmStruct.FigureHandles{2};
hSV = svmStruct.FigureHandles{3};
% unscale the data for plotting purposes
[~,hClassLines] = svmplotdata(sampleOrig,outclass,hAxis);
trainingString = strcat(cellstr(groupString),' (training)');
sampleString = strcat(cellstr(groupString),' (classified)');
legendHandles = {hLines(1),hClassLines{1},...
hLines(2),hClassLines{2},hSV};
legendNames = {trainingString{1},sampleString{1},...
trainingString{2},sampleString{2},'Support Vectors'};
ok = ~cellfun(@isempty,legendHandles);
legend([legendHandles{ok}],legendNames(ok));
catch ME
warning(message('stats:svmclassify:DisplayFailed', ME.message));
end
end
end
outclass(outclass == -1) = 2;
unClassified = isnan(outclass);
outclass = glevels(outclass(~unClassified),:);
if any(unClassified)
try
outclass = statinsertnan(unClassified,outclass);
catch ME
if ~isequal(ME.identifier,'stats:statinsertnan:LogicalInput')
rethrow(ME);
else
error(message('stats:svmclassify:logicalwithNaN'));
end
end
end
else
outclass = [];
end
function [svm_struct, svIndex] = svmtrain_r2015a(training, groupnames, varargin)
%SVMTRAIN Train a support vector machine classifier
% SVMTRAIN will be removed in a future release. Use FITCSVM instead.
%
% SVMSTRUCT = SVMTRAIN(TRAINING, Y) trains a support vector machine (SVM)
% classifier on data taken from two groups. TRAINING is a numeric matrix
% of predictor data. Rows of TRAINING correspond to observations; columns
% correspond to features. Y is a column vector that contains the known
% class labels for TRAINING. Y is a grouping variable, i.e., it can be a
% categorical, numeric, or logical vector; a cell vector of strings; or a
% character matrix with each row representing a class label (see help for
% groupingvariable). Each element of Y specifies the group the
% corresponding row of TRAINING belongs to. TRAINING and Y must have the
% same number of rows. SVMSTRUCT contains information about the trained
% classifier, including the support vectors, that is used by SVMCLASSIFY
% for classification. SVMTRAIN treats NaNs, empty strings or 'undefined'
% values as missing values and ignores the corresponding rows in
% TRAINING and Y.
%
% SVMSTRUCT = SVMTRAIN(TRAINING, Y, 'PARAM1',val1, 'PARAM2',val2, ...)
% specifies one or more of the following name/value pairs:
%
% Name Value
% 'kernel_function' A string or a function handle specifying the
% kernel function used to represent the dot
% product in a new space. The value can be one of
% the following:
% 'linear' - Linear kernel or dot product
% (default). In this case, SVMTRAIN
% finds the optimal separating plane
% in the original space.
% 'quadratic' - Quadratic kernel
% 'polynomial' - Polynomial kernel with default
% order 3. To specify another order,
% use the 'polyorder' argument.
% 'rbf' - Gaussian Radial Basis Function
% with default scaling factor 1. To
% specify another scaling factor,
% use the 'rbf_sigma' argument.
% 'mlp' - Multilayer Perceptron kernel (MLP)
% with default weight 1 and default
% bias -1. To specify another weight
% or bias, use the 'mlp_params'
% argument.
% 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 M and N are
% the number of rows in U and V
% respectively.
%
% 'rbf_sigma' A positive number specifying the scaling factor
% in the Gaussian radial basis function kernel.
% Default is 1.
%
% 'polyorder' A positive integer specifying the order of the
% polynomial kernel. Default is 3.
%
% 'mlp_params' A vector [P1 P2] specifying the parameters of MLP
% kernel. The MLP kernel takes the form:
% K = tanh(P1*U*V' + P2),
% where P1 > 0 and P2 < 0. Default is [1,-1].
%
% 'method' A string specifying the method used to find the
% separating hyperplane. Choices are:
% 'SMO' - Sequential Minimal Optimization (SMO)
% method (default). It implements the L1
% soft-margin SVM classifier.
% 'QP' - Quadratic programming (requires an
% Optimization Toolbox license). It
% implements the L2 soft-margin SVM
% classifier. Method 'QP' doesn't scale
% well for TRAINING with large number of
% observations.
