Of &SSKrSSKJr SSKJr SSKJr SSKrSSK J r "SS\ 5r Sr S r "S S 5r"S S 5rg!\a N4f=f)N)abstractmethod)sqrt)stdout)ClusterIcj\rSrSrSrSSjrSSjr\S5rSr \S5r S r S r S r S rS rg)VectorSpaceClustererz Abstract clusterer which takes tokens and maps them into a vector space. Optionally performs singular value decomposition to reduce the dimensionality. Nc*SUlXlX lg)z :param normalise: should vectors be normalised to length 1 :type normalise: boolean :param svd_dimensions: number of dimensions to use in reducing vector dimensionsionality with SVD :type svd_dimensions: int N_Tt_should_normalise_svd_dimensions)self normalisesvd_dimensionss 3/usr/lib/python3/dist-packages/nltk/cluster/util.py__init__VectorSpaceClusterer.__init__s!*-cN[U5S:deUR(a[[URU55nUR (GaUR [US5:a[ RR[ R"[ R"U555upEnUSUR [ R"UR [ R5-nUSS2SUR 24nUSUR 2SS24n [ R"[ R"Xy55n[ R"U5UlURX5 U(a!UV s/sHoR!U 5PM sn $gs sn fNr)lenr listmap _normalisernumpylinalgsvd transposearrayidentityfloat64dotr cluster_vectorspaceclassify) rvectorsassign_clusterstraceudvtSTDtvectors rclusterVectorSpaceClusterer.cluster(sK7|a  ! !3t89G    D$8$83wqz?$J))%//%++g:N*OPJQ2(D(()ENN$$emm-A!+t++++,A*d***A-.Booeii&67Gq)DH   0 8?@fMM&)@ @ @sF"cg)z4 Finds the clusters using the given set of vectors. N)rr&r(s rr$(VectorSpaceClusterer.cluster_vectorspaceArcUR(aURU5nURb![R"URU5nUR U5nUR U5$N)r rr rr#classify_vectorspace cluster_name)rr/r0s rr%VectorSpaceClusterer.classifyGsX  ! !__V,F 88 YYtxx0F++F3  ))rcg)z> Returns the index of the appropriate cluster for the vector. Nr3rr/s rr8)VectorSpaceClusterer.classify_vectorspaceOr5rcUR(aURU5nURb![R"URU5nUR X5$r7)r rr rr#likelihood_vectorspace)rr/labels r likelihoodVectorSpaceClusterer.likelihoodUsH  ! !__V,F 88 YYtxx0F**699rc6URU5nX#:XaS$S$)z@ Returns the likelihood of the vector belonging to the cluster. g?g)r8)rr/r0 predicteds rr?+VectorSpaceClusterer.likelihood_vectorspace\s$--f5 *s33rcUR(aURU5nURb![R"URU5nU$)zE Returns the vector after normalisation and dimensionality reduction )r rr rr#r<s rr/VectorSpaceClusterer.vectorcs>  ! !__V,F 88 YYtxx0F rcFU[[R"X55- $)z' Normalises the vector to unit length. rrr#r<s rrVectorSpaceClusterer._normalisemsUYYv6777rr )FN)FF)__name__ __module__ __qualname____firstlineno____doc__rr0rr$r%r8rAr?r/r__static_attributes__r3rrrrsS .A2  *  :48rrcHX- n[[R"X"55$)zq Returns the euclidean distance between vectors u and v. This is equivalent to the length of the vector (u - v). rI)r)vdiffs reuclidean_distancerTts 5D  $% &&rc S[R"X5[[R"X55[[R"X55-- - $)zk Returns 1 minus the cosine of the angle between vectors v and u. This is equal to ``1 - (u.v / |u||v|)``. )rr#r)r)rRs rcosine_distancerW}s;  !4 !#84 !;P#PQ RRrc4\rSrSrSrSrS SjrSrSrSr g) _DendrogramNodezTree node of a dendrogram.cXlX lgr7)_value _children)rvaluechildrens rr_DendrogramNode.