This files provides all the instruction for running the NewEleusis Player Phase -2 Following is the description of all the files for the working of NewEleusis Player phase -2 code

Game.py -

-This is the main file which would need to be run for getting following output

• Confidence of the player
• Rule prediction
• Score of prediction of new eleusis player

NewEleusisPlayer.py -

This file contains following function:

generate_random_card()

• This function provides the random generation of card for the player after everyplay getValidCard()
• This function helps in appending a random generated card to new eleusis players hand

boardlist() -

• This cards returnt the boardlist from the board state

scientist() -

• This is actual new eleusis player function which return the rule predicted

rule_equivalence() -

• This function is used in predicting equivalent god rule by the new eleusis player

checkBoardDescription()-

• This function evaluates the board state

score -

• Function returns the score of the new eleusis player

populateDecisionTable.py -

• The file contains following function populate_attribute() -
• This file deals with populating the decision table called when a new card is played

decisionTree.py -

• The file contains following class and function

Class Node() --

• Node class initializes the attributes required for decision tree node

Class decisiontree() -

• decision tree class initializes the attributes required for decision tree

populateAttributes() -

• This function get the inputs from the populate attribuet file for every new card played

build_decision_tree() -

• The function builds and returns a decision tree

getResult() -

• getResult function returns result from the decision tree

print_tree() -

• The function return the child nodes values of the decision tree

createDecisiontree() -

• The function creates the decision tree with all the input available from the populate attribute file

getSplitAttributesWithCombination() -

• The function returns output of all possible hypothesis with max information gain

getSplitAttributes() -

• The function returns output of all possible hypothesis with max information gain

getInformationGain()-

• Function the return the count of the information gain calculated.

getRules() -

• The function return final rule value evaluated from the decision tree.