Exercises

1. Higher-order functions. Define a function make_accumulator in Python that returns an accumulator function, which takes one numerical argument and returns the sum of all arguments ever passed to the accumulator. Do not define any classes for this problem.

   def make_accumulator():
       """Return an accumulator function.
   
       The accumulator function takes a single numeric argument and
       accumulates that argument into a running total, then returns
       total.
   
       >>> acc = make_accumulator()
       >>> acc(15)
       15
       >>> acc(10)
       25
       >>> acc2 = make_accumulator()
       >>> acc2(7)
       7
       >>> acc3 = acc2
       >>> acc3(6)
       13
       >>> acc2(5)
       18
       >>> acc(4)
       29
       """
       # add your solution below
   

2. Decorators and memoization. Memoization is an optimization in recursive algorithms that have repeated computations, where the arguments and result of a function call are stored when a function is first called with that set of arguments. Then if the function is called again with the same arguments, the stored value is looked up and returned rather than being recomputed.

   For this problem, implement a memoize decorator in Python that takes in a function and returns a version that performs memoization. The decorator should work on functions that take in any number of non-keyword arguments. You may assume that all arguments are hashable.

   Hint: We recommend using a dictionary to store previously computed values, and variable argument lists to handle functions with any number of parameters.

   def memoize(func):
       """Return a version of func that memoizes computation.
   
       Returns a function that computes the same result as func, but
       if a given set of arguments has already been seen before,
       returns the previously computed result instead of repeating
       the computation. Assumes func is a pure function (i.e. has no
       side affects), and that all arguments to func are hashable.
   
       >>> @memoize
       ... def sum_to_n(n):
       ...     return 1 if n == 1 else n + sum_to_n(n - 1)
       >>> try:
       ...     sum_to_n(300)
       ...     sum_to_n(600)
       ...     sum_to_n(900)
       ...     sum_to_n(1200)
       ... except RecursionError:
       ...     print('recursion limit exceeded')
       45150
       180300
       405450
       720600
       >>> @memoize
       ... def sum_k_to_n(k, n):
       ...     return k if n == k else n + sum_k_to_n(k, n - 1)
       >>> try:
       ...     sum_k_to_n(2, 300)
       ...     sum_k_to_n(2, 600)
       ...     sum_k_to_n(2, 900)
       ...     sum_k_to_n(2, 1200)
       ... except RecursionError:
       ...     print('recursion limit exceeded')
       45149
       180299
       405449
       720599
       """
       # add your code below
   

3. Chain. Recall that the compose() higher-order function takes in two single-parameter functions as arguments and returns the composition of the two functions. Thus, compose(f, g)(x) computes f(g(x)).

   The following is a definition of compose() in Python:

   def compose(f, g):
       return lambda x: f(g(x))
   

   Composition can be generalized to function chains, so that chain(f, g, h)(x) computes f(g(h(x))), chain(f, g, h, k)(x) computes f(g(h(k(x)))), and so on. Implement the variadic chain() function in Python.

   def chain(*funcs):
       """Return a function that is the compositional chain of funcs.
   
       If funcs is empty, returns the identity function.
   
       >>> chain()(3)
       3
       >>> chain(lambda x: 3 * x)(3)
       9
       >>> chain(lambda x: x + 1, lambda x: 3 * x)(3)
       10
       >>> chain(lambda x: x // 2, lambda x: x + 1, lambda x: 3 * x)(3)
       5
       """
       # add your code below
   

   Your solution must use recursion.

4. Generators. This problem will give you practice in working with generators that represent infinite sequences.

   You may not use any built-in sequences (e.g. lists, tuples, dicts) for this problem.

   1. Implement a scale() generator that, given an iterator of numbers, scales them by a constant to produce a new iterator. For example, given a generator naturals() for the natural numbers, scale(naturals(), 2) produces an iterator of the even natural numbers.

      def scale(items, factor):
          """Produce an iterator of the elements in items scaled by
          factor.
      
          Consumes the elements from items.
      
          >>> def naturals():
          ...     num = 0
          ...     while True:
          ...         yield num
          ...         num += 1
          >>> values = scale(naturals(), 3)
          >>> [next(values) for i in range(5)]
          [0, 3, 6, 9, 12]
          """
          # add your code below
      

   2. Implement a merge() generator that, given two infinite iterators whose elements are in strictly increasing order, produces a new iterator that contains the items from both input iterators, in increasing order and without duplicates.

      def merge(items1, items2):
          """Produce an ordered iterator that is the merge of the inputs.
      
          The resulting iterator contains the elements in increasing
          order from items1 and items2, without duplicates. Requires
          each of items1 and items2 to be infinite iterators in
          strictly increasing order. Consumes the elements from
          items1 and items2.
      
          The input iterators should only be advanced when
          necessary, after yielding or discarding the previous item
          produced by the iterator.
      
          >>> def naturals():
          ...     num = 0
          ...     while True:
          ...         yield num
          ...         num += 1
          >>> values = merge(naturals(), naturals())
          >>> [next(values) for i in range(5)]
          [0, 1, 2, 3, 4]
          >>> values2 = merge(scale(naturals(), 2), scale(naturals(), 3))
          >>> [next(values2) for i in range(10)]
          [0, 2, 3, 4, 6, 8, 9, 10, 12, 14]
          >>> class Wrapper:  # used to test for advancing too early
          ...     def __init__(self, iterator):
          ...         self._iterator = iterator
          ...         self._last = None
          ...     def __next__(self):
          ...         self._last = next(self._iterator)
          ...         return self._last
          ...     def __iter__(self):
          ...         return self
          ...     def last(self):
          ...         return self._last
          >>> it2 = Wrapper(scale(naturals(), 2))
          >>> it3 = Wrapper(scale(naturals(), 3))
          >>> values3 = merge(it2, it3)
          >>> next(values3), it2.last(), it3.last()
          (0, 0, 0)
          >>> next(values3), it2.last(), it3.last()
          (2, 2, 3)
          >>> next(values3), it2.last(), it3.last()
          (3, 4, 3)
          >>> next(values3), it2.last(), it3.last()
          (4, 4, 6)
          >>> next(values3), it2.last(), it3.last()
          (6, 6, 6)
          """
          # add your code below
      

   3. A famous problem, first raised by R. Hamming, is to enumerate, in ascending order with no repetitions, all positive integers with no prime factors other than 2, 3, or 5. One obvious way to do this is to simply test each integer in turn to see whether it has any factors other than 2, 3, and 5. But this is very inefficient, since, as the integers get larger, fewer and fewer of them fit the requirement. As an alternative, we can build an iterator of such numbers using a generator. Let us call the required iterator of numbers s and notice the following facts about it.

      • s begins with 1.
      • The elements of scale(s, 2) are also elements of s.
      • The same is true for scale(s, 3) and scale(s, 5).
      • These are all of the elements of s.

      All that is left is to combine the elements from these sources, which can be done with the merge() generator above. Fill in the make_s() generator that produces the required iterator s.

      def make_s():
          """Produce iterator of ints that only have factors in {2, 3, 5}.
      
          >>> values = make_s()
          >>> [next(values) for i in range(18)]
          [1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 15, 16, 18, 20, 24, 25, 27, 30]
          """
          # add your code below
      

Submission

Place your solutions to all questions in the provided hw2.py file. Submit hw2.py to the autograder before the deadline. Be sure to register your partnership on the autograder if you are working with a partner.