| Name | datatransformationslibrary JSON |
| Version |
0.1.2
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| upload_time | 2023-08-17 13:26:12 |
| maintainer | |
| docs_url | None |
| author | Your Name |
| requires_python | >=3.9,<4.0 |
| license | |
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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](#installation)
- [Functions](#functions)
- [transpose2d](#transpose2d)
- [window1d](#window1d)
- [convolution2d](#convolution2d)
- [Running Tests](#running-tests)
- [Contribution](#contribution)
- [License](#license)
## Installation
# Clone the repository
```bash
git clone git@github.com:TuringCollegeSubmissions/smizak-DE2.1.git
```
# Navigate to the directory
``` bash
cd smizak-DE2.1
```
# Install necessary packages
``` bash
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/
``` bash
pip install datatransformationslibrary
```
## Functions
### transpose2d
Transposes a 2D matrix.
**Usage:**
```python
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:**
```python
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:**
```python
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
```bash
pip install pytest
```
# Navigate to the tests directory
``` bash
# Navigate to tests directory
cd test
# Run the tests
pytest
```
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"description": "# Data Transformations Library\n\nThis library provides essential functions for manipulating and transforming data structures commonly used in data science and machine learning.\n\n## Table of Contents\n\n- [Installation](#installation)\n- [Functions](#functions)\n - [transpose2d](#transpose2d)\n - [window1d](#window1d)\n - [convolution2d](#convolution2d)\n- [Running Tests](#running-tests)\n- [Contribution](#contribution)\n- [License](#license)\n\n## Installation\n\n\n# Clone the repository\n```bash\ngit clone git@github.com:TuringCollegeSubmissions/smizak-DE2.1.git\n```\n\n# Navigate to the directory\n``` bash\ncd smizak-DE2.1\n```\n\n# Install necessary packages\n``` bash\npip install -r requirements.txt\n```\n\n# Install from pypi\nAlternatively one can install the package from version published on pypi.\npackage can be found on https://pypi.org/project/datatransformationslibrary/\n``` bash\npip install datatransformationslibrary\n```\n\n## Functions\n\n### transpose2d\n\nTransposes a 2D matrix.\n\n**Usage:**\n\n```python\nfrom src.DataTransformationsLibrary import transpose2d\nresult = transpose2d(input_matrix)\n```\n\n**Arguments:**\n\n- `input_matrix (list[list[float]])`: A 2D matrix to transpose.\n\n**Returns:**\n\n- A list representing the transposed matrix.\n\n**Raises:**\n\n- `ValueError`: If the input matrix is empty.\n- `ValueError`: If all rows of the matrix do not have the same length.\n\n### window1d\n\nGenerates windows of the specified size, shift, and stride from the input array.\n\n**Usage:**\n\n```python\nfrom src.DataTransformationsLibrary import window1d\nresult = window1d(input_array, size, shift=1, stride=1)\n```\n\n**Arguments:**\n\n- `input_array (Union[list, np.ndarray])`: 1D list or numpy array to generate windows from.\n- `size (int)`: The size of each window.\n- `shift (int, optional)`: Number of positions to move the window each iteration. Defaults to 1.\n- `stride (int, optional)`: The step between consecutive elements within each window. Defaults to 1.\n\n**Returns:**\n\n- A list of windows.\n\n**Raises:**\n\n- `ValueError`: If input_array is not a list or 1D numpy array.\n- `ValueError`: If input_array is not 1D.\n\n### convolution2d\n\nPerforms a 2D convolution operation on the input matrix with a specified kernel.\n\n**Usage:**\n\n```python\nfrom src.DataTransformationsLibrary import convolution2d\nresult = convolution2d(input_matrix, kernel, stride=1)\n```\n\n**Arguments:**\n\n- `input_matrix (np.ndarray)`: A 2D input matrix for convolution.\n- `kernel (np.ndarray)`: A 2D kernel for convolution.\n- `stride (int, optional)`: The step size to use when applying the kernel. Defaults to 1.\n\n**Returns:**\n\n- A matrix resulting from the convolution operation.\n\n**Raises:**\n\n- `ValueError`: If stride is less than or equal to 0.\n\n## Running Tests\n\nTo ensure the integrity of this library, a comprehensive set of tests are provided. To run the tests:\n\n# Ensure pytest is installed\n```bash\npip install pytest\n```\n\n# Navigate to the tests directory\n``` bash\n# Navigate to tests directory\ncd test\n\n# Run the tests\npytest\n```\n",
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