³ò 4ÒÇIc@s¤ddklZd„Zd„Zedƒd„ƒZd„Zd„Zd„Zd „Zd „Z d „Z d „Z d „Z d„Z edjo e ƒndS(iÿÿÿÿ(t deprecatedcCsg}x(t|ƒD]}|idg|ƒqWx"t|ƒD]}|||dWx"t|ƒD]}||d| "sain" -> "shin" -> "shine". These operations could have been done in other orders, but at least three steps are needed. @param s1, s2: The strings to be analysed @type s1: C{string} @type s2: C{string} @rtype C{int} i(tlenRRR(RRRRRRR((s+/p/zhu/06/nlp/nltk/nltk/metrics/distance.pyR!s  .cCs||jodSndSdS(s§Simple equality test. 0.0 if the labels are identical, 1.0 if they are different. >>> binary_distance(1,1) 0.0 >>> binary_distance(1,3) 1.0 ggð?N((tlabel1tlabel2((s+/p/zhu/06/nlp/nltk/nltk/metrics/distance.pytbinary_distance;s cCs?t|i|ƒƒt|i|ƒƒtt|i|ƒƒƒS(s/Distance metric comparing set-similarity. (Rtuniont intersectiontfloat(RR((s+/p/zhu/06/nlp/nltk/nltk/metrics/distance.pytjaccard_distanceMscCs<dtt|i|ƒƒƒttt|ƒt|ƒƒƒS(s Distance metric that takes into account partial agreement when multiple labels are assigned. >>> masi_distance(set([1,2]),set([1,2,3,4])) 0.5 Passonneau 2005, Measuring Agreement on Set-Valued Items (MASI) for Semantic and Pragmatic Annotation. i(RRRtmax(RR((s+/p/zhu/06/nlp/nltk/nltk/metrics/distance.pyt masi_distanceTs cCs(yt||dƒSWn dGHnXdS(s¡Krippendorff'1 interval distance metric >>> interval_distance(1,10) 81 Krippendorff 1980, Content Analysis: An Introduction to its Methodology is7non-numeric labels not supported with interval distanceN(tpow(RR((s+/p/zhu/06/nlp/nltk/nltk/metrics/distance.pytinterval_distanceascs ‡fd†S(s=Higher-order function to test presence of a given label csdˆ|jˆ|jjS(gð?((txty(tlabel(s+/p/zhu/06/nlp/nltk/nltk/metrics/distance.pytts((R"((R"s+/p/zhu/06/nlp/nltk/nltk/metrics/distance.pytpresencepscs ‡fd†S(NcsÅttdt|ƒƒtdt|ƒƒƒˆ|jo ˆ|jp}dˆ|jo ˆ|jp_ttdt|ƒƒƒˆ|jo ˆ|jp+tdt|ƒƒˆ|jo ˆ|jS(gð?g(tabsRR(R R!(R"(s+/p/zhu/06/nlp/nltk/nltk/metrics/distance.pyR#xs((R"((R"s+/p/zhu/06/nlp/nltk/nltk/metrics/distance.pytfractional_presencewscs‚h‰xlt|ƒD]^}|iƒidƒ\}}}t|gƒ}t|gƒ}t|ƒˆt||gƒ s