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_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_mat_ltimes2(self):
mat = np.random.rand(10,100)
p = self.matrix_A2.ltimes(mat)
p_true = np.dot( mat,A2 )
self.assertTrue( np.linalg.norm(p-p_true)/np.linalg.norm(p_true) < 1e-5 )
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 )
