semvec/pypi-packages huggingface dataset:
django-rest-kakao-auto-reply 0.1.6
Make you easy to create kakaotalk plusfriend bot based on Django + DRF
License: MIT
Keywords: django-rest-kakao-auto-reply

=============================
django-rest-kakao-auto-reply
=============================

WIP




History
-------

0.1.0 (2018-07-12)
++++++++++++++++++

* First release on PyPI.
lad 0.1.dev4
Least absolute deviations with L1 regularization using majorization-minimization
License: MIT
Keywords: statistics optimization

lad
===
|ci-badge| |cov-badge| |zenodo-badge|

.. |ci-badge| image:: https://travis-ci.org/mirca/lad.svg?branch=master
    :target: https://travis-ci.org/mirca/lad
.. |cov-badge| image:: https://codecov.io/gh/mirca/lad/branch/master/graph/badge.svg
    :target: https://codecov.io/gh/mirca/lad/branch/master/
.. |zenodo-badge| image:: https://zenodo.org/badge/136721899.svg
   :target: https://zenodo.org/badge/latestdoi/136721899

Least absolute deviations with L1 regularization using majorization-minimization.
In estimation theory terms, this is the Maximum A Posterior (MAP) estimator for
a Laplacian likelihood with Laplacian prior, i.e.

.. image:: lad.png
    :align: center

The MM algorithm update turns out to be

.. image:: lad2.png
    :align: center

which belongs to the class of iterative reweighted least-squares: https://en.wikipedia.org/wiki/Iteratively_reweighted_least_squares

Python Version
--------------

To install the development version, proceed as follows::

    git clone https://github.com/mirca/lad.git
    pip install -e lad

Or install the lastest version on PyPi::

    pip install lad

Installation dependencies::

    - tensorflow

Test dependencies::

    - numpy
    - tensorflow
    - pytest
    - pytest-cov

R version
---------

Inside the R console, type::

    devtools::install_github("mirca/lad/r/lad")
jschema2py 0.1.0
jsonschema to python class converter
License: MIT
Keywords: jschema2py,json,schema,jsonschema,library,python3

# JSchema2py #
JSchema2py is able to generate python classes starting from JSONSchema and provides the automatic support for types
and constraints checking.

## Installation
The package can be installed through pip:

    $ pip install jschema2py

or downloaded from [GitHub](https://github.com/jacopodl/jschema2py).

## Examples
For example, given the following schema:
```json
{
  "title": "UserInfo",
  "type": "object",
  "properties": {
    "name": {
      "type": "string",
      "pattern": "^[^a-z0-9]"
    },
    "userName": {
      "type": "string"
    },
    "age": {
      "type": "integer",
      "minimum": 0,
      "maximum": 100
    }
  }
}
```
jschema2py can easily convert it into python class in this way (Assume here that the schema is stored into variable 
called schema):
```python
from jschema2py import build_class
UserInfo = build_class(schema)
user = UserInfo()
user.name = "Jacopo"
user.userName = "JDL"
user.age = 24
print(user)
```
validation will be performed on the object manipulation:
```python
user.name = "jacopo" # raise ConstraintError (pattern: ^[^a-z0-9])
user.age = "24" # raise TypeError
```
The object can be serialized into a JSON document:
```python
jsdoc = str(user)
```
### Accessing generated classes
If one of the property of the schema refers to another object, you can access the class that represents the referred 
object by using method get_class:
```json
{
  "title": "Nested",
  "type": "object",
  "properties": {
    "inner": {
      "title": "Inner",
      "type": "object",
      "properties": {
        "string": {
          "type": "string",
          "default": "I'm inner!"
        }
      }
    }
  }
}
```
```python
from jschema2py import build_class
Nested = build_class(schema)
nested = Nested()
nested.inner = nested.get_class("inner")() # Gets the class associated with the property "inner" 
print(nested.inner.string)
```