ó ù`]c@ shddlmZmZmZddlmZmZddlmZeeƒde fd„ƒYƒZ dS(iÿÿÿÿ(tdivisiontprint_functiontunicode_literals(tABCMetatabstractmethod(t add_metaclasstFeaturecB s¶eZdZdZdZdd„Zd„Zed„ƒZ d„Z ee d„ƒZ d„Z d„Zd „Zd „Zd „Zd „Zd „Zd„Zeed„ƒƒZRS(u An abstract base class for Features. A Feature is a combination of a specific property-computing method and a list of relative positions to apply that method to. The property-computing method, M{extract_property(tokens, index)}, must be implemented by every subclass. It extracts or computes a specific property for the token at the current index. Typical extract_property() methods return features such as the token text or tag; but more involved methods may consider the entire sequence M{tokens} and for instance compute the length of the sentence the token belongs to. In addition, the subclass may have a PROPERTY_NAME, which is how it will be printed (in Rules and Templates, etc). If not given, defaults to the classname. unltk.tbl.FeaturecC s½d|_|dkr=tttd„|Dƒƒƒƒ|_nay5||krUt‚ntt||dƒƒ|_Wn)tk rtdj||ƒƒ‚nX|j j p³|j j |_ dS(uO Construct a Feature which may apply at C{positions}. #For instance, importing some concrete subclasses (Feature is abstract) >>> from nltk.tag.brill import Word, Pos #Feature Word, applying at one of [-2, -1] >>> Word([-2,-1]) Word([-2, -1]) #Positions need not be contiguous >>> Word([-2,-1, 1]) Word([-2, -1, 1]) #Contiguous ranges can alternatively be specified giving the #two endpoints (inclusive) >>> Pos(-3, -1) Pos([-3, -2, -1]) #In two-arg form, start <= end is enforced >>> Pos(2, 1) Traceback (most recent call last): File "", line 1, in File "nltk/tbl/template.py", line 306, in __init__ raise TypeError ValueError: illegal interval specification: (start=2, end=1) :type positions: list of int :param positions: the positions at which this features should apply :raises ValueError: illegal position specifications An alternative calling convention, for contiguous positions only, is Feature(start, end): :type start: int :param start: start of range where this feature should apply :type end: int :param end: end of range (NOTE: inclusive!) where this feature should apply cs s|]}t|ƒVqdS(N(tint(t.0ti((s/lib/python2.7/site-packages/nltk/tbl/feature.pys Rsiu4illegal interval specification: (start={0}, end={1})N( tNonet positionsttupletsortedtsett TypeErrortranget ValueErrortformatt __class__t PROPERTY_NAMEt__name__(tselfR tend((s/lib/python2.7/site-packages/nltk/tbl/feature.pyt__init__'s)  (    cC s|jS(N(R (R((s/lib/python2.7/site-packages/nltk/tbl/feature.pytencode_json_objcscC s|}||ƒS(N((tclstobjR ((s/lib/python2.7/site-packages/nltk/tbl/feature.pytdecode_json_objfscC sd|jjt|jƒfS(Nu%s(%r)(RRtlistR (R((s/lib/python2.7/site-packages/nltk/tbl/feature.pyt__repr__ksc sstd„|Dƒƒs.tdj|ƒƒ‚n‡fd†|Dƒ}g|D]$}|o`d|ksK||ƒ^qKS(u¸ Return a list of features, one for each start point in starts and for each window length in winlen. If excludezero is True, no Features containing 0 in its positions will be generated (many tbl trainers have a special representation for the target feature at [0]) For instance, importing a concrete subclass (Feature is abstract) >>> from nltk.tag.brill import Word First argument gives the possible start positions, second the possible window lengths >>> Word.expand([-3,-2,-1], [1]) [Word([-3]), Word([-2]), Word([-1])] >>> Word.expand([-2,-1], [1]) [Word([-2]), Word([-1])] >>> Word.expand([-3,-2,-1], [1,2]) [Word([-3]), Word([-2]), Word([-1]), Word([-3, -2]), Word([-2, -1])] >>> Word.expand([-2,-1], [1]) [Word([-2]), Word([-1])] a third optional argument excludes all Features whose positions contain zero >>> Word.expand([-2,-1,0], [1,2], excludezero=False) [Word([-2]), Word([-1]), Word([0]), Word([-2, -1]), Word([-1, 