* changed queue to set in AC3

Changed queue to set in AC3 (as in the pseudocode of the original algorithm) to reduce the number of consistency-check due to the redundancy of the same arcs in queue. For example, on the harder1 configuration of the Sudoku CSP the number consistency-check has been reduced from 40464 to 12562!

* re-added test commented by mistake

* added the mentioned AC4 algorithm for constraint propagation

AC3 algorithm has non-optimal worst case time-complexity O(cd^3 ), while AC4 algorithm runs in O(cd^2) worst case time

* added doctest in Sudoku for AC4 and and the possibility of choosing the constant propagation algorithm in mac inference

* removed useless doctest for AC4 in Sudoku because AC4's tests are already present in test_csp.py

* added map coloring SAT problems

* fixed typo errors and removed unnecessary brackets

* reformulated the map coloring problem

* Revert "reformulated the map coloring problem"

This reverts commit 20ab0e5afa238a0556e68f173b07ad32d0779d3b.

* Revert "fixed typo errors and removed unnecessary brackets"

This reverts commit f743146c43b28e0525b0f0b332faebc78c15946f.

* Revert "added map coloring SAT problems"

This reverts commit 9e0fa550e85081cf5b92fb6a3418384ab5a9fdfd.

* Revert "removed useless doctest for AC4 in Sudoku because AC4's tests are already present in test_csp.py"

This reverts commit b3cd24c511a82275f5b43c9f176396e6ba05f67e.

* Revert "added doctest in Sudoku for AC4 and and the possibility of choosing the constant propagation algorithm in mac inference"

This reverts commit 6986247481a05f1e558b93b2bf3cdae395f9c4ee.

* Revert "added the mentioned AC4 algorithm for constraint propagation"

This reverts commit 03551fbf2aa3980b915d4b6fefcbc70f24547b03.

* added map coloring SAT problem

* fixed build error

* Revert "added map coloring SAT problem"

This reverts commit 93af259e4811ddd775429f8a334111b9dd9e268c.

* Revert "fixed build error"

This reverts commit 6641c2c861728f3d43d3931ef201c6f7093cbc96.

* added map coloring SAT problem

* removed redundant parentheses

* added Viterbi algorithm

* chapter 18 learning

* add chapter 19

* move init dataset in NN learner

* add adam optimizer, add nn learner

* remove cpt 19 for debug

* change while loop in games4e

* add chapter 19

* add sgd and adam optimizer

* add chpt19 deep nn

* add rnn

* add auto encoder

* add comments, correct tests

* add more comments, change algorithms according to orders of chapter sections

* add keras and numpy to requirements

* add tf as requirement

* add gc in test agent

* fix agent bugs for running test_agent and test_agent_4e together

* fix build error

* add chapter 21 and 22

* add chapter 12 and part of 13

* remove chapter 12 and 13, add test of rl

* modify rnn test

* add chapter 12 and 13

* change gaussian kernel util function

* fix example bugs

* fix build bug

* chapter 18 learning

* add chapter 19

* move init dataset in NN learner

* add adam optimizer, add nn learner

* remove cpt 19 for debug

* change while loop in games4e

* add chapter 19

* add sgd and adam optimizer

* add chpt19 deep nn

* add rnn

* add auto encoder

* add comments, correct tests

* add more comments, change algorithms according to orders of chapter sections

* add keras and numpy to requirements

* add tf as requirement

* add gc in test agent

* fix agent bugs for running test_agent and test_agent_4e together

* fix build error

* add chapter 21 and 22

* add chapter 12 and part of 13

* remove chapter 12 and 13, add test of rl

* modify rnn test

* fix build error

* Update utils4e.py

* changed queue to set in AC3

Changed queue to set in AC3 (as in the pseudocode of the original algorithm) to reduce the number of consistency-check due to the redundancy of the same arcs in queue. For example, on the harder1 configuration of the Sudoku CSP the number consistency-check has been reduced from 40464 to 12562!

* re-added test commented by mistake

* added the mentioned AC4 algorithm for constraint propagation

AC3 algorithm has non-optimal worst case time-complexity O(cd^3 ), while AC4 algorithm runs in O(cd^2) worst case time

* added doctest in Sudoku for AC4 and and the possibility of choosing the constant propagation algorithm in mac inference

* removed useless doctest for AC4 in Sudoku because AC4's tests are already present in test_csp.py

* added map coloring SAT problems

* fixed typo errors and removed unnecessary brackets

* reformulated the map coloring problem

* Revert "reformulated the map coloring problem"

This reverts commit 20ab0e5afa238a0556e68f173b07ad32d0779d3b.

* Revert "fixed typo errors and removed unnecessary brackets"

This reverts commit f743146c43b28e0525b0f0b332faebc78c15946f.

* Revert "added map coloring SAT problems"

This reverts commit 9e0fa550e85081cf5b92fb6a3418384ab5a9fdfd.

