FILEMAIL ENGLISH MANUAL
FILEMAIL ENGLISH CODE

P0Vect = p0Num/p0Denom # utilize Numpy Array calculation p(wi/c0), To avoid underflow, It will be changed to log()Д«ecause when p0Num It will be reported in time RuntimeWarning: invalid value encountered in true_divide, This is from 0/0 Lead to, So it's setting p0Denom Cannot be set to 0.ДЇirst, Calculation documents are insulting documents пј€class=1пј‰ Probability, namely Pпј€1пј‰.Pпј€0пј‰ May by 1-Pпј€1пј‰ obtain. P1Vect = p1Num/p1Denom # utilize Numpy Array calculation p(wi/c1), Namely class 1 The probability of the occurrence of each entry under the condition P0Num = zeros(numWords) p1Num = zeros(numWords)# Create two lists of the same length as the entry vector, Smoothing пјљ The initial value is set to 1 PAbusive = sum(trainCategory)/float(numWords) Naive Bayes Classifier training function пјљ numTrainDocs = len(trainMatrix)# Text matrix

FILEMAIL ENGLISH CODE

The pseudo code for this function is as follows пјљДЄccording to the basic theory of the former part, we first get P(w|ci), Calculate again Pпј€ciпј‰. Two гЂЃ Training algorithm пјљ Calculate the probability from the word vector Print(setOfWords2Vec(myVocabList,listOPosts)) Д¬an check whether the function is working properly пјљ myVocabList=createVocabList(listOPosts) The first function creates some experimental samples, The second function creates a non repeating list of all documents that appear, The third function input parameter is the vocabulary and a document, The output is the document vector, Each element of a vector is 1 or 0, Indicates whether the words in the vocabulary appear in the input document. Print('the word:%s is not in my Vocabulary!'' % word') ReturnVec = *len(vocaList)# Create a file in which all elements are 0 Instead of text # Process the sample output in vector form VocabSet = vocabSet | set(document) # Create a union of two sets Cross out repeated words

FILEMAIL ENGLISH MANUAL

Return postingList,classVec # The second variable returned is a manual tag used to distinguish insulting and non insulting tags. ]Д¬lassVec = #1 For insulting words ,0 For normal speech

,# This document is a message board for spotted dog lovers PostingList =, # Participle available wordcloud One гЂЃ Prepare the dataД¬reate a bayes.py Program, Building word vectors from text, Realize the conversion function from thesaurus to vector. This article has participated in гЂЊ New people's creation ceremony гЂЌ Activities, Start the road of nuggets creation together.

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