semvec/pypi-packages huggingface dataset:
darkhex 0.0.1
Set of tools to analyze and research the game Dark Hex.
Requires Python: >=3.6

# DarkHex

Dark Hex is the imperfect information version of the game Hex. This game has a really scarce work on it. In this repository I am including an implementation of the game along with some algorithms implemented specifically for DarkHex. Also I included the results of some of the experiments.

For details on DarkHex and any of the algorithms used as well as the experiments please check my thesis [-link-]().

- [What is Hex](<https://en.wikipedia.org/wiki/Hex_(board_game)>)
- [Dark-Hex Open Spiel Implementation](https://github.com/deepmind/open_spiel/blob/master/open_spiel/games/dark_hex.h)
- [Sample Game](Sample_game.md)

## Implementations

- [x] pONE
- [x] Vanilla CFR
- [x] FSI-CFR
- [x] Backward Induction Best Response
- [ ] AlphaZero Approximate Best Response
- [ ] CFR+
- [ ] MCCFR
- [ ] NFSP
- [ ] Deep CFR

### Installation

The library is uploaded on Pypi and can be installed using pip. Before pip called make sure you have the following packages installed:

- [Python 3.6](https://www.python.org/downloads/)
- [PyGObject - bindings for GTK3](https://pypi.org/project/pygobject/)
- [Graphviz](https://www.graphviz.org/)
- [GCC](https://gcc.gnu.org/)
- [Libcairo](https://www.cairographics.org/)

They are mostly required for graph drawing and setup reasons. Please make sure you have these packages installed before installing the library.

#### For Debian / Ubuntu

```bash
sudo apt-get install libgirepository1.0-dev gcc libcairo2-dev pkg-config gir1.2-gtk-3.0 graphviz
```

#### For MacOS

```bash
brew install gobject-introspection graphviz cairo pkg-config gtk+3 gcc
```

After installing the packages you can install the library using pypi.

```bash
pip install darkhex
```

#### Setup from source using pip

Installing from source would be helpful if you wanted a custom version of the library / helping with development. Make sure you have the packages above installed.

##### Virtual Environment

We recommend using a virtual environment for the development.

E.g. for Ubuntu or Debian:

```bash
sudo apt-get install virtualenv python3-virtualenv
virtualenv venv
source venv/bin/activate
```

Rest of the installation is just installing python requirements.

E.g. for Ubuntu or Debian:

```bash
# Install the libraries listed above
sudo apt-get install libgirepository1.0-dev gcc libcairo2-dev pkg-config gir1.2-gtk-3.0 graphviz

# Install the python packages
pip install -r requirements.txt
```

**Update PYTHONPATH on your .bashrc or .zshrc file (or .venv/bin/activate)**
convnet-morpho 1.0.1
convnet-morpho is a cell morphology analysis library
Requires Python: >=3.6

It includes functions to read and process cell segmentations, that are made either from automatic or manual segmentations. It also includes a deep-learning based morphology analysis library - for this it uses the convolutional neural network AlexNet for image feature extraction. Finally it contains feature aggregation, dimensionality reduction and visualization, and a module for integrative and joint analysis of morphology and gene expression.
spotifymoods 0.0.5
A simple ML model to classify Spotify tracks using audio features.
Keywords: spotify,machine learning,spotify moods,spotify emotions

# spotifymoods
 
A simple ML model to classify Spotify tracks using audio features.

## Installation
```shell
pip install spotifymoods
```

## Usage
### Train the model
```python
import pandas as pd
from spotifymoods import train

# create a Pandas DataFrame from the file 'training_data.csv'
train_data = pd.read_csv('training_data.csv')

# save the trained model to the files 'trained_model.pkl' & 'scaled.pkl'
train(data=train_data, trained_output='trained.pkl', scaled_output='scaled.pkl')
```


### Predict moods
```python
import pandas as pd
from spotifymoods import predict

# create a Pandas DataFrame from the file 'test_data.csv'
test_data = pd.read_csv('test_data.csv')

# use the trained model from the files 'trained.pkl' & 'scaled.pkl', and return the results as DataFrame
result = predict(data=test_data, trained_path='trained.pkl', scaled_path='scaled.pkl')

# output the results to a .csv file
result.to_csv('result.csv', index=False)
```

### CSV data examples
``training_data.csv``
```text
energy,liveness,tempo,speechiness,acousticness,instrumentalness,danceability,duration_ms,loudness,valence,id,mood
0.549,0.22,130.749,0.0698,0.000798,0.00485,0.357,244573,-7.843,0.531,3kdMzXOcrDIdSWLdONHNK5,Energetic
0.975,0.16,129.022,0.0618,5.13e-05,0.713,0.594,205760,-3.21,0.899,3rFEKOClXOdNFO6fQGuQ9j,Energetic
```

``test_data.csv``
```text
energy,liveness,tempo,speechiness,acousticness,instrumentalness,danceability,duration_ms,loudness,valence,id
0.549,0.22,130.749,0.0698,0.000798,0.00485,0.357,244573,-7.843,0.531,3kdMzXOcrDIdSWLdONHNK5
0.975,0.16,129.022,0.0618,5.13e-05,0.713,0.594,205760,-3.21,0.899,3rFEKOClXOdNFO6fQGuQ9j
```