Initial matrix mpi

src/matrix.py

@@ -219,32 +219,32 @@ class Matrix:
     def __rmul__(self, other):
         return self * other
 
-    def __norm_frobenius__(self):
+    def __abs_sum__(self):
         rows = self.__shape__[0]
         cols = self.__shape__[1]
         abs_sum = 0
         for i in range(rows):
             for j in range(cols):
                 abs_sum += abs(self.__data__[i][j]) ** 2
-        return math.sqrt(abs_sum)
+        return abs_sum
 
-    def __norm_colsum__(self):
+    def __col_sums__(self):
         rows = self.__shape__[0]
         cols = self.__shape__[1]
         col_sums = [0] * cols
         for j in range(cols):
             for i in range(rows):
                 col_sums[j] += abs(self.__data__[i][j])
-        return max(col_sums)
+        return col_sums
 
-    def __norm_rowsum__(self):
+    def __row_sums__(self):
         rows = self.__shape__[0]
         cols = self.__shape__[1]
         row_sums = [0] * rows
         for i in range(rows):
             for j in range(cols):
                 row_sums[i] += abs(self.__data__[i][j])
-        return max(row_sums)
+        return row_sums
 
     def norm(self, f: str = "frobenius"):
         """
@@ -258,11 +258,11 @@ class Matrix:
         :return: the norm as a number
         """
         if f == "frobenius":
-            norm = self.__norm_frobenius__()
+            norm = math.sqrt(self.__abs_sum__())
         elif f == "col sum":
-            norm = self.__norm_colsum__()
+            norm = max(self.__col_sums__())
         elif f == "row sum":
-            norm = self.__norm_rowsum__()
+            norm = max(self.__row_sums__())
         else:
             raise ValueError(f"Parameter f must be either \"frobenius\", \"row sum\" or \"col sum\"")
         return norm

src/matrix_mpi.py

@@ -0,0 +1,38 @@
+import numpy
+from mpi4py import MPI
+
+from matrix import Matrix
+
+
+class MatrixMPI:
+    __comm__ = MPI.COMM_WORLD
+    __size__ = __comm__.Get_size()
+    __rank__ = __comm__.Get_rank()
+
+    __matrix__: Matrix = None
+    __chunk__: list = None
+
+    def __init__(self, data=None, shape=None, structure=None, model=None, offset=None, n=None):
+        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()
+
+    def __str__(self):
+        return str(self.__matrix__)
+
+    def get_rank_data(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()
+
+    def T(self):
+        return self.transpose()
+
+
+mpi_matrix = MatrixMPI(list(range(1, 25)), (12, 2))
+print(mpi_matrix.transpose())