'''simple docstring'''
a=	[
    ['''ab-e1''', '''ac-e3''', '''ad-e5''', '''bc-e4''', '''bd-e2''', '''be-e6''', '''bh-e12''', '''cd-e2''', '''ce-e4''',
     '''de-e1''', '''df-e8''', '''dg-e5''', '''dh-e10''', '''ef-e3''', '''eg-e2''', '''fg-e6''', '''gh-e6''', '''hi-e3'''],
    ['''ab-e1''', '''ac-e3''', '''ad-e5''', '''bc-e4''', '''bd-e2''', '''be-e6''', '''cd-e2''', '''de-e1''', '''df-e8''',
     '''ef-e3''', '''eg-e2''', '''fg-e6'''],
    ['''ab-e1''', '''ac-e3''', '''bc-e4''', '''bd-e2''', '''de-e1''', '''df-e8''', '''dg-e5''', '''ef-e3''', '''eg-e2''',
     '''eh-e12''', '''fg-e6''', '''fh-e10''', '''gh-e6'''],
    ['''ab-e1''', '''ac-e3''', '''bc-e4''', '''bd-e2''', '''bh-e12''', '''cd-e2''', '''df-e8''', '''dh-e10'''],
    ['''ab-e1''', '''ac-e3''', '''ad-e5''', '''bc-e4''', '''bd-e2''', '''cd-e2''', '''ce-e4''', '''de-e1''', '''df-e8''',
     '''dg-e5''', '''ef-e3''', '''eg-e2''', '''fg-e6''']
]







def 			_UpperCamelCase			(  _a						:						int      ):




    """simple docstring"""



    __UpperCamelCase						:						Any		     =	set()
    for row in edge_array:
        for item in row:
            distinct_edge.add(item[0]      )
    return list(_a      )







def 			_UpperCamelCase			(  _a						:						Tuple					,  _a						:						Any      ):




    """simple docstring"""



    __UpperCamelCase						:						Any		     =	['0'] * len(_a      )
    for i, row in enumerate(_a      ):
        for item in row:
            if distinct_edge in item[0]:
                __UpperCamelCase						:						Union[str, Any]		     =	'1'
                break
    return "".join(_a      )







def 			_UpperCamelCase			(  _a						:						Optional[int]      ):




    """simple docstring"""



    __UpperCamelCase						:						Union[str, Any]		     =	get_distinct_edge(_a      )
    __UpperCamelCase						:						str		     =	{}

    for item in distinct_edge:
        __UpperCamelCase						:						List[Any]		     =	get_bitcode(_a					,  _a      )
        # print('bit',bit)
        # bt=''.join(bit)
        __UpperCamelCase						:						Any		     =	bit.count('1'      )
        __UpperCamelCase						:						str		     =	[s, bit]
    # Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
    __UpperCamelCase						:						str		     =	[
        [k, v[0], v[1]]
        for k, v in sorted(frequency_table.items()					,  key=lambda _a      : v[1][0]					,  reverse=_a      )
    ]
    return sorted_frequency_table







def 			_UpperCamelCase			(  _a						:						Optional[Any]      ):




    """simple docstring"""



    __UpperCamelCase						:						Optional[int]		     =	{}
    for _, item in enumerate(_a      ):
        nodes.setdefault(item[2]					,  []      ).append(item[0]      )
    return nodes







def 			_UpperCamelCase			(  _a						:						List[str]      ):




    """simple docstring"""



    __UpperCamelCase						:						Tuple		     =	{}
    for key, value in nodes.items():
        __UpperCamelCase						:						List[Any]		     =	value
    return cluster







def 			_UpperCamelCase			(  _a						:						Optional[int]      ):




    """simple docstring"""



    return [i * 1_0_0 / len(_a      ) for i in cluster]







def 			_UpperCamelCase			(  ):




