svm - Box constraint in libsvm package (compare MATLAB fitcsvm and libsvm options) -

these options of libsvm package:

   options:     -s svm_type : set type of svm (default 0)         0 -- c-svc         1 -- nu-svc         2 -- one-class svm         3 -- epsilon-svr         4 -- nu-svr     -t kernel_type : set type of kernel function (default 2)         0 -- linear: u'*v         1 -- polynomial: (gamma*u'*v + coef0)^degree         2 -- radial basis function: exp(-gamma*|u-v|^2)         3 -- sigmoid: tanh(gamma*u'*v + coef0)     -d degree : set degree in kernel function (default 3)     -g gamma : set gamma in kernel function (default 1/num_features)     -r coef0 : set coef0 in kernel function (default 0)     -c cost : set parameter c of c-svc, epsilon-svr, , nu-svr (default 1)     -n nu : set parameter nu of nu-svc, one-class svm, , nu-svr (default 0.5)     -p epsilon : set epsilon in loss function of epsilon-svr (default 0.1)     -m cachesize : set cache memory size in mb (default 100)     -e epsilon : set tolerance of termination criterion (default 0.001)     -h shrinking: whether use shrinking heuristics, 0 or 1 (default 1)     -b probability_estimates: whether train svc or svr model probability estimates, 0 or 1 (default 0)     -wi weight: set parameter c of class weight*c, c-svc (default 1) 

which 1 svm box-constraint? -c? if not, how can calculate that? i'm converting libsvm code matlab fitcsvm function. have these options in matlab:

    'cost' — misclassification cost     square matrix | structure array     misclassification cost, specified comma-separated pair consisting of 'cost' , square matrix or structure. if specify:      square matrix cost, cost(i,j) cost of classifying point class j if true class (i.e., rows correspond true class , columns correspond predicted class). specify class order corresponding rows , columns of cost, additionally specify classnames name-value pair argument.     structure s, must have 2 fields:     s.classnames, contains class names variable of same data type y     s.classificationcosts, contains cost matrix rows , columns ordered in s.classnames     two-class learning, if specify cost matrix, software updates prior probabilities incorporating penalties described in cost matrix. subsequently, cost matrix resets default. more details on relationships , algorithmic behavior of boxconstraint, cost, prior, standardize, , weights, see algorithms.      defaults are:      one-class learning, cost = 0.     two-class learning, cost(i,j) = 1 if ~= j, , cost(i,j) = 0 if = j.     example: 'cost',[0,1;2,0]      data types: double | single | struct 

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'boxconstraint' — box constraint 1 (default) | positive scalar box constraint, specified comma-separated pair consisting of 'boxconstraint' , positive scalar.  one-class learning, software sets box constraint 1.  more details on relationships , algorithmic behavior of boxconstraint, cost, prior, standardize, , weights, see algorithms.  example: 'boxconstraint',100  data types: double | single 

what differences between cost , boxconstraint comparing above libsvm package options?

google , found following:

-c = boxconstraint

-wi ≈ cost

-g = 1/(kernelscale^2) if rbf used