[Sum of Multiples] Add Approaches

exercises/practice/sum-of-multiples/.approaches/config.json

@@ -0,0 +1,18 @@
+{
+ "introduction": {
+ "authors": [
+ "MatthijsBlom"
+ ]
+ },
+ "approaches": [
+ {
+ "uuid": "7dd85d5b-12bd-48a6-97fe-8eb7dd87af72",
+ "slug": "filter-for-multiples",
+ "title": "Filter for multiples",
+ "blurb": "Use the built-in filter function to select the numbers that are multiples, then sum these.",
+ "authors": [
+ "MatthijsBlom"
+ ]
+ }
+ ]
+}

exercises/practice/sum-of-multiples/.approaches/filter-for-multiples/content.md

@@ -0,0 +1,253 @@
+# `filter` for multiples
+
+```python
+def sum_of_multiples(limit, factors):
+ return sum(filter(
+ lambda n: any(n % f == 0 for f in factors if f != 0),
+ range(limit)
+ ))
+```
+
+Probably the most straightforward way of solving this problem is to
+
+1. look at every individual integer between `0` and `limit`,
+2. check that it is a multiple of any of the given `factors`, and
+3. add it to the sum when it is.
+
+
+## Notable language features used in this solution
+
+### Built-in function: `sum`
+
+Adding all the numbers in a collection together is a very common operation.
+Therefore, Python provides the built-in function [`sum`][builtin-sum].
+
+`sum` takes one argument, and requires that it be **iterable**.
+A value is iterable whenever it makes sense to use it in a `for` loop like this:
+
+```python
+for element in iterable_value: # 👈
+ ...
+```
+
+The `list` is the most commonly used iterable data structure.
+Many other containers are also iterable, such as `set`s, `tuple`s, `range`s, and even `dict`s and `str`ings.
+Still other examples include iterators and generators, which are discussed below.
+
+When given a collection of numbers, `sum` will look at the elements one by one and add them up.
+The result is a single number.
+
+```python
+numbers = range(1, 100 + 1) # 1, 2, …, 100
+sum(numbers) # ⟹ 5050
+```
+
+Had the highlighted solution not used `sum`, it might have looked like this:
+
+```python
+def sum_of_multiples(limit, factors):
+ multiples = filter(
+ lambda n: any(n % f == 0 for f in factors if f != 0),
+ range(limit))
+ total = 0
+ for multiple in multiples:
+ total += multiple
+ return total
+```
+
+
+### Built-in function: `filter`
+
+Selecting elements of a collection for having a certain property is also a very common operation.
+Therefore, Python provides the built-in function [`filter`][builtin-filter].
+
+`filter` takes two arguments.
+The first is a **predicate**.
+The second is the iterable the elements of which should be filtered.
+
+A predicate is a function that takes one argument (of any particular type) and returns a `bool`.
+Such functions are commonly used to encode properties of values.
+An example is `str.isupper`, which takes a `str` and returns `True` whenever it is uppercase:
+
+```python
+str.isupper("AAAAH! 😱") # ⟹ True
+str.isupper("Eh? 😕") # ⟹ False
+str.isupper("⬆️💼") # ⟹ False
+```
+
+Thus, the function `str.isupper` represents the property of _being an uppercase string_.
+
+Contrary to what you might expect, `filter` does not return a data structure like the one given as the iterable argument:
+
+```python
+filter(str.isupper, ["THUNDERBOLTS", "and", "LIGHTNING"])
+# ⟹ <filter object at 0x000002F46B107BE0>
+```
+
+Instead, it returns an **iterator**.
+
+An iterator is an object whose sole purpose is to guide iteration through some data structure.
+In particular, `filter` makes sure that elements that do not satisfy the predicate are skipped:
+
+```python
+for word in filter(str.isupper, ["THUNDERBOLTS", "and", "LIGHTNING"]):
+ print(word)
+# prints:
+# THUNDERBOLTS
+# LIGHTNING
+```
+
+An iterator is a bit like a cursor that can move only to the right.
+
+The main differences between containers (such as `list`s) and iterators are
+
+- Containers can, depending on their contents, take up a lot of space in memory, but iterators are typically very small regardless of how many elements they 'contain'.
+- Containers can be iterated over multiple times, but iterators can be used only once.
+
+To illustrate the latter difference:
+
+```python
+def is_even(n):
+ return n % 2 == 0
+
+numbers = range(20) # 0, 1, …, 19
+even_numbers = filter(is_even, numbers) # 0, 2, …, 18
+sum(numbers) # ⟹ 190
+sum(numbers) # ⟹ 190
+sum(even_numbers) # ⟹ 90
+sum(even_numbers) # ⟹ 0
+```
+
+Here, `sum` iterates over both `numbers` and `even_numbers` twice.
+
+In the case of `numbers` everything is fine.
