Insect combinatorics challenge












1














Below is the problem taken from Berkeley's Cs61A page here




Question 9: Insect Combinatorics*



Consider an insect in an M by N grid. The insect starts at the bottom left corner, (0, 0), and wants to end up at the top right corner, (M-1, N-1). The insect is only capable of moving right or up. Write a function paths that takes a grid length and width and returns the number of different paths the insect can take from the start to the goal. (There is a closed-form solution to this problem, but try to answer it procedurally using recursion.)
enter image description here



For example, the 2 by 2 grid has a total of two ways for the insect to move from the start to the goal. For the 3 by 3 grid, the insect has 6 diferent paths (only 3 are shown above).



def paths(m, n):
"""Return the number of paths from one corner of an
M by N grid to the opposite corner.

>>> paths(2, 2)
2
>>> paths(5, 7)
210
>>> paths(117, 1)
1
>>> paths(1, 157)
1
"""
"*** YOUR CODE HERE ***"



This solution is with the knowledge of 'higher order function' and 'recursion'. I've yet to learn data structures and algorithms (if required).



Idea: Started from the destination and found the possibilities. As per the skill level, the solution took 3 hours of my time. Please provide feedback on this.



def paths(m, n):
"""Return the number of paths from one corner of an
M by N grid to the opposite corner.

>>> paths(2, 2)
2
>>> paths(5, 7)
210
>>> paths(117, 1)
1
>>> paths(1, 157)
1
"""
count_paths = 0
def find_number_of_paths(x, y):
if x == 0 and y == 0:
nonlocal count_paths
count_paths += 1
return
if x > 0:
find_number_of_paths(x-1, y)
if y > 0:
find_number_of_paths(x, y-1)
find_number_of_paths(m-1, n-1)
return count_paths



  1. Can we avoid re-assignment operator on count_paths?

  2. Can we avoid nested function definitions?

  3. Is there a name for above solution approach in algorithm world? Any better approach?


Note: As per this assignment, no usage of data model is recommended.










share|improve this question





























    1














    Below is the problem taken from Berkeley's Cs61A page here




    Question 9: Insect Combinatorics*



    Consider an insect in an M by N grid. The insect starts at the bottom left corner, (0, 0), and wants to end up at the top right corner, (M-1, N-1). The insect is only capable of moving right or up. Write a function paths that takes a grid length and width and returns the number of different paths the insect can take from the start to the goal. (There is a closed-form solution to this problem, but try to answer it procedurally using recursion.)
    enter image description here



    For example, the 2 by 2 grid has a total of two ways for the insect to move from the start to the goal. For the 3 by 3 grid, the insect has 6 diferent paths (only 3 are shown above).



    def paths(m, n):
    """Return the number of paths from one corner of an
    M by N grid to the opposite corner.

    >>> paths(2, 2)
    2
    >>> paths(5, 7)
    210
    >>> paths(117, 1)
    1
    >>> paths(1, 157)
    1
    """
    "*** YOUR CODE HERE ***"



    This solution is with the knowledge of 'higher order function' and 'recursion'. I've yet to learn data structures and algorithms (if required).



    Idea: Started from the destination and found the possibilities. As per the skill level, the solution took 3 hours of my time. Please provide feedback on this.



    def paths(m, n):
    """Return the number of paths from one corner of an
    M by N grid to the opposite corner.

    >>> paths(2, 2)
    2
    >>> paths(5, 7)
    210
    >>> paths(117, 1)
    1
    >>> paths(1, 157)
    1
    """
    count_paths = 0
    def find_number_of_paths(x, y):
    if x == 0 and y == 0:
    nonlocal count_paths
    count_paths += 1
    return
    if x > 0:
    find_number_of_paths(x-1, y)
    if y > 0:
    find_number_of_paths(x, y-1)
    find_number_of_paths(m-1, n-1)
    return count_paths



    1. Can we avoid re-assignment operator on count_paths?

    2. Can we avoid nested function definitions?

    3. Is there a name for above solution approach in algorithm world? Any better approach?


    Note: As per this assignment, no usage of data model is recommended.










