naive bayes python

This paper illustrates the Python implementation method of naive Bayesian algorithm. Share to everyone for your reference. The implementation methods are as follows: Advantages and disadvantages of naive Bayesian algorithm Advantages: It is still valid in the case of less data, can deal with many kinds of problems Disadvantage: Sensitive to the way the input d

to the overall probability of 0, there is a probability of multiplication after the value will be relatively small, take log convenient comparison.Test the Code Function section:def classifynb (Vec2classify, P0vec, P1vec, PClass1): p1 = SUM (vec2classify * P1vec) + log (pClass1) #element-wise mult p0 = SUM (vec2classify * P0vec) + log (1.0-PCLASS1) if p1 > P0: return 1 else: return 0def tes TINGNB (): listoposts,listclasses = Loaddataset () myvocablist = crea

Again, here's why the title is "using" instead of "Implementing": First, professionals provide algorithms that are higher than our own algorithms, whether efficient or accurate. Secondly, for those who are not good at maths, it is very painful to study a bunch of formulas in order to realize the algorithm. Again, there is no need to "reinvent the wheel" unless the algorithms provided by others meet their own needs. Below the point, do not understand the Bayesian algorithm can go to check the re

" | b= "No"), P (a1= "3" | b= "No"); P (a2= "1" | b= "No"), P (a2= "2" | b= "No"),... 2. The probability of classifying test data according to the Bayes theorem: calculation: P (b= "hard" |test_a), p (b= "soft" |test_a), p (b= "no" |test_a), the probability of the largest category, is the classification result of naive Bayesian classifier. Code Implementation Def train (dataset,labels): Uniquelabels =

Misc.forsale 0.91 0.70 0.79 257 the Rec.autos 0.89 0.89 0.89 238 - Rec.motorcycles 0.98 0.92 0.95 276 - Rec.sport.baseball 0.98 0.91 0.95 251 the Rec.sport.hockey 0.93 0.99 0.96 233 the Sci.crypt 0.86 0.98 0.91 238 the sci.electronics 0.85 0.88 0.86 249 the sci.med 0.92 0.94 0.93 245 - sci.space 0.89 0.96 0.92 221 the Soc.religion.christian 0.78 0.96 0.86 232 the talk.politics.guns 0.88 0.96 0.92 251 the talk.politics.mideast 0.90 0.98 0.94 23194 Talk.politics.misc 0.79 0.89 0.84 188 the Talk.r

=train_model (train_x,train_y,classnum)For I in Range (Classnum):Print (Prior_probability[i]) #输出一下每个标签的总共数量Time3=time.time ()Print ("Train data Cost", Time3-time2, "second")Print ("Start predicting data ...")Predict_y=predict (test_x,test_y,prior_probability,conditional_probability)Time4=time.time ()Print ("Predict data Cost", Time4-time3, "second")Print ("Start calculate accuracy ...")Acc=cal_accuracy (test_y,predict_y)Time5=time.time ()Print ("Accuarcy", ACC)Print ("Calculate Accuarcy cost",

In this paper, the Python implementation method of naive Bayesian algorithm is described. Share to everyone for your reference. The implementation method is as follows: Advantages and disadvantages of naive Bayesian algorithm Pros: Still effective with less data, can handle multiple categories of problems Cons: Sensitive to the way the input data is prepared App

# server.pyfrom socket import *HOST = ‘‘PORT = 50007s = socket(AF_INET, SOCK_STREAM)s.bind((HOST, PORT))s.listen(1)(conn, addr) = s.accept()while True: data = conn.recv(1024) if not data: break conn.send(data)conn.close()# client.pyfrom socket import *HOST = ‘‘PORT = 50007s = socket(AF_INET, SOCK_STREAM)s.connect((HOST, PORT))while True: line = input() if not line: break s.send(str.encode(line)) data = s.recv(1024) print(data)s.close()[Mess] [P