C | |
| calc_accuracy [Libsvm.Stats] | calc_accuracy expected predicted
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| calc_mse [Libsvm.Stats] | calc_mse expected predicted
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| calc_n_correct [Libsvm.Stats] | calc_n_correct expected predicted
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| calc_scc [Libsvm.Stats] | calc_scc expected predicted
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| create [Libsvm.Svm.Problem] | create x y constructs a problem from a feature matrix x and target
vector y.
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| create_k [Libsvm.Svm.Problem] | create_k k y constructs a problem from a matrix k and target vector
y.
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| cross_validation [Libsvm.Svm] | cross_validation params problem n_folds conducts n-fold
cross-validation on the given problem and parameters params.
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G | |
| get_labels [Libsvm.Svm.Model] | get_labels model
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| get_n_classes [Libsvm.Svm.Model] | get_n_classes model
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| get_n_feats [Libsvm.Svm.Problem] | get_n_feats prob
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| get_n_samples [Libsvm.Svm.Problem] | get_n_samples prob
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| get_n_sv [Libsvm.Svm.Model] | get_n_sv model
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| get_svm_type [Libsvm.Svm.Model] | get_svm_type model
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| get_svr_probability [Libsvm.Svm.Model] | get_svr_probability model
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| get_targets [Libsvm.Svm.Problem] | get_targets prob
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L | |
| load [Libsvm.Svm.Model] | load filename loads a model from the file filename.
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| load [Libsvm.Svm.Problem] | load filename loads a problem from the file filename.
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M | |
| min_max_feats [Libsvm.Svm.Problem] | min_max_feats prob
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O | |
| output [Libsvm.Svm.Problem] | output prob oc outputs the problem prob to an output channel oc.
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P | |
| predict [Libsvm.Svm] | predict model x applies predict_one to each row of the matrix x.
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| predict_from_file [Libsvm.Svm] | predict_from_file model filename does classification or regression
on the testing data given in filename.
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| predict_one [Libsvm.Svm] | predict_one model x does classification or regression on a test vector
x given a model.
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| predict_probability [Libsvm.Svm] | predict_probability m x does classification or regression on a test
vector x based on a model with probability information.
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| predict_values [Libsvm.Svm] | predict_values model x
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| print [Libsvm.Svm.Problem] | print prob prints the internal representation of a problem.
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S | |
| save [Libsvm.Svm.Model] | save model filename saves a model to the file filename.
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| save [Libsvm.Svm.Problem] | save prob filename saves the problem prob to the file filename.
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| scale [Libsvm.Svm.Problem] | scale ?lower ?upper prob min_feats max_feats
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T | |
| train [Libsvm.Svm] | train params problem trains a SVM model on the given problem and
parameters params: svm_type - type of SVM classification/regression (default C_SVC), kernel - type of the SVM kernel (default RBF), degree - the exponent in the POLY kernel (default 3), gamma - parameter for POLY, RBF and SIGMOID kernel (default 0), coef0 - parameter for POLY and SIGMOID kernel (default 0), c - the cost of constraints violation in C_SVC, EPSILON_SVR, and
NU_SVR (default 1), nu - the parameter in NU_SVM, NU_SVR and ONE_CLASS (default 0.5), eps - the epsilon in the epsilon-sensitive loss function of
EPSILON_SVR (default 0.1), cachesize - the size of the kernel cache in megabytes (default 100), tol - the stopping criterion (default 1e-3), shrinking - use on to conduct shrinking, otherwise off (default on), probability - if probability = true, then a model with probability
information will be obtained (default false), weights - weights to penalize classes (default = []), verbose - if verbose = true, then train the SVM in verbose mode
(default false)
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