semvec/pypi-packages huggingface dataset:
BCalc 1.0.1
A Calculator with many uses
License: MIT
Requires Python: >=3.0

List of functions:

**Notice** Add 'p' infront of any function to make it automaticly print, ex: 'padd(1,2)'


add(x,y)
adds x and y

sub(x,y)
subtracts y from x

mult(x,y)
multiplys x by y

div(x,y)
divides x by y

average(list)
takes the numbers of the list variable and averages them out

exponent(x,y)
x to the power of y

compare(x,y)
compares x to y, 'x < y', 'x > y', 'x = y'

isequal(x,y)
Returns 'Equal' if x and y are equal or 'Not Equal' if x and y are not equal
mapz 1.1.28
Extension of dict features
License: MIT
Requires Python: >=3.8,<4.0
Keywords: map,dict,mapping,interface

# MapZ

<h2>Extension of standard `dict` capabilities.</h2>

<p>
    <a href="https://github.com/ilexconf/mapz/actions"><img alt="GitHub Workflow Status" src="https://img.shields.io/github/workflow/status/ilexconf/mapz/release?logo=github"></a>
    <a href="https://codecov.io/gh/ilexconf/mapz/"><img alt="Codecov" src="https://img.shields.io/codecov/c/github/ilexconf/mapz?logo=codecov"></a>
    <a href="https://pypi.org/project/mapz/"><img alt="PyPI" src="https://img.shields.io/pypi/v/mapz?color=blue&logo=pypi"></a>
</p>
hd-clustering 0.1.0
Smart Hyperdimensional Clustering algorithm: FebHD
License: MIT
Requires Python: >=3.8,<4.0

# hd-clustering

**Authors**: Alejandro Hernández Cano, Mohsen Imani.

## Installation

In order to install the package, simply run the following:

```
pip install febhd_clustering
```

Visit the PyPI [project page](https://pypi.org/project/febhd-clustering/) for
more information about releases.

## Documentation

Read the [documentation](https://febhd-clustering.readthedocs.io/en/latest/)
of this project. 

## Quick start

The following code generates dummy data and trains a FebHD clustering model
with it.

```python
>>> import hd_clustering
>>> dim = 10000
>>> n_samples = 1000
>>> features = 100
>>> clusters = 5
>>> x = torch.randn(n_samples, features) # dummy data
>>> model = hd_clustering.FebHD(clusters, features, dim=dim)
>>> if torch.cuda.is_available():
...     print('Training on GPU!')
...     model = model.to('cuda')
...     x = x.to('cuda')
...
Training on GPU!
>>> model.fit(x, epochs=10)
>>> ypred = model(x)
>>> ypred.size()
torch.Size([1000])
```

For more examples, see the `examples/` directory.

## Citation request

If you use hd-clustering, please cite the following papers:

1. Alejandro Hernández-Cano, Yeseong Kim, Mohsen Imani. "A Framework for
   Efficient and Binary Clustering in High-Dimensional Space". IEEE/ACM Design
   Automation and Test in Europe Conference (DATE), 2021.

2. Mohsen Imani, et al. "DUAL: Acceleration of Clustering Algorithms using
   Digital-based Processing In-Memory"r IEEE/ACM International Symposium on
   Microarchitecture (MICRO), 2020.