private static double |
Linear.calc_start_C(Problem prob,
Parameter param) |
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static void |
Linear.crossValidation(Problem prob,
Parameter param,
int nr_fold,
double[] target) |
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static ParameterCSearchResult |
Linear.find_parameter_C(Problem prob,
Parameter param_tmp,
double start_C,
double max_C,
int[] fold_start,
int[] perm,
Problem[] subprob,
int nr_fold) |
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static ParameterSearchResult |
Linear.findParameters(Problem prob,
Parameter param,
int nr_fold,
double start_C,
double start_p) |
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private static int |
Linear.solve_l1r_l2_svc(Problem prob_col,
Parameter param,
double[] w,
double Cp,
double Cn,
double eps,
int max_iter) |
A coordinate descent algorithm for
L1-regularized L2-loss support vector classification
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private static int |
Linear.solve_l1r_lr(Problem prob_col,
Parameter param,
double[] w,
double Cp,
double Cn,
double eps,
int max_iter) |
A coordinate descent algorithm for
L1-regularized logistic regression problems
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private static int |
Linear.solve_l2r_l1l2_svc(Problem prob,
Parameter param,
double[] w,
double Cp,
double Cn,
int max_iter) |
A coordinate descent algorithm for
L1-loss and L2-loss SVM dual problems
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private static int |
Linear.solve_l2r_l1l2_svr(Problem prob,
Parameter param,
double[] w,
int max_iter) |
A coordinate descent algorithm for
L1-loss and L2-loss epsilon-SVR dual problem
min_\beta 0.5\beta^T (Q + diag(lambda)) \beta - p \sum_{i=1}^l|\beta_i| + \sum_{i=1}^l yi\beta_i,
s.t.
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private static int |
Linear.solve_l2r_lr_dual(Problem prob,
Parameter param,
double[] w,
double Cp,
double Cn,
int max_iter) |
A coordinate descent algorithm for
the dual of L2-regularized logistic regression problems
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(package private) static int |
Linear.solve_oneclass_svm(Problem prob,
Parameter param,
double[] w,
MutableDouble rho,
int max_iter) |
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static Model |
Linear.train(Problem prob,
Parameter param) |
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private static void |
Linear.train_one(Problem prob,
Parameter param,
double[] w,
double Cp,
double Cn) |
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