³ò ’B_Kc@sedZddklZddkTddkTdefd„ƒYZd„Zedjo eƒndS( s A classifier based on the Naive Bayes algorithm. In order to find the probability for a label, this algorithm first uses the Bayes rule to express P(label|features) in terms of P(label) and P(features|label):: P(label) * P(features|label) P(label|features) = ------------------------------ P(features) The algorithm then makes the 'naive' assumption that all features are independent, given the label:: P(label) * P(f1|label) * ... * P(fn|label) P(label|features) = -------------------------------------------- P(features) Rather than computing P(featues) explicitly, the algorithm just calculates the denominator for each label, and normalizes them so they sum to one:: P(label) * P(f1|label) * ... * P(fn|label) P(label|features) = -------------------------------------------- SUM[l]( P(l) * P(f1|l) * ... * P(fn|l) ) iÿÿÿÿ(t defaultdict(t*tNaiveBayesClassifiercBs\eZdZd„Zd„Zd„Zd„Zdd„Zdd„Ze e d „ƒZ RS( s  A Naive Bayes classifier. Naive Bayes classifiers are paramaterized by two probability distributions: - P(label) gives the probability that an input will receive each label, given no information about the input's features. - P(fname=fval|label) gives the probability that a given feature (fname) will receive a given value (fval), given that the label (label). If the classifier encounters an input with a feature that has never been seen with any label, then rather than assigning a probability of 0 to all labels, it will ignore that feature. The feature value 'None' is reserved for unseen feature values; you generally should not use 'None' as a feature value for one of your own features. cCs%||_||_|iƒ|_dS(s5 @param label_probdist: P(label), the probability distribution over labels. It is expressed as a L{ProbDistI} whose samples are labels. I.e., P(label) = C{label_probdist.prob(label)}. @param feature_probdist: P(fname=fval|label), the probability distribution for feature values, given labels. It is expressed as a dictionary whose keys are C{(label,fname)} pairs and whose values are L{ProbDistI}s over feature values. I.e., P(fname=fval|label) = C{feature_probdist[label,fname].prob(fval)}. If a given C{(label,fname)} is not a key in C{feature_probdist}, then it is assumed that the corresponding P(fname=fval|label) is 0 for all values of C{fval}. N(t_label_probdistt_feature_probdisttsamplest_labels(tselftlabel_probdisttfeature_probdist((s./p/zhu/06/nlp/nltk/nltk/classify/naivebayes.pyt__init__@s  cCs|iS(N(R(R((s./p/zhu/06/nlp/nltk/nltk/classify/naivebayes.pytlabelsUscCs|i|ƒiƒS(N(t prob_classifytmax(Rt featureset((s./p/zhu/06/nlp/nltk/nltk/classify/naivebayes.pytclassifyXsc Cs,|iƒ}xJ|iƒD]<}x3|iD]!}||f|ijoPq)q)W||=qWh}x'|iD]}|ii|ƒ|| scSsdS(gð?((((s./p/zhu/06/nlp/nltk/nltk/classify/naivebayes.pyR,¡siR!csˆ|ˆ|S(((tfeature(tmaxprobtminprob(s./p/zhu/06/nlp/nltk/nltk/classify/naivebayes.pyR,°s( tsetRRRRtaddRR tmintdiscardR$( RR&tfeaturesRRtprobdistRR-tp((R.R/s./p/zhu/06/nlp/nltk/nltk/classify/naivebayes.pyR#’s"     c Cstƒ}ttƒ}ttƒ}tƒ}xs|D]k\}}|i|ƒxO|iƒD]A\}} |||fi| ƒ||i| ƒ|i|ƒqWWq1Wxn|D]f}||} xS|D]K}|||fiƒ} |||fit| | ƒ||itƒq¾Wq§W||ƒ} h} xL|iƒD]>\\}}}||dt||ƒƒ}|| ||f!s  ¹