class SparseRowMatrixTestCase(unittest.TestCase):
def setUp(self):
self.matrix_A = SparseRowMatrix(sparse_matrix_rdd, 'test_data', 1000,
100)
self.matrix_A2 = SparseRowMatrix(sparse_matrix_rdd2, 'test_data', 100,
1000)
def test_size(self):
c = self.matrix_A.rdd.count()
self.assertEqual(c, 1000)
def test_size2(self):
c = self.matrix_A2.rdd.count()
self.assertEqual(c, 100)
def test_gaussian_projection(self):
p = self.matrix_A.gaussian_projection(20)
self.assertEqual(p.shape, (20, 100))
#def test_mat_ltimes(self):
# mat = np.random.rand(10,1000)
# p = self.matrix_A.ltimes(mat)
# p_true = np.dot( mat,A )
# self.assertTrue( np.linalg.norm(p-p_true)/np.linalg.norm(p_true) < 1e-5 )
def test_atamat(self):
mat = np.random.rand(100, 20)
p = self.matrix_A.atamat(mat)
p_true = np.dot(A.T, np.dot(A, mat))
self.assertTrue(
np.linalg.norm(p - p_true) / np.linalg.norm(p_true) < 1e-5)
def test_gaussian_projection2(self):
p = self.matrix_A2.gaussian_projection(20)
self.assertEqual(p.shape, (20, 1000))
def test_atamat2(self):
mat = np.random.rand(1000, 20)
p = self.matrix_A2.atamat(mat)
p_true = np.dot(A2.T, np.dot(A2, mat))
self.assertTrue(
np.linalg.norm(p - p_true) / np.linalg.norm(p_true) < 1e-5)
class SparseRowMatrixTestCase(unittest.TestCase):
def setUp(self):
self.matrix_A = SparseRowMatrix(sparse_matrix_rdd,'test_data',1000,100)
self.matrix_A2 = SparseRowMatrix(sparse_matrix_rdd2,'test_data',100,1000)
def test_size(self):
c = self.matrix_A.rdd.count()
self.assertEqual(c, 1000)
def test_size2(self):
c = self.matrix_A2.rdd.count()
self.assertEqual(c, 100)
def test_gaussian_projection(self):
p = self.matrix_A.gaussian_projection(20)
self.assertEqual(p.shape, (20,100))
#def test_mat_ltimes(self):
# mat = np.random.rand(10,1000)
# p = self.matrix_A.ltimes(mat)
# p_true = np.dot( mat,A )
# self.assertTrue( np.linalg.norm(p-p_true)/np.linalg.norm(p_true) < 1e-5 )
def test_atamat(self):
mat = np.random.rand(100,20)
p = self.matrix_A.atamat(mat)
p_true = np.dot( A.T, np.dot(A, mat) )
self.assertTrue( np.linalg.norm(p-p_true)/np.linalg.norm(p_true) < 1e-5 )
def test_gaussian_projection2(self):
p = self.matrix_A2.gaussian_projection(20)
self.assertEqual(p.shape, (20,1000))
def test_atamat2(self):
mat = np.random.rand(1000,20)
p = self.matrix_A2.atamat(mat)
p_true = np.dot( A2.T, np.dot(A2, mat) )
self.assertTrue( np.linalg.norm(p-p_true)/np.linalg.norm(p_true) < 1e-5 )