    share|improve this question



























      1












      1








      1


      1





      Below is the problem taken from Berkeley's Cs61A page here




      Question 9: Insect Combinatorics*



      Consider an insect in an M by N grid. The insect starts at the bottom left corner, (0, 0), and wants to end up at the top right corner, (M-1, N-1). The insect is only capable of moving right or up. Write a function paths that takes a grid length and width and returns the number of different paths the insect can take from the start to the goal. (There is a closed-form solution to this problem, but try to answer it procedurally using recursion.)
      enter image description here



      For example, the 2 by 2 grid has a total of two ways for the insect to move from the start to the goal. For the 3 by 3 grid, the insect has 6 diferent paths (only 3 are shown above).



      def paths(m, n):
      """Return the number of paths from one corner of an
      M by N grid to the opposite corner.

      >>> paths(2, 2)
      2
      >>> paths(5, 7)
      210
      >>> paths(117, 1)
      1
      >>> paths(1, 157)
      1
      """
      "*** YOUR CODE HERE ***"



      This solution is with the knowledge of 'higher order function' and 'recursion'. I've yet to learn data structures and algorithms (if required).



      Idea: Started from the destination and found the possibilities. As per the skill level, the solution took 3 hours of my time. Please provide feedback on this.



      def paths(m, n):
      """Return the number of paths from one corner of an
      M by N grid to the opposite corner.

      >>> paths(2, 2)
      2
      >>> paths(5, 7)
      210
      >>> paths(117, 1)
      1
      >>> paths(1, 157)
      1
      """
      count_paths = 0
      def find_number_of_paths(x, y):
      if x == 0 and y == 0:
      nonlocal count_paths
      count_paths += 1
      return
      if x > 0:
      find_number_of_paths(x-1, y)
      if y > 0:
      find_number_of_paths(x, y-1)
      find_number_of_paths(m-1, n-1)
      return count_paths



      1. Can we avoid re-assignment operator on count_paths?

      2. Can we avoid nested function definitions?

      3. Is there a name for above solution approach in algorithm world? Any better approach?


      Note: As per this assignment, no usage of data model is recommended.










      share|improve this question















      Below is the problem taken from Berkeley's Cs61A page here




      Question 9: Insect Combinatorics*



      Consider an insect in an M by N grid. The insect starts at the bottom left corner, (0, 0), and wants to end up at the top right corner, (M-1, N-1). The insect is only capable of moving right or up. Write a function paths that takes a grid length and width and returns the number of different paths the insect can take from the start to the goal. (There is a closed-form solution to this problem, but try to answer it procedurally using recursion.)
      enter image description here



      For example, the 2 by 2 grid has a total of two ways for the insect to move from the start to the goal. For the 3 by 3 grid, the insect has 6 diferent paths (only 3 are shown above).



      def paths(m, n):
      """Return the number of paths from one corner of an
      M by N grid to the opposite corner.

      >>> paths(2, 2)
      2
      >>> paths(5, 7)
      210
      >>> paths(117, 1)
      1
      >>> paths(1, 157)
      1
      """
      "*** YOUR CODE HERE ***"



      This solution is with the knowledge of 'higher order function' and 'recursion'. I've yet to learn data structures and algorithms (if required).



      Idea: Started from the destination and found the possibilities. As per the skill level, the solution took 3 hours of my time. Please provide feedback on this.



      def paths(m, n):
      """Return the number of paths from one corner of an
      M by N grid to the opposite corner.

