svmtl

usage: svmtl train [options] trainfile
       svmtl classify [options] testfile
       svmtl crossval [options] trainfile
       svmtl gridsearch [options] trainfile

Please use 'svmtl train --help', etc. for
furter help.

svmtl train

usage: svmtl train [options] trainfile

where options are:


file input output
-----------------
   -m, --modelfile filename   
                              filename for model file. Default: training file
                              name with ".model" extension

training
--------
   -dt, --details 
                         0   no details are printed / written to model-file
                         1   print statistics and write them to model-file
                              (default)
                         2   print training infos from all two-class
                              trainings and write them to model-file
   -pt, --print_train_error 
                         0   do not calculate training error (default)
                         1   calculate the training error (which is just
                              classification of each training vector) and
                              print statistics

progress reporting
------------------
   -vb, --verbose_level 
                         0   report nothing
                         1   report parameter tuning
                         2   report cross validation
                         3   report multi class svm
                         4   report two class svm
                         5   report addtitional training infos
   -p, --draw_progress_bar 
                         0   no acsii progress bars
                         1   draw acsii progress bars (default)

select algorithms
-----------------
   -mc, --multi_class_type 
               one_vs_one    multi-class SVM by using the 'one versus one'
                              approach
               one_vs_rest   multi-class SVM by using the One versus Rest
                              approach
   -tc, --two_class_type 
                    c_svc    Two class SVM using C-SVC algorithm for
                              training
                    nu_svc   Two class SVM using nu-SVC algorithm for
                              training
   -oc, --one_class_type xxx   
                              no algorithms available
   -kf, --kernel_type 
           kmatrix_rbf       cached kernel matrix for radial basis function
                              kernel: exp(-gamma*|u-v|^2)
           kmatrix_linear    cached kernel matrix for linear kernel: u'*v
           kmatrix_poly      cached kernel matrix for polynomial kernel:
                              (gamma*u'*v + coef0)^degree
           kmatrix_sigmoid   cached kernel matrix for polynomial kernel:
                              (gamma*u'*v + coef0)^degree
           kmatrix_scaled_rbf   
                              cached kernel matrix for scaled feature vectors
                              passed to radial basis function kernel:
                              exp(-gamma*|u-v|^2)
           kmatrix_scaled_linear   
                              cached kernel matrix for scaled feature vectors
                              passed to linear kernel: u'*v
           kmatrix_scaled_poly   
                              cached kernel matrix for scaled feature vectors
                              passed to polynomial kernel: (gamma*u'*v +
                              coef0)^degree
           kmatrix_scaled_sigmoid   
                              cached kernel matrix for scaled feature vectors
                              passed to polynomial kernel: (gamma*u'*v +
                              coef0)^degree
           rbf               radial basis function kernel:
                              exp(-gamma*|u-v|^2)
           linear            linear kernel: u'*v
           poly              polynomial kernel: (gamma*u'*v + coef0)^degree
           sigmoid           polynomial kernel: (gamma*u'*v + coef0)^degree

svm parameters
--------------
   -cs, --cache_size size    cache memory size in MB (default 40)
   -c, --cost value          penalty cost for wrong training vectors in
                              C-SVC. (default 1)
   -e, --epsilon value       tolerance of termination criterion (default
                              0.001)
   -n, --nu value            parameter nu in nu-SVC. (default 0.5)
   -sh, --shrinking 
                         0   don't use the shrinking heuristics
                         1   use the shrinking heuristics (default)
   -w, --weight value        weight for positive class samples in two-class
                              C-SVC. (default 1)

kernel parameters
-----------------
   -r, --coef0 value         coef0 for polynomial kernel. (default 0)
   -r, --coef0 value         coef0 for sigmoid kernel. (default 0)
   -d, --degree value        degree for polynomial kernel. (default 3)
   -g, --gamma value         gamma for polynomial kernel. (default 1)
   -g, --gamma value         gamma for rbf-kernel. (default 1.0, or 1/k, when
                              feature_vector_dim is given
   -g, --gamma value         gamma for sigmoid kernel. (default 1)
   -sa, --scale_algorithm 
                    minmax   scale each feature that min becomes -1 and max
                              becomes +1
                    stddev   scale each feature that mean becomes 0 and
                              standard deviation becomes 1
   -sf, --scale_factor array   
                              array containing scale factors for each feature
                              -- usually you don't want to specify this
                              manually
   -so, --scale_offset array   
                              array containing offsets for each feature --
                              usually you don't want to specify this manually

svmtl classify

usage: svmtl classify [options] testfile

where options are:


file input output
-----------------
   -m, --modelfile filename   
                              filename of model file. Default: 'svmtl.model'
   -o, --outfile filename    filename for output file with classification
                              results. Default: test file name with ".output"
                              extension

classification
--------------
   -dt, --details 
                         0   no details
                         1   print correct/wrong classifications (requires a
                              labeled test set) (default)
                         2   save results from each two-class classifications
                              to output file

