R

R interface for thundersvm.

Before you use the R interface, you must build ThunderSVM.

Instructions for building ThunderSVM

• Please refer to Installation for building ThunderSVM.

• Then, under ./build/lib/ of the ThunderSVM root directory, you should be able to see a library of ThunderSVM (e.g., libthundersvm.so on Linux machines).

• After you have successfully done the above two steps, it is ready to start using Python interfaces.

Methods

By default, the directory for storing the training data and results is the working directory.

svm_train_R(svm_type = 0, kernel = 2,degree = 3,gamma = 'auto', coef0 = 0.0, nu = 0.5, cost = 1.0, epsilon = 0.1, tol = 0.001, probability = FALSE, class_weight = 'None', cv = -1, verbose = FALSE, max_iter = -1, n_cores = -1, dataset = 'None', model_file = 'None')

svm_predict_R(test_dataset = 'None', model_file = 'None', out_file = 'None')

Example

• Step 1: go to the R interface.

# in thundersvm root directory
cd R

• Step 2: start R in the terminal by typing R and press the Enter key.
• Step 3: execute the following code in R.

source("svm.R")
svm_train_R(dataset = "../dataset/test_dataset.txt", model_file = "../dataset/test_dataset.txt.model", cost = 100, gamma = 0.5)
svm_predict_R(test_dataset = "../dataset/test_dataset.txt", model_file = "../dataset/test_dataset.txt.model", out_file="test_dataset.txt.out")

Parameters

svm_type: int, optional(default=0) set type of SVM (default 0)
0 -- C-SVC (multi-class classification)
1 -- nu-SVC (multi-class classification)
2 -- one-class SVM
3 -- epsilon-SVR (regression)
4 -- nu-SVR (regression)

kernel: int, optional(default=2)
set type of kernel function
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)
4 -- precomputed kernel (kernel values in training_set_file)

degree: int, optional(default=3)
set degree in kernel function

gamma: float, optional(default='auto')
set gamma in kernel function (auto:1/num_features)

coef0: float, optional(default=0.0)
set coef0 in kernel function

cost: float, optional(default=1.0)
set the parameter C of C-SVC, epsilon-SVR, and nu-SVR

nu: float, optional(default=0.5)
set the parameter nu of nu-SVC, one-class SVM, and nu-SVR

epsilon: float, optional(default=0.1)
set the epsilon in loss function of epsilon-SVR

tol: float, optional(default=0.001)
set tolerance of termination criterion (default 0.001)

probability: boolean, optional(default=False)
whether to train a SVC or SVR model for probability estimates, True or False

class_weight: {dict, ‘balanced’}, optional(default=None)
set the parameter C of class i to weight*C, for C-SVC

cv: int, optional(default=-1)
specify the number of folds in cross-validation, or -1 for no cross-validation.

verbose: bool(default=False)
enable verbose output. Note that this setting takes advantage of a per-process runtime setting; if enabled, ThunderSVM may not work properly in a multithreaded context.

max_iter: int, optional (default=-1)
hard limit on the number of iterations within the solver, or -1 for no limit.

n_cores: int, optional (default=-1)
set the number of cpu cores to use, -1 for maximum

dataset: string
file path to training dataset.

model_file: string, optional
file path to save the trained model.

test_dataset: string
file path to test set

out_file: string, optional
file path to save predicted outcomes.