def testLearnClassify(self):
docs = getDocs()
l = Learner()
class_values = [1, 1, 1, -1, -1]
model = l.learn(docs, class_values)
judgments = [model.classify(d) for d in docs]
for i in range(len(class_values)):
binary = 1 if judgments[i] > 0 else -1
self.assertEqual(class_values[i], binary)
def testLearn(self):
docs = getDocs()
l = Learner()
model = l.learn(docs, [1, 1, 1, -1, -1])
print model, model.bias
self.assertEqual(5, model.num_docs)
self.assertEqual(10, len(model.plane))
self.assertNotEqual(model.bias, 0)
print model.plane
def testLearnClassify(self):
docs = getDocs()
l = Learner()
class_values = [1, 1, 1, -1, -1]
model = l.learn(docs, class_values)
judgments = [model.classify(d) for d in docs]
for i in range(len(class_values)):
binary = 1 if judgments[i] > 0 else -1
self.assertEqual(class_values[i], binary)
def testLearn(self):
docs = getDocs()
l = Learner()
model = l.learn(docs, [1, 1, 1, -1, -1])
print model, model.bias
self.assertEqual(5, model.num_docs)
self.assertEqual(10, len(model.plane))
self.assertNotEqual(model.bias, 0)
print model.plane
def testLearnUnbiased(self):
docs = getDocs()
l = Learner()
l.biased_hyperplane = False
model = l.learn(docs, [1, 1, 1, -1, -1])
print model, model.bias
self.assertEqual(5, model.num_docs)
self.assertEqual(10, len(model.plane))
self.assertEqual(model.bias, 0)
print model.plane
def testLearnUnbiased(self):
docs = getDocs()
l = Learner()
l.biased_hyperplane = False
model = l.learn(docs, [1, 1, 1, -1, -1])
print model, model.bias
self.assertEqual(5, model.num_docs)
self.assertEqual(10, len(model.plane))
self.assertEqual(model.bias, 0)
print model.plane
from svmlight import DocumentFactory, Learner
artists = [line.strip() for line in open('artistList.txt')]
tags = [line.strip() for line in open('tagData.txt')]
classes = [line.strip() for line in open('classification.txt')]
f = DocumentFactory()
docs = [f.new(x.split(',')) for x in tags]
l = Learner()
l.set_kernel_type(0)
model = l.learn(docs[50:], [int(s) for s in classes[50:]])
judgments = [model.classify(d) for d in docs[:50]]
print model.plane, model.bias
print judgments
i = 0;
while (i < len(judgments)):
print str(i) + '. ' + artists[i]
if (judgments[i] >= 0.0):
print 'yes'
else :
print 'no'
i += 1
def testSetBiasedHyperplane(self):
l = Learner()
l.biased_hyperplane = False
self.assertEqual(str(l), "Learner(biased_hyperplane=False)")
def testConstruction(self):
self.assertEqual(str(Learner()), "Learner(biased_hyperplane=True)")
def test_set_biased_hyperplane(self):
l = Learner()
l.biased_hyperplane = False
self.assertEqual(str(l), 'Learner(biased_hyperplane=False)')
def testSetBiasedHyperplane(self):
l = Learner()
l.biased_hyperplane = False
self.assertEqual(str(l), "Learner(biased_hyperplane=False)")