% 'LS' - Least-squares method. It implements the
% L2 soft-margin SVM classifier.
%
% 'options' Options structure created using either STATSET or
% OPTIMSET.
% * When you set 'method' to 'SMO' (default),
% create the options structure using STATSET.
% Applicable options:
% 'Display' Level of display output. Choices
% are 'off' (the default), 'iter', and
% 'final'. Value 'iter' reports every
% 500 iterations.
% 'MaxIter' A positive integer specifying the
% maximum number of iterations allowed.
% Default is 15000 for method 'SMO'.
% * When you set method to 'QP', create the
% options structure using OPTIMSET. For details
% of applicable options choices, see QUADPROG
% options. SVM uses a convex quadratic program,
% so you can choose the 'interior-point-convex'
% algorithm in QUADPROG.
%
% 'tolkkt' A positive scalar that specifies the tolerance
% with which the Karush-Kuhn-Tucker (KKT) conditions
% are checked for method 'SMO'. Default is
% 1.0000e-003.
%
% 'kktviolationlevel' A scalar specifying the fraction of observations
% that are allowed to violate the KKT conditions for
% method 'SMO'. Setting this value to be positive
% helps the algorithm to converge faster if it is
% fluctuating near a good solution. Default is 0.
%
% 'kernelcachelimit' A positive scalar S specifying the size of the
% kernel matrix cache for method 'SMO'. The
% algorithm keeps a matrix with up to S * S
% double-precision numbers in memory. Default is
% 5000. When the number of points in TRAINING
% exceeds S, the SMO method slows down. It's
% recommended to set S as large as your system
% permits.
%
% 'boxconstraint' The box constraint C for the soft margin. C can be
% a positive numeric scalar or a vector of positive
% numbers with the number of elements equal to the
% number of rows in TRAINING.
% Default is 1.
% * If C is a scalar, it is automatically rescaled
% by N/(2*N1) for the observations of group one,
% and by N/(2*N2) for the observations of group
% two, where N1 is the number of observations in
% group one, N2 is the number of observations in
% group two. The rescaling is done to take into
% account unbalanced groups, i.e., when N1 and N2
% are different.
% * If C is a vector, then each element of C
% specifies the box constraint for the
% corresponding observation.
%
% 'autoscale' A logical value specifying whether or not to
% shift and scale the data points before training.
% When the value is true, the columns of TRAINING
% are shifted and scaled to have zero mean unit
% variance. Default is true.
%
% 'showplot' A logical value specifying whether or not to show
% a plot. When the value is true, SVMTRAIN creates a
% plot of the grouped data and the separating line
% for the classifier, when using data with 2
% features (columns). Default is false.
%
% SVMSTRUCT is a structure having the following properties:
%
% SupportVectors Matrix of data points with each row corresponding
% to a support vector.
% Note: when 'autoscale' is false, this field
% contains original support vectors in TRAINING.
% When 'autoscale' is true, this field contains
% shifted and scaled vectors from TRAINING.
% Alpha Vector of Lagrange multipliers for the support
% vectors. The sign is positive for support vectors
% belonging to the first group and negative for
% support vectors belonging to the second group.
% Bias Intercept of the hyperplane that separates
% the two groups.
% Note: when 'autoscale' is false, this field
% corresponds to the original data points in
% TRAINING. When 'autoscale' is true, this field
% corresponds to shifted and scaled data points.
% KernelFunction The function handle of kernel function used.
% KernelFunctionArgs Cell array containing the additional arguments
% for the kernel function.
% GroupNames A column vector that contains the known
% class labels for TRAINING. Y is a grouping
% variable (see help for groupingvariable).
% SupportVectorIndices A column vector indicating the indices of support
% vectors.
% ScaleData This field contains information about auto-scale.
% When 'autoscale' is false, it is empty. When
% 'autoscale' is set to true, it is a structure
% containing two fields:
% shift - A row vector containing the negative
% of the mean across all observations
% in TRAINING.
% scaleFactor - A row vector whose value is
% 1./STD(TRAINING).
% FigureHandles A vector of figure handles created by SVMTRAIN
% when 'showplot' argument is TRUE.
