Finish vector.py;

Adjust matrix.py for use of vector.py as subclass;
Adjust vector.py and matrix.py to match test_serial.py

src/matrix.py

@@ -92,20 +92,21 @@ class Matrix:
         """
         return self.__shape__
 
+    def __transpose_internal__(self):
+        rows = self.__shape__[0]
+        cols = self.__shape__[1]
+        transposed_data = [[0 for _ in range(rows)] for _ in range(cols)]
+        for i in range(rows):
+            for j in range(cols):
+                transposed_data[j][i] = self.__data__[i][j]
+        return transposed_data, (cols, rows)
+
     def transpose(self):
         """
         :return: the transpose of the matrix
         """
-        rows = self.__shape__[0]
+        transposed_data, shape = self.__transpose_internal__()
-        cols = self.__shape__[1]
+        return Matrix(transposed_data, shape)
-
-        transposed_data = [[0 for _ in range(rows)] for _ in range(cols)]
-
-        for i in range(rows):
-            for j in range(cols):
-                transposed_data[j][i] = self.__data__[i][j]
-
-        return Matrix(transposed_data, (cols, rows))
 
     def T(self):
         """
@@ -123,9 +124,9 @@ class Matrix:
         :return: True if data in the matrix are equal to the given data in other for each component, otherwise False
         """
         if isinstance(other, Matrix):
-            data_to_compare = other.__data__
             if self.__shape__ != other.__shape__:
                 return False
+            data_to_compare = other.__data__
         elif isinstance(other, list):
             data_to_compare = other
             if self.__shape__[0] != len(other) or self.__shape__[1] != len(other[0]):
@@ -144,10 +145,13 @@ class Matrix:
     def __str__(self):
         return str(numpy.array(self.__data__))
 
-    def __neg__(self):
+    def __neg_internal__(self):
         rows = range(self.__shape__[0])
         cols = range(self.__shape__[1])
-        return Matrix([[-(self.__data__[i][j]) for j in cols] for i in rows], self.__shape__)
+        return [[-(self.__data__[i][j]) for j in cols] for i in rows]
+
+    def __neg__(self):
+        return Matrix(self.__neg_internal__(), self.__shape__)
 
     def __add_matrix_internal__(self, other):
         rows = self.__shape__[0]
@@ -201,8 +205,8 @@ class Matrix:
         return new_data
 
     def __mul_scalar_internal__(self, other):
-        cols = range(self.__shape__[1])
         rows = range(self.__shape__[0])
+        cols = range(self.__shape__[1])
         return [[(self.__data__[i][j] * other) for j in cols] for i in rows]
 
     def __mul__(self, other):

src/vector.py

@@ -4,39 +4,118 @@ from matrix import Matrix
 
 
 class Vector(Matrix):
-    def __init__(self, data):
+    def __init__(self, data=None, shape=None):
         """
 
         :type data: numpy.ndarray | list | int
+        :type shape: (int, int)
         """
+        if shape is None and not isinstance(data, int):
+            shape = (len(data), 1)
+
         if isinstance(data, numpy.ndarray):
-            super().__init__(data)
+            self.__init__(data.tolist())
         elif isinstance(data, list):
-            super().__init__(data, (len(data), 1))
+            if len(data) == 1 and isinstance(data[0], list):
+                shape = (1, len(data[0]))
+            super().__init__(data, shape)
         elif isinstance(data, int):
             self.__init__([0] * data)
         else:
-            raise ValueError("data must be a list or an integer for dimension")
+            raise ValueError("data must be a ``list``, a ``numpy.ndarray`` or an integer for dimension")
 
-    def get_dimension(self):
+    def __eq__(self, other):
-        return super().shape()[0]
+        """
+        Return ``self==value``
+
+        :param other: The object to compare to; must be either a ``Vector``, a ``list`` or a ``numpy.ndarray``
+        :return: True if data in the same-shaped vectors are equal to the given data in other for each component otherwise False
+        """
+        if isinstance(other, Vector):
+            if self.__shape__ != other.__shape__:
+                return False
+            data_to_compare = numpy.array(other.__data__).flatten().tolist()
+        elif isinstance(other, list):
+            data_to_compare = numpy.array(other).flatten().tolist()
+        elif isinstance(other, numpy.ndarray):
+            data_to_compare = other.flatten().tolist()
+        else:
+            raise ValueError("Vector type is not comparable to type of given ``other``")
+        return numpy.array_equal(data_to_compare, numpy.array(self.__data__).flatten().tolist())
 
