Vh{ddZddlZddlZddlmZmZdZGddZGddeZ Gd d eZ Gd d e Z Gd de Z GddZ GddZdZej j"dZdZej j"dZdZdZdZGddZy)z>Unit tests for the :mod:`networkx.algorithms.tree.mst` module.N) edges_equal nodes_equalcdtjt5tjtj ddddtjtd5tj tj ddddy#1swYWxYw#1swYyxYw)Nrandom algorithmz.random is not a valid choice for an algorithm.)match)pytestraises ValueErrornxminimum_spanning_treeGraphmaximum_spanning_edgesW/home/dcms/DCMS/lib/python3.12/site-packages/networkx/algorithms/tree/tests/test_mst.pytest_unknown_algorithmr s z "A   x@A J B !!"((*ABBAABBs*B'*B&B#&B/cdeZdZdZdZdZdZdZdZdZ dZ d Z d Z d Z d Zd ZdZdZy)MinimumSpanningTreeTestBaseaBase class for test classes for minimum spanning tree algorithms. This class contains some common tests that will be inherited by subclasses. Each subclass must have a class attribute :data:`algorithm` that is a string representing the algorithm to run, as described under the ``algorithm`` keyword argument for the :func:`networkx.minimum_spanning_edges` function. Subclasses can then implement any algorithm-specific tests. c B|j|_gd}tj|_|jj |ddddifddddifddddifd dddifdddd ifdd dd ifg|_ddddifdd dd ifdddd ifdddd ifdd dd ifdd ddifg|_y)zjCreates an example graph and stores the expected minimum and maximum spanning tree edges. )r)rr)rr )rr)rr!r)rr!)rr)r!rr)r!r#r )rr# rrweightrrrr!rr#r rr"r$N)ralgor rGadd_weighted_edges_fromminimum_spanning_edgelistmaximum_spanning_edgelist)selfmethodedgess r setup_methodz(MinimumSpanningTreeTestBase.setup_methods NN    &&u- Ha= ! Ha= ! Ha= ! Ha= ! Ha= ! Ha= ! * &Ha= ! Ha= ! Ha= ! Hb> " Ha= ! 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 $ $Q$))J O6!9d58n5556!''),vv.>???rN)__name__ __module__ __qualname____doc__r.rArErNrYr]rcrgrmrqrwr{rrrrrrrsS& PCC-* *2*2C C > ( . @rrceZdZdZdZdZy) TestBoruvkaubUnit tests for computing a minimum (or maximum) spanning tree using Borůvka's algorithm. boruvkactj|jd}td|D}t ||j sJy)u_Tests that using a Unicode string can correctly indicate Borůvka's algorithm. uborůvkarc3VK|]!\}}}t||t|||f#ywr1r2r5s rr:z0TestBoruvka.test_unicode_name..r;r<N)r r=r'r>rr)r?s rtest_unicode_namezTestBoruvka.test_unicode_namesB))$&&JGGGG64#A#ABBBrN)rrrrrrrrrrrsICrrceZdZdZdZy)MultigraphMSTTestBasectj}|jdddd|jddddtj}|||jd}t d gt |sJy ) z[Tests that the minimum spanning edges of a multigraph preserves edge keys. rrarkeyr%bFrG)rrrNr r\rVr=r&rrX)r+r' min_edges mst_edgess rtest_multigraph_keys_minz.MultigraphMSTTestBase.test_multigraph_keys_minj MMO 1aS + 1aS +-- a4995A K=$y/:::rctj}|jdddd|jddddtj}|||jd}t d gt |sJy ) z[Tests that the maximum spanning edges of a multigraph preserves edge keys. rrrrrrFrG)rrrN)r r\rVrr&rrX)r+r' max_edgesrs rtest_multigraph_keys_maxz.MultigraphMSTTestBase.test_multigraph_keys_maxrrN)rrrrrrrrrrs  ; ;rrceZdZdZdZdZy) TestKruskalzaUnit tests for computing a minimum (or maximum) spanning tree using Kruskal's algorithm. kruskalctj}|jdddd|jdddd|jdddd|jddddtj||jdd}t d d gt |sJtj||jdd}t ddd difddd difgt |sJtj||jdd}t d d gt |sJtj||jdd}t dddd difdddd difgt |sJy)zTests that the keys and data values are included in MST edges based on whether keys and data parameters are true or falserrrrr!TF)rkeysrH)rrr)rrrr%rrtNr)r+r'rs rtest_key_data_boolzTestKruskal.test_key_data_bools MMO 1aQq ) 1aQq ) 1aQq ) 1aQq )-- E Iy14 ?CCC-- T XqM "QHa=$9 :DO   -- U FF+T)_===-- D A!