%
% Example:
% % Load the data and select features for classification
% load fisheriris
% X = [meas(:,1), meas(:,2)];
% % Extract the Setosa class
% Y = nominal(ismember(species,'setosa'));
% % Randomly partitions observations into a training set and a test
% % set using stratified holdout
% P = cvpartition(Y,'Holdout',0.20);
% % Use a linear support vector machine classifier
% svmStruct = svmtrain(X(P.training,:),Y(P.training),'showplot',true);
% C = svmclassify(svmStruct,X(P.test,:),'showplot',true);
% errRate = sum(Y(P.test)~= C)/P.TestSize %mis-classification rate
% conMat = confusionmat(Y(P.test),C) % the confusion matrix
%
% See also SVMCLASSIFY, CLASSIFY, TREEBAGGER, GROUPINGVARIABLE, fitcsvm.
% Copyright 2004-2014 The MathWorks, Inc.
% References:
%
% [1] Cristianini, N., Shawe-Taylor, J An Introduction to Support
% Vector Machines, Cambridge University Press, Cambridge, UK. 2000.
% http://www.support-vector.net
% [2] Kecman, V, Learning and Soft Computing,
% MIT Press, Cambridge, MA. 2001.
% [3] Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B.,
% Vandewalle, J., Least Squares Support Vector Machines,
% World Scientific, Singapore, 2002.
% [4] J.C. Platt: A Fast Algorithm for Training Support Vector
% Machines, Advances in Kernel Methods - Support Vector Learning,
% MIT Press, 1998.
% [5] J.C. Platt: Fast Training of Support Vector Machines using
% Sequential Minimal Optimization Microsoft Research Technical
% Report MSR-TR-98-14, 1998.
% [6] http://www.kernel-machines.org/papers/tr-30-1998.ps.gz
%
% SVMTRAIN(...,'KFUNARGS',ARGS) allows you to pass additional
% arguments to kernel functions.
narginchk(2, Inf);
% check group is a vector or a char array
if ~isvector(groupnames) && ~ischar(groupnames)
error(message('stats:svmtrain:GroupNotVector'));
end
% make sure that the data are correctly oriented.
if size(groupnames,1) == 1
groupnames = groupnames';
end
if ~isnumeric(training) || ~ismatrix(training)
error(message('stats:svmtrain:TrainingBadType'));
end
% grp2idx sorts a numeric grouping var ascending, and a string grouping
% var by order of first occurrence
[groupIndex, groupString] = grp2idx(groupnames);
% make sure data is the right size
if size(training,1) ~= size(groupIndex,1)
if size(training,2) == size(groupIndex,1)
training = training';
else
error(message('stats:svmtrain:DataGroupSizeMismatch'))
end
end
if isempty(training)
error(message('stats:svmtrain:NoData'))
end
nans = isnan(groupIndex) | any(isnan(training),2);
if any(nans)
training(nans,:) = [];
groupIndex(nans) = [];
end
if isempty(training)
error(message('stats:svmtrain:NoData'))
end
ngroups = length(unique(groupIndex));
nPoints = length(groupIndex);
if ngroups > 2
error(message('stats:svmtrain:TooManyGroups', ngroups))
end
if length(groupString) > ngroups
warning(message('stats:svmtrain:EmptyGroups'));
end
% convert to groupIndex from 2 to -1.
groupIndex = 1 - (2* (groupIndex-1));
pnames = {'kernel_function','method','showplot', 'polyorder','mlp_params',...
'boxconstraint','rbf_sigma','autoscale', 'options',...
'tolkkt','kktviolationlevel','kernelcachelimit'...
'kfunargs', 'quadprog_opts','smo_opts'};
dflts = { 'linear', [], false, [], [], ....
1, [], true , [] , ....
[], [], [],...
