def train(self, kernel_width, c, epsilon):
self._svm_new(kernel_width, c, epsilon)
x = RealFeatures(self.x)
self.svm.io.enable_progress()
self.svm.train(x)
self.svm.io.disable_progress()
def classify(self, matrix):
cl = self.svm.classify(
RealFeatures(
np.reshape(matrix, newshape=(com.FEATURE_DIM, 1),
order='F'))).get_label(0)
return int(cl + 1.0) % 10
def _svm_new(self, kernel_width, c, epsilon):
if self.x == None or self.y == None:
raise Exception("No training data loaded.")
x = RealFeatures(self.x)
y = Labels(self.y)
self.svm = GMNPSVM(c, GaussianKernel(x, x, kernel_width), y)
self.svm.set_epsilon(epsilon)
def get_test_error(self):
self.svm.io.enable_progress()
l = self.svm.apply(RealFeatures(self.x_test)).get_labels()
self.svm.io.disable_progress()
return 1.0 - np.mean(l == self.y_test)