} %1a(A'? 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UA66''*Dzz$##**6$**$T*2R+ST Urc d}d}tj|jD]Y}t|j dd}t |Dcgc] }|dd c}}||j vsJ||k\sJ|dz }[ycc}w)rrTrrr%rNr rr'r>r-sumrr+r last_weightrr@rr%s r.test_minimum_spanning_tree_iterator_multigraphzQTestSpanningTreeMultiGraphIterator.test_minimum_spanning_tree_iterator_multigraphs  ++DFF3 DDJJDtJ<=F&9Q!A$x.9:FT000 00[( (( !OJ  9s B cd}d}tj|jdD]Y}t|j dd}t |Dcgc] }|dd c}}||j vsJ||ksJ|d z}[y cc}w) r2FrTrrr%rNrrs r.test_maximum_spanning_tree_iterator_multigraphzQTestSpanningTreeMultiGraphIterator.test_maximum_spanning_tree_iterator_multigraphs  ++DFFEB DDJJDtJ<=F&9Q!A$x.9:FT000 00[( (( !OJ  9sB N)rrrrr.rrrrrrrsU:  rrc ddlm}tjdddddddddd d }t j }|D]$\}}|j ||||||f &gd }t j }|j|t j|d d}tjj|j|jsJy)z@ Using a fixed seed, sample one tree for repeatability. rexpscipyw-!lU??5^I ҿDJտ燧W2￩ rsrrrr)rr!rt)rrrr!)r!r lambda_key)rtrr!rr)r!rr*seedN) mathrr importorskipr rrVadd_edges_fromrandom_spanning_treeutilsrr-)rgammar'r7r8solution_edgessolution sampled_trees r.test_random_spanning_tree_multiplicative_smallrs   E  A81 1aCq!f $6 78>NxxzH N+**1lDL 88   0B0B CC Crc ddlm}ddlm}t j dt j d}ddddddd d d d }t j}|D]$\}}|j||||||f &d}i}t j|D].} d} | jdD] \}}} | | z} | || <|| z }0t|dk(sJd} i} |D]} || |z | z|| <d| | <|d}t| D]{}t j|d|}t j|sJ|D]E} t jj!| j|js8| | xxdz cc<{}|j#t%| j't%|j'\}}|dkrJy)zJ Sample many trees from the distribution created in the last test rrRandomnumpy scipy.statsrrrrrrrrrrrjKi%r皙?N)rrrrr rr rrVrr-lenrurrrr chisquarerXvalues)rrstatsrr'r7r8 total_weight tree_expectedtr%r9 sample_size tree_actualrng_rps r.test_random_spanning_tree_multiplicative_larger*s       .E E  A81 1aCq!f $6 78LM  $ $Q 'wwLw1 GAq! aKF ! a  }  ## #KK )!,|;{J a A *C ; ..q,SI zz,''' Axx##AGG\-?-?@A!#  " ??4 2 2 45tMNxxzH N+** (5rL 88   0B0B CC Crc ddlm}tjdtjd}dddddddd dd }t j }|D]\}}|j |||||f  d}i}t j|D].}d} |jd D] \}}} | | z } | ||<|| z }0t|dk(sJd} i} |D]}|||z | z||<d| |<|d} t| D]|}t j|d d| }t j|sJ|D]E}tjj|j|js8| |xxdz cc<|~|jt!| j#t!|j#\}}|dkrJy)z> Sample many spanning trees from the additive method. rrrrrrrr!rrrRr%rjrir F)rrr N)rrr rr rrVrr-r rurrrrr rXr )rrr-r'r7r8rrrr%r9rrrrrrs r(test_random_spanning_tree_additive_largers       .E E  A/1 1aq!f ./LM  $ $Q 'wwHw- GAq! aKF ! a  }  ## #KK )!,|;{J a A *C ;  .. xC zz,''' Axx##AGG\-?-?@A!#  & ??4 2 2 45tMQ   syy>Q  rctj}|jdtjj |}t |j dk(sJt |jdk(sJyNrr)r rrfrrr rr-rs r+test_random_spanning_tree_single_node_graphr$ sX  AJJqM '' & &q )C syy>Q   syy>Q  rctj}|jd|jddtjj |}t |jdk(sJt |jdk(sJyr#) r rrfrVrrr rr-rs r*test_random_spanning_tree_single_node_loopr&sf  AJJqMJJq! 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