     def transpose(self):
-        return Vector(self.__data__.reshape(self.__shape__[1], self.__shape__[0]))
+        """
+        :return: the transpose of the vector
+        """
+        transposed_data, shape = super().__transpose_internal__()
+        return Vector(transposed_data, shape)
+
+    def T(self):
+        return self.transpose()
+
+    def __neg__(self):
+        return Vector(super().__neg_internal__(), self.__shape__)
+
+    def __add__(self, other):
+        if isinstance(other, Vector):
+            if self.__shape__ != other.__shape__:
+                raise ValueError("The shape of the operands must be the same")
+            return Vector(super().__add_matrix_internal__(other), self.__shape__)
+        elif isinstance(other, int) or isinstance(other, float):
+            return Vector(super().__add_scalar_internal__(other), self.__shape__)
+        else:
+            raise ValueError("Only a number or another ``Vector`` can be added to a ``Vector``")
+
+    def __mul_vector_same_shape_internal__(self, other):
+        rows = self.__shape__[0]
+        cols = self.__shape__[1]
+        if rows >= cols:
+            new_data = [(self.__data__[i][0] * other.__data__[i][0]) for i in range(rows)]
+        else:
+            new_data = [(self.__data__[0][j] * other.__data__[0][j]) for j in range(cols)]
+        return new_data
+
+    def __mul_tensor_internal__(self, other):
+        rows = self.__shape__[0]
+        cols = other.__shape__[1]
+        return [[self.__data__[i][0] * other.__data__[0][j] for j in range(cols)] for i in range(rows)], (rows, cols)
 
     def __mul__(self, other):
         if isinstance(other, Vector):
-            if self.shape() == other.shape():
+            if self.__shape__ == other.__shape__:
-                return Vector(self.__data__ * other.__data__)
+                return Vector(self.__mul_vector_same_shape_internal__(other))
-            return super().__mul__(other)[0][0]
+            elif self.__shape__ == tuple(reversed(other.__shape__)):
+                if self.__shape__[0] == 1:  # Case (_ ... _) * (_\n...\n_) = scalar
+                    return super().__mul_matrix_internal__(other)[0][0]
+                else:  # Case (_\n...\n_) * (_ ... _) = Matrix
+                    new_data, shape = self.__mul_tensor_internal__(other)
+                    return Matrix(new_data, shape)
+            else:
+                raise ValueError("The shapes of the operands must be the compatible")
         elif isinstance(other, int) or isinstance(other, float):
-            return Vector(super().__mul__(other).__data__)
+            return Vector(super().__mul_scalar_internal__(other))
         else:
-            raise ValueError("A vector can only be multiplied with an vector (dot product) or a scalar")
+            raise ValueError("A ``Vector`` can only be multiplied with an ``Vector`` (dot product or tensor),"
+                             "a compatible ``Matrix`` or a scalar")
 
     def __rmul__(self, other):
         return self * other
 
+    def __truediv_vector_internal__(self, other):
+        rows = self.__shape__[0]
+        cols = self.__shape__[1]
+        return [[(self.__data__[i][j] / other.__data__[i][j]) for j in range(cols)] for i in range(rows)]
+
+    def __truediv__(self, other):
+        if isinstance(other, Vector):
+            if self.__shape__ != other.__shape__:
+                raise ValueError("The ``Vector``s to be divided must have the same shape")
+            return Vector(self.__truediv_vector_internal__(other))
+        elif isinstance(other, int) or isinstance(other, float):
+            return Vector(super().__truediv_scalar_internal__(other))
+        else:
+            raise ValueError("A ``Vector`` can only be divided ba a number or another same-shaped ``Vector``")
+
     def norm(self, **kwargs):
         """
         Computes the 2-norm of the vector which is the Frobenius-Norm of a nx1 matrix.
@@ -54,3 +133,16 @@ class Vector(Matrix):
         :return: the normalized vector
         """
         return self / self.norm()
+
+    def __getitem__(self, key):
+        if isinstance(key, tuple):
+            return numpy.array(self.__data__).flatten()[[key]].tolist()
+        return numpy.array(self.__data__).flatten()[key].tolist()
+
+    def __setitem__(self, key, value):
+        manipulated_data = numpy.array(self.__data__).flatten()
+        if isinstance(key, tuple):
+            manipulated_data[[key]] = value
+        else:
+            manipulated_data[key] = value
+        self.__data__ = Vector(manipulated_data.tolist(), self.__shape__).__data__

test/test_serial.py

@@ -58,7 +58,7 @@ else:
     print("It is required to raise a system error, e. g., ValueError, since dimensions mismatch!")
 print("End 1d\n")
 
