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Commit 802e9d3

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Merge pull request #75 from amejiarosario/feat/set-questions
feat(book/set): add questions and solutions
2 parents 641b338 + 75aa67a commit 802e9d3

16 files changed

+316
-45
lines changed

‎book/D-interview-questions-solutions.asc

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@@ -577,35 +577,81 @@ The sum is 1, however `sum - k` is `0`. If it doesn't exist on the map, we will
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- Time: `O(n)`. We visit every number once.
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- Space: `O(n)`. The map size will be the same as the original array.
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// :leveloffset: +1
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// === Solutions for TOPIC Questions
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// (((Interview Questions Solutions, TOPIC)))
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// :leveloffset: -1
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:leveloffset: +1
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=== Solutions for Set Questions
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(((Interview Questions Solutions, Set)))
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:leveloffset: -1
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[#set-q-most-common-word]
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include::content/part03/set.asc[tag=set-q-most-common-word]
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This problem requires multiple steps. We can use a `Set` for quickly looking up banned words. For getting the count of each word, we used a `Map`.
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*Algorithm*:
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- Convert text to lowercase.
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- Remove any special characters `!?',;.`.
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- Convert the paragraph into words array.
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- Count how many times words occur.
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- Exclude banned words from counts.
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- Return the word (or first one) that is the most repeated.
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*Implementation*:
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[source, javascript]
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----
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include::interview-questions/most-common-word.js[tags=description;solution]
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----
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Here are heavily relying on Regular Expressions:
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- `\W+` would match all non-words.
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- `\s+` catches all whitespace.
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The line that is mapping words to count seems very busy. Here's another version of the same code a little bit more explicit:
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[source, javascript]
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----
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include::interview-questions/most-common-word.js[tags=explicit]
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----
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*Complexity Analysis*:
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- Time: `O(m + n)`, where `n` is paragraph length and `m` is the number of banned words. If we were NOT using a `Set` for prohibited words, then the runtime would have been `O(mn)`.
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- Space: `O(m + n)`. The extra space complexity is given by the size of the `Map` and `Set`.
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// [#TOPIC-q-FILENAME]
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// include::content/part03/TOPIC_FILE.asc[tag=TOPIC-q-FILENAME]
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// RESTATE REQUIREMENTS AND DESCRIPTIONS
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// *Algorithm*:
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// - STEP 1
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// - STEP 2
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// - STEP 2.1
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// - STEP 2.2
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[#set-q-longest-substring-without-repeating-characters]
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include::content/part03/set.asc[tag=set-q-longest-substring-without-repeating-characters]
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// *Implementation*:
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One of the most efficient ways to find repeating characters is using a `Map` or `Set`. Use a `Map` when you need to keep track of the count/index (e.g., string -> count) and use a `Set` when you only need to know if there are repeated characters or not.
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// [source, javascript]
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// ----
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// include::interview-questions/FILENAME.js[tags=description;solution]
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// ----
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*Algorithm*:
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* Visit each letter.
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** Insert the letter on a Set.
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** Keep track of the maximum size of the Set in `max`.
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** If the letter has been seen before, delete until there's no duplicate.
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* Return max.
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// IMPLEMENTATION NOTES
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*Implementation*:
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// *Complexity Analysis*:
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[source, javascript]
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----
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include::interview-questions/longest-substring-without-repeating-characters.js[tags=description;solution]
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----
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We could also have used a Map and keep track of the indexes, but that's not necessary. In this case, the `Set` is all we need.
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*Complexity Analysis*:
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// - Time: `O(?)`. WHY?
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// - Space: `O(?)`. WHY?
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- Time: `O(n)`. We visit each letter once.
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- Space: `O(W)`, where `W` is the max length of non-repeating characters. The maximum size of the Set gives the space complexity. In the worst-case scenario, all letters are unique (`W = n`), so our space complexity would be `O(n)`. In the avg. case where there are one or more duplicates, it uses less space than `n`, because `W < n`.

