def test_copy(self):
shapes_copy = np.copy(self.shapes)
gpa = prince.GPA(copy=True)
gpa.fit(shapes_copy)
np.testing.assert_array_equal(self.shapes, shapes_copy)
gpa = prince.GPA(copy=False)
gpa.fit(shapes_copy)
self.assertRaises(AssertionError, np.testing.assert_array_equal,
self.shapes, shapes_copy)
def test_fit_transform_single(self):
"""Aligning a single shape should return the same shape, just normalized."""
gpa = prince.GPA()
shapes = self.shapes[0:1]
aligned_shapes = gpa.fit_transform(shapes)
np.testing.assert_array_almost_equal(shapes / np.linalg.norm(shapes),
aligned_shapes)
def test_fit_transform_equal(self):
"""In our specific case of all-same-shape circles, the shapes should
align perfectly."""
gpa = prince.GPA()
aligned_shapes = gpa.fit_transform(self.shapes)
self.assertIsInstance(aligned_shapes, np.ndarray)
np.testing.assert_array_almost_equal(aligned_shapes[:-1],
aligned_shapes[1:])
def test_transform(self):
gpa = prince.GPA(copy=True)
aligned_shapes = gpa.fit(self.shapes).transform(self.shapes)
self.assertIsInstance(aligned_shapes, np.ndarray)
self.assertEqual(self.shapes.shape, aligned_shapes.shape)
def test_fit_bad_input_size(self):
gpa = prince.GPA()
with self.assertRaises(ValueError):
gpa.fit(self.shapes[0])
def test_fit_bad_init(self):
gpa = prince.GPA(init='bad init type')
with self.assertRaises(ValueError):
gpa.fit(self.shapes)
def test_fit_mean(self):
gpa = prince.GPA(init='mean')
self.assertIsInstance(gpa.fit(self.shapes), prince.GPA)
def test_fit_random(self):
gpa = prince.GPA(init='random')
self.assertIsInstance(gpa.fit(self.shapes), prince.GPA)
def test_fit(self):
gpa = prince.GPA()
self.assertIsInstance(gpa.fit(self.shapes), prince.GPA)