progress reporting
------------------
   -vb, --verbose_level 
                         0   report nothing
                         1   report parameter tuning
                         2   report cross validation
                         3   report multi class svm
                         4   report two class svm
                         5   report addtitional training infos
   -p, --draw_progress_bar 
                         0   no acsii progress bars
                         1   draw acsii progress bars (default)

svmtl crossval

usage: svmtl crossval [options] trainfile

where options are:


file input output
-----------------
   -o, --outfile filename    filename for output file with classification
                              results. Default: test file name with ".output"
                              extension

cross validation
----------------
   -v, --nfold nsubsets      number of subsets for cross validation (default
                              10)
   -ss, --shuffle_subsets 
                         0   no shuffling
                         1   shuffle data before splitting into subsets for
                              cross validation (default)
   -td, --train_details 
                         0   no details
                         1   print training statistics (default)
   -cd, --class_details 
                         0   no details
                         1   print correct/wrong classifications per class
                              (default)

progress reporting
------------------
   -vb, --verbose_level 
                         0   report nothing
                         1   report parameter tuning
                         2   report cross validation
                         3   report multi class svm
                         4   report two class svm
                         5   report addtitional training infos
   -p, --draw_progress_bar 
                         0   no acsii progress bars
                         1   draw acsii progress bars (default)

select algorithms
-----------------
   -mc, --multi_class_type 
               one_vs_one    multi-class SVM by using the 'one versus one'
                              approach
               one_vs_rest   multi-class SVM by using the One versus Rest
                              approach
   -tc, --two_class_type 
                    c_svc    Two class SVM using C-SVC algorithm for
                              training
                    nu_svc   Two class SVM using nu-SVC algorithm for
                              training
   -oc, --one_class_type xxx   
                              no algorithms available
   -kf, --kernel_type 
           kmatrix_rbf       cached kernel matrix for radial basis function
                              kernel: exp(-gamma*|u-v|^2)
           kmatrix_linear    cached kernel matrix for linear kernel: u'*v
           kmatrix_poly      cached kernel matrix for polynomial kernel:
                              (gamma*u'*v + coef0)^degree
           kmatrix_sigmoid   cached kernel matrix for polynomial kernel:
                              (gamma*u'*v + coef0)^degree
           kmatrix_scaled_rbf   
                              cached kernel matrix for scaled feature vectors
                              passed to radial basis function kernel:
                              exp(-gamma*|u-v|^2)
           kmatrix_scaled_linear   
                              cached kernel matrix for scaled feature vectors
                              passed to linear kernel: u'*v
           kmatrix_scaled_poly   
                              cached kernel matrix for scaled feature vectors
                              passed to polynomial kernel: (gamma*u'*v +
                              coef0)^degree
           kmatrix_scaled_sigmoid   
                              cached kernel matrix for scaled feature vectors
                              passed to polynomial kernel: (gamma*u'*v +
                              coef0)^degree
           rbf               radial basis function kernel:
                              exp(-gamma*|u-v|^2)
           linear            linear kernel: u'*v
           poly              polynomial kernel: (gamma*u'*v + coef0)^degree
           sigmoid           polynomial kernel: (gamma*u'*v + coef0)^degree

svm parameters
--------------
   -cs, --cache_size size    cache memory size in MB (default 40)
   -c, --cost value          penalty cost for wrong training vectors in
                              C-SVC. (default 1)
   -e, --epsilon value       tolerance of termination criterion (default
                              0.001)
   -n, --nu value            parameter nu in nu-SVC. (default 0.5)
   -sh, --shrinking 
                         0   don't use the shrinking heuristics
                         1   use the shrinking heuristics (default)
   -w, --weight value        weight for positive class samples in two-class
                              C-SVC. (default 1)

kernel parameters
-----------------
   -r, --coef0 value         coef0 for polynomial kernel. (default 0)
   -r, --coef0 value         coef0 for sigmoid kernel. (default 0)
   -d, --degree value        degree for polynomial kernel. (default 3)
   -g, --gamma value         gamma for polynomial kernel. (default 1)
   -g, --gamma value         gamma for rbf-kernel. (default 1.0, or 1/k, when
                              feature_vector_dim is given
   -g, --gamma value         gamma for sigmoid kernel. (default 1)
   -sa, --scale_algorithm 
                    minmax   scale each feature that min becomes -1 and max
                              becomes +1
                    stddev   scale each feature that mean becomes 0 and
                              standard deviation becomes 1
   -sf, --scale_factor array   
                              array containing scale factors for each feature
                              -- usually you don't want to specify this
                              manually
   -so, --scale_offset array   
                              array containing offsets for each feature --
                              usually you don't want to specify this manually

svmtl gridsearch

usage: svmtl gridsearch [options] trainfile

where options are:


