Identifying Binary Search Trees from their Prufer Sequence












3












$begingroup$


If you ignore its root, a Binary Search Tree generated by some permutation of ${1, ldots, n}$ is a labeled tree. Which means you can calculate its Prufer Sequence. I did this in Python and I found that there are A000245 distinct Prufer sequences.



import itertools

def generate_tree(l):
'''Return the root and a set of edges'''
if len(l) == 0:
return None, None
if len(l) == 1:
return l[0], set()
left_root, left_edges = generate_tree([x for x in l if x < l[0]])
right_root, right_edges = generate_tree([x for x in l if x > l[0]])
edges = set()
if left_root is not None:
edges.add((left_root, l[0]))
edges.update(left_edges)
if right_root is not None:
edges.add((l[0], right_root))
edges.update(right_edges)
return l[0], edges

def pruffer_sequence(edge_set):
degrees = {}
for v1, v2 in edge_set:
degrees[v1] = degrees.get(v1, 0) + 1
degrees[v2] = degrees.get(v2, 0) + 1
r =
while len(edge_set) > 1:
smallest_leaf = min([v for v in degrees.keys() if degrees[v] == 1])
degrees[smallest_leaf] -= 1
for e in edge_set:
if smallest_leaf in e:
edge_set = edge_set - {e}
neighbor = [v for v in e if v != smallest_leaf][0]
degrees[neighbor] -= 1
r.append(neighbor)
break
return r

for number_of_vertices in range(2, 10):
R = set()
for l in itertools.permutations(range(number_of_vertices)):
root, edge_set = generate_tree(l)
R.add( tuple( pruffer_sequence(edge_set) ))
print number_of_vertices, len(R)


Is there some way to characterize a BST by its Prufer sequence without first calculating its tree?










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$endgroup$

















    3












    $begingroup$


    If you ignore its root, a Binary Search Tree generated by some permutation of ${1, ldots, n}$ is a labeled tree. Which means you can calculate its Prufer Sequence. I did this in Python and I found that there are A000245 distinct Prufer sequences.



    import itertools

    def generate_tree(l):
    '''Return the root and a set of edges'''
    if len(l) == 0:
    return None, None
    if len(l) == 1:
    return l[0], set()
    left_root, left_edges = generate_tree([x for x in l if x < l[0]])
    right_root, right_edges = generate_tree([x for x in l if x > l[0]])
    edges = set()
    if left_root is not None:
    edges.add((left_root, l[0]))
    edges.update(left_edges)
    if right_root is not None:
    edges.add((l[0], right_root))
    edges.update(right_edges)
    return l[0], edges

    def pruffer_sequence(edge_set):
    degrees = {}
    for v1, v2 in edge_set:
    degrees[v1] = degrees.get(v1, 0) + 1
    degrees[v2] = degrees.get(v2, 0) + 1
    r =
    while len(edge_set) > 1:
    smallest_leaf = min([v for v in degrees.keys() if degrees[v] == 1])
    degrees[smallest_leaf] -= 1
    for e in edge_set:
    if smallest_leaf in e:
    edge_set = edge_set - {e}
    neighbor = [v for v in e if v != smallest_leaf][0]
    degrees[neighbor] -= 1
    r.append(neighbor)
    break
    return r

    for number_of_vertices in range(2, 10):
    R = set()
    for l in itertools.permutations(range(number_of_vertices)):
    root, edge_set = generate_tree(l)
    R.add( tuple( pruffer_sequence(edge_set) ))
    print number_of_vertices, len(R)


    Is there some way to characterize a BST by its Prufer sequence without first calculating its tree?










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    $endgroup$















      3












      3








      3


      1



      $begingroup$


      If you ignore its root, a Binary Search Tree generated by some permutation of ${1, ldots, n}$ is a labeled tree. Which means you can calculate its Prufer Sequence. I did this in Python and I found that there are A000245 distinct Prufer sequences.



