turing_data_transformation_library 0.1.3

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Data Transformation Library

Overview

This Python library is designed to assist data scientists in performing essential data transformations, particularly for machine learning models. It provides a suite of functions that simplify common operations in data processing and analysis.

Features

The library currently offers three primary functions:

• Transpose: A function to transpose matrices (2D tensors), switching their axes.
• Time Series Windowing: A tool for creating windows in time series data, essential for time series analysis and modeling.
• Cross-Correlation: An implementation of the cross-correlation function, commonly used in convolutional
• neural networks.

Transpose

• Function: transpose2d(input_matrix)
• Input: input_matrix - a list of lists of real numbers representing a 2D matrix.
• Output: Transposed matrix as a list of lists of real numbers.
• Implementation: Pure Python, using standard library.

Time Series Windowing

• Function: window1d(input_array, size, shift=1, stride=1)
• Inputs:
  • input_array: List or 1D Numpy array of real numbers.
  • size: Integer, size (length) of the window.
  • shift: Integer, shift (step size) between windows.
  • stride: Integer, stride (step size) within each window.
• Output: List of lists or 1D Numpy arrays of real numbers.
• Implementation: Python and Numpy.

Cross-Correlation

• Function: convolution2d(input_matrix, kernel, stride=1)
• Inputs:
  • input_matrix: 2D Numpy array of real numbers.
  • kernel: 2D Numpy array of real numbers.
  • stride: Integer, stride value.
• Output: 2D Numpy array of real numbers.
• Implementation: Python and Numpy.

Installation

The library is available on PyPI and can be installed using pip:

pip install turing_data_transformation_library

Usage

After installation, the functions can be imported and used in Python scripts or Jupyter notebooks.

Example usage:

from turing_data_transformation_library.transpose import transpose2d
from turing_data_transformation_library.time_series_windowing import window1d
from turing_data_transformation_library.cross_correlation import convolution2d

# Example for transpose2d
matrix = [[1, 2], [3, 4]]
transpose2d(matrix)

# Example for window1d
input_array = [1, 2, 3, 4, 5]
size = 2
shift = 2
window1d(input_array, size, shift)

# Example for convolution2d
input_matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
kernel = [[1, 0], [0, -1]]
stride = 2
convolution2d(input_matrix, kernel, stride)