{} , [] , []};
[kfun,optimMethod, plotflag, polyOrder, mlpParams, boxC, rbf_sigma, ...
autoScale, opts, tolkkt, kktvl,kerCL, kfunargs, qpOptsInput, ...
smoOptsInput] = internal.stats.parseArgs(pnames, dflts, varargin{:});
usePoly = false;
useMLP = false;
useSigma = false;
%parse kernel functions
if ischar(kfun)
okfuns = {'linear','quadratic', 'radial','rbf','polynomial','mlp'};
[~,i] = internal.stats.getParamVal(kfun,okfuns,'kernel_function');
switch i
case 1
kfun = @linear_kernel;
case 2
kfun = @quadratic_kernel;
case {3,4}
kfun = @rbf_kernel;
useSigma = true;
case 5
kfun = @poly_kernel;
usePoly = true;
case 6
kfun = @mlp_kernel;
useMLP = true;
end
elseif ~isa(kfun, 'function_handle')
error(message('stats:svmtrain:BadKernelFunction'));
end
%parse optimization method
optimList ={'QP','SMO','LS'};
i = 2; % set to 'SMO'
if ~isempty(optimMethod)
[~,i] = internal.stats.getParamVal(optimMethod,optimList,'Method');
if i==1 && ( ~license('test', 'optimization_toolbox') ...
|| isempty(which('quadprog')))
warning(message('stats:svmtrain:NoOptim'));
i = 2;
end
end
if i == 2 && ngroups==1
error(message('stats:svmtrain:InvalidY'));
end
optimMethod = optimList{i};
% The large scale solver cannot handle this type of problem, so turn it off.
% qp_opts = optimset('LargeScale','Off','display','off');
% We can use the 'interior-point-convex' option
qp_opts = optimset('Algorithm','interior-point-convex','display','off');
smo_opts = statset('Display','off','MaxIter',15000);
%parse opts. opts will override 'quadprog_opt' and 'smo_opt' argument
if ~isempty(opts)
qp_opts = optimset(qp_opts,opts);
smo_opts = statset(smo_opts,opts);
else
% only consider undocumented 'quadprog_opts' arguments
% when 'opts' is empty; Otherwise, ignore 'quadprog_opts'
if ~isempty(qpOptsInput)
if isstruct(qpOptsInput)
qp_opts = optimset(qp_opts,qpOptsInput);
elseif iscell(qpOptsInput)
qp_opts = optimset(qp_opts,qpOptsInput{:});
else
error(message('stats:svmtrain:BadQuadprogOpts'));
end
end
end
% Turn off deprecation warning for svmsmoset
warning('off','stats:obsolete:ReplaceThisWith');
cleanupObj = onCleanup(@() warning('on','stats:obsolete:ReplaceThisWith'));
if ~isempty(smoOptsInput) && isempty(tolkkt) && isempty(kktvl) ...
&& isempty(kerCL) && isempty(opts)
%back-compatibility.
smo_opts = svmsmoset(smoOptsInput);
else
if isempty(tolkkt)
tolkkt = 1e-3;
end
if isempty(kerCL)
kerCL = 5000;
end
if isempty(kktvl)
kktvl = 0;
end
smo_opts = svmsmoset(smo_opts,'tolkkt',tolkkt,'KernelCacheLimit',kerCL,....
'KKTViolationLevel',kktvl);
end
if ~isscalar(smo_opts.TolKKT) || ~isnumeric(smo_opts.TolKKT) || smo_opts.TolKKT <= 0
error(message('stats:svmtrain:badTolKKT'));
end
if ~isscalar(smo_opts.KKTViolationLevel) || ~isnumeric(smo_opts.KKTViolationLevel)...
|| smo_opts.KKTViolationLevel < 0 || smo_opts.KKTViolationLevel > 1
error(message('stats:svmtrain:badKKTVL'));
end
if ~isscalar(smo_opts.KernelCacheLimit) || ~isnumeric(smo_opts.KernelCacheLimit)...
