D. jhumigas

## validator-decorator-with-panderas.py
"""

Existing builtin decorators in panderas are ok, but they will raise an exception once a check fails.
If that is not the expected behavior, we can create our own decorators, to filter out corrupted rows to analyse them later without
creating a failure on the whole dataset.

In the following, we will create our own decorator which will filter out rows that did not pass our checks.
"""

import inspect

## trust_score.py
"""
The MIT License (MIT)
Copyright (c) 2018 David Mugisha

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

## LSTMAug.ipynb

      
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                Augmenting the LSTM PoS tagger with Character-level features (PyTorch)
              
          
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## cbow.py
"""
My proposal to the exercise in the tutorial about Deep Learning for NLP with Pytorch
This is one is about Word Embeddings that encodes Lexical Semantics.

Continuous Bag-of-Words model (CBOW) is model that tries to predict a word given the context
of a few words before and after the target.

.. _Source:
   http://pytorch.org/tutorials/beginner/nlp/word_embeddings_tutorial.html#exercise-computing-word-embeddings-continuous-bag-of-words
"""

## Purity score with scikit-learn
"""
The MIT License (MIT)
Copyright (c) 2017 David Mugisha

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

## extract_data.py
"""
Small example of function to reorder a data set.
"""
#!/usr/bin/env python
# -*- coding: utf-8 -*-

import numpy as np

def extract_k(labels, k):
    """Extract k percent of the dataset.

## Sinus.java
import java.awt.geom.Point2D;

public class Sinus{
  /**
	 * Computes the sine of the angle between three points.
	 * Use the properties of the cross product between two vectors in a 2D euclidean space.
	 * The positive angles are counter-clockwise.
	 * @param pt1 Point defined by a location in a coordinate space
	 * @param pt2 Point defined by a location in a coordinate space
	 * @param pt3 Point defined by a location in a coordinate space

## lagfib.py
#!usr/bin/python

from random import randint
from math import pow

_lag1 = 55
_lag2 = 24
_modulus = 31
#_firstterms = [randint(0,pow(2,_modulus)) for x in range(0,_lag1)]
_firstterms = [773, 744, 844, 228, 13, 1011, 691, 1, 1106, 730, 438, 102, 498, 1004, 111, 1230, 217, 1133, 703, 686, 78, 551, 60, 1009, 772, 922, 1223, 1205, 511, 876, 992, 162, 85, 296, 837, 755, 579, 268, 64, 194, 811, 645, 626, 140, 395, 1162, 322, 64, 97, 477, 117, 803, 1233, 288, 117, 594]
	"""

	Existing builtin decorators in panderas are ok, but they will raise an exception once a check fails.
	If that is not the expected behavior, we can create our own decorators, to filter out corrupted rows to analyse them later without
	creating a failure on the whole dataset.

	In the following, we will create our own decorator which will filter out rows that did not pass our checks.
	"""

	import inspect
	"""
	The MIT License (MIT)
	Copyright (c) 2018 David Mugisha

	Permission is hereby granted, free of charge, to any person obtaining a copy
	of this software and associated documentation files (the "Software"), to deal
	in the Software without restriction, including without limitation the rights
	to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
	copies of the Software, and to permit persons to whom the Software is
	furnished to do so, subject to the following conditions:
	"""
	My proposal to the exercise in the tutorial about Deep Learning for NLP with Pytorch
	This is one is about Word Embeddings that encodes Lexical Semantics.

	Continuous Bag-of-Words model (CBOW) is model that tries to predict a word given the context
	of a few words before and after the target.

	.. _Source:
	http://pytorch.org/tutorials/beginner/nlp/word_embeddings_tutorial.html#exercise-computing-word-embeddings-continuous-bag-of-words
	"""
	"""
	The MIT License (MIT)
	Copyright (c) 2017 David Mugisha

	Permission is hereby granted, free of charge, to any person obtaining a copy
	of this software and associated documentation files (the "Software"), to deal
	in the Software without restriction, including without limitation the rights
	to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
	copies of the Software, and to permit persons to whom the Software is
	furnished to do so, subject to the following conditions:
	"""
	Small example of function to reorder a data set.
	"""
	#!/usr/bin/env python
	# -- coding: utf-8 --

	import numpy as np

	def extract_k(labels, k):
	"""Extract k percent of the dataset.
	import java.awt.geom.Point2D;

	public class Sinus{
	/**
	* Computes the sine of the angle between three points.
	* Use the properties of the cross product between two vectors in a 2D euclidean space.
	* The positive angles are counter-clockwise.
	* @param pt1 Point defined by a location in a coordinate space
	* @param pt2 Point defined by a location in a coordinate space
	* @param pt3 Point defined by a location in a coordinate space
	#!usr/bin/python

	from random import randint
	from math import pow

	_lag1 = 55
	_lag2 = 24
	_modulus = 31
	#_firstterms = [randint(0,pow(2,_modulus)) for x in range(0,_lag1)]
	_firstterms = [773, 744, 844, 228, 13, 1011, 691, 1, 1106, 730, 438, 102, 498, 1004, 111, 1230, 217, 1133, 703, 686, 78, 551, 60, 1009, 772, 922, 1223, 1205, 511, 876, 992, 162, 85, 296, 837, 755, 579, 268, 64, 194, 811, 645, 626, 140, 395, 1162, 322, 64, 97, 477, 117, 803, 1233, 288, 117, 594]