+Even after looping through the whole of `numbers`, all its elements are still there, and so `sum` can ask to see them again without problem.
+
+The situation with `even_numbers` is less simple.
+To use the _cursor_ analogy: after going through all of `even_number`'s 'elements' &ndash; actually elements of `numbers` &ndash; the cursor has moved all the way to the right.
+It cannot move backwards, so if you wish to iterate over all even numbers again then you need a new cursor.
+We say that the `even_numbers` iterator is _exhausted_. When `sum` asks for its elements again, `even_numbers` comes up empty and so `sum` returns `0`.
+
+Had the highlighted solution not used `filter`, it might have looked like this:
+
+```python
+def sum_of_multiples(limit, factors):
+ def is_multiple(n):
+ return any(n % f == 0 for f in factors if f != 0)
+
+ multiples = [candidate for candidate in range(limit) if is_multiple(candidate)]
+ return sum(multiples)
+```
+
+This variant stores all the multiples in a `list` before summing them.
+Such a list can become very big.
+For example, if `limit = 1_000_000_000` and `factors = [1]` then `multiples` will take up 8 gigabytes of memory!
+It is to avoid unnecessarily creating such large intermediate data structures that iterators are often used.
+
+
+### A function expression: `lambda`
+
+Typically, when using higher-order functions like `filter` and `map`, the function to pass as an argument does not yet exist and needs to be defined first.
+
+The standard way of defining functions is through the `def` statement:
+
+```python
+def name(parameters):
+ statements
+```
+
+Downsides of this construct include
+
+- the syntax can be a bit bulky
+- it requires coming up with a fresh name
+
+These qualities can be quite bothersome when you just need a simple function of no particular significance for single use only.
+In situations like this you might like to use a **lambda expression** instead.
+
+A lambda expression is a specific kind of expression that evaluates to a function.
+It looks like this:
+
+```python
+lambda parameters: expression # general form
+lambda a, b, x: a * x + b # specific example
+```
+
+This latter lambda expression evaluates to a function that takes three arguments (`a`, `b`, `x`) and returns the value `a * x + b`.
+Except for not having a name, it is equivalent to the function defined by
+
+```python
+def some_name(a, b, x):
+ return a * x + b
+```
+
+A lambda expression need not necessarily be passed as an argument.
+It can also be applied to arguments immediately, or assigned to a variable:
+
+```python
+lambda a, b, x: a * x + b
+# ⟹ <function <lambda> at 0x000001F36A274CC0>
+
+(lambda a, b, x: a * x + b)(2, 3, 5)
+# ⟹ 13
+
+some_function = lambda a, b, x: a * x + b
+some_function(2, 3, 5)
+# ⟹ 13
+
+list(filter(
+ lambda s: len(s) <= 3, 
+ ["aaaa", "b", "ccccc", "dd", "eee"]
+))
+# ⟹ ['b', 'dd', 'eee']
+```
+
+~~~~exercism/note
+Immediately applying a lambda expression is possible, but generally pointless:
+
+```python
+# Instead of
+(lambda a, b, x: a * x + b)(2, 3, y)
+# you might as well write
+2 * y + 3
+```
+~~~~
+
+~~~~exercism/caution
+Assigning a lambda expressions to variables is unidiomatic.
+When you want to give a lambda expression a name, use `def` instead.
+~~~~
+
+Only functions that can be defined using a single (`return`) statement can be written as a lambda expression.
+If you need multiple statements, you have no choice but to use `def`.
+
+Had the highlighted solution not used `lambda`, it might have looked like this:
+
+```python
+def sum_of_multiples(limit, factors):
+ def is_multiple(n): 
+ return any(n % f == 0 for f in factors if f != 0)
+
+ return sum(filter(is_multiple, range(limit)))
+```
+
+
+### Built-in function: `any`
+
+...
+
+
+### A generator expression
+
+...
+
+
+## Reflections on this approach
+
+An important advantage of this approach is that it is very easy to understand.
+However, it suffers from potentially performing a lot of unnecessary work, for example when all `factors` are large, or when there are no `factors` at all.
+
+<!-- TODO elaborate -->
+
+
+[builtin-sum]: https://docs.python.org/3/library/functions.html#sum "Built-in Functions: sum"
+[builtin-filter]: https://docs.python.org/3/library/functions.html#filter "Built-in Functions: filter"

exercises/practice/sum-of-multiples/.approaches/filter-for-multiples/snippet.txt

@@ -0,0 +1,5 @@
+def sum_of_multiples(limit, factors):
+ return sum(filter(
+ lambda n: any(n % f == 0 for f in factors if f != 0),
+ range(limit)
+ ))