* add failure_test method to utils

* comment fix

* Update test_learning.py

* Update test_csp.py

* add a simple naive bayes classifier

* Update test_learning.py

* spacing

* minor fix

* lists to strings

* Update README.md

* add powerset to utils

* add powerset test

* Create knowledge.py

* Create test_knowledge.py

* add header docstring to knowledge.py

* update learning docstring

* minor edits in knowledge.py

* Fixed truncated_svd

* Moved SVD algorithm to learning.py

* Minor fixes

* Typo fix

* Added decisiontreelearner to notebook

* Added RandomForest

* Update test_learning.py

* Update learning.py

* DataFile Update

* Update learning.py

* Update search.py

* Update test_learning.py

* Update test_text.py

* Add files via upload

* Update text.ipynb

* Update search-4e.ipynb

* Create CONTRIBUTING.md

is supposed to return an answer when errT converges, not errV
used to return size of when err_val converges but is supposed to return the size with minimum err_val

* Create function to initialize random weights

* Add sigmoid derivative function

* Fix flake8 warnings

* Remove unnecessary #noqa

* Fix doctest

* Update learning.ipynb

* Delete perceptron.png

* Add new Perceptron image

* Update Perceptron Implementation

* Add Gaussian Function

* Added Tests

Add tests for Continuous Naive Bayes + Means/Standard Deviation

* Update learning.py

* Commenting Fix

* Add test for gaussian

* test for every class

* Update test_learning.py

* Round float results to make sure test passes

* Update learning.py

* Update test_learning.py

* Update test_learning.py

* Add grade_learner

* Update test_learning.py

* Update test_learning.py

* Update learning.py

* set max_score to -1 (for now)

* Update learning.py

* Make find_max more pythonic

* Update learning.py

* Add Euclidean Distance

* Update learning.ipynb

* minor fix in notebook

* minor spacing in learning.py

* Added Euclidean Test

* Exclude test files from flake8 check

* Fix flake8 for main files

* Add flake8 check to build

* Bugfixing

* Test for "exclude"

* Update test_learning.py

* update_values

* Bug fixing

* Spacing

* Update test_agents.py

pep8 changes, showed flake8 errors

* Update test_agents.py

* Update test_agents.py

* Update test_agents.py

* Update test_text.py

added missing whitespace after comma

* Update utils.py

added space after comma

* Update search.py

added space after comma

* Update probability.py

added space after comma

* Update learning.py

added space after comma

* Update planning.py

added double_tennis_problem

* Update rl.py

In the pseudocode figure 21.8, the first 'if' starts with argument 's', which is the previous state, not s1(i.e, the current state).

* Update search.py

the 'uniform_cost_search' in notebook 'search-4e.ipynb' resembles more to the pseudocode in book.

* Update search.py

* Update search.py

* Update search.py

created a parameter size in the function leave_one_out(learner, dataset, size=None):

* Update learning.py

* Added remove_examples function

In learning.py, I added the Hamming Distance metric.

* Update some comments

* Cleanup and remove some duplicate initialization

* Fix some comments quotation and little bugs: raising Exception instead of raising Failure, random.randrange(a,b) instead of random(a, b)

* Fix some comments quotation and remove explicit inheritance from object

* Fix 'r' in front of the ```parse_csv``` comments

* Fix quotation in comments

* Fix quotation in comments and fix bug in 'KB_AgentProgram'

* Update utils.py

in pseudo code the sequence of arguments is " WEIGHTED-SAMPLE-WITH-REPLACEMENT(N, S, W)"  same must follow in function "particle_filtering" in the file probability.py

* Update learning.py

weight_sample_with_replacement sequence of args

* Update probability.py

 weighted_sample_with_replacement sequence of args

* Update search.py

* Update Comments on learning.py + PluralityLearner

- Fixed some capitalization, spelling and quotation mistakes in comments

- In the PluralityLearner function, the nested function "predict" always returns the same output for a dataset, without taking into account the input ("example"). I defaulted the input as an empty list, so that we don't have to create (or find) a dummy example when we want to simply find the most popular class.

* Update learning.py

* Update learning.py

* Update learning.py

* Made Requested Changes

* changed mean boolean error

* Update learning.py

Stop using statistics.mode, because it throws an error when there are ties.

* Modern string formatting in csp.py.

* Modern string formatting in games.py.

* Modern string formatting in learning.py.

* Modern string formatting in nlp.py.

* Modern string formatting in probability.py.

* Modern string formatting in search.py.

* Replaced {0\!r} by {} if %s.

* Corrected a typo.

Syntax error. Used the function len instead of count.

* 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

this classifier of learning module is not optimized to run on this huge MNIST data

having this flag drastically reduces time to load & sanity check large image datasets for practical ML tasks