def test_liblinear(self):
# If LIBLINEAR can be loaded,
# assert that it is used for linear SVC (= 10x faster).
try:
from pattern.vector import svm
except ImportError as e:
print(e)
return
if svm.LIBLINEAR:
classifier1 = vector.SVM(
type=vector.CLASSIFICATION,
kernel=vector.LINEAR,
extensions=(vector.LIBSVM, vector.LIBLINEAR))
classifier2 = vector.SVM(
type=vector.CLASSIFICATION,
kernel=vector.RBF,
extensions=(vector.LIBSVM, vector.LIBLINEAR))
classifier3 = vector.SVM(
type=vector.CLASSIFICATION,
kernel=vector.LINEAR,
extensions=(vector.LIBSVM,))
self.assertEqual(classifier1.extension, vector.LIBLINEAR)
self.assertEqual(classifier2.extension, vector.LIBSVM)
self.assertEqual(classifier3.extension, vector.LIBSVM)
print("pattern.vector.svm.LIBSVM")
print("pattern.vector.svm.LIBLINEAR")
#print(A, P, R, F, o)
self.assertTrue(P >= 0.93)
self.assertTrue(R >= 0.93)
self.assertTrue(F >= 0.93)
def test_liblinear(self):
# If LIBLINEAR can be loaded,
# assert that it is used for linear SVC (= 10x faster).
try:
from pattern.vector import svm
except ImportError, e:
print(e)
return
if svm.LIBLINEAR:
classifier1 = vector.SVM(
type = vector.CLASSIFICATION,
kernel = vector.LINEAR,
extensions = (vector.LIBSVM, vector.LIBLINEAR))
classifier2 = vector.SVM(
type = vector.CLASSIFICATION,
kernel = vector.RBF,
extensions = (vector.LIBSVM, vector.LIBLINEAR))
classifier3 = vector.SVM(
type = vector.CLASSIFICATION,
kernel = vector.LINEAR,
extensions = (vector.LIBSVM,))
self.assertEqual(classifier1.extension, vector.LIBLINEAR)
self.assertEqual(classifier2.extension, vector.LIBSVM)
self.assertEqual(classifier3.extension, vector.LIBSVM)
print("pattern.vector.svm.LIBSVM")
print("pattern.vector.svm.LIBLINEAR")