      >>> paths(2, 2)
      2
      >>> paths(5, 7)
      210
      >>> paths(117, 1)
      1
      >>> paths(1, 157)
      1
      """
      count_paths = 0
      def find_number_of_paths(x, y):
      if x == 0 and y == 0:
      nonlocal count_paths
      count_paths += 1
      return
      if x > 0:
      find_number_of_paths(x-1, y)
      if y > 0:
      find_number_of_paths(x, y-1)
      find_number_of_paths(m-1, n-1)
      return count_paths



      1. Can we avoid re-assignment operator on count_paths?

      2. Can we avoid nested function definitions?

      3. Is there a name for above solution approach in algorithm world? Any better approach?


      Note: As per this assignment, no usage of data model is recommended.







      python python-3.x recursion homework combinatorics






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Apr 2 '18 at 22:31









      Grant Miller

      2232416




      2232416










      asked Jun 30 '15 at 9:44









      overexchangeoverexchange

      1,59852347




      1,59852347






















          1 Answer
          1






          active

          oldest

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          5














          Using nonlocal rather than global is certainly good, but better yet would be returning values.



          def paths(m, n):
          def find_number_of_paths(x, y):
          if x == 0 and y == 0:
          return 1

          ret = 0

          if x > 0:
          ret += find_number_of_paths(x-1, y)

          if y > 0:
          ret += find_number_of_paths(x, y-1)

          return ret

          return find_number_of_paths(m-1, n-1)


          That lets us elide the outer function entirely:



          def paths(x, y):
          if x == 1 and y == 1:
          return 1

          ret = 0

          if x > 1:
          ret += paths(x-1, y)

          if y > 1:
          ret += paths(x, y-1)

          return ret


          It's a bit strange to critique this since "no closed-form solution" basically means no good solution. There are ways of speeding this up, though, that avoid that. A trivial one is memoization:



          _paths_cache = {(1, 1): 1}
          def paths(x, y):
          if (x, y) in _paths_cache:
          return _paths_cache[x, y]

          ret = 0

          if x > 1:
          ret += paths(x-1, y)

          if y > 1:
          ret += paths(x, y-1)

          _paths_cache[x, y] = ret
          return ret





          share|improve this answer





















          • What is closed-form solution?
            – overexchange
            Jun 30 '15 at 12:34










          • So, ret=0, executes only once?
            – overexchange
            Jun 30 '15 at 12:35










          • ${(x-1) + (y-1)} choose {y-1}$
            – Veedrac
            Jun 30 '15 at 13:35












          • ret = 0 executes nearly once per call to paths, which is approximately $xy$ times.
            – Veedrac
            Jun 30 '15 at 13:40






          • 1




            Each call to the function has a different ret. Look at each function call in isolation: find_number_of_paths(4, 3) is equal to find_number_of_paths(3, 3) + find_number_of_paths(4, 2), so we add each to ret and return the answer.
            – Veedrac
            Jul 1 '15 at 0:28











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          1 Answer
          1






          active

          oldest

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          1 Answer
          1






          active

          oldest

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          active

          oldest

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          active

          oldest

          votes









          5














          Using nonlocal rather than global is certainly good, but better yet would be returning values.



          def paths(m, n):
          def find_number_of_paths(x, y):
          if x == 0 and y == 0:
          return 1

          ret = 0

          if x > 0:
          ret += find_number_of_paths(x-1, y)

          if y > 0:
          ret += find_number_of_paths(x, y-1)

          return ret

          return find_number_of_paths(m-1, n-1)


          That lets us elide the outer function entirely:



          def paths(x, y):
          if x == 1 and y == 1:
          return 1

          ret = 0

          if x > 1:
          ret += paths(x-1, y)

          if y > 1:
          ret += paths(x, y-1)

          return ret


          It's a bit strange to critique this since "no closed-form solution" basically means no good solution. There are ways of speeding this up, though, that avoid that. A trivial one is memoization:



          _paths_cache = {(1, 1): 1}
          def paths(x, y):
          if (x, y) in _paths_cache:
          return _paths_cache[x, y]

          ret = 0

          if x > 1:
          ret += paths(x-1, y)

          if y > 1:
          ret += paths(x, y-1)

          _paths_cache[x, y] = ret
          return ret





          share|improve this answer





















          • What is closed-form solution?
            – overexchange
            Jun 30 '15 at 12:34