||smo_opts.KernelCacheLimit < 0
error(message('stats:svmtrain:badKerCL'));
end
%parse plot flag
plotflag = opttf(plotflag,'showplot');
if plotflag && size(training,2) ~=2
plotflag = false;
warning(message('stats:svmtrain:OnlyPlot2D'));
end
if ~isempty(kfunargs) && ~iscell(kfunargs)
kfunargs = {kfunargs};
end
%polyOrder
if ~isempty(polyOrder)
%setPoly = true;
if ~usePoly
warning(message('stats:svmtrain:PolyOrderNotPolyKernel'));
else
kfunargs = {polyOrder};
end
end
% mlpparams
if ~isempty(mlpParams)
if ~isnumeric(mlpParams) || numel(mlpParams)~=2
error(message('stats:svmtrain:BadMLPParams'));
end
if mlpParams(1) <= 0
error(message('stats:svmtrain:MLPWeightNotPositive'))
end
if mlpParams(2) >= 0
warning(message('stats:svmtrain:MLPBiasNotNegative'))
end
if ~useMLP
warning(message('stats:svmtrain:MLPParamNotMLPKernel'));
else
kfunargs = {mlpParams(1), mlpParams(2)};
end
end
%rbf_sigma
if ~isempty(rbf_sigma)
if useSigma
kfunargs = {rbf_sigma};
else
warning(message('stats:svmtrain:RBFParamNotRBFKernel'))
end
end
% box constraint: it can be a positive numeric scalar or a numeric vector
% of the same length as the number of data points
if isscalar(boxC) && isnumeric(boxC) && boxC > 0
% scalar input: adjust to group size and transform into vector
% set default value of box constraint
boxconstraint = ones(nPoints, 1);
n1 = length(find(groupIndex==1));
n2 = length(find(groupIndex==-1));
c1 = 0.5 * boxC * nPoints / n1;
c2 = 0.5 * boxC * nPoints / n2;
boxconstraint(groupIndex==1) = c1;
boxconstraint(groupIndex==-1) = c2;
elseif isvector(boxC) && isnumeric(boxC) && all(boxC > 0) && (length(boxC) == nPoints)
% vector input
boxconstraint = boxC;
else
error(message('stats:svmtrain:InvalidBoxConstraint'));
end
% If boxconstraint == Inf then convergence will not
% happen so fix the value to 1/sqrt(eps).
boxconstraint = min(boxconstraint,repmat(1/sqrt(eps(class(boxconstraint))),...
size(boxconstraint)));
autoScale = opttf(autoScale,'autoscale');
% plot the data if requested
if plotflag
[hAxis,hLines] = svmplotdata(training,groupIndex);
hLines = [hLines{1} hLines{2}];
legend(hLines,cellstr(groupString));
end
% autoscale data if required,
scaleData = [];
if autoScale
scaleData.shift = - mean(training);
stdVals = std(training);
scaleData.scaleFactor = 1./stdVals;
% leave zero-variance data unscaled:
scaleData.scaleFactor(~isfinite(scaleData.scaleFactor)) = 1;
% shift and scale columns of data matrix:
for c = 1:size(training, 2)
training(:,c) = scaleData.scaleFactor(c) * ...
(training(:,c) + scaleData.shift(c));
end
end
if strcmpi(optimMethod, 'SMO')
% if we have a kernel that takes extra arguments we must define a new
% kernel function handle to be passed to seqminopt
if ~isempty(kfunargs)
tmp_kfun = @(x,y) feval(kfun, x,y, kfunargs{:});
else
tmp_kfun = kfun;
end
[alpha, bias] = seqminopt(training, groupIndex, ...
boxconstraint, tmp_kfun, smo_opts);
svIndex = find(alpha > sqrt(eps));
sv = training(svIndex,:);
alphaHat = groupIndex(svIndex).*alpha(svIndex);
else % QP and LS both need the kernel matrix:
% calculate kernel function and add additional term required
% for two-norm soft margin
try
kx = feval(kfun,training,training,kfunargs{:});
% ensure function is symmetric
kx = (kx+kx')/2 + diag(1./boxconstraint);
catch ME
m = message('stats:svmtrain:KernelFunctionError',func2str(kfun));
throw(addCause(MException(m.Identifier,'%s',getString(m)),ME));
end
% create Hessian
H =((groupIndex * groupIndex').*kx);
if strcmpi(optimMethod, 'QP')
if strncmpi(qp_opts.Algorithm,'inte',4)
X0 = [];
else
X0= ones(nPoints,1);
end
[alpha, ~, exitflag, output] = quadprog(H,-ones(nPoints,1),[],[],...
groupIndex',0,zeros(nPoints,1), Inf *ones(nPoints,1),...
X0, qp_opts);
if exitflag <= 0
error(message('stats:svmtrain:UnsolvableOptimization', output.message));
end
% The support vectors are the non-zeros of alpha.
% We could also use the zero values of the Lagrangian (fifth output of
% quadprog) though the method below seems to be good enough.
svIndex = find(alpha > sqrt(eps));
sv = training(svIndex,:);
% calculate the parameters of the separating line from the support
% vectors.
alphaHat = groupIndex(svIndex).*alpha(svIndex);
% Calculate the bias by applying the indicator function to the support
% vector with largest alpha.
