Major changes preparing to satisfy given test_serial.py

src/matrix.py

@@ -87,6 +87,14 @@ class Matrix:
         """
         return Matrix(self.__data__.transpose())
 
+    def T(self):
+        """
+        Same as ``matrix.transpose()``
+
+        :return: see ``matrix.transpose()``
+        """
+        return self.transpose()
+
     def __eq__(self, other):
         """
         Return ``self==value``

src/vector.py

@@ -1,13 +1,17 @@
+import numpy
+
 from matrix import Matrix
 
 
 class Vector(Matrix):
-    def __init__(self, data: list | int):
+    def __init__(self, data):
         """
 
-        :type data: list | int
+        :type data: numpy.ndarray | list | int
         """
-        if isinstance(data, list):
+        if isinstance(data, numpy.ndarray):
+            super().__init__(data)
+        elif isinstance(data, list):
             super().__init__(data, (len(data), 1))
         elif isinstance(data, int):
             self.__init__([0] * data)
@@ -17,11 +21,16 @@ class Vector(Matrix):
     def get_dimension(self):
         return super().shape()[0]
 
+    def transpose(self):
+        return Vector(self.__data__.reshape(self.__shape__[1], self.__shape__[0]))
+
     def __mul__(self, other):
         if isinstance(other, Vector):
-            return (super().transpose().__mul__(other))[0][0]
+            if self.shape() == other.shape():
+                return Vector(self.__data__ * other.__data__)
+            return super().__mul__(other)[0][0]
         elif isinstance(other, int) or isinstance(other, float):
-            return super().__mul__(other)
+            return Vector(super().__mul__(other).__data__)
         else:
             raise ValueError("A vector can only be multiplied with an vector (dot product) or a scalar")
 
@@ -29,7 +38,19 @@ class Vector(Matrix):
         return self * other
 
     def norm(self, **kwargs):
+        """
+        Computes the 2-norm of the vector which is the Frobenius-Norm of a nx1 matrix.
+
+        :param kwargs: ignored
+        :return: the 2-norm of the vector
+        """
         return super().norm()
 
     def normalize(self):
+        """
+        A normalized vector has the length (norm) 1.
+        To achieve that the vector is divided by the norm of itself.
+
+        :return: the normalized vector
+        """
         return self / self.norm()