    """simple docstring"""



    print('\nNodes\n'      )
    for key, value in nodes.items():
        print(_a					,  _a      )
    print('\nSupport\n'      )
    print(_a      )
    print('\n Cluster \n'      )
    for key, value in sorted(cluster.items()					,  reverse=_a      ):
        print(_a					,  _a      )
    print('\n Graph\n'      )
    for key, value in graph.items():
        print(_a					,  _a      )
    print('\n Edge List of Frequent subgraphs \n'      )
    for edge_list in freq_subgraph_edge_list:
        print(_a      )







def 			_UpperCamelCase			(  _a						:						Tuple					,  _a						:						Optional[Any]					,  _a						:						List[str]					,  _a						:						List[Any]      ):




    """simple docstring"""



    for i in cluster[ca]:
        __UpperCamelCase						:						Optional[int]		     =	0
        __UpperCamelCase						:						Optional[int]		     =	ca + 1
        while ca < max(cluster.keys()      ):
            for j in cluster[ca]:
                if int(_a					,  2      ) & int(_a					,  2      ) == int(_a					,  2      ):
                    if tuple(nodes[i]      ) in graph:
                        graph[tuple(nodes[i]      )].append(nodes[j]      )
                    else:
                        __UpperCamelCase						:						Optional[int]		     =	[nodes[j]]
                    count += 1
            if count == 0:
                __UpperCamelCase						:						int		     =	ca + 1
            else:
                break







def 			_UpperCamelCase			(  _a						:						Optional[Any]					,  _a						:						Optional[int]      ):




    """simple docstring"""



    __UpperCamelCase						:						Union[str, Any]		     =	cluster[max(cluster.keys()      )]
    __UpperCamelCase						:						List[Any]		     =	'Header'
    __UpperCamelCase						:						Dict		     =	{}
    for i in x:
        if (["Header"],) in graph:
            graph[(["Header"],)].append(x[i]      )
        else:
            __UpperCamelCase						:						int		     =	[x[i]]
    for i in x:
        __UpperCamelCase						:						Dict		     =	[['Header']]
    __UpperCamelCase						:						Tuple		     =	1
    while i < max(_a      ) - 1:
        create_edge(_a					,  _a					,  _a					,  _a      )
        __UpperCamelCase						:						List[Any]		     =	i + 1
    return graph







def 			_UpperCamelCase			(  _a						:						Optional[int]					,  _a						:						Optional[Any]					,  _a						:						Dict					,  _a						:						List[Any]=None      ):




    """simple docstring"""



    __UpperCamelCase						:						int		     =	(path or []) + [start]
    if start == end:
        paths.append(_a      )
    for node in graph[start]:
        if tuple(_a      ) not in path:
            my_dfs(_a					,  tuple(_a      )					,  _a					,  _a      )







def 			_UpperCamelCase			(  _a						:						List[str]					,  _a						:						Optional[int]					,  _a						:						Tuple      ):




    """simple docstring"""



    __UpperCamelCase						:						List[str]		     =	int(s / 1_0_0 * (len(_a      ) - 1)      )
    for i in cluster[k]:
        my_dfs(_a					,  tuple(cluster[k][i]      )					,  (['Header'],)      )







def 			_UpperCamelCase			(  _a						:						Tuple      ):




    """simple docstring"""



    __UpperCamelCase						:						Tuple		     =	[]
    for edges in paths:
        __UpperCamelCase						:						Optional[Any]		     =	[]
        for j in range(len(_a      ) - 1      ):
            __UpperCamelCase						:						Union[str, Any]		     =	list(edges[j]      )
            for e in temp:
                __UpperCamelCase						:						Optional[Any]		     =	(e[0], e[1])
                el.append(_a      )
        freq_sub_el.append(_a      )
    return freq_sub_el







def 			_UpperCamelCase			(  _a						:						int      ):




