def create_learner(self):
kernel = ["linear", "poly", "rbf", "sigmoid"][self.kernel_type]
common_args = {
'kernel': kernel,
'degree': self.degree,
'gamma': self.gamma or self._default_gamma,
'coef0': self.coef0,
'tol': self.tol,
'max_iter': self.max_iter if self.limit_iter else -1,
'preprocessors': self.preprocessors
}
if self.svm_type == self.SVM:
return SVMLearner(C=self.C, epsilon=self.epsilon, **common_args)
else:
return NuSVMLearner(nu=self.nu, C=self.nu_C, **common_args)
def create_learner(self):
kernel = ["linear", "poly", "rbf", "sigmoid"][self.kernel_type]
common_args = {
"kernel": kernel,
"degree": self.degree,
"gamma": self.gamma or self._default_gamma,
"coef0": self.coef0,
"probability": True,
"tol": self.tol,
"max_iter": self.max_iter if self.limit_iter else -1,
"preprocessors": self.preprocessors,
}
if self.svm_type == self.SVM:
return SVMLearner(C=self.C, epsilon=self.epsilon, **common_args)
else:
return NuSVMLearner(nu=self.nu, C=self.nu_C, **common_args)
