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Computers and Technology, 23.05.2020 00:59, Knownothing
In class, we learned a Nave Bayes classi er for binary feature values, i. e., xj 2 f0; 1g where we model the class conditional distribution to be Bernoulli. In this exercise, you are going to extend the result to the case where features that are non-binary. We are given a training set f(x(i); y(i)); i = f1; ;mgg, where x(i) 2 f1; 2; ; sgn and y(i) 2 f0; 1g. Again, we model the label as a biased coin with 0 = P(y(i) = 0) and 1 0 = P(y(i) = 1). We model each non-binary feature value x(i) j (an element of x(i)) as a biased dice for each class. This is parameterized by:
P(zj-s|y = 1) = θ.sly=1
Notice that we do not model P(aj sly = 0) and Pu; = sly = 1) directly. Instead we use the above equations to guarantee all probabilities for each class sum to 1.
A) Using the Naive Bayes (NB) assumption, write down the joint probability of the data: in terms of the parameters θο, θjkly-0 and ,,k y-1. You mlay find the indicator function 1(-) useful.
B) Maximizing the joint probability you get in (a) with respect to o, ,ky-o and jk y-1. Write down your resulting θο, θ, k y 0 and θ.kl,-1 and show intermediate steps.
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Answers: 2
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Computers and Technology, 23.06.2019 09:00, vetterk1400
Design a class tictactoe that: holds the following information about the game: two-dimensional array (3 by 3), and winner. add additional variables as needed. includes the functions to perform the various operations on objects. for example, function to print the board, getting the move, checking if move is valid, determining if there is a winner after each move. add additional operations as needed. includes constructor(s). write the functions of the class, and write a program that uses the class. the program should declare an object of type tictactoe. the program will create the board and store it in the array. the program will allow two players to play the tic-tac-toe game. after every valid move update the array, check if there is a winner. if there is no winner and no tie, then print the board again to continue.
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In class, we learned a Nave Bayes classi er for binary feature values, i. e., xj 2 f0; 1g where we m...
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