__init__s  !rcUR(a7/nURH#nURURU55 M% U$U(a UR/$U/$r7)r]extendleavesr\)rvaluesrcchilds rrc_DendrogramNode.leavessL >>F ell623(M KK= 6MrcURU4/n[U5U:aUR5up4UR(dUR X445 OwURHFnUR(aUR URU45 M3UR SU45 MH UR 5 [U5U:aM/nUH$up4UR UR55 M& U$r)r\rpopr]pushappendsortrc)rnqueueprioritynoderegroupss rrp_DendrogramNode.groupss++t$%%j1n"YY[NH>> H+,??LL%,,!67LL!U, ( JJL%j1n#NH MM$++- ($ rcH[URUR5S:$r)rWr\)r comparators r__lt___DendrogramNode.__lt__st{{J,=,=>BBr)r]r\N)T) rKrLrMrNrOrrcrprtrPr3rrrYrYs$" *CrrYcB\rSrSrSr/4SjrSrSr/4SjrSr Sr g ) Dendrogramz Represents a dendrogram, a tree with a specified branching order. This must be initialised with the leaf items, then iteratively call merge for each branch. This class constructs a tree representing the order of calls to the merge function. cUVs/sHn[U5PM snUl[R"UR5UlSUlgs snf)z[ :param items: the items at the leaves of the dendrogram :type items: sequence of (any) rVN)rY_itemscopy_original_items_merge)ritemsitems rrDendrogram.__init__sA :??t,? #yy5 @sAc^[U5S:de[TR/U4SjU5Q76nT=RS- slUTRUS'USSHnTRU M g)a  Merges nodes at given indices in the dendrogram. The nodes will be combined which then replaces the first node specified. All other nodes involved in the merge will be removed. :param indices: indices of the items to merge (at least two) :type indices: seq of int c3B># UHnTRUv M g7fr7)rz).0irs r #Dendrogram.merge..s-Ngdkk!ngsrVrN)rrYr}rz)rindicesrors` rmergeDendrogram.mergesk7|q   t{{O-Ng-NO q "& GAJA Arc[UR5S:a![UR/URQ76nOURSnUR U5$)zr Finds the n-groups of items (leaves) reachable from a cut at depth n. :param n: number of groups :type n: int rVr)rrzrYr}rp)rrlroots rrpDendrogram.groupssG t{{ a "4;;==D;;q>D{{1~rcX^^^Sup#n[UR5S:a![UR/URQ76nOURSnURnU(aUnOUVs/sHnSUR -PM nn[ [[U55S-mTS-m[TT- S- 5mSUU4Sjjn Sn UR U4/n UVs/sH o"S5PM n nU (GaU R5up[[S UR55n[[URU55nU(a[U5n[ U5n[[U55HnUUU;aIUW:XaU "U "USU55 O%UW:XaU "U "X#S55 OU "U "X#U55 U "U5U U'MUWUs=::aW::aO OU "U "X3U55 MvU "U U5 M U "S 5 URH3nUR(dMU RUR U45 M5 U R!5 U H nU "U5 M U "S 5 U (aGMU "S R#U4S jU555 U "S 5 g s snfs snf)z Print the dendrogram in ASCII art to standard out. :param leaf_labels: an optional list of strings to use for labeling the leaves :type leaf_labels: list )+-|rVrz%sr c >TU-UUT-3$r7r3)centreleftrightlhalfrhalfs rformatDendrogram.show..formatsdl^F8EEM?; ;rc0[R"U5 gr7)rwrite)strs rdisplay Dendrogram.show..displays LL rc*URS5S$)NFr)rc)cs r!Dendrogram.show..s%1Cr c3D># UHoRT5v M g7fr7)center)rrwidths rr"Dendrogram.show..#s@xt E**xs N)rr)rrzrYr}r|r\maxrintrhrr]indexminrangerjrkjoin)r leaf_labelsJOINHLINKVLINKrrclast_rowleafrrrm verticalsrnrochild_left_leafrmin_idxmax_idxrreverticalrrrs @@@rshowDendrogram.showsP+U t{{ a "4;;==D;;q>D%% "H7=>vtt{{*vH>CX&'!+ EEMA%& < < ++t$%-34VTVC[V 4"YY[NH"3'CT^^#TUO3v||_=>Gg,g,3v;'!9/G|tS% 89gtC 89tE :;#)%=IaL,W,F578IaL)( DM???LL%,,!67( JJL%!& DM9e> @x@@A c? 5s ,J"J'c[UR5S:a![UR/URQ76nOURSnUR S5nS[U5-$)NrVrFz)rrzrYr}rc)rrrcs r__repr__Dendrogram.__repr__&sT t{{ a "4;;==D;;q>DU#,s6{::r)rzr}r|N) rKrLrMrNrOrrrprrrPr3rrrwrws,   "GR;rrw)r{abcrmathrsysrr ImportErrornltk.cluster.apirrrTrWrYrwr3rrrsh  &\88\8~'S(C(CV|;|;C  sAAA