semvec/pypi-packages huggingface dataset:
mlglass 0.0.1
MLglass: A Transparency with models
License: MIT
Keywords: Machine Learning,Visualizations,Weights,Decision Tree,Naive Bayes

# Machine Learning Models with glass (Transparency)

### Links: **[Github](https://github.com/Nikeshbajaj/mlglass)**  |  **[PyPi - project](https://pypi.org/project/mlglass/)**
### Installation: *[pip install spkit](https://pypi.org/project/mlglass/)*


-----
## Table of contents
-[Logistic Regression](#logistic-regression---view-in-notebook)
-[Naive Bayes](#naive-bayes---view-in-notebook)
-[Decision Trees](#decision-trees---view-in-notebook)
-----


## Installation

**Requirement**:  numpy, matplotlib

### with pip

```
pip install mlglass
```

### Build from the source
Download the repository or clone it with git, after cd in directory build it from source with

```
python setup.py install
```

#### Machine Learning models - with visualizations
* Logistic Regression
* Naive Bayes
* Decision Trees
* DeepNet (to be updated)


## Machine Learning
### [Logistic Regression](https://nbviewer.jupyter.org/github/Nikeshbajaj/spkit/blob/master/notebooks/2.1_LogisticRegression_examples.ipynb) - *View in notebook*
<p align="center"><img src="https://raw.githubusercontent.com/Nikeshbajaj/MachineLearningFromScratch/master/LogisticRegression/img/example5.gif" width="600"/></p>

### [Naive Bayes](https://nbviewer.jupyter.org/github/Nikeshbajaj/spkit/blob/master/notebooks/2.2_NaiveBayes_example.ipynb) - *View in notebook*
<p align="center"><img src="https://raw.githubusercontent.com/Nikeshbajaj/MachineLearningFromScratch/master/Probabilistic/img/FeatureDist.png" width="600"/></p>

### [Decision Trees](https://nbviewer.jupyter.org/github/Nikeshbajaj/spkit/blob/master/notebooks/2.3_Tree_Example_Classification_and_Regression.ipynb) - *View in notebook*

[**[source code]**](https://github.com/Nikeshbajaj/spkit/blob/master/examples/trees_example.py) | [**[jupyter-notebook]**](https://nbviewer.jupyter.org/github/Nikeshbajaj/spkit/blob/master/notebooks/2.3.1_Trees_Classification_Example.ipynb)
<p align="center">
<img src="https://raw.githubusercontent.com/Nikeshbajaj/spkit/master/figures/tree_sinusoidal.png" width="800"/>
<img src="https://raw.githubusercontent.com/Nikeshbajaj/spkit/master/figures/trees.png" width="800"/>
</p>


#### Plottng tree while training

<p align="center"><img src="https://raw.githubusercontent.com/Nikeshbajaj/MachineLearningFromScratch/master/Trees/img/a123_nik.gif" width="600"/></p>

[**view in repository **](https://github.com/Nikeshbajaj/spkit/tree/master/notebooks)

______________________________________

# Contacts:

* **Nikesh Bajaj**
* http://nikeshbajaj.in
* n.bajaj@qmul.ac.uk
* n.bajaj@uel.ac.uk
* bajaj.nikkey@gmail.com
### PhD from Queen Mary University of London, Postdoctoral at University of East London
______________________________________
windows-spotlight-extractor 1.0.0
CLI Tool for exporting Windows 10 Spotlight Images as Wallpapers
License: MIT

# Windows 10 Spotlight Extractor

This application will scrape the windows spotlight assets folder for wallpapers.
The folder is located at
```
%LOCALAPPDATA%/Packages/Microsoft.Windows.ContentDeliveryManager_cw5n1h2txyewy/LocalState/Assets
```

Once found the wallpapers will be copied into the Spotlight images directory. By default this is:
```
%USERPROFILE%/OneDrive/Pictures/Spotlight
```

The application will filter only for images larger than 1920 x 1080


## Getting Started

Set the script to run on a schedule or just call using the CLI command ```spotlight```
You should see an output similar to the following
```
[2019-12-26 19:37:40,819] [root] [root] - Copying Windows Spotlight Images Into C:\Users\jonat\OneDrive\Pictures\Spotlight
[2019-12-26 19:37:40,844] [root] [root] - Successfully Copied 3 files
```

Modify the Settings in ```C:\Users\jonat\AppData\Local\w10-spotlight-extractor\settings.ini```

I recommend adding the following to the settings to silence the output under the LOGGING section
CORRECT THE USERNAME FIRST!
```
file = C:\Users\jonat\AppData\Local\w10-spotlight-extractor\app.log
```


### Installing
I Highly recommend using PIPX to install the spotlight extractor to ensure you do not run into issues with other environments and CLI apps installed using PIP   
https://packaging.python.org/guides/installing-stand-alone-command-line-tools/

Install the spotlight extractor with pip into a Python3 Environment.
```
pip3 install windows-spotlight-extractor
```

## Contributing

Contributions are most welcome to the project, please raise issues first and contribute in response to the issue with a pull request.

## Versioning

We use [SemVer](http://semver.org/) for versioning. For the versions available, see the [tags on this repository](https://bitbucket.org/fxquants/fxq-ioc-core/downloads/?tab=tags). 

## Authors

* [Jonathan Turnock](https://bitbucket.org/Jonathan_Turnock/) - *Initial work*

## License

This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details
generi 0.2.0
Manage huge sets of Docker images using matrix builds
License: MIT
Requires Python: >=3.7
Keywords: docker,CI,templating

![Generi logo](/docs/images/icon.png?raw=true "Generi logo")

�� [Documentation](https://generi.nicklehmann.sh/)

�� About
========

Generi is a tool to automatically create Dockerfiles and build images for a large combination of different factors. It is the right tool if you need to build many similar images with slightly different parameters. 

For example, say you are developing an app. You might want to build one docker image for python 2.7, 3.6, 3.8. And for each python version, you need one with a database included or not. And all that for each tag. If you have experienced a scenario like this, try out Generi.

�� Features
===========

- Configuration in yaml
- Specify different parameter to form a build matrix
- Generate Dockerfile for each combination
- Build all variations of your image with one command
- Push to the repository of your choice
- Everything customisable using [Jinja](https://jinja.palletsprojects.com/en/2.10.x/)

✈️ Quickstart
=============

`Generi` can be configured using a simple yaml file that defines your matrix build. 

*schema.yaml*

```yaml
parameters:
  python_version:
    - 2.7
    - 3.5
    - 3.6
    - 3.7
  operating_system:
    - buster
    - alpine

template: templates
output: "output/{{ python_version }}/{{ operating_system }}"
image: "nicklehmann/myapplication:py{{ python_version }}-{{ operating_system }}"

registry:
  username: nicklehmann
```

*templates/Dockerfile*

```dockerfile
FROM python:{{ python_version }}-{{ operating_system }}

COPY main.py main.py

CMD ["python", "main.py"]
```

First, render your dockerfiles by running

```bash
$ generi write schema.yaml
```

After that, build and optionally push your image.

```bash
$ generi build schema.yaml
$ generi push schema.yaml
```

For more examples, please see the [usage](https://generi.nicklehmann.sh/usage/usage.html) section of the documentation.