datatransformationslibrary 0.1.1

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Data Transformations Library

This library provides essential functions for manipulating and transforming data structures commonly used in data science and machine learning.

Table of Contents

• Installation
• Functions
  • transpose2d
  • window1d
  • convolution2d
• Running Tests
• Contribution
• License

Installation

Clone the repository

git clone [e-mail]:TuringCollegeSubmissions/smizak-DE2.1.git

Navigate to the directory

cd smizak-DE2.1

Install necessary packages

pip install -r requirements.txt

Install from pypi

Alternatively one can install the package from version published on pypi. package can be found on https://pypi.org/project/datatransformationslibrary/

pip install datatransformationslibrary

Functions

transpose2d

Transposes a 2D matrix.

Usage:

from src.DataTransformationsLibrary import transpose2d
result = transpose2d(input_matrix)

Arguments:

• input_matrix (list[list[float]]): A 2D matrix to transpose.

Returns:

• A list representing the transposed matrix.

Raises:

• ValueError: If the input matrix is empty.
• ValueError: If all rows of the matrix do not have the same length.

window1d

Generates windows of the specified size, shift, and stride from the input array.

Usage:

from src.DataTransformationsLibrary import window1d
result = window1d(input_array, size, shift=1, stride=1)

Arguments:

• input_array (Union[list, np.ndarray]): 1D list or numpy array to generate windows from.
• size (int): The size of each window.
• shift (int, optional): Number of positions to move the window each iteration. Defaults to 1.
• stride (int, optional): The step between consecutive elements within each window. Defaults to 1.

Returns:

• A list of windows.

Raises:

• ValueError: If input_array is not a list or 1D numpy array.
• ValueError: If input_array is not 1D.

convolution2d

Performs a 2D convolution operation on the input matrix with a specified kernel.

Usage:

from src.DataTransformationsLibrary import convolution2d
result = convolution2d(input_matrix, kernel, stride=1)

Arguments:

• input_matrix (np.ndarray): A 2D input matrix for convolution.
• kernel (np.ndarray): A 2D kernel for convolution.
• stride (int, optional): The step size to use when applying the kernel. Defaults to 1.

Returns:

• A matrix resulting from the convolution operation.

Raises:

• ValueError: If stride is less than or equal to 0.

Running Tests

To ensure the integrity of this library, a comprehensive set of tests are provided. To run the tests:

Ensure pytest is installed

pip install pytest

Navigate to the tests directory

# Navigate to tests directory
cd test

# Run the tests
pytest