semvec/pypi-packages huggingface dataset:
cloudssh 1.4
EC2 SSH connections helper

cloudssh
========

|Build Status| |codecov| |MIT licensed|

EC2 SSH connections helper

An SSH connection helper for AWS
--------------------------------

``cloudssh`` allows you to quickly connect to EC2 instances using their
names or instance IDs.

It will call the AWS SDK to find the instance public IP address and open
a SSH connection in a subprocess.

Example:

.. figure:: https://github.com/gabfl/cloudssh/blob/master/img/ec2.png?raw=true
   :alt: EC2

   EC2

You can connect to this instance with:

::

   cssh dev

You can also use the **auto-completion feature** by building a local
index of your AWS instances then calling ``cloudssh`` without any
argument.

::

   cssh --build_index
   # The instances index has been stored in ~/.cloudssh/.

   cssh
   # Start typing an instance name and press [TAB] to auto complete.

Or search instances by name with:

::

   cssh --build_index
   # The instances index has been stored in ~/.cloudssh/.

   cssh --search http
   # Found "web-http-prod", connect? [Y/n]: 

Or lookup an instance details:

::

   cssh web-http-prod --info
   # * Network
   # Public ID: 123.***.***.***
   # Private ID: 10.***.***.***
   #
   # * VPC/subnet
   # VPC ID: vpc-******
   # Subnet ID: subnet-******
   #
   # * Misc
   # Instance name: web-http-prod
   # Instance ID: i-******
   # Instance type: t2.xlarge
   # Launch date: 2019-10-12 15:44:36+00:00
   #
   # * Tags
   #   product = web_cluster
   #   environment = production
   #   Name = web-http-prod

Example:

.. figure:: https://github.com/gabfl/cloudssh/blob/master/img/autocomplete_demo.gif?raw=true
   :alt: EC2

   EC2

Installation and usage
----------------------

.. code:: bash

   pip3 install cloudssh
   aws configure # To configure your AWS credentials

   cssh myserver # Call the module followed by the name of one of your servers

Advanced configuration
----------------------

You can optionally create a file ``~/.cloudssh/cloudssh.cfg`` (see
`example <cloudssh.cfg.sample>`__).

.. |Build Status| image:: https://travis-ci.org/gabfl/cloudssh.svg?branch=master
   :target: https://travis-ci.org/gabfl/cloudssh
.. |codecov| image:: https://codecov.io/gh/gabfl/cloudssh/branch/master/graph/badge.svg
   :target: https://codecov.io/gh/gabfl/cloudssh
.. |MIT licensed| image:: https://img.shields.io/badge/license-MIT-green.svg
   :target: https://raw.githubusercontent.com/gabfl/cloudssh/master/LICENSE
dataimport 0.1.0
read datafiles via the import system

# data-import

`import` data files via import-hook trickery

## Installation

```
 $ pip install dataimport
```

## Usage

```python
>>> import dataimport.test.csv as csv_file
>>> print(list(csv_file))
[['col1', 'col2', 'col3', 'col4'], ['1', '2', '3', '4'], ['5', '6', '7', '8'], ['9', '10', '11', '12'], ['13', '14', '15', '16'], ['17', '18', '19', '20']]
```

`csv` and `json` loaders are included, but adding or replacing a loader is easy!

```python
>>> import dataimport
>>> import pandas
>>> dataimport.register('csv', pandas.read_csv)
>>> import dataimport.test.csv as datatable
>>> print(datatable)
   col1  col2  col3  col4
0     1     2     3     4
1     5     6     7     8
2     9    10    11    12
3    13    14    15    16
4    17    18    19    20
```
basic-knn 0.0.2
K-Nearest Neighbors algorithm for classification problems.
Requires Python: >=3.6

# KNN Algorithm Module

### What is KNN?
In k-NN classification, the output is a class membership. An object is classified by a plurality vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor.

k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until function evaluation. Since this algorithm relies on distance for classification, normalizing the training data can improve its accuracy dramatically. 

(Wikipedia Article about KNN, https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)

### How to Install?
It is always better to use "pip" (Package manager for Python).
```
pip install basic_knn
```

### Sample Usage

#### Create Model
```
# import knn classifier
from basic_knn import KNNClassifier

# sample data
data_x = [...]
data_y = [...]
labels = [...]

# create model
model = KNNClassifier(xs = xs, ys = ys, labels = labels)
```

#### Make Predictions
```
# sample input for predictions
sample_input = (..., ...)

# make prediction
model.predict(sample_input)
```