          • So, ret=0, executes only once?
            – overexchange
            Jun 30 '15 at 12:35










          • ${(x-1) + (y-1)} choose {y-1}$
            – Veedrac
            Jun 30 '15 at 13:35












          • ret = 0 executes nearly once per call to paths, which is approximately $xy$ times.
            – Veedrac
            Jun 30 '15 at 13:40






          • 1




            Each call to the function has a different ret. Look at each function call in isolation: find_number_of_paths(4, 3) is equal to find_number_of_paths(3, 3) + find_number_of_paths(4, 2), so we add each to ret and return the answer.
            – Veedrac
            Jul 1 '15 at 0:28
















          5














          Using nonlocal rather than global is certainly good, but better yet would be returning values.



          def paths(m, n):
          def find_number_of_paths(x, y):
          if x == 0 and y == 0:
          return 1

          ret = 0

          if x > 0:
          ret += find_number_of_paths(x-1, y)

          if y > 0:
          ret += find_number_of_paths(x, y-1)

          return ret

          return find_number_of_paths(m-1, n-1)


          That lets us elide the outer function entirely:



          def paths(x, y):
          if x == 1 and y == 1:
          return 1

          ret = 0

          if x > 1:
          ret += paths(x-1, y)

          if y > 1:
          ret += paths(x, y-1)

          return ret


          It's a bit strange to critique this since "no closed-form solution" basically means no good solution. There are ways of speeding this up, though, that avoid that. A trivial one is memoization:



          _paths_cache = {(1, 1): 1}
          def paths(x, y):
          if (x, y) in _paths_cache:
          return _paths_cache[x, y]

          ret = 0

          if x > 1:
          ret += paths(x-1, y)

          if y > 1:
          ret += paths(x, y-1)

          _paths_cache[x, y] = ret
          return ret





          share|improve this answer





















          • What is closed-form solution?
            – overexchange
            Jun 30 '15 at 12:34










          • So, ret=0, executes only once?
            – overexchange
            Jun 30 '15 at 12:35










          • ${(x-1) + (y-1)} choose {y-1}$
            – Veedrac
            Jun 30 '15 at 13:35












          • ret = 0 executes nearly once per call to paths, which is approximately $xy$ times.
            – Veedrac
            Jun 30 '15 at 13:40






          • 1




            Each call to the function has a different ret. Look at each function call in isolation: find_number_of_paths(4, 3) is equal to find_number_of_paths(3, 3) + find_number_of_paths(4, 2), so we add each to ret and return the answer.
            – Veedrac
            Jul 1 '15 at 0:28














          5












          5








          5






          Using nonlocal rather than global is certainly good, but better yet would be returning values.



          def paths(m, n):
          def find_number_of_paths(x, y):
          if x == 0 and y == 0:
          return 1

          ret = 0

          if x > 0:
          ret += find_number_of_paths(x-1, y)

          if y > 0:
          ret += find_number_of_paths(x, y-1)

          return ret

          return find_number_of_paths(m-1, n-1)


          That lets us elide the outer function entirely:



          def paths(x, y):
          if x == 1 and y == 1:
          return 1

          ret = 0

          if x > 1:
          ret += paths(x-1, y)

          if y > 1:
          ret += paths(x, y-1)

          return ret


          It's a bit strange to critique this since "no closed-form solution" basically means no good solution. There are ways of speeding this up, though, that avoid that. A trivial one is memoization:



          _paths_cache = {(1, 1): 1}
          def paths(x, y):
          if (x, y) in _paths_cache:
          return _paths_cache[x, y]

          ret = 0

          if x > 1:
          ret += paths(x-1, y)

          if y > 1:
          ret += paths(x, y-1)

          _paths_cache[x, y] = ret
          return ret





          share|improve this answer












          Using nonlocal rather than global is certainly good, but better yet would be returning values.



