³ò 4ÒÇIc@sLdZddklZlZdefd„ƒYZdefd„ƒYZdS(s Interfaces for labeling tokens with category labels (or X{class labels}). L{ClassifierI} is a standard interface for X{single-category classification}, in which: - The set of categories is known. - The number of categories is finite. - Each text belongs to exactly one category. L{MultiClassifierI} is a standard interface for C{multi-category classification}, in which: - The set of categories is known. - The number of categories is finite. - Each text belongs to zero or more categories. iÿÿÿÿ(t deprecatedt overriddent ClassifierIcBseeZdZd„Zd„Zd„Zd„Zd„Zedƒd„ƒZ edƒd „ƒZ RS( sã A processing interface for labeling tokens with a single category label (or X{class}). Labels are typically C{string}s or C{integer}s, but can be any immutable type. The set of labels that the classifier chooses from must be fixed and finite. Subclasses must define: - L{labels()} - either L{classify()} or L{batch_classify()} (or both) Subclasses may define: - either L{prob_classify()} or L{batch_prob_classify()} (or both) cCs tƒ‚dS(sv @return: the list of category labels used by this classifier. @rtype: C{list} of (immutable) N(tNotImplementedError(tself((s'/p/zhu/06/nlp/nltk/nltk/classify/api.pytlabels/scCs5t|iƒo|i|gƒdSn tƒ‚dS(se @return: the most appropriate label for the given featureset. @rtype: label iN(Rtbatch_classifyR(Rt featureset((s'/p/zhu/06/nlp/nltk/nltk/classify/api.pytclassify6scCs5t|iƒo|i|gƒdSn tƒ‚dS(s¡ @return: a probability distribution over labels for the given featureset. @rtype: L{ProbDistI } iN(Rtbatch_prob_classifyR(RR((s'/p/zhu/06/nlp/nltk/nltk/classify/api.pyt prob_classify@scCs(g}|D]}||i|ƒq ~S(s» Apply L{self.classify()} to each element of C{featuresets}. I.e.: >>> return [self.classify(fs) for fs in featuresets] @rtype: C{list} of I{label} (R(Rt featuresetst_[1]tfs((s'/p/zhu/06/nlp/nltk/nltk/classify/api.pyRKscCs(g}|D]}||i|ƒq ~S(sæ Apply L{self.prob_classify()} to each element of C{featuresets}. I.e.: >>> return [self.prob_classify(fs) for fs in featuresets] @rtype: C{list} of L{ProbDistI } (R (RR R R ((s'/p/zhu/06/nlp/nltk/nltk/classify/api.pyR Uss#Use .batch_prob_classify() instead.cCs |i|ƒS(N(R (RR ((s'/p/zhu/06/nlp/nltk/nltk/classify/api.pytbatch_probdist`ssUse .prob_classify() instead.cCs |i|ƒS(N(R (RR((s'/p/zhu/06/nlp/nltk/nltk/classify/api.pytprobdistcs( t__name__t __module__t__doc__RRR RR RRR(((s'/p/zhu/06/nlp/nltk/nltk/classify/api.pyR!s   tMultiClassifierIcBseeZdZd„Zd„Zd„Zd„Zd„Zedƒd„ƒZ edƒd „ƒZ RS( sï A processing interface for labeling tokens with zero or more category labels (or X{labels}). Labels are typically C{string}s or C{integer}s, but can be any immutable type. The set of labels that the multi-classifier chooses from must be fixed and finite. Subclasses must define: - L{labels()} - either L{classify()} or L{batch_classify()} (or both) Subclasses may define: - either L{prob_classify()} or L{batch_prob_classify()} (or both) cCs tƒ‚dS(sv @return: the list of category labels used by this classifier. @rtype: C{list} of (immutable) N(R(R((s'/p/zhu/06/nlp/nltk/nltk/classify/api.pyRvscCs5t|iƒo|i|gƒdSn tƒ‚dS(sz @return: the most appropriate set of labels for the given featureset. @rtype: C{set} of I{label} iN(RRR(RR((s'/p/zhu/06/nlp/nltk/nltk/classify/api.pyR}scCs5t|iƒo|i|gƒdSn tƒ‚dS(s© @return: a probability distribution over sets of labels for the given featureset. @rtype: L{ProbDistI } iN(RR R(RR((s'/p/zhu/06/nlp/nltk/nltk/classify/api.pyR ‡scCs(g}|D]}||i|ƒq ~S(sÓ Apply L{self.classify()} to each element of C{featuresets}. I.e.: >>> return [self.classify(fs) for fs in featuresets] @rtype: C{list} of (C{set} of I{label}) (R(RR R R ((s'/p/zhu/06/nlp/nltk/nltk/classify/api.pyR’scCs(g}|D]}||i|ƒq ~S(sò Apply L{self.prob_classify()} to each element of C{featuresets}. I.e.: >>> return [self.prob_classify(fs) for fs in featuresets] @rtype: C{list} of L{ProbDistI } (R (RR R R ((s'/p/zhu/06/nlp/nltk/nltk/classify/api.pyR œss#Use .batch_prob_classify() instead.cCs |i|ƒS(N(R (RR ((s'/p/zhu/06/nlp/nltk/nltk/classify/api.pyR§ssUse .prob_classify() instead.cCs |i|ƒS(N(R (RR((s'/p/zhu/06/nlp/nltk/nltk/classify/api.pyRªs( RRRRRR RR RRR(((s'/p/zhu/06/nlp/nltk/nltk/classify/api.pyRhs   N(Rtnltk.internalsRRtobjectRR(((s'/p/zhu/06/nlp/nltk/nltk/classify/api.pyssG