def test_ml_mllib_matrix_conversion(self):
# to ml
# dense
mllibDM = Matrices.dense(2, 2, [0, 1, 2, 3])
mlDM1 = newlinalg.Matrices.dense(2, 2, [0, 1, 2, 3])
mlDM2 = mllibDM.asML()
self.assertEqual(mlDM2, mlDM1)
# transposed
mllibDMt = DenseMatrix(2, 2, [0, 1, 2, 3], True)
mlDMt1 = newlinalg.DenseMatrix(2, 2, [0, 1, 2, 3], True)
mlDMt2 = mllibDMt.asML()
self.assertEqual(mlDMt2, mlDMt1)
# sparse
mllibSM = Matrices.sparse(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4])
mlSM1 = newlinalg.Matrices.sparse(2, 2, [0, 2, 3], [0, 1, 1],
[2, 3, 4])
mlSM2 = mllibSM.asML()
self.assertEqual(mlSM2, mlSM1)
# transposed
mllibSMt = SparseMatrix(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4], True)
mlSMt1 = newlinalg.SparseMatrix(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4],
True)
mlSMt2 = mllibSMt.asML()
self.assertEqual(mlSMt2, mlSMt1)
# from ml
# dense
mllibDM1 = Matrices.dense(2, 2, [1, 2, 3, 4])
mlDM = newlinalg.Matrices.dense(2, 2, [1, 2, 3, 4])
mllibDM2 = Matrices.fromML(mlDM)
self.assertEqual(mllibDM1, mllibDM2)
# transposed
mllibDMt1 = DenseMatrix(2, 2, [1, 2, 3, 4], True)
mlDMt = newlinalg.DenseMatrix(2, 2, [1, 2, 3, 4], True)
mllibDMt2 = Matrices.fromML(mlDMt)
self.assertEqual(mllibDMt1, mllibDMt2)
# sparse
mllibSM1 = Matrices.sparse(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4])
mlSM = newlinalg.Matrices.sparse(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4])
mllibSM2 = Matrices.fromML(mlSM)
self.assertEqual(mllibSM1, mllibSM2)
# transposed
mllibSMt1 = SparseMatrix(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4], True)
mlSMt = newlinalg.SparseMatrix(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4],
True)
mllibSMt2 = Matrices.fromML(mlSMt)
self.assertEqual(mllibSMt1, mllibSMt2)
def test_ml_mllib_matrix_conversion(self):
# to ml
# dense
mllibDM = Matrices.dense(2, 2, [0, 1, 2, 3])
mlDM1 = newlinalg.Matrices.dense(2, 2, [0, 1, 2, 3])
mlDM2 = mllibDM.asML()
self.assertEqual(mlDM2, mlDM1)
# transposed
mllibDMt = DenseMatrix(2, 2, [0, 1, 2, 3], True)
mlDMt1 = newlinalg.DenseMatrix(2, 2, [0, 1, 2, 3], True)
mlDMt2 = mllibDMt.asML()
self.assertEqual(mlDMt2, mlDMt1)
# sparse
mllibSM = Matrices.sparse(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4])
mlSM1 = newlinalg.Matrices.sparse(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4])
mlSM2 = mllibSM.asML()
self.assertEqual(mlSM2, mlSM1)
# transposed
mllibSMt = SparseMatrix(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4], True)
mlSMt1 = newlinalg.SparseMatrix(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4], True)
mlSMt2 = mllibSMt.asML()
self.assertEqual(mlSMt2, mlSMt1)
# from ml
# dense
mllibDM1 = Matrices.dense(2, 2, [1, 2, 3, 4])
mlDM = newlinalg.Matrices.dense(2, 2, [1, 2, 3, 4])
mllibDM2 = Matrices.fromML(mlDM)
self.assertEqual(mllibDM1, mllibDM2)
# transposed
mllibDMt1 = DenseMatrix(2, 2, [1, 2, 3, 4], True)
mlDMt = newlinalg.DenseMatrix(2, 2, [1, 2, 3, 4], True)
mllibDMt2 = Matrices.fromML(mlDMt)
self.assertEqual(mllibDMt1, mllibDMt2)
# sparse
mllibSM1 = Matrices.sparse(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4])
mlSM = newlinalg.Matrices.sparse(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4])
mllibSM2 = Matrices.fromML(mlSM)
self.assertEqual(mllibSM1, mllibSM2)
# transposed
mllibSMt1 = SparseMatrix(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4], True)
mlSMt = newlinalg.SparseMatrix(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4], True)
mllibSMt2 = Matrices.fromML(mlSMt)
self.assertEqual(mllibSMt1, mllibSMt2)