0])] >>> Word.expand([-2,-1,0], [1,2], excludezero=True) [Word([-2]), Word([-1]), Word([-2, -1])] All window lengths must be positive >>> Word.expand([-2,-1], [0]) Traceback (most recent call last): File "", line 1, in File "nltk/tag/tbl/template.py", line 371, in expand :param starts: where to start looking for Feature ValueError: non-positive window length in [0] :param starts: where to start looking for Feature :type starts: list of ints :param winlens: window lengths where to look for Feature :type starts: list of ints :param excludezero: do not output any Feature with 0 in any of its positions. :type excludezero: bool :returns: list of Features :raises ValueError: for non-positive window lengths cs s|]}|dkVqdS(iN((Rtx((s/lib/python2.7/site-packages/nltk/tbl/feature.pys  su!non-positive window length in {0}c3 sA|]7}ttˆƒ|dƒD]}ˆ|||!Vq!qdS(iN(Rtlen(RtwR (tstarts(s/lib/python2.7/site-packages/nltk/tbl/feature.pys ¢si(tallRR(RR"twinlenst excludezerotxsR((R"s/lib/python2.7/site-packages/nltk/tbl/feature.pytexpandns2cC s.|j|jko-t|jƒt|jƒkS(uQ Return True if this Feature always returns True when other does More precisely, return True if this feature refers to the same property as other; and this Feature looks at all positions that other does (and possibly other positions in addition). #For instance, importing a concrete subclass (Feature is abstract) >>> from nltk.tag.brill import Word, Pos >>> Word([-3,-2,-1]).issuperset(Word([-3,-2])) True >>> Word([-3,-2,-1]).issuperset(Word([-3,-2, 0])) False #Feature subclasses must agree >>> Word([-3,-2,-1]).issuperset(Pos([-3,-2])) False :param other: feature with which to compare :type other: (subclass of) Feature :return: True if this feature is superset, otherwise False :rtype: bool (RRR (Rtother((s/lib/python2.7/site-packages/nltk/tbl/feature.pyt issuperset¥s!cC s2t|j|jko.t|jƒt|jƒ@ƒS(u Return True if the positions of this Feature intersects with those of other More precisely, return True if this feature refers to the same property as other; and there is some overlap in the positions they look at. #For instance, importing a concrete subclass (Feature is abstract) >>> from nltk.tag.brill import Word, Pos >>> Word([-3,-2,-1]).intersects(Word([-3,-2])) True >>> Word([-3,-2,-1]).intersects(Word([-3,-2, 0])) True >>> Word([-3,-2,-1]).intersects(Word([0])) False #Feature subclasses must agree >>> Word([-3,-2,-1]).intersects(Pos([-3,-2])) False :param other: feature with which to compare :type other: (subclass of) Feature :return: True if feature classes agree and there is some overlap in the positions they look at :rtype: bool (tboolRRR (RR(((s/lib/python2.7/site-packages/nltk/tbl/feature.pyt intersectsÅscC s"|j|jko!|j|jkS(N(RR (RR(((s/lib/python2.7/site-packages/nltk/tbl/feature.pyt__eq__ëscC s(|jj|jjkp'|j|jkS(N(RRR (RR(((s/lib/python2.7/site-packages/nltk/tbl/feature.pyt__lt__îscC s ||k S(N((RR(((s/lib/python2.7/site-packages/nltk/tbl/feature.pyt__ne__öscC s ||kS(N((RR(((s/lib/python2.7/site-packages/nltk/tbl/feature.pyt__gt__ùscC s ||k S(N((RR(((s/lib/python2.7/site-packages/nltk/tbl/feature.pyt__ge__üscC s||kp||kS(N((RR(((s/lib/python2.7/site-packages/nltk/tbl/feature.pyt__le__ÿscC sdS(u@ Any subclass of Feature must define static method extract_property(tokens, index) :param tokens: the sequence of tokens :type tokens: list of tokens :param index: the current index :type index: int :return: feature value :rtype: any (but usually scalar) N((ttokenstindex((s/lib/python2.7/site-packages/nltk/tbl/feature.pytextract_propertytN(Rt __module__t__doc__tjson_tagR RRRt classmethodRRtFalseR'R)R+R,R-R.R/R0R1t staticmethodRR4(((s/lib/python2.7/site-packages/nltk/tbl/feature.pyRs& <  6 &      N( t __future__RRRtabcRRtsixRtobjectR(((s/lib/python2.7/site-packages/nltk/tbl/feature.pyt s