file input output
-----------------

grid search
-----------
   -p1, --param1 
           <keyname>:<from>,add<step>,<to>   
                              specify linear range for row axis. e.g.,
                              'cost:1,add1,5' results in 1,2,3,4,5
           <keyname>:<from>,mul<factor>,<to>   
                              specify logarithmic range for row axis. e.g.,
                              'gamma:1,mul2,16' results in 1,2,4,8,16
           <keyname>:<v1>,<v2>,<v3>,...   
                              specify list of values for rowaxis. e.g.
                              'cost:-2,5,42,3'
   -p2, --param2 <keyname>:<valuespec>   
                              range and parameter for column axis -- syntax of
                              valuespec is the same as for 'param1'
   -pg, --print_grid 
                         0   no output during evaluation
                         1   print the grid during evaluation (default)

cross validation
----------------
   -v, --nfold nsubsets      number of subsets for cross validation (default
                              10)
   -ss, --shuffle_subsets 
                         0   no shuffling
                         1   shuffle data before splitting into subsets for
                              cross validation (default)
   -td, --train_details 
                         0   no details
                         1   print training statistics (default)
   -cd, --class_details 
                         0   no details
                         1   print correct/wrong classifications per class
                              (default)

progress reporting
------------------
   -vb, --verbose_level 
                         0   report nothing
                         1   report parameter tuning
                         2   report cross validation
                         3   report multi class svm
                         4   report two class svm
                         5   report addtitional training infos
   -p, --draw_progress_bar 
                         0   no acsii progress bars
                         1   draw acsii progress bars (default)

select algorithms
-----------------
   -mc, --multi_class_type 
               one_vs_one    multi-class SVM by using the 'one versus one'
                              approach
               one_vs_rest   multi-class SVM by using the One versus Rest
                              approach
   -tc, --two_class_type 
                    c_svc    Two class SVM using C-SVC algorithm for
                              training
                    nu_svc   Two class SVM using nu-SVC algorithm for
                              training
   -oc, --one_class_type xxx   
                              no algorithms available
   -kf, --kernel_type 
           kmatrix_rbf       cached kernel matrix for radial basis function
                              kernel: exp(-gamma*|u-v|^2)
           kmatrix_linear    cached kernel matrix for linear kernel: u'*v
           kmatrix_poly      cached kernel matrix for polynomial kernel:
                              (gamma*u'*v + coef0)^degree
           kmatrix_sigmoid   cached kernel matrix for polynomial kernel:
                              (gamma*u'*v + coef0)^degree
           kmatrix_scaled_rbf   
                              cached kernel matrix for scaled feature vectors
                              passed to radial basis function kernel:
                              exp(-gamma*|u-v|^2)
           kmatrix_scaled_linear   
                              cached kernel matrix for scaled feature vectors
                              passed to linear kernel: u'*v
           kmatrix_scaled_poly   
                              cached kernel matrix for scaled feature vectors
                              passed to polynomial kernel: (gamma*u'*v +
                              coef0)^degree
           kmatrix_scaled_sigmoid   
                              cached kernel matrix for scaled feature vectors
                              passed to polynomial kernel: (gamma*u'*v +
                              coef0)^degree
           rbf               radial basis function kernel:
                              exp(-gamma*|u-v|^2)
           linear            linear kernel: u'*v
           poly              polynomial kernel: (gamma*u'*v + coef0)^degree
           sigmoid           polynomial kernel: (gamma*u'*v + coef0)^degree

svm parameters
--------------
   -cs, --cache_size size    cache memory size in MB (default 40)
   -c, --cost value          penalty cost for wrong training vectors in
                              C-SVC. (default 1)
   -e, --epsilon value       tolerance of termination criterion (default
                              0.001)
   -n, --nu value            parameter nu in nu-SVC. (default 0.5)
   -sh, --shrinking 
                         0   don't use the shrinking heuristics
                         1   use the shrinking heuristics (default)
   -w, --weight value        weight for positive class samples in two-class
                              C-SVC. (default 1)

kernel parameters
-----------------
   -r, --coef0 value         coef0 for polynomial kernel. (default 0)
   -r, --coef0 value         coef0 for sigmoid kernel. (default 0)
   -d, --degree value        degree for polynomial kernel. (default 3)
   -g, --gamma value         gamma for polynomial kernel. (default 1)
   -g, --gamma value         gamma for rbf-kernel. (default 1.0, or 1/k, when
                              feature_vector_dim is given
   -g, --gamma value         gamma for sigmoid kernel. (default 1)
   -sa, --scale_algorithm 
                    minmax   scale each feature that min becomes -1 and max
                              becomes +1
                    stddev   scale each feature that mean becomes 0 and
                              standard deviation becomes 1
   -sf, --scale_factor array   
                              array containing scale factors for each feature
                              -- usually you don't want to specify this
                              manually
   -so, --scale_offset array   
                              array containing offsets for each feature --
                              usually you don't want to specify this manually