def test_compose_exact(self):
test_cases = [(self.m11, self.m21, self.ph1, 5 / (3.0)),
(self.m13, self.m23, self.ph3, 6),
(self.m14, self.m24, self.ph4, 6)]
for arg1, arg2, phrase, lambda_ in test_cases:
m = Dilation()
m._solve(arg1, arg2, phrase)
res = m._compose(arg1, arg2)
np.testing.assert_array_almost_equal(res.mat, phrase.mat, 2)
m = Dilation(lambda_)
res = m._compose(arg1, arg2)
np.testing.assert_array_almost_equal(res.mat, phrase.mat, 2)
def test_compose_exact(self):
test_cases = [(self.m11, self.m21, self.ph1, 5 / (3.0)),
(self.m13, self.m23, self.ph3, 6),
(self.m14, self.m24, self.ph4, 6)
]
for arg1, arg2, phrase, lambda_ in test_cases:
m = Dilation()
m._train(arg1, arg2, phrase)
res = m._compose(arg1, arg2)
np.testing.assert_array_almost_equal(res.mat, phrase.mat, 2)
m = Dilation(lambda_)
res = m._compose(arg1, arg2)
np.testing.assert_array_almost_equal(res.mat, phrase.mat, 2)
def test_train_random(self):
test_cases = [1.0, 2.0, 3.0]
rows = 4
cols = 3
m1 = np.random.rand(rows, cols)
m2 = np.random.rand(rows, cols)
for lambda_ in test_cases:
m = Dilation(lambda_)
result_p = m._compose(DenseMatrix(m1), DenseMatrix(m2))
m = Dilation()
m._solve(DenseMatrix(m1), DenseMatrix(m2), result_p)
self.assertAlmostEqual(lambda_, m._lambda)
def test_train_random(self):
test_cases = [1.0,2.0,3.0]
rows = 4
cols = 3
m1 = np.random.rand(rows,cols)
m2 = np.random.rand(rows,cols)
for lambda_ in test_cases:
m = Dilation(lambda_)
result_p = m._compose(DenseMatrix(m1), DenseMatrix(m2))
m = Dilation()
m._train(DenseMatrix(m1),DenseMatrix(m2),result_p)
self.assertAlmostEqual(lambda_, m._lambda)
