def test_trivial_crossvalidation(self):
for i in range(1, 10):
m_a = DenseMatrix(np.mat(np.random.random((i + 1,4))))
m_b = DenseMatrix(np.mat(np.random.random((i + 1,4))))
tmp_a = m_a.mat.copy()
tmp_b = m_b.mat.copy()
learner = RidgeRegressionLearner(param_range=[3], intercept=False)
solution = learner.train(m_a, m_b)
learner2 = RidgeRegressionLearner(param = 3, intercept=False)
solution2 = learner2.train(m_a, m_b)
np.testing.assert_array_equal(tmp_a, m_a.mat)
np.testing.assert_array_equal(tmp_b, m_b.mat)
np.testing.assert_array_equal(solution.mat, solution2.mat)
learner = RidgeRegressionLearner(param_range=[3], intercept=False)
solution = learner.train(m_a, m_b)
np.testing.assert_array_equal(tmp_a, m_a.mat)
np.testing.assert_array_equal(tmp_b, m_b.mat)
np.testing.assert_array_equal(solution.mat, solution2.mat)
learner = RidgeRegressionLearner(param_range=[0], intercept=False)
solution = learner.train(m_a, m_b)
learner2 = LstsqRegressionLearner(intercept=False)
solution2 = learner2.train(m_a, m_b)
np.testing.assert_array_almost_equal(solution.mat, solution2.mat, 3)
def test_trivial_crossvalidation(self):
for i in range(1, 10):
m_a = DenseMatrix(np.mat(np.random.random((i + 1, 4))))
m_b = DenseMatrix(np.mat(np.random.random((i + 1, 4))))
tmp_a = m_a.mat.copy()
tmp_b = m_b.mat.copy()
learner = RidgeRegressionLearner(param_range=[3], intercept=False)
solution = learner.train(m_a, m_b)
learner2 = RidgeRegressionLearner(param=3, intercept=False)
solution2 = learner2.train(m_a, m_b)
np.testing.assert_array_equal(tmp_a, m_a.mat)
np.testing.assert_array_equal(tmp_b, m_b.mat)
np.testing.assert_array_equal(solution.mat, solution2.mat)
learner = RidgeRegressionLearner(param_range=[3], intercept=False)
solution = learner.train(m_a, m_b)
np.testing.assert_array_equal(tmp_a, m_a.mat)
np.testing.assert_array_equal(tmp_b, m_b.mat)
np.testing.assert_array_equal(solution.mat, solution2.mat)
learner = RidgeRegressionLearner(param_range=[0], intercept=False)
solution = learner.train(m_a, m_b)
learner2 = LstsqRegressionLearner(intercept=False)
solution2 = learner2.train(m_a, m_b)
np.testing.assert_array_almost_equal(solution.mat, solution2.mat,
3)
def test_crossvalidation(self):
a = DenseMatrix(np.matrix([[1, 1],[2, 3],[4, 6]]))
b = DenseMatrix(np.matrix([[12, 15, 18],[21, 27, 33],[35, 46, 57]]))
res = DenseMatrix(np.matrix([[1, 2, 3],[4, 5, 6],[7, 8, 9]]))
learner = RidgeRegressionLearner(intercept=True, param_range=[0])
learner2 = LstsqRegressionLearner(intercept=False)
res1 = learner2.train(a, b)
res2 = learner.train(a, b)
np.testing.assert_array_almost_equal(res2.mat[:-1,:], res[0:2,:].mat, 6)
np.testing.assert_array_almost_equal(res2.mat[-1,:], res[2:3,:].mat, 6)
new_a = padd_matrix(a, 1)
self.assertGreater(((a * res1) - b).norm(), ((new_a * res2) - b).norm())
def test_crossvalidation(self):
a = DenseMatrix(np.matrix([[1, 1], [2, 3], [4, 6]]))
b = DenseMatrix(np.matrix([[12, 15, 18], [21, 27, 33], [35, 46, 57]]))
res = DenseMatrix(np.matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]))
learner = RidgeRegressionLearner(intercept=True, param_range=[0])
learner2 = LstsqRegressionLearner(intercept=False)
res1 = learner2.train(a, b)
res2 = learner.train(a, b)
np.testing.assert_array_almost_equal(res2.mat[:-1, :], res[0:2, :].mat,
6)
np.testing.assert_array_almost_equal(res2.mat[-1, :], res[2:3, :].mat,
6)
new_a = padd_matrix(a, 1)
self.assertGreater(((a * res1) - b).norm(),
((new_a * res2) - b).norm())
