Package: algebraML
Basic algebra for Machine LearningThis package performs basic operations between two matrices that are used in the field of machine learning such as:addition, subtraction, multiplication and division. It can also perform single matrix operations such as:dot product, inverse matrix finding and determinant.InstallationRun the following command in your terminal:$ pip install -i https://test.pypi.org/simple/ basic-algebra-ml==0.0.1UsageThe package is composed of two classes:1) Basic_OperationsParent class that has the following methods:check_type_matrix:checks if the parameters are a matrix of type<<numpy.ndarray>>.addition:adds two matrices together. For example:# Declare the matrix
[In]:   from basic_operations import Basic_Operations

        # Declare the matrix
        matrix1 = ([1, 6, 5], [6, 14, 34], [21, 12, 4])
        matrix2 = ([3, 2, 54], [21, 12, 9], [1, 6, 5])

        # Instantiate the class
        test = Basic_Operations(matrix1, matrix2)


        print(test.addition())

[Out]:  [[ 4.  8. 59.]
        [27. 26. 43.]
        [22. 18.  9.]]subtraction:subtraction of two matrices. With the matrices of the previous example, then we have the following result:[In]:   print(test.subtraction())

[Out]:  [[ -2.   4. -49.]
        [-15.   2.  25.]
        [ 20.   6.  -1.]]multiplication:multiply two matrices.Note: Remember that to perform multiplication between matrices the number of rows must match the number of columns.[In]:   matrix3 = ([8, 56, 7], [6, 14, 34], [9, 63, 5])
        matrix4 = ([36, 1, 2], [94, 9, 12], [3, 66, 1])

        test2 = Basic_Operations(matrix3, matrix4)

        print(test2.multiplication())

[Out]:  [[ 288.   56.   14.]
        [ 564.  126.  408.]
        [  27. 4158.    5.]]Note the following example where the dimensions do not match between more matrices. The program will give a message indicating the error.[In]:   matrix3 = ([8, 56, 7], [6, 14, 34], [9, 63, 5])
        matrix4 = ([36, 1, 2], [94, 9, 12])

        print(test2.multiplication())

[Out]:  Error: operands could not be broadcast together with shapes (3,3) (2,3)division:divide two matrices. See the following example:Below are several examples of how you can use this package:[In]:   print(test2.division())

[Out]:  [[ 0.22222222 56.          3.5       ]
        [ 0.06382979  1.55555556  2.83333333]
        [ 3.          0.95454545  5.        ]]dot:Dot product of two arrays. See the following example:[In]:   matrix5 = ([8, 56, 7], [6, 14, 34], [9, 63, 5])
        matrix6 = ([3, 2, 54], [21, 12, 9], [1, 6, 5])

        test3 = Basic_Operations(matrix5, matrix6)

        print(test3.dot())

[Out]:  [[1207.  730.  971.]
        [ 346.  384.  620.]
        [1355.  804. 1078.]]2) Operations_One_Matrix:Child class that inherits fromBasic_Operations.  With this class you can perform the following operations with a matrix:scalar:multiply a matrix by a scalar[In]:   from matrix_manipulations import Operations_One_Matrix


        matrix = ([36, 1, 2], [94, 9, 12], [3, 66, 1])

        test = Operations_One_Matrix(matrix)

        print(test.scalar(2))


[Out]:  [[ 72.   2.   4.]
        [188.  18.  24.]
        [  6. 132.   2.]]shape:checks the dimensions of a matrix.[In]:   print(test.scalar(2))


[Out]:  (3, 3)inverse:calculate the inverse of a matrixtranspose:Invert or permute the axes of a matrix