      import itertools

      def generate_tree(l):
      '''Return the root and a set of edges'''
      if len(l) == 0:
      return None, None
      if len(l) == 1:
      return l[0], set()
      left_root, left_edges = generate_tree([x for x in l if x < l[0]])
      right_root, right_edges = generate_tree([x for x in l if x > l[0]])
      edges = set()
      if left_root is not None:
      edges.add((left_root, l[0]))
      edges.update(left_edges)
      if right_root is not None:
      edges.add((l[0], right_root))
      edges.update(right_edges)
      return l[0], edges

      def pruffer_sequence(edge_set):
      degrees = {}
      for v1, v2 in edge_set:
      degrees[v1] = degrees.get(v1, 0) + 1
      degrees[v2] = degrees.get(v2, 0) + 1
      r =
      while len(edge_set) > 1:
      smallest_leaf = min([v for v in degrees.keys() if degrees[v] == 1])
      degrees[smallest_leaf] -= 1
      for e in edge_set:
      if smallest_leaf in e:
      edge_set = edge_set - {e}
      neighbor = [v for v in e if v != smallest_leaf][0]
      degrees[neighbor] -= 1
      r.append(neighbor)
      break
      return r

      for number_of_vertices in range(2, 10):
      R = set()
      for l in itertools.permutations(range(number_of_vertices)):
      root, edge_set = generate_tree(l)
      R.add( tuple( pruffer_sequence(edge_set) ))
      print number_of_vertices, len(R)


      Is there some way to characterize a BST by its Prufer sequence without first calculating its tree?










      share|cite|improve this question









      $endgroup$




      If you ignore its root, a Binary Search Tree generated by some permutation of ${1, ldots, n}$ is a labeled tree. Which means you can calculate its Prufer Sequence. I did this in Python and I found that there are A000245 distinct Prufer sequences.



      import itertools

      def generate_tree(l):
      '''Return the root and a set of edges'''
      if len(l) == 0:
      return None, None
      if len(l) == 1:
      return l[0], set()
      left_root, left_edges = generate_tree([x for x in l if x < l[0]])
      right_root, right_edges = generate_tree([x for x in l if x > l[0]])
      edges = set()
      if left_root is not None:
      edges.add((left_root, l[0]))
      edges.update(left_edges)
      if right_root is not None:
      edges.add((l[0], right_root))
      edges.update(right_edges)
      return l[0], edges

      def pruffer_sequence(edge_set):
      degrees = {}
      for v1, v2 in edge_set:
      degrees[v1] = degrees.get(v1, 0) + 1
      degrees[v2] = degrees.get(v2, 0) + 1
      r =
      while len(edge_set) > 1:
      smallest_leaf = min([v for v in degrees.keys() if degrees[v] == 1])
      degrees[smallest_leaf] -= 1
      for e in edge_set:
      if smallest_leaf in e:
      edge_set = edge_set - {e}
      neighbor = [v for v in e if v != smallest_leaf][0]
      degrees[neighbor] -= 1
      r.append(neighbor)
      break
      return r

      for number_of_vertices in range(2, 10):
      R = set()
      for l in itertools.permutations(range(number_of_vertices)):
      root, edge_set = generate_tree(l)
      R.add( tuple( pruffer_sequence(edge_set) ))
      print number_of_vertices, len(R)


      Is there some way to characterize a BST by its Prufer sequence without first calculating its tree?







      combinatorics trees






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      asked Jan 27 '15 at 3:10









      ybermanyberman

      1,496714




      1,496714






















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          $begingroup$

          Is there a reason you chose to take the BST of the permutations? There are multiple ways to uniquely represent a permutation as a tree, where you would end up with $n!$ different Prufer sequences. Categorizing such sequences might be more interesting.



          But in any case, I couldn't find a way to enumerate without iterating all the $n!$ permutations. I tried to find a bijection between Prufer sequences and something counted by the difference of Catalan numbers $C_n-C_{n-1}$, but to no avail. I was however, able to speed up your code.



          Given two permutations $p_1, p_2 in S_n$



          $$ text{Prufer}(p_1) ;;;= ;;; text{Prufer}(p_2) \
          Longleftrightarrow \
          text{Tree}(p_1) ;;; = ;;; text{Tree}(p_2) \
          Longleftrightarrow \
          text{Edges}(text{BST}(p_1)) ;;; = ;;; text{Edges}(text{BST}(p_2)) \
          $$

          So you only need to find the Prufer sequence when you come across a new edge set. Also, if you actually construct the BST, you can find the Prufer sequence in linear time. Here is my python code (takes 6 seconds rather than 18).






          share|cite|improve this answer











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            active

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            oldest

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            active

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            $begingroup$

            Is there a reason you chose to take the BST of the permutations? There are multiple ways to uniquely represent a permutation as a tree, where you would end up with $n!$ different Prufer sequences. Categorizing such sequences might be more interesting.



            But in any case, I couldn't find a way to enumerate without iterating all the $n!$ permutations. I tried to find a bijection between Prufer sequences and something counted by the difference of Catalan numbers $C_n-C_{n-1}$, but to no avail. I was however, able to speed up your code.



