ó ů`]c@ s;ddlmZd„Zd„Zd„Zdd„ZdS(i˙˙˙˙(tdivisioncc s\t|ƒ}t|ƒ}x=|D]5}y|||||fVWqtk rSqXqWdS(s¸Finds the difference between the values in ranks1 and ranks2 for keys present in both dicts. If the arguments are not dicts, they are converted from (key, rank) sequences. N(tdicttKeyError(tranks1tranks2tk((s4lib/python2.7/site-packages/nltk/metrics/spearman.pyt _rank_distss    cC s}d}d}x5t||ƒD]$\}}|||7}|d7}qWy dd||||dSWntk rxdSXdS(sUReturns the Spearman correlation coefficient for two rankings, which should be dicts or sequences of (key, rank). The coefficient ranges from -1.0 (ranks are opposite) to 1.0 (ranks are identical), and is only calculated for keys in both rankings (for meaningful results, remove keys present in only one list before ranking).iiigN(RtZeroDivisionError(RRtntresRtd((s4lib/python2.7/site-packages/nltk/metrics/spearman.pytspearman_correlations  cC sd„t|ƒDƒS(s„Given a sequence, yields each element with an increasing rank, suitable for use as an argument to ``spearman_correlation``. cs s!|]\}}||fVqdS(N((t.0tiR((s4lib/python2.7/site-packages/nltk/metrics/spearman.pys 2s(t enumerate(tseq((s4lib/python2.7/site-packages/nltk/metrics/spearman.pytranks_from_sequence.sgV瞯Ňs