          def paths(m, n):
          def find_number_of_paths(x, y):
          if x == 0 and y == 0:
          return 1

          ret = 0

          if x > 0:
          ret += find_number_of_paths(x-1, y)

          if y > 0:
          ret += find_number_of_paths(x, y-1)

          return ret

          return find_number_of_paths(m-1, n-1)


          That lets us elide the outer function entirely:



          def paths(x, y):
          if x == 1 and y == 1:
          return 1

          ret = 0

          if x > 1:
          ret += paths(x-1, y)

          if y > 1:
          ret += paths(x, y-1)

          return ret


          It's a bit strange to critique this since "no closed-form solution" basically means no good solution. There are ways of speeding this up, though, that avoid that. A trivial one is memoization:



          _paths_cache = {(1, 1): 1}
          def paths(x, y):
          if (x, y) in _paths_cache:
          return _paths_cache[x, y]

          ret = 0

          if x > 1:
          ret += paths(x-1, y)

          if y > 1:
          ret += paths(x, y-1)

          _paths_cache[x, y] = ret
          return ret






          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered Jun 30 '15 at 9:56









          VeedracVeedrac

          9,1131634




          9,1131634












          • What is closed-form solution?
            – overexchange
            Jun 30 '15 at 12:34










          • So, ret=0, executes only once?
            – overexchange
            Jun 30 '15 at 12:35










          • ${(x-1) + (y-1)} choose {y-1}$
            – Veedrac
            Jun 30 '15 at 13:35












          • ret = 0 executes nearly once per call to paths, which is approximately $xy$ times.
            – Veedrac
            Jun 30 '15 at 13:40






          • 1




            Each call to the function has a different ret. Look at each function call in isolation: find_number_of_paths(4, 3) is equal to find_number_of_paths(3, 3) + find_number_of_paths(4, 2), so we add each to ret and return the answer.
            – Veedrac
            Jul 1 '15 at 0:28


















          • What is closed-form solution?
            – overexchange
            Jun 30 '15 at 12:34










          • So, ret=0, executes only once?
            – overexchange
            Jun 30 '15 at 12:35










          • ${(x-1) + (y-1)} choose {y-1}$
            – Veedrac
            Jun 30 '15 at 13:35












          • ret = 0 executes nearly once per call to paths, which is approximately $xy$ times.
            – Veedrac
            Jun 30 '15 at 13:40






          • 1




            Each call to the function has a different ret. Look at each function call in isolation: find_number_of_paths(4, 3) is equal to find_number_of_paths(3, 3) + find_number_of_paths(4, 2), so we add each to ret and return the answer.
            – Veedrac
            Jul 1 '15 at 0:28
















          What is closed-form solution?
          – overexchange
          Jun 30 '15 at 12:34




          What is closed-form solution?
          – overexchange
          Jun 30 '15 at 12:34












          So, ret=0, executes only once?
          – overexchange
          Jun 30 '15 at 12:35




          So, ret=0, executes only once?
          – overexchange
          Jun 30 '15 at 12:35












          ${(x-1) + (y-1)} choose {y-1}$
          – Veedrac
          Jun 30 '15 at 13:35






          ${(x-1) + (y-1)} choose {y-1}$
          – Veedrac
          Jun 30 '15 at 13:35














          ret = 0 executes nearly once per call to paths, which is approximately $xy$ times.
          – Veedrac
          Jun 30 '15 at 13:40




          ret = 0 executes nearly once per call to paths, which is approximately $xy$ times.
          – Veedrac
          Jun 30 '15 at 13:40




          1




          1




          Each call to the function has a different ret. Look at each function call in isolation: find_number_of_paths(4, 3) is equal to find_number_of_paths(3, 3) + find_number_of_paths(4, 2), so we add each to ret and return the answer.
          – Veedrac
          Jul 1 '15 at 0:28




          Each call to the function has a different ret. Look at each function call in isolation: find_number_of_paths(4, 3) is equal to find_number_of_paths(3, 3) + find_number_of_paths(4, 2), so we add each to ret and return the answer.
          – Veedrac
          Jul 1 '15 at 0:28


















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