* Adds hashable dict type

* Implemented permutation decoder

* added test for permutation decode

* Optimized permutationdecoder

* relaxed tests

* uses isinstance

* Adds hashable dict type

* Implemented permutation decoder

* added test for permutation decode

* Optimized permutationdecoder

* relaxed tests

* Exclude test files from flake8 check

* Fix flake8 for main files

* Add flake8 check to build

* Add NgramCharModel to text.py

* Update text.py

* Update

* Fix NgramTextModel bug

* Add new tests for NgramTextModel

* Update search.py

Commenting issues fixed (spacing and punctuation was off sometimes).

* Update agents.py

* Update canvas.py

Grammar

* Update grid.py

* Update learning.py

Added period

* Update logic.py

Fix quoting

* Update mdp.py

Fixed quoting

* Update nlp.py

Capitalization and punctuation fixes

* Update planning.py

* Update probability.py

* Update rl.py

'th' to 'the'

* Update search.py

* Update text.py

* Update utils.py

* Update utils.py

* Update utils.py

* Update learning.py

Typo

* Update utils.py

* Made Log Usage Consistent in text.py

* Added Explicit Imports from utils

doccument -> document

In hindsight, the variable name "doc" implies we are manipulating the
document itself when, in reality, we are manipulating the index of the
document. "docid" better expresses this concept.

Added self to argument

This change is for consistency.

Cast was not needed since the return value was already being cast. Query
function changed to use heapq.nlargest() for simplicity

Problems fixed:
Replaced cmp: 1 instance in line 323 of search.py
Convert set() to {}: 3 instances in line 29 of MDP.py, line 181 and 215
of search.py
Convert .sort() to sorted(): 1 instance in line 150 of text.py
Cast map to list: 1 instance in line 47 of grid.py

- Fix relative imports in base of is a package or not
- Fix not working notebooks
- Add notebooks to packages sources

Used tool 2to3 to finish the porting to Python 3 plus some fixes by
hand.

These functions, which I had to implement in Python 2.2, are now
available in standard Python.

Move CountingProbDist to learning.py and use it in NaiveBayesLearner. Make NaiveBayesLearner use target-value frequencies too.

 * Use weighted_sampler() for the sampling.
 * By not using a defaultdict, keep n_obs always up to date.
 * Don't inherit from ProbDist, at least for now, since we don't
   use anything from the superclass, and if we did it would break
   (e.g. __setitem__).
 * Remove __len__ since nobody uses it.
 * Use heapq.nlargest.

Also separate random doctests from others, and tweak docs.