semvec/pypi-packages huggingface dataset:
postgres_kernel 0.2.2
A PostgreSQL kernel for IPython

# A simple Jupyter kernel for PostgreSQL

Install with `pip install postgres_kernel`

To use, run one of:

```bash
jupyter notebook
# In the notebook interface, select PostgreSQL from the 'New' menu
jupyter qtconsole --kernel postgres
jupyter console --kernel postgres
```

## How to use:

There are a couple of specially formatted comments for controlling the connection string and autocommit mode.

```sql
-- connection: postgres://brian:password@localhost:5432/dbname
-- autocommit: true
-- (or false)
```

For details of how this works, see Jupyter's docs on [wrapper kernels](http://jupyter-client.readthedocs.io/en/latest/wrapperkernels.html).
This is heavily based on [takluyver/bash_kernel](https://github.com/takluyver/bash_kernel). Just look at our git log :)

![](images/console.png)

![](images/notebook.png)


Related
-------

- Catherine Devlin has an ipython magic that seems very full featured: [catherinedevlin/ipython-sql](https://github.com/catherinedevlin/ipython-sql)

- As noted, this is based on [takluyver/bash_kernel](https://github.com/takluyver/bash_kernel)
yandex.rasp 0.0.2
A Python wrapper for Yandex.Schedules API
Keywords: yandex yandex.rasp rest

Yandex-Rasp-Api
=======================

A Python wrapper for the [Yandex.Schedules](https://tech.yandex.ru/rasp/raspapi/) API.


What is Yandex.Schedules?
=========================
As per the [documentation page](https://tech.yandex.ru/rasp/raspapi/), the API enables one to access data about different types of transport systems on the service.

Namely:
* Flight/transit schedule between selected stations;
* The schedule of flights/transits departing from the specified station;
* Route with the indication of travel time and all stops;
* Contact information of carriers;
* List of stations closest to the specified point.


Installation
======================
Use `pip`:

```bash
pip install yandex.rasp
```

Usage
=======================
Coming soon...
pyDML 0.1.0
Distance Metric Learning algorithms for Python
Keywords: Distance Metric Learning,Classification,Mahalanobis Distance,Dimensionality,Similarity

# pyDML

[![](https://travis-ci.org/jlsuarezdiaz/pyDML.svg?branch=master)](https://travis-ci.org/jlsuarezdiaz/pyDML)
[![](https://img.shields.io/badge/language-Python-green.svg)](https://www.python.org/)
[![](https://img.shields.io/badge/license-GPL-orange.svg)](https://www.gnu.org/licenses/gpl.html)
[![](https://badge.fury.io/py/pyDML.svg)](http://badge.fury.io/py/pyDML)

Distance Metric Learning Algorithms for Python

## What is Distance Metric Learning?

Many machine learning algorithms need a similarity measure to carry out their tasks. Usually, standard distances, like euclidean distance, are used to measure this similarity. Distance Metric Learning algorithms try to learn an optimal distance from the data.

## How to learn a distance?

There are two main ways to learn a distance in Distance Metric Learning:

- Learning a metric matrix M, that is, a positive semidefinite matrix. In this case, the distance is measured as
<a href="https://www.codecogs.com/eqnedit.php?latex=d(x,y)&space;=&space;\sqrt{(x-y)^TM(x-y)}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?d(x,y)&space;=&space;\sqrt{(x-y)^TM(x-y)}" title="d(x,y) = \sqrt{(x-y)^TM(x-y)}" /></a>

- Learning a linear map L. This map is also represented by a matrix, not necessarily definite or squared. Here, the distance between two elements is the euclidean distance after applying the transformation.

Every linear map defines a single metric (M = L'L), and two linear maps that define the same metric only differ in an isometry. So both approaches are equivalent.

## Some applications

### Improve distance based classifiers

![](./plots/ex_learning_nca.png)
*Improving 1-NN classification.*

### Dimensionality reduction

![](./plots/ex_red_dim.png)
*Learning a projection onto a plane for the digits dataset (dimension 64).*

## Documentation

See the available algorithms, the additional functionalities and the full documentation [here](https://pydml.readthedocs.io/en/latest/).

## Stats

The distance metric learning algorithms in pyDML are being evaluated in several datasets. The results of these experiments are available in the [pyDML-Stats](https://github.com/jlsuarezdiaz/pyDML-Stats) repository.

## Installation

- PyPI latest version: `pip install pyDML`

- From GitHub: clone or download this repository and run the command `python setup.py install` on the root directory.



## Authors

- Juan Luis Suárez Díaz ([jlsuarezdiaz](https://github.com/jlsuarezdiaz))