semvec/pypi-packages huggingface dataset:
paddle-sparse-dense 0.0.5
Sparse-dense operators for paddle.
Requires Python: ~=3.6

# Sparse-dense operators implementation for Paddle
This module implements `coo`, `csc` and `csr` matrix formats and their inter-ops with dense matrices.

Feel free to open an issue when you feel that something is incorrect.

## Requirements
It only needs `paddle`. It is tested on `paddle >= 2.1.0, <= 2.2.0rc1`, but should work for any recent paddle versions.

## Usage
Most functions are implemented within classes that encapsulate sparse formats: `COO`, `CSR` and `CSC`.

Cross-format operators are implemented in dedicated sub-modules: `spgemm` and `batching`.

## Supported operations

### Conversion
```plain
coo -> csc, csr, dense
csc -> coo
csr -> coo
```

### Batch MVP (Matrix-Vector Product) or SpMM (Sparse-Dense Matmul)
Note that in this library, the batch dimensions are appended instead of prepended to the dot dimension (which makes batch MVP essentially regular matmul). Use `utils.swap_axes` or `paddle.transpose` when necessary.
```plain
coo, dense -> dense
```

### Point-wise
Supports broadcast on the dense side.
```plain
coo + coo -> coo
coo * scalar -> coo
coo * dense -> coo (equiv. coo @ diag(vec) if dense is a vector)
```

### SpGEMM (Sparse-Sparse Matmul)
```plain
coo, csr -> coo (via row-wise mixed product)
```

### Batching and unbatching
Many batched operations can be efficiently represented via operation on block-diagonal sparse matrix. We also provide batching and unbatching operations for homogeneously-shaped sparse matrices.

For COO matrices, this is constructing (destructing) a block-diagonal COO matrix given (into) several small COO matrices.

If you know the expected shapes of matrices after unbatching you may construct it explicitly by calling `BatchingInfo(shapes: [n, 2] numpy array of int)`. Otherwise: 1) most operations keep shapes, and there is no need to change BatchingInfo; 2) `batch_info_dot` is provided, for merging info between two batches of matrices that go through `SpGeMM` to obtain a final batch of matrices.

```plain
batch [coo] -> coo
unbatch coo -> [coo]
```

## Installation
`pip install paddle-sparse-dense`

## Caveats
Currently all stuff is implemented with pure python and no CUDA code has been written. As a result, the routines have good run-time performance in general but have a memory overhead of order `O(nnz/n)`.
check-git-repos 1.0.0
A command line interface to check git repositories within a directory
Requires Python: >=3.7,<4.0
Keywords: maintenance,tooling,git,cli

# check_git_repos

A simple utility script to check git repos contained underneath a given directory, reporting if any of the repos contain changes that
need to be upstreamed.

## Usage

```bash
usage: check_git_repos [-h] [--include-hidden] [path]

Check the status of git repositories in a directory

positional arguments:
  path              if given, the directory to start looking from, otherwise the directory will be used

optional arguments:
  -h, --help        show this help message and exit
  --include-hidden  whether to look in hidden directories
```

## Checked issues:
### Repo level issues:
- `untracked_files` the repo has files which aren't tracked
- `dirty_files` the repo has dirty files which need to be committed

### Branch level issues:
The reporting format is `branch_issues: [branch] ([issue])`

- `missing_upstream` a branch exists that has no upstream
- `unpushed_changes` a branch has commits that don't exist upstream

## Development

The project is built using [poetry](https://github.com/python-poetry/poetry), and has a makefile for development:

- `clean`: clean up build artifacts
- `develop`: install the project to a virtual environment
- `format`: format files using black
- `publish`: publish the project to pypi (given you have credentials)

Once `make develop` is run, you can run the development version with `poetry run check_git_repos`
wseg 0.0.1a3
A python library to perform word segmentation in various models
Requires Python: >=3.6, <4
Keywords: wseg,word segmentation,text analysis

#  WSeg

A Python library to perform word segmentation in various fields

## Functions

1. Dictionary-based word segmentation methods (BMM, FMM, and BIMM)
2. Hidden Markov Model (HMM)-based word segmentation method

## License
The `wseg` project is provided by [Donghua Chen](https://github.com/dhchenx/umls-graph).