semvec/pypi-packages huggingface dataset:
reflowrst 1.1.0
Modify valid rst text to fit within given width
License: MIT

=========
reflowrst
=========

Installation
============

>>> sudo pip install reflowrst

Usage
=====

Example:

>>> from reflowrst import reflow
>>> text = 'Here is a paragraph that is far too long and will need to wrap.'
>>> print(reflow(text, 47))
Here is a paragraph that is far too long and
will need to wrap.

You can also get the longest possible form of the rst text by using 0
for the space. For example:

>>> paragraph = '''Here is a paragraph that is far too long and
will need to wrap.'''
>>> print(reflow(paragraph, 0))
Here is a paragraph that is far too long and will need to wrap.

Todo
====
* |+| Titles
* |+| Transitions
* |+| Paragraphs
* |+| Bullet Lists
* |+| Enumerated Lists
* |+| Definitions
* |+| Fields
* |+| Options
* |+| Literal Blocks
* |-| Quoted Literal Blocks
* |+| Line Blocks
* |+| Block Quotes
* |-| Doctest Blocks
* |+| Grid Tables
* |+| Simple Tables
* |+| Footnotes
* |+| Citations
* |+| Directives

.. |+| unicode:: U+2611
.. |-| unicode:: U+2610
gradient-api 1.2.2
API of gradient descent.
License: MIT
Keywords: gradient_api,Gradient,gradient,GradientApi,gradientAPI

**news_api**
==================

*Usage:*
--------


>>> from gradient_api import gradient

>>> r1 = gradient.generate(expr_or_poly1d='x**2+1')
>>> r2 = gradient.generate(expr_or_poly1d=[1,0,1])

>>> print(r1 == r2)
True

>>> print(r1)
{'X': -0.00011417981541647683, 'Y': 1.0000000130370303, 'Gradient': -0.00022835963083295366, 'Numloop': 51}



>>> trainData = pandas.DataFrame(numpy.array([[1,3,5,7],[2,4,6,8]]))
>>> r3 = gradient.generate_2d(trainData)
>>> print(r3)
{'Gradient': array([-1020.54, -1589.21, -3630.29, -5671.37]),
 'Linear_Coef': array([-16.94, -38.6 , -60.26]),
 'Linear_Intercept': -10.83,
 'Numloop': 2,
 'Y': 263786.0329,
 'theta': array([-10.83, -16.94, -38.6 , -60.26])}


*Tips:*
-------

>>> generate_1d(expr_or_poly1d,init_x=-0.5,step=0.01,num_iters=1e4,showPlot=False,xlimArr=None,ylimArr=None)

>>> generate_2d(dataFrame,step=0.1,num_iters=1e4)