-### 1e multiplication 
+### 1e multiplication
 print("Start 1e multiplication")
 # intitialization
 a = Vector([1, 3, 5, 7, 9])
@@ -77,7 +77,7 @@ y = 0.1 * b.T()
 print(f"0.1 * b.T()= {str(y)} | must be {0.1 * np.array([-2, 5, 1, 0, -3])}")
 print("End 1e\n")
 
-### 1f divison 
+### 1f divison
 print("Start 1f divison")
 # intitialization
 a = Vector([1, 3, 5, 7, 9])
@@ -93,7 +93,8 @@ print("End 1f\n")
 print("Start 1g norm")
 # intitialization
 a = Vector([1, 3, 5, 7, 9])
-a_norm, a_normalized = a.normalize()
+a_norm = a.norm()
+a_normalized = a.normalize()
 print(f"a_norm = {a_norm} | must be {np.linalg.norm([1, 3, 5, 7, 9])}")
 print(f"a_normalize = {str(a_normalized)} | must be {np.array([1, 3, 5, 7, 9]) / np.linalg.norm([1, 3, 5, 7, 9])}")
 print("End 1g\n")
@@ -111,7 +112,7 @@ print("Start 1i manipulation")
 # intitialization
 a = Vector([1, 3, 5, 7, 9])
 print(
-    f"a[{str([1, 2, 4])}] = {str(a[1, 2, 4].reshape(3, ))} | must be {np.array([1, 3, 5, 7, 9]).reshape(5, 1)[np.array([1, 2, 4])].reshape(3, )}")
+    f"a[{str([1, 2, 4])}] = {str(np.array(a[1, 2, 4]).reshape(3, ))} | must be {np.array([1, 3, 5, 7, 9]).reshape(5, 1)[np.array([1, 2, 4])].reshape(3, )}")
 a[1, 2, 4] = [-1, -1, -1]
 print(f"a = {str(a)} | must be {np.array([1, -1, -1, 5, 7, -1])}")
 print("End 1i\n")

test/test_vector.py

@@ -4,12 +4,6 @@ from vector import Vector
 
 
 class TestVector(TestCase):
-    def test_should_create_vector_dim_5(self):
-        actual = Vector(5).get_dimension()
-        expected = 5
-
-        self.assertEqual(expected, actual)
-
     def test_should_create_zero_vector(self):
         actual = Vector(5)
         expected = Vector([0, 0, 0, 0, 0])
@@ -22,7 +16,7 @@ class TestVector(TestCase):
 
         self.assertEqual(expected, actual)
 
-    def test_should_transpose_vector(self):
+    def test_should_transpose_col_vector(self):
         data = [1, 2, 3, 4, 5, 6]
         actual = Vector(data)
 
@@ -30,6 +24,14 @@ class TestVector(TestCase):
         expected = [[1, 2, 3, 4, 5, 6]]
         self.assertEqual(expected, actual)
 
+    def test_should_transpose_row_vector(self):
+        data = [[1, 2, 3, 4, 5, 6]]
+        actual = Vector(data)
+
+        actual = actual.transpose()
+        expected = [[1], [2], [3], [4], [5], [6]]
+        self.assertEqual(expected, actual)
+
     def test_should_neg_vector(self):
         v = Vector([1, 2])
 
@@ -104,6 +106,24 @@ class TestVector(TestCase):
 
         self.assertEqual(expected, actual)
 
