gem "thundersvm"

x = [[1, 2], [3, 4], [5, 6], [7, 8]]
y = [1, 2, 3, 4]

model = ThunderSVM::Regressor.new
model.fit(x, y)

model.predict(x)

model.save_model("model.txt")

model = ThunderSVM.load_model("model.txt")

model.support_vectors

model.cv(x, y)

model.cv(x, y, folds: 5)

ThunderSVM::Model.new(
  svm_type: :c_svc,    # type of SVM (c_svc, nu_svc, one_class, epsilon_svr, nu_svr)
  kernel: :rbf,        # type of kernel function (linear, polynomial, rbf, sigmoid)
  degree: 3,           # degree in kernel function
  gamma: nil,          # gamma in kernel function
  coef0: 0,            # coef0 in kernel function
  c: 1,                # parameter C of C-SVC, epsilon-SVR, and nu-SVR
  nu: 0.5,             # parameter nu of nu-SVC, one-class SVM, and nu-SVR
  epsilon: 0.1,        # epsilon in loss function of epsilon-SVR
  max_memory: 8192,    # constrain the maximum memory size (MB) that thundersvm uses
  tolerance: 0.001,    # tolerance of termination criterion
  probability: false,  # whether to train a SVC or SVR model for probability estimates
  gpu: 0,              # specify which gpu to use
  cores: nil,          # number of cpu cores to use (defaults to all)
  verbose: false       # verbose mode
)

[[1, 2], [3, 4], [5, 6], [7, 8]]

Numo::DFloat.cast([[1, 2], [3, 4], [5, 6], [7, 8]])

model.fit("train.txt")
model.predict("test.txt")

ThunderSVM.ffi_lib = "path/to/build/lib/libthundersvm.so"

ThunderSVM.ffi_lib = "path/to/build/lib/libthundersvm.dll"