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update #1
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* probability notebook * Update test_probability.py * Update README.md
* fixed Key Error bug in CustomMDP and reordered the return values of T function to be consistent with GridMDP * removing idea files from git
* tic-tac-toe gui added * Added GUI for Searching * Added Legend and Minor Fix * Minor Fix and Options added * Added Breadth-First Tree Search * Added Depth-First Tree Search * Minor Fix * Added Depth-First Graph Search
Closes #673
* tic-tac-toe gui added * Added GUI for Searching * Added Legend and Minor Fix * Minor Fix and Options added * Added Breadth-First Tree Search * Added Depth-First Tree Search * Minor Fix * Added Depth-First Graph Search * Minor Fix * Breadth-First Search and Minor Fix * Added Depth-First Graph Search in notebook * Added Depth-First Tree Search in notebook * Cell Placement
* tic-tac-toe gui added * Added GUI for Searching * Added Legend and Minor Fix * Minor Fix and Options added * Added Breadth-First Tree Search * Added Depth-First Tree Search * Minor Fix * Added Depth-First Graph Search * Minor Fix * Breadth-First Search and Minor Fix * Added Depth-First Graph Search in notebook * Added Depth-First Tree Search in notebook * Cell Placement * Added Uniform Cost Search in GUI
Partially solves #687
* added example for Decision Tree Learner * fixed docstring in learning.py and description in learning.ipynb
* Added Vacuum Agent * Minor Fix * Improved Font * Added XYVacuumEnv * Minor Fix * Review changes
* Update PeakFindingProblem code to allow diagonal motion * Fix unit test issues * update PeakFindingProblem to take actions as input param * Refactor code in search.py
Add features like selecting cities to be part of tsp, controlling temperature and speed of animation.
* rebase with master * solve error * solve error * solve error * Update search.ipynb
* added Best First search * fixed minor conflicts * minor changes
* Added dropdown option to solve using genetic algorithm * Added option to solve using Hill Climbing * Added messagebox to confirm exit
* Update in NeuralNetLearner function * made the changes as suggested
…1039) * update in Readme.md * updated as per the suggestions in the review.
* 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] 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
* 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 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
* 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
* Update GSOC link to correctly refer aimacode@GSOC2019 * Correct the underlying typo
Update CONTRIBUTING.md
Improvement in train_test_split function Shuffling has been removed
* added necessary and unique tests * some required changes * required changes
* added text classification in nlp_apps * updated as per the changes suggested.
Added: - .coveragerc file to configure report generation Modified: - .travis.yml to start generating reports during build
* Implementing Class Tfidf * added class of Tfidf * added scoring function BM25 * Revert "Implementing Class Tfidf"
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