    """simple docstring"""



    for i in range(len(_a      )      ):
        for j in range(len(edge_array[i]      )      ):
            __UpperCamelCase						:						int		     =	edge_array[i][j].split('-'      )
            __UpperCamelCase						:						List[Any]		     =	t


if __name__ == "__main__":
  preprocess(edge_array)
  a=	get_frequency_table(edge_array)
  a=	get_nodes(frequency_table)
  a=	get_cluster(nodes)
  a=	get_support(cluster)
  a=	construct_graph(cluster, nodes)
  find_freq_subgraph_given_support(6_0, cluster, graph)
  a=	[]
  a=	freq_subgraphs_edge_list(paths)
  print_all()




'''simple docstring'''
import numpy as np







def 			_UpperCamelCase			(  _a						:						Union[str, Any]					,  _a						:						Any					,  _a						:						Dict      ):




    """simple docstring"""



    __UpperCamelCase						:						Tuple		     =	n_draws * class_counts / class_counts.sum()
    # floored means we don't overshoot n_samples, but probably undershoot
    __UpperCamelCase						:						Optional[Any]		     =	np.floor(_a      )
    # we add samples according to how much "left over" probability
    # they had, until we arrive at n_samples
    __UpperCamelCase						:						Tuple		     =	int(n_draws - floored.sum()      )
    if need_to_add > 0:
        __UpperCamelCase						:						Any		     =	continuous - floored
        __UpperCamelCase						:						Any		     =	np.sort(np.unique(_a      )      )[::-1]
        # add according to remainder, but break ties
        # randomly to avoid biases
        for value in values:
            ((__UpperCamelCase)     ,       )						:						Any		     =	np.where(remainder == value      )
            # if we need_to_add less than what's in inds
            # we draw randomly from them.
            # if we need to add more, we add them all and
            # go to the next value
            __UpperCamelCase						:						List[Any]		     =	min(len(_a      )					,  _a      )
            __UpperCamelCase						:						Tuple		     =	rng.choice(_a					,  size=_a					,  replace=_a      )
            floored[inds] += 1
            need_to_add -= add_now
            if need_to_add == 0:
                break
    return floored.astype(np.intaa      )







def 			_UpperCamelCase			(  _a						:						Optional[int]					,  _a						:						Union[str, Any]					,  _a						:						int					,  _a						:						Union[str, Any]					,  _a						:						str=1_0      ):




    """simple docstring"""



    __UpperCamelCase     ,       __UpperCamelCase						:						int		     =	np.unique(_a					,  return_inverse=_a      )
    __UpperCamelCase						:						str		     =	classes.shape[0]
    __UpperCamelCase						:						str		     =	np.bincount(_a      )
    if np.min(_a      ) < 2:
        raise ValueError('Minimum class count error'      )
    if n_train < n_classes:
        raise ValueError(
            'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)      )
    if n_test < n_classes:
        raise ValueError(
            'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)      )
    __UpperCamelCase						:						Optional[int]		     =	np.split(np.argsort(_a					,  kind='mergesort'      )					,  np.cumsum(_a      )[:-1]      )
    for _ in range(_a      ):
        __UpperCamelCase						:						List[str]		     =	approximate_mode(_a					,  _a					,  _a      )
        __UpperCamelCase						:						Tuple		     =	class_counts - n_i
        __UpperCamelCase						:						int		     =	approximate_mode(_a					,  _a					,  _a      )

        __UpperCamelCase						:						List[Any]		     =	[]
        __UpperCamelCase						:						str		     =	[]

        for i in range(_a      ):
            __UpperCamelCase						:						Optional[Any]		     =	rng.permutation(class_counts[i]      )
            __UpperCamelCase						:						List[Any]		     =	class_indices[i].take(_a					,  mode='clip'      )
            train.extend(perm_indices_class_i[: n_i[i]]      )
            test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]      )
        __UpperCamelCase						:						int		     =	rng.permutation(_a      )
        __UpperCamelCase						:						int		     =	rng.permutation(_a      )

        yield train, test