[~,maxPos] = max(alpha);
bias = groupIndex(maxPos) - sum(alphaHat.*kx(svIndex,maxPos));
% an alternative method is to average the values over all support vectors
% bias = mean(groupIndex(sv)' - sum(alphaHat(:,ones(1,numSVs)).*kx(sv,sv)));
% An alternative way to calculate support vectors is to look for zeros of
% the Lagrangian (fifth output from QUADPROG).
%
% [alpha,fval,output,exitflag,t] = quadprog(H,-ones(nPoints,1),[],[],...
% groupIndex',0,zeros(nPoints,1),inf *ones(nPoints,1),zeros(nPoints,1),opts);
%
% sv = t.lower < sqrt(eps) & t.upper < sqrt(eps);
else % Least-Squares
% now build up compound matrix for solver
A = [0 groupIndex';groupIndex,H];
b = [0;ones(size(groupIndex))];
x = A\b;
% calculate the parameters of the separating line from the support
% vectors.
sv = training;
bias = x(1);
alphaHat = groupIndex.*x(2:end);
svIndex = (1:nPoints)';
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.SupportVectorIndices = svIndex;
svm_struct.ScaleData = scaleData;
svm_struct.FigureHandles = [];
if plotflag
hSV = svmplotsvs(hAxis,hLines,groupString,svm_struct);
svm_struct.FigureHandles = {hAxis,hLines,hSV};
end
function [out,f] = svmdecision_r2015a(Xnew,svm_struct)
%SVMDECISION Evaluates the SVM decision function
% Copyright 2004-2012 The MathWorks, Inc.
sv = svm_struct.SupportVectors;
alphaHat = svm_struct.Alpha;
bias = svm_struct.Bias;
kfun = svm_struct.KernelFunction;
kfunargs = svm_struct.KernelFunctionArgs;
f = (feval(kfun,sv,Xnew,kfunargs{:})'*alphaHat(:)) + bias;
out = sign(f);
% points on the boundary are assigned to class 1
out(out==0) = 1;
function options = svmsmoset_r2015a(varargin)
%SVMSMOSET Obsolete function.
% SVMSMOSET will be removed in a future release. Use FITCSVM instead.
%
% OPTIONS = SVMSMOSET('NAME1',VALUE1,'NAME2',VALUE2,...) creates an
% options structure OPTIONS in which the named properties have the
% specified values. Any unspecified properties have default values. It is
% sufficient to type only the leading characters that uniquely identify
% the property. Case is ignored for property names.
%
% OPTIONS = SVMSMOSET(OLDOPTS,'NAME1',VALUE1,...) alters an existing
% options structure OLDOPTS.
%
% OPTIONS = SVMSMOSET(OLDOPTS,NEWOPTS) combines an existing options
% structure OLDOPTS with a new options structure NEWOPTS. Any new
% properties overwrite corresponding old properties.
%
% SVMSMOSET with no input arguments displays all property names and their
% possible values.
%
% SVMSMOSET has the following properties:
%
% TolKKT
% Tolerance with which the Karush-Kuhn-Tucker (KKT) conditions are
% checked. Default value is 1e-3.
%
% MaxIter
% Maximum number of iterations of main loop. If this number is exceeded
% before the algorithm converges then the algorithm stops and gives an
% error. Default value is 15000.
%
% Display
% Controls the level of information about the optimization iterations
% that is displayed as the algorithm runs. The value can be 'off', which
% displays nothing, 'iter', which reports every 500 iterations, and
% 'final', which reports when the algorithm finishes. Default value is
% 'off'.
%
% KKTViolationLevel
% This number specifies the fraction of alphas that are allowed to
% violate the KKT conditions. Setting this to a value greater than 0 will
% help the algorithm to converge if it is fluctuating near a good
% solution. Default value is 0.
%
% KernelCacheLimit
% This number specifies the size of the kernel matrix cache. The
% algorithm keeps a matrix with up to KernelCacheLimit * KernelCacheLimit
% double numbers in memory. Default value is 5000.
%
% Examples:
%
% opts = svmsmoset('Display','final','MaxIter',20000,...