* Revert "removed useless doctest for AC4 in Sudoku because AC4's tests are already present in test_csp.py"

This reverts commit b3cd24c511a82275f5b43c9f176396e6ba05f67e.

* Revert "added doctest in Sudoku for AC4 and and the possibility of choosing the constant propagation algorithm in mac inference"

This reverts commit 6986247481a05f1e558b93b2bf3cdae395f9c4ee.

* Revert "added the mentioned AC4 algorithm for constraint propagation"

This reverts commit 03551fbf2aa3980b915d4b6fefcbc70f24547b03.

* added map coloring SAT problem

* fixed build error

* Revert "added map coloring SAT problem"

This reverts commit 93af259e4811ddd775429f8a334111b9dd9e268c.

* Revert "fixed build error"

This reverts commit 6641c2c861728f3d43d3931ef201c6f7093cbc96.

* added map coloring SAT problem

* removed redundant parentheses

* add perceotion and tests

* upadte requirements

* fix typo

* add utils and images for perception

* fix build error

* comment the last 2 agent tests

* fix build error

* change cnn test

The "search-4e.ipynb" in line 21 is a typo for search4e.ipynb. File search-4e.ipynb does not exist. It will show 404 error.

* Implementing Class Tfidf

* added class of Tfidf
* added scoring function BM25

* Revert "Implementing Class Tfidf"

Added:
 - .coveragerc file to configure report generation
Modified:
 - .travis.yml to start generating reports during build

* add monte carlo tree search

* add comments to mcts

* change model based reflex agent

* add games4e.py

* recover games4e.ipynb

* add tests to mcts

* added text classification in nlp_apps

* updated as per the changes suggested.

Cover or use at least once in tests classes, methods, and non-debugging functions of csp.py (version 18.04.2019).  Issue #287.

* added necessary and unique tests

* some required changes

* required changes

Improvement in train_test_split function

Shuffling has been removed

Update CONTRIBUTING.md

* Update GSOC link to correctly refer aimacode@GSOC2019

* Correct the underlying typo

* Added WumpusWorld testcases

Added:
 - Testcases in test_agents.py
Modified:
 - Duplicate walls are not added in corners now

* Tests for VacuumEnvironment and WumpusEnvironment

Added:
 - Test cases for Explorer actions in WumpusEnvironment
 - Test cases for VacuumEnvironment
Modified:
 - VacuumAgent correctly disables bump percept when agent sucks dirt after bumping into a wall

* Added spaces in tuples to comply with PEP8

* Reworked Introduction and 1-D environment in agents.py

Added:
 - Table of Contents and overview
 - A miniscule explanation of all required code from agents.py
Modified:
 - Some grammar and sentences
 - Structure of the notebook in 1-D environments to make it more coherent
Removed:
 - Outputs from notebook (Makes VCS tough and bugs tough to detect)

* Reworked agents in a 2D environment

Modified:
 - Removed global variable turn from 2D park model: Agent programs are not supposed to see anything except percepts
 - Bump percept is now generated when Dog is about to bump into a wall
 - Replaced all XYEnvironment with GraphicEnvironment - Gives better readability to both code and output (Previous way of just showing GraphicEnvironment in the end was redundant imo)
 - Restructured the 2D park and EnergeticBlindDog environment scenario to be more readable
Removed:
 - Redundant Park2D without graphics (subclass of XYEnvironment)

* Fixed issue #1030

Added:
 - ipython and ipythonblocks packages to requirements.txt

* Has some typographic improvements in agents.ipynb

* Added output to agents.ipynb

* changed queue to set in AC3

Changed queue to set in AC3 (as in the pseudocode of the original algorithm) to reduce the number of consistency-check due to the redundancy of the same arcs in queue. For example, on the harder1 configuration of the Sudoku CSP the number consistency-check has been reduced from 40464 to 12562!

* re-added test commented by mistake

* Reworked PriorityQueue spec

Modified:
- Priority Queue methods:
    queue[elem] now returns the first value of elem stored in queue
    elem in queue now correctly returns whether a copy of element is present regardless of the function value. Apparently the bug was introduced while trying to meet heapq spec
    del queue[elem] deletes the first instance of elem in queue correctly
- Algorithms
    Same change in best_first_graph_search in romania_problem.py and search.py to make them compatible with the new spec
- Tests
    Introduced 3 tests in test_utils.py to comprehensively test PriorityQueue's new spec

* Reworked PriorityQueue spec

Modified:
- Priority Queue methods:
    queue[elem] now returns the first value of elem stored in queue
    elem in queue now correctly returns whether a copy of element is present regardless of the function value. Apparently the bug was introduced while trying to meet heapq spec
    del queue[elem] delet...

* update in Readme.md

* updated as per the suggestions in the review.

* Update in NeuralNetLearner function

* made the changes as suggested

* no need of if else

* no need of if - else .   

As if hidden_layer_sizes is zero then it will not affect layer_sizes

* removed comment

* Update learning.py