def test_transform(self):
ldamodule = LDA(solver='svd')
self.assertIsNone(ldamodule.fit(self.data, self.labels))
totransform = np.random.rand(100, 10)
transformed = ldamodule.transform(totransform, 5)
self.assertEqual(transformed.shape, (100, 5))
def test_fitpredicteigen(self):
ldamodule = LDA(solver='eigen')
self.assertIsNone(ldamodule.fit(self.data, self.labels))
testdata = np.random.rand(100, 10)
# testlabels = np.array([i%10 for i in range(testdata.shape[0])])
scores = ldamodule.predict_log_prob(testdata)
for scoreset in scores:
for score in scoreset:
self.assertLessEqual(score, 0)
class LDATest(unittest.TestCase):
def setUp(self):
self.ldamodule1 = LDA('svd')
self.ldamodule2 = SKLDA()
self.data = np.random.rand(10, 10)
self.labels = np.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 3])
def test_fitpredict(self):
np.set_printoptions(precision=4)
self.ldamodule1.fit(self.data, self.labels)
self.ldamodule2.fit(self.data, self.labels)
testdata = np.random.rand(10, 10)
# testlabels = np.array([i%10 for i in range(testdata.shape[0])])
scores = self.ldamodule1.predict_log_prob(testdata)
class LDATest(unittest.TestCase):
def setUp(self):
self.ldamodule1 = LDA('svd')
self.ldamodule2 = SKLDA()
self.data = np.random.rand(10, 10)
self.labels = np.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 3])
def test_fitpredict(self):
np.set_printoptions(precision=4)
self.ldamodule1.fit(self.data, self.labels)
self.ldamodule2.fit(self.data, self.labels)
testdata = np.random.rand(10, 10)
# testlabels = np.array([i%10 for i in range(testdata.shape[0])])
scores = self.ldamodule1.predict_log_prob(testdata)
def setUp(self):
self.ldamodule1 = LDA('svd')
self.ldamodule2 = SKLDA()
self.data = np.random.rand(10, 10)
self.labels = np.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 3])
def setUp(self):
self.ldamodule1 = LDA('svd')
self.ldamodule2 = SKLDA()
self.data = np.random.rand(10, 10)
self.labels = np.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 3])
