def apply(self):
kernel = ["linear", "poly", "rbf", "sigmoid"][self.kernel_type]
common_args = dict(
kernel=kernel,
degree=self.degree,
gamma=self.gamma,
coef0=self.coef0,
tol=self.tol,
max_iter=self.max_iter if self.limit_iter else -1,
probability=True,
preprocessors=self.preprocessors
)
if self.svmtype == 0:
learner = SVMLearner(C=self.C, **common_args)
else:
learner = NuSVMLearner(nu=self.nu, **common_args)
learner.name = self.learner_name
classifier = None
sv = None
if self.data is not None:
self.error(0)
if not learner.check_learner_adequacy(self.data.domain):
self.error(0, learner.learner_adequacy_err_msg)
else:
classifier = learner(self.data)
classifier.name = self.learner_name
sv = self.data[classifier.skl_model.support_]
self.send("Learner", learner)
self.send("Classifier", classifier)
self.send("Support vectors", sv)
def apply(self):
kernel = ["linear", "poly", "rbf", "sigmoid"][self.kernel_type]
common_args = dict(kernel=kernel,
degree=self.degree,
gamma=self.gamma,
coef0=self.coef0,
tol=self.tol,
max_iter=self.max_iter if self.limit_iter else -1,
probability=True,
preprocessors=self.preprocessors)
if self.svmtype == 0:
learner = SVMLearner(C=self.C, **common_args)
else:
learner = NuSVMLearner(nu=self.nu, **common_args)
learner.name = self.learner_name
classifier = None
sv = None
if self.data is not None:
self.error([0, 1])
if not learner.check_learner_adequacy(self.data.domain):
self.error(0, learner.learner_adequacy_err_msg)
elif len(np.unique(self.data.Y)) < 2:
self.error(1, "Data contains only one target value.")
else:
classifier = learner(self.data)
classifier.name = self.learner_name
sv = self.data[classifier.skl_model.support_]
self.send("Learner", learner)
self.send("Classifier", classifier)
self.send("Support vectors", sv)
