Add distributed computing for negation and addition (no memory distributing)

src/matrix.py

@@ -84,6 +84,25 @@ class Matrix:
         """
         return self.__data__
 
+    @staticmethod
+    def flatten(matrices: list):
+        """
+        Flattens a list of matrices into one bigger matrix.
+        The columns must match the first ``Matrix`` in the list and the rows can be arbitrarily.
+
+        :param matrices: A list of matrices.
+        :type matrices: list
+        :return: A ``Matrix`` extended by all matrices in the list.
+        :rtype: ``Matrix``
+        """
+        flattened_data = []
+        rows = 0
+        for matrix in matrices:
+            flattened_data.extend(matrix.get_data())
+            rows += matrix.__shape__[0]
+        cols = matrices[0].__shape__[1]
+        return Matrix(flattened_data, (rows, cols))
+
     def shape(self):
         """
         :return: the shape of the matrix, which is ``(rows, columns)``
@@ -219,7 +238,7 @@ class Matrix:
     def __rmul__(self, other):
         return self * other
 
-    def __abs_sum__(self):
+    def __abs_sum_of_squares__(self):
         rows = self.__shape__[0]
         cols = self.__shape__[1]
         abs_sum = 0
@@ -258,7 +277,7 @@ class Matrix:
         :return: the norm as a number
         """
         if f == "frobenius":
-            norm = math.sqrt(self.__abs_sum__())
+            norm = math.sqrt(self.__abs_sum_of_squares__())
         elif f == "col sum":
             norm = max(self.__col_sums__())
         elif f == "row sum":

src/matrix_mpi.py

@@ -5,9 +5,9 @@ from matrix import Matrix
 
 
 class MatrixMPI:
-    __comm__ = MPI.COMM_WORLD
-    __size__ = __comm__.Get_size()
-    __rank__ = __comm__.Get_rank()
+    __mpi_comm__ = MPI.COMM_WORLD
+    __mpi_size__ = __mpi_comm__.Get_size()
+    __mpi_rank__ = __mpi_comm__.Get_rank()
 
     __matrix__: Matrix = None
     __chunk__: list = None
@@ -16,23 +16,103 @@ class MatrixMPI:
         self.__matrix__ = Matrix(data=data, shape=shape, structure=structure, model=model, offset=offset, n=n)
 
         total_amount_of_rows = self.__matrix__.shape()[0]
-        chunks = numpy.array_split(list(range(total_amount_of_rows)), self.__size__)
-        self.__chunk__ = chunks[self.__rank__].tolist()
+        chunks = numpy.array_split(list(range(total_amount_of_rows)), self.__mpi_size__)
+        self.__chunk__ = chunks[self.__mpi_rank__].tolist()
+
+    @staticmethod
+    def of(matrix: Matrix):
+        return MatrixMPI(matrix.get_data(), matrix.shape())
 
     def __str__(self):
         return str(self.__matrix__)
 
-    def get_rank_data(self):
+    def get_rank_submatrix(self):
         rows = len(self.__chunk__)
         cols = self.__matrix__.shape()[1]
         return Matrix(self.__matrix__[self.__chunk__], (rows, cols))
 
     def transpose(self):
-        return self.__matrix__.transpose()
+        """
+        :return: the transpose of the matrix
+        """
+        return MatrixMPI.of(self.__matrix__.transpose())
 
     def T(self):
+        """
+        Same as ``matrix.transpose()``
+
+        :return: see ``matrix.transpose()``
+        """
         return self.transpose()
 
+    def __eq__(self, other):
+        """
+        Return ``self==value``
 
-mpi_matrix = MatrixMPI(list(range(1, 25)), (12, 2))
-print(mpi_matrix.transpose())
+        :param other: The object to compare to; must be either a ``MatrixMPI``, ``Matrix``, a ``list`` or a ``numpy.ndarray``
+        :return: True if data in the matrix are equal to the given data in other for each component, otherwise False
+        """
+        if isinstance(other, MatrixMPI):
+            return self.__matrix__ == other.__matrix__
+        else:
+            return self.__matrix__ == other
+
+    def __neg__(self):
+        gathered_data = self.__mpi_comm__.gather(-self.get_rank_submatrix())
+        data = self.__mpi_comm__.bcast(gathered_data)
+        return MatrixMPI.of(Matrix.flatten(data))
+
+    def __add__(self, other):
+        if isinstance(other, MatrixMPI):
+            other = other.get_rank_submatrix()
+        gathered_data = self.__mpi_comm__.gather(self.get_rank_submatrix() + other)
+        data = self.__mpi_comm__.bcast(gathered_data)
+        return MatrixMPI.of(Matrix.flatten(data))
+
+    def __radd__(self, other):
+        return self + other
+
+    def __sub__(self, other):
+        return self + (-other)
+
+    def __rsub__(self, other):
+        return -self + other
+
+    def __truediv__(self, other):
+        gathered_data = self.__mpi_comm__.gather(self.get_rank_submatrix() / other)
+        data = self.__mpi_comm__.bcast(gathered_data)
+        return MatrixMPI.of(Matrix.flatten(data))
+
+    def __mul__(self, other):
+        if isinstance(other, MatrixMPI):
+            return self.__matrix__ * other.__matrix__
+        else:
+            return self.__matrix__ * other
+
+    def __rmul__(self, other):
+        return self * other
+
+    def norm(self, f: str = "frobenius"):
+        """
+        Calculates the norm of the matrix.
+
+        A norm is a positive definit, absolute homogeneous and subadditive function.
+        For Matrices a norm is also sub-multiplicative.
+
+        :param f: The norm to be used, could be either "frobenius", "row sum" or "col sum"
+
+        :return: the norm as a number
+        """
+        return self.__matrix__.norm(f)
+
+    def __getitem__(self, key):
+        return self.__matrix__[key]
+
+    def __setitem__(self, key, value):
+        self.__matrix__[key] = value
+
+
+# m1 = MatrixMPI(numpy.random.uniform(0, 1, 1_000_000), (1000, 1000))
+m1 = MatrixMPI([1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6], (5, 3))
+
+print(m1 - m1)

test/test_matrix.py

@@ -378,3 +378,12 @@ class TestMatrix(TestCase):
         expected = Matrix([1, 2, 3, 4, 5, 60, 70, 8, 9, 100, 110, 12, 13, 14, 15, 16], (4, 4))
 
         self.assertEqual(expected, actual)
+
+    def test_should_flatten_list_of_matrices(self):
+        m1 = Matrix([1, 2, 3, 4, 5, 6, 7, 8, 9], (3, 3))
+        m2 = Matrix([1, 2, 3, 4, 5, 6], (2, 3))
+
+        actual = m1.flatten([m1, m2])
+        expected = Matrix([1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6], (5, 3))
+
+        self.assertEqual(expected, actual)