semvec/pypi-packages huggingface dataset:
pygraphml 2.4.2
Library to parse GraphML files in Python

# pygraphml

[![Build Status](https://travis-ci.org/hadim/pygraphml.svg?branch=master)](https://travis-ci.org/hadim/pygraphml)
[![PyPI version](https://img.shields.io/pypi/v/pygraphml.svg?maxAge=2591000)](https://pypi.org/project/pygraphml/)

`pygraphml` is a Python library designed to parse GraphML file. To
see specification about GraphML, see http://graphml.graphdrawing.org/

A documentation is provided as a [Jupyter notebook](notebooks/documentation.ipynb).

# Requirements

- Python > 2.7 and > 3.6.
- `matplotlib` and [`networkx`](http://networkx.lanl.gov/) for visualization (optional).

# Install

`pip install pygraphml`

Or you can use Anaconda and `conda-forge` :

```
conda config --add channels conda-forge
conda install pygraphml
```

# License

Under BSD license. See [LICENSE](LICENSE).

# Authors

- Hadrien Mary <hadrien.mary@gmail.com>
- Nick Hamilton <n.hamilton@imb.uq.edu.au>
fotonower 0.9.16
A module to connect to Fotonower' services
Keywords: setuptools development

# Fotonower's connection tools

A package to connect to fotonower's services and API.

ulploading medias, creating feeds, get access to results from classification


----

### to modify and add some info too

[packaging guide]: https://packaging.python.org
[distribution tutorial]: https://packaging.python.org/en/latest/distributing.html
[src]: https://github.com/pypa/sampleproject
[rst]: http://docutils.sourceforge.net/rst.html
[md]: https://tools.ietf.org/html/rfc7764#section-3.5 "CommonMark variant"
[md use]: https://packaging.python.org/specifications/core-metadata/#description-content-type-optional
priority-memory 0.0.2
A prioritized sampling tool.

# Priority Memory

A prioritized sampling tool for priority memory replay.

The implementation is based on sum tree, or segmentation tree.

- Set the priority of each sample at anytime. 
- When you do not know the priority of the sample, you can append 
  them to the buffer, and they will show up in the next sampling batch.
- When the buffer is full, drop the samples with lowest priority.

The time complexity for sampling a batch with batch size m 
from a dataset with n samples is O(mlogn), for setting priority 
for the batch is O(mlogn).


# Usage

> pip install priority_memory

```python

from priority_memory import FastPriorReplayBuffer

buffer = FastPriorReplayBuffer(8000)
buffer.append(features=[0.1, 0.1, 0.1], prior=1)
buffer.append(features=[0.2, 0.2, 0.2], prior=2)
buffer.append(features=[0.3, 0.3, 0.3], prior=3)
buffer.append(features=[0.4, 0.4, 0.4], prior=4)
indexes, data, weights = buffer.sample_with_weights(batch_size=2)

mae = [10, 20]
buffer.set_weights(indexes, mae)

```