algebraML 0.0.5

This package performs basic algebraic operations

Basic algebra for Machine Learning

This 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.

Installation

Run the following command in your terminal:

$ pip install -i https://test.pypi.org/simple/ basic-algebra-ml==0.0.1 

Usage

The package is composed of two classes:

1) Basic_Operations

Parent 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 from Basic_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 matrix

• transpose: Invert or permute the axes of a matrix