semvec/pypi-packages huggingface dataset:
idsprites 1.0.1
Easily generate simple continual learning benchmarks.
Requires Python: >=3.10

# ♾ Infinite dSprites

Easily generate simple continual learning benchmarks. Inspired by [dSprites](https://github.com/google-deepmind/dsprites-dataset).

![A grid of 2D shapes undergoing rotation, translation, and scaling.](img/shapes.gif)

## Install

Install the package from PyPI:

```bash
python -m pip install idsprites
```

Verify the installation:

```bash
python -c "import idsprites"
```

## Usage

See the [examples](examples) directory for notebooks demonstrating how to use the package.

## Contribute

Clone the repo:

```bash
git clone git@github.com:sbdzdz/idsprites.git
cd idsprites
```

It's a good idea to install the package in interactive mode inside a virtual environment:

```bash
python -m virtualenv venv
source venv/bin/activate

python -m pip install -r requirements.txt
python -m pip install -e .
```
data_transform_lib_turing 1.1
3 functions for data transformation
Requires Python: <4.0,>=3.12

# Data Transformation Library

## Description
The Data Transformation Library is a Python package designed for performing various matrix operations. This package includes functions for matrix transposition, sliding window generation on 1D arrays, and applying 2D convolution on matrices. 

## Installation

To install the Data Transformation Library, you will need Python 3.8 or later. The package can be installed via pip directly from PyPI:

```bash
pip install data_transformation_library
```

## Usage

Here is how you can use the functions in the Data Transformation Library:

### Transpose a 2D matrix

```python
from data_transformation_library import transpose2d

matrix = [
    [1, 2],
    [3, 4]
]
transposed = transpose2d(matrix)
print(transposed)
```

### Create a sliding window from a 1D array

```python
from data_transformation_library import window1d
import numpy as np

input_array = np.array([1, 2, 3, 4, 5])
windows = window1d(input_array, size=2, shift=1, stride=1)
print(windows)
```

### Apply a 2D convolution to a matrix

```python
from data_transformation_library import convolution2d
import numpy as np

input_matrix = np.array([
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
])
kernel = np.array([
    [1, 0],
    [0, -1]
])

convol_result = convolution2d(input_matrix, kernel)
print(convol_result)
```