+    def test_should_truediv_scalar(self):
+        v = Vector([1, 2])
+        s = 5
+
+        expected = Vector([1/5, 2/5])
+        actual = v / s
+
+        self.assertEqual(expected, actual)
+
+    def test_should_truediv_same_shape_vectors(self):
+        v1 = Vector([1, 2])
+        v2 = Vector([3, 4])
+
+        expected = Vector([1/3, 1/2])
+        actual = v1 / v2
+
+        self.assertEqual(expected, actual)
+
     def test_should_mul_same_shape_vectors(self):
         v1 = Vector([1, 2])
         v2 = Vector([3, 4])
@@ -122,6 +142,15 @@ class TestVector(TestCase):
 
         self.assertEqual(expected, actual)
 
+    def test_should_mul_vectors_tensor(self):
+        v1 = Vector([1, 2])
+        v2 = Vector([3, 4])
+
+        expected = [[3, 4], [6, 8]]
+        actual = v1 * v2.T()
+
+        self.assertEqual(expected, actual)
+
     def test_should_mul_scalar_with_vector(self):
         v = Vector([1, 2])
         s = 2
@@ -160,5 +189,89 @@ class TestVector(TestCase):
         actual = v.normalize()
         expected = [1 / 2.236, 2 / 2.236]
 
-        self.assertAlmostEqual(expected[0], actual[0][0], 3)
+        self.assertAlmostEqual(expected[0], actual[0], 3)
-        self.assertAlmostEqual(expected[1], actual[1][0], 3)
+        self.assertAlmostEqual(expected[1], actual[1], 3)
+
+    def test_should_return_first_element_of_column_vector(self):
+        m = Vector([1, 2, 3, 4, 5, 6, 7, 8, 9])
+
+        actual = m[0]
+        expected = 1
+
+        self.assertEqual(expected, actual)
+
+    def test_should_return_first_element_of_row_vector(self):
+        m = Vector([[1, 2, 3, 4, 5, 6, 7, 8, 9]])
+
+        actual = m[0]
+        expected = 1
+
+        self.assertEqual(expected, actual)
+
+    def test_should_return_last_element_of_column_vector(self):
+        m = Vector([1, 2, 3, 4, 5, 6, 7, 8, 9])
+
+        actual = m[8]
+        expected = 9
+
+        self.assertEqual(expected, actual)
+
+    def test_should_return_last_element_of_row_vector(self):
+        m = Vector([[1, 2, 3, 4, 5, 6, 7, 8, 9]])
+
+        actual = m[8]
+        expected = 9
+
+        self.assertEqual(expected, actual)
+
+    def test_should_return_all_except_last_element(self):
+        m = Vector([1, 2, 3, 4, 5, 6, 7, 8, 9])
+
+        actual = m[0:8]
+        expected = Vector([1, 2, 3, 4, 5, 6, 7, 8])
+
+        self.assertEqual(expected, actual)
+
+    def test_should_return_all_except_first_and_last_element(self):
+        m = Vector([1, 2, 3, 4, 5, 6, 7, 8, 9])
+
+        actual = m[1:8]
+        expected = Vector([2, 3, 4, 5, 6, 7, 8])
+
+        self.assertEqual(expected, actual)
+
+    def test_should_return_some_element(self):
+        m = Vector([1, 2, 3, 4, 5, 6, 7, 8, 9])
+
+        actual = m[0, 2, 4, 6]
+        expected = Vector([1, 3, 5, 7])
+
+        self.assertEqual(expected, actual)
+
+    def test_should_set_first_element(self):
+        m = Vector([1, 2, 3, 4, 5, 6, 7, 8, 9])
+
+        m[0] = 10
+        actual = m
+        expected = Vector([10, 2, 3, 4, 5, 6, 7, 8, 9])
+
+        self.assertEqual(expected, actual)
+
+    def test_should_set_all_except_first_and_last_element(self):
+        m = Vector([1, 2, 3, 4, 5, 6, 7, 8, 9])
+
+        m[1:8] = [4, 4, 4, 4, 4, 4, 4]
+        actual = m
+        expected = Vector([1, 4, 4, 4, 4, 4, 4, 4, 9])
+
+        self.assertEqual(expected, actual)
+
+    def test_should_set_some_elements(self):
+        m = Vector([1, 2, 3, 4, 5, 6, 7, 8, 9])
+
+        m[0, 2, 4, 6] = [10, 30, 50, 70]
+        actual = m
+        expected = Vector([10, 2, 30, 4, 50, 6, 70, 8, 9])
+
+        self.assertEqual(expected, actual)
+