            Given two permutations $p_1, p_2 in S_n$



            $$ text{Prufer}(p_1) ;;;= ;;; text{Prufer}(p_2) \
            Longleftrightarrow \
            text{Tree}(p_1) ;;; = ;;; text{Tree}(p_2) \
            Longleftrightarrow \
            text{Edges}(text{BST}(p_1)) ;;; = ;;; text{Edges}(text{BST}(p_2)) \
            $$

            So you only need to find the Prufer sequence when you come across a new edge set. Also, if you actually construct the BST, you can find the Prufer sequence in linear time. Here is my python code (takes 6 seconds rather than 18).






            share|cite|improve this answer











            $endgroup$


















              1












              $begingroup$

              Is there a reason you chose to take the BST of the permutations? There are multiple ways to uniquely represent a permutation as a tree, where you would end up with $n!$ different Prufer sequences. Categorizing such sequences might be more interesting.



              But in any case, I couldn't find a way to enumerate without iterating all the $n!$ permutations. I tried to find a bijection between Prufer sequences and something counted by the difference of Catalan numbers $C_n-C_{n-1}$, but to no avail. I was however, able to speed up your code.



              Given two permutations $p_1, p_2 in S_n$



              $$ text{Prufer}(p_1) ;;;= ;;; text{Prufer}(p_2) \
              Longleftrightarrow \
              text{Tree}(p_1) ;;; = ;;; text{Tree}(p_2) \
              Longleftrightarrow \
              text{Edges}(text{BST}(p_1)) ;;; = ;;; text{Edges}(text{BST}(p_2)) \
              $$

              So you only need to find the Prufer sequence when you come across a new edge set. Also, if you actually construct the BST, you can find the Prufer sequence in linear time. Here is my python code (takes 6 seconds rather than 18).






              share|cite|improve this answer











              $endgroup$
















                1












                1








                1





                $begingroup$

                Is there a reason you chose to take the BST of the permutations? There are multiple ways to uniquely represent a permutation as a tree, where you would end up with $n!$ different Prufer sequences. Categorizing such sequences might be more interesting.



                But in any case, I couldn't find a way to enumerate without iterating all the $n!$ permutations. I tried to find a bijection between Prufer sequences and something counted by the difference of Catalan numbers $C_n-C_{n-1}$, but to no avail. I was however, able to speed up your code.



                Given two permutations $p_1, p_2 in S_n$



                $$ text{Prufer}(p_1) ;;;= ;;; text{Prufer}(p_2) \
                Longleftrightarrow \
                text{Tree}(p_1) ;;; = ;;; text{Tree}(p_2) \
                Longleftrightarrow \
                text{Edges}(text{BST}(p_1)) ;;; = ;;; text{Edges}(text{BST}(p_2)) \
                $$

                So you only need to find the Prufer sequence when you come across a new edge set. Also, if you actually construct the BST, you can find the Prufer sequence in linear time. Here is my python code (takes 6 seconds rather than 18).






                share|cite|improve this answer











                $endgroup$



                Is there a reason you chose to take the BST of the permutations? There are multiple ways to uniquely represent a permutation as a tree, where you would end up with $n!$ different Prufer sequences. Categorizing such sequences might be more interesting.



                But in any case, I couldn't find a way to enumerate without iterating all the $n!$ permutations. I tried to find a bijection between Prufer sequences and something counted by the difference of Catalan numbers $C_n-C_{n-1}$, but to no avail. I was however, able to speed up your code.



                Given two permutations $p_1, p_2 in S_n$



                $$ text{Prufer}(p_1) ;;;= ;;; text{Prufer}(p_2) \
                Longleftrightarrow \
                text{Tree}(p_1) ;;; = ;;; text{Tree}(p_2) \
                Longleftrightarrow \
                text{Edges}(text{BST}(p_1)) ;;; = ;;; text{Edges}(text{BST}(p_2)) \
                $$

                So you only need to find the Prufer sequence when you come across a new edge set. Also, if you actually construct the BST, you can find the Prufer sequence in linear time. Here is my python code (takes 6 seconds rather than 18).







                share|cite|improve this answer














                share|cite|improve this answer



                share|cite|improve this answer








                edited Jan 15 at 9:22

























                answered Jan 27 '15 at 21:27









                Andrew SzymczakAndrew Szymczak

                1,177616




                1,177616






























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