B Naive Bayesian classifier The Naive Bayesian Classifier algorithm is a classification algorithm based on calculating the probability of belonging to each class. That is to say that the test data $($to be classified$)$ will be assigned to the most likely class. The probability of belonging to each class is calculated from the W learning data as follows: class$($x$)=$ argmax $($IP$()$P$()\}$ Here P$()$ is the a priori probability of belonging to the class k$.$ in other words its the probability of obtaining a data of class k if we draw a data at random. P$()$ is the probability that a data x has the value x for the variable i$,$ if we know its class w$.$ Here we will calculate this probability by calculating the distance between the data x$,$ and each barycenter of the classes $($i$.$ e$.$ the class average$),$ divided by the sum distances between this data and each barycenter. List of useful functions: mean, sum: calculate the mean and the sum of a list of values. unique: returns the list of list values, without repeating values. asarray: transforms a list into a vector. vector.prod: makes the product of the values of a vector. naive bayes.GaussianNB: the naive Bayesian Classifier from sklearn. Create a CBN $($X$,$ Y$)$ function that takes x data and labels as input Y and which returns a label, for each data, predicted from class la more likely according to equation $(1).$ Here again, we take each data, one by one, as test data and we consider all data as training data. It is advisable to first calculate the barycentres and the a priori probabilities P$()$ for each class, then calculate the conditional probabilities P$()$ for each class and each variable.