semvec/pypi-packages huggingface dataset:
mezzanine-theme-moderna 0.2
Mezzanine CMS theme: Moderna
License: MIT

Mezzanine Theme Moderna
=======================

Maintained and packaged version of awesome `moderna theme <https://github.com/thecodinghouse/mezzanine-themes/tree/master/moderna>`__.

Supporting
----------

- Mezzanine 4.2.0
- Django 1.10.1
- Python 3.5.2

It may run with other versions

Install
-------

After installing mezzanine

- ``pip install mezzanine-theme-moderna``
- Add ``"moderna"`` on your ``settings.py``

.. code-block:: python

  INSTALLED_APPS = (
      ...
      "django.contrib.staticfiles",
      "moderna",
      ...
  )
sampled 0.1.2
Decorator for reusable models in PyMC3
License: MIT

|Build Status| |Coverage Status|

========
sampled
========


*Decorator for reusable models in PyMC3*

Provides syntactic sugar for reusable models with PyMC3.  This lets you separate creating a generative model from using the model.

Here is an example of creating a model:

::

    import numpy as np
    import pymc3 as pm
    from sampled import sampled

    @sampled
    def linear_model(X, y):
        shape = X.shape
        X = pm.Normal('X', mu=np.mean(X, axis=0), sd=np.std(X, axis=0), shape=shape)
        coefs = pm.Normal('coefs', mu=np.zeros(shape[1]), sd=np.ones(shape[1]), shape=shape[1])
        pm.Normal('y', mu=np.dot(X, coefs), sd=np.ones(shape[0]), shape=shape[0])

Now here is how to use the model:
::

    X = np.random.normal(size=(1000, 10))
    w = np.random.normal(size=10)
    y = X.dot(w) + np.random.normal(scale=0.1, size=1000)

    with linear_model(X=X, y=y):
        sampled_coefs = pm.sample(draws=1000, tune=500)

    np.allclose(sampled_coefs.get_values('coefs').mean(axis=0), w, atol=0.1) # True

You can also use this to build graphical networks -- here is a continuous version of the `STUDENT` example from Koller and Friedman's "Probabilistic Graphical Models", chapter 3:

::

    @sampled
    def student():
        difficulty = pm.Beta('difficulty', alpha=5, beta=5)
        intelligence = pm.Beta('intelligence', alpha=5, beta=5)
        SAT = pm.Beta('SAT', alpha=20 * intelligence, beta=20 * (1 - intelligence))
        grade_avg = 0.5 + 0.5 * tt.sqrt((1 - difficulty) * intelligence)
        grade = pm.Beta('grade', alpha=20 * grade_avg, beta=20 * (1 - grade_avg))
        recommendation = pm.Binomial('recommendation', n=1, p=0.7 * grade)

Observations may be passed into any node, and we can observe how that changes posterior expectations:

::

    # no prior knowledge
    with student():
        prior = pm.sample(draws=1000, tune=500)

    prior.get_values('recommendation').mean()  # 0.502

    # 99th percentile SAT score --> higher chance of a recommendation
    with student(SAT=0.99):
        good_sats = pm.sample(draws=1000, tune=500)

    good_sats.get_values('recommendation').mean()  # 0.543

    # A good grade in a hard class --> very high chance of recommendation
    with student(difficulty=0.99, grade=0.99):
        hard_class_good_grade = pm.sample(draws=1000, tune=500)

    hard_class_good_grade.get_values('recommendation').mean()  # 0.705


**References**

*  Koller, Daphne, and Nir Friedman. *Probabilistic graphical models: principles and techniques.* MIT press, 2009.

.. |Build Status| image:: https://travis-ci.org/ColCarroll/sampled.svg?branch=master
   :target: https://travis-ci.org/ColCarroll/sampled
.. |Coverage Status| image:: https://coveralls.io/repos/github/ColCarroll/sampled/badge.svg?branch=master
   :target: https://coveralls.io/github/ColCarroll/sampled?branch=master
synapy 0.1
Synapy, a synaptic matrix implementation for Python
License: MIT
Keywords: synaptic matrix,synapy,neurons,machine learning

Synapy is a Python implementation of a synaptic matrix. This project represents an ongoing search for new neurobiologically inspired learning algorithms and computational techniques. This work is inspired by, and based on, Dr. Arnold Trehub's work. Particularly, this project is based on his concept of the synaptic matrix.
FileSorter 0.2.0
A Python file sorter by common data types
License: MIT
Keywords: sorting files file sort

File Sorter
This program will let you sort files by using one sorta easy command!

`file_sort [path] [levels]`

The path will be os.getcwd() by default.
The levels are a number from 0 to ∞ indicating how far into subfolders the sort may look. The default is 0 for no folders may be searched.