naive bayes classifier

training samples. For example, y = 1 has M1 and training samples have M, then P (y = 1) = m1/m. However, I still cannot figure out the p (x | Y) computation. Naive Bayes hypothesis: P (x1, x2 ,.., XN | y) = P (X1 | Y )... P (XN | y) (x1, x2 ,..., XN is the component of X, that is, the condition is independent. When I! When J is used, P (XI | y, XJ) = P (XI | Y). If y is specified, the occurrence of Xi is

; - for(Auto d:data) { Wu for(inti =0; I i) { -C_p[make_pair (D[i], label)] + = (1.0/(Prior *data.size ())); About } $ } - } - } - A intNaivebayes::p redict (Constvectorint> Item) { + intresult; the DoubleMax_prob =0.0; - for(Auto p:p_p) { $ intLabel =P.first; the DoublePrior =P.second; the DoubleProb =Prior; the for(inti =0; I 1; ++i) { theProb *=C_p[make_pair (Item[i], label)]; - } in the

Take the test tomorrow. You can bring your computer to your computer and write the program first. Save your effort to use a calculator ...... Directly use the Python source code. [Python] # Naive Bayes # Calculate the Prob. of class: clsdef P (data, cls_val, cls_name = "class"): cnt = 0.0 for e in data: if e [cls_name] = cls_val: cnt + = 1 return cnt/len (data) # Calculate the Prob (attr | cls) def PT (data

user requests a request, we need to traverse the probability of each grid in the computed database and return the center point of the maximum probability grid. Assuming that our lattice is 10*10 meters in size, then all the grid in Beijing will have 160 million lattice, traverse computation overhead is very huge. A method to improve the computational efficiency is to solve the approximate spatial range based on the user's signal vectors, and then calculate the probability of each lattice in the

section $ " " -X_train, X_test, y_train, y_test =train_test_split (News.data, - News.target, thetest_size=0.25, -Random_state=33)Wuyi the " " - 3 Bayesian classifier predicts news Wu " " - #convert text to features AboutVEC =Countvectorizer () $X_train =vec.fit_transform (X_train) -X_test =vec.transform (x_test) - #Initialize naive Bayesian model -MNB =MULTINOMIALNB () A #Training set, estimating paramete

1. Background When I was outside the company internship, a great God told me that learning computer is to a Bayesian formula applied to apply. Well, it's finally used. Naive Bayesian classifier is said to be a lot of anti-Vice software used in the algorithm, Bayesian formula is also relatively simple, the university to do probability problems often used. The core idea is to find out the most likely effect

)]=1 else:print "The word:%s is not in my vocabulary!" %word return returnvecdef TRAINNBC (trainsamples,traincategory): Numtrainsamp=len (Trainsamples) NumWords=len (train Samples[0]) pabusive=sum (traincategory)/float (numtrainsamp) #y =1 or 0 feature Count P0num=np.ones (numwords) P1NUM=NP.O NES (numwords) #y =1 or 0 category count P0numtotal=numwords p1numtotal=numwords for I in Range (Numtrainsamp): if Traincategory[i]==1:p0num+=trainsamples[i] P0numtotal+=sum (Trainsamples[i]) E