% 'KernelCacheLimit',1000);
% alt_opts = svmsmoset(opts,'Display','iter','KKTViolationLevel',.05);
%
% See also SVMCLASSIFY, SVMTRAIN, fitcsvm.
% References:
%
% [1] Cristianini, N., Shawe-Taylor, J An Introduction to Support
% Vector Machines, Cambridge University Press, Cambridge, UK. 2000.
% http://www.support-vector.net
% [2] J.C. Platt: A Fast Algorithm for Training Support Vector
% Machines, http://research.microsoft.com/users/jplatt/smo.html
% [3] R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working Set Selection Using
% Second Order Information for Training SVM. Journal of Machine
% Learning Research, 6(2005), 1889-1918.
% [4] L. Bottou and C.-J. Lin. Support Vector Machine Solvers. 2006,
% available from http://www.csie.ntu.edu.tw/~cjlin/papers.html
% Copyright 2006-2014 The MathWorks, Inc.
%
% MaxNonBoundsIter -- may get added at a later date. Currently hardcoded
% Maximum number of iterations of the loop which tries to make the set of
% non-bound alphas (true support vectors) consistent. If this number is
% exceeded the algorithm continues with loop over the full set of alphas.
% Tuning this number can speed up the algorithm. Default value is 25.
warning(message('stats:obsolete:ReplaceThisWith','svmsmoset','fitcsvm'));
% Print out possible values of properties.
if (nargin == 0) && (nargout == 0)
fprintf(' Display: [ off | iter | final ]\n');
fprintf(' TolKKT: [ positive scalar ]\n');
fprintf(' MaxIter: [ positive scalar ]\n');
fprintf(' KernelCacheLimit: [ positive scalar ]\n');
fprintf(' KKTViolationLevel: [ positive scalar]\n');
fprintf('\n');
return;
end
% Create a struct of all the fields with all values set to
Options = {...
'Display', 'off';
'TolKKT', 1e-3;
'MaxIter', 15000;
'KKTViolationLevel', 0;
'KernelCacheLimit', 5000;};
Names = Options(:,1);
Defaults = Options(:,2);
m = size(Names,1);
% Combine all leading options structures o1, o2, ... in odeset(o1,o2,...).
for j = 1:m
options.(Names{j}) = Defaults{j};
end
% work through the inputs until we find a parameter name. Handle options
% structures as we go.
i = 1;
while i <= nargin
arg = varargin{i};
if ischar(arg) % arg is an option name
break;
end
if ~isempty(arg) % [] is a valid options argument
if ~isa(arg,'struct')
error(message('stats:svmtrain:NoPropNameOrStruct', i));
end
for j = 1:m
if any(strcmp(fieldnames(arg),Names{j}))
val = arg.(Names{j});
else
val = [];
end
if ~isempty(val)
options.(Names{j}) = val;
end
end
end
i = i + 1;
end
% A finite state machine to parse name-value pairs.
if rem(nargin-i+1,2) ~= 0
error(message('stats:svmtrain:ArgNameValueMismatch'));
end
expectval = 0; % start expecting a name, not a value
while i <= nargin
arg = varargin{i};
if ~expectval
if ~ischar(arg)
error(message('stats:svmtrain:NoPropName', i));
end
k = find(strncmpi(arg, Names,numel(arg)));
if isempty(k)
error(message('stats:svmtrain:UnknownParameterName', arg));
elseif length(k)>1
error(message('stats:svmtrain:AmbiguousParameterName', arg));
end
expectval = 1; % we expect a value next
else
options.(Names{k}) = arg;
expectval = 0;
end
i = i + 1;
end
if expectval
error(message('stats:svmtrain:NoValueForProp', arg));
end
%check tolkkt
其他
刚开始在网上搜索到 下载libsvm包并将其添加到Matlab toolbox中，可以继续使用svmtrain和svmclassify/svmpredict函数，尝试之后发现还是无法直接调用原来训练好的svm model，只能重新训练model
参考：
- 关于matlab2018a版本错误使用 svmclassify 分类器
- Matlab代码提示“svmtrain已删除 请改用fitcsvm”，以及svmpredict没有返回结果label和精度accuracy的解决办法
- LIBSVM – A Library for Support Vector Machines
- Old Version of LIBSVM