‎book/content/part03/set.asc

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=== Set
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(((Set)))
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(((Data Structures, Non-Linear, Set)))
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A set is a data structure where duplicated entries are not allowed. Set is like an array with unique values.
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A set is a data structure where duplicated entries are not allowed. A Set is like an array with only unique values.
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NOTE: JavaScript has already a built-in Set data structure.
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NOTE: JavaScript already has a built-in Set data structure.
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Take a look at the following
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example:
@@ -38,15 +38,15 @@ TIP: A hint... it should perform all operations in *O(1)** or at most *O(log n)*
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If we use a `map`, we can accomplish this. However, maps use a key/value pair. If we only use the keys, we can avoid duplicates. Since in a `map` you can only have one key at a time.
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As you might remember from the <<part03-graph-data-structures#map>> chapter, there are two ways of implementing a `map` and both can be used to create a `set`. Let's explore the difference between the two implementations are.
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As you might remember from the <<part03-graph-data-structures#map>> chapter, there are two ways of implementing a `map`, and both can be used to create a `set`. Let's explore the difference between the two implementations are.
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==== HashSet vs TreeSet
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We can implement a `map` using a *balanced BST* and using a *hash function*. If we use them to implement a `Set`, then we would have a `HashSet` and `TreeSet` respectively.
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We can implement a `map` using a *balanced BST* or a *hash function*. If we use them to implement a `Set`, we would have a `HashSet` and `TreeSet`.
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* `TreeSet`, would return the values sorted in ascending order.
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* `HashSet`, would return the values in insertion order.
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* Operations on a `HashSet` would take on average O(1) and in the worst case (rehash is due), it would take O(n).
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* Operations on a `HashSet` would take on average O(1), and in the worst case (rehash is due), it would take O(n).
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* Operation on a `TreeSet` is always O(log n).
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Let’s implement both!
@@ -65,7 +65,7 @@ include::{codedir}/data-structures/sets/tree-set.js[tag=constructor]
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----
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<1> Converts an array or any iterable data structure to a set.
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A common use case for Sets is to remove duplicated values from an array. We can do that by passing them in the constructor as follows:
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An everyday use case for Sets is to remove duplicated values from an array. We can do that bypassing them in the constructor as follows:
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.Removing duplicates from an Array using a Set
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[source, javascript]
@@ -115,7 +115,7 @@ Voilà! That’s it!
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===== Converting TreeSet to Array
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A common use case for a Set is to convert it to an array or use in an iterator (for loops, forEach, ...). Let’s provide the method for that:
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Another use case for a Set is to convert it to an array or use an iterator (for loops, forEach, ...). Let’s provide the method for that:
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.TreeSet's iterator
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[source, javascript]
@@ -151,7 +151,7 @@ No more duplicates in our array!
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Check out our https://github.com/amejiarosario/dsa.js/blob/f69b744a1bddd3d99243ca64b3ad46f3f2dd7342/src/data-structures/sets/tree-set.js#L12[GitHub repo for the full TreeSet implementation].
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Let’s now, implement a `HashSet`.
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Let’s now implement a `HashSet`.
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[[hashset]]
157157
==== HashSet
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===== Inserting values to a HashSet
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To insert items in a HashSet we use the `set` method of the `HashMap`:
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To insert items in a HashSet, we use the `set` method of the `HashMap`:
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.HashSet's `add` method
178178
[source, javascript]
@@ -181,7 +181,7 @@ include::{codedir}/data-structures/sets/hash-set.js[tag=add, indent=0]
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}
182182
----
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184-
`HashMap` stores key/value pairs, but for this, we only need the key, and we ignore the value.
184+
`HashMap` stores key/value pairs, but we only need the keys for Set, so we ignore the value.
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186186
===== Finding values in a HashSet
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===== Deleting values from a HashSet
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For deleting a value from a hashSet we use the HashMap’s delete method:
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For deleting a value from a hashSet, we use the HashMap’s delete method:
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.HashSet's `delete` method
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[source, javascript]
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==== HashSet vs HashMap Time Complexity
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We can say that `HashMap` in on average more performant O(1) vs. O(log n). However, if a
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We can say that `HashMap` in on average, more performant O(1) vs. O(log n). However, if a
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rehash happens, it will take *O(n)* instead of *O(1)*. A `TreeSet` is always *O(log n)*.
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(((Tables, Non-Linear DS, HashSet/TreeSet complexities)))
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.TreeSet vs HashSet
237237
* HashSet keeps data in insertion order
238238
* TreeSet keeps data sorted in ascending order.
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==== Practice Questions
242+
(((Interview Questions, Set)))
243+
244+
// tag::set-q-most-common-word[]
245+
===== Most common word
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*ST-1*) _Given a text and a list of banned words.
248+
Find the most common word that is not on the banned list.
249+
You might need to sanitize the text and strip out punctuation `?!,'.`_
250+
// end::set-q-most-common-word[]
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// _Seen in interviews at: Amazon._
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Examples:
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[source, javascript]
257+
----
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mostCommonWord(
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`How much wood, would a Woodchuck chuck,
260+
if a woodchuck could chuck?`,
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['a'],
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); // woodchuck or chuck (both show up twice)
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mostCommonWord(
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`It's a blue ball and its shade... Very BLUE!`,
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['and']); // blue (it show up twice, "it" and "its" once)
267+
----
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269+
Starter code:
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[source, javascript]
272+
----
273+
include::../../interview-questions/most-common-word.js[tags=description;placeholder]
274+
----
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_Solution: <<set-q-most-common-word>>_
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// tag::set-q-longest-substring-without-repeating-characters[]
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===== Longest Without Repeating
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*ST-2*) _Find the length of the longest substring without repeating characters._
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// end::set-q-longest-substring-without-repeating-characters[]
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// _Seen in interviews at: Amazon, Facebook, Bloomberg._
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Examples:
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[source, javascript]
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----
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lenLongestSubstring('aaaaa'); // 1 ('a')
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lenLongestSubstring('abccdefg'); // 5 ('cdefg')
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lenLongestSubstring('abc'); // 3 ('abc')
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----
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Starter code:
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[source, javascript]
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----
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include::../../interview-questions/longest-substring-without-repeating-characters.js[tags=description;placeholder]
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----
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_Solution: <<set-q-longest-substring-without-repeating-characters>>_

‎book/interview-questions/binary-tree-right-side-view.js

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/**
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* Find the rightmost nodes by level.
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*
7-
* @example
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* @example rightSideView(bt([1,2,3,4])); // [1, 3, 4]
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* 1 <- 1
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* / \
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* 2 3 <- 3

‎book/interview-questions/buy-sell-stock.js

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* maxProfit([1, 2, 3]); // => 2
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* maxProfit([3, 2, 1]); // => 0
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* @param {number[]} prices - Array with daily stock prices
9+
* @returns {number} - Max profit
910
*/
1011
function maxProfit(prices) {
1112
// end::description[]

‎book/interview-questions/daily-temperatures.js

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* dailyTemperatures([73, 69, 72, 76, 73]); // [3, 1, 1, 0, 0]
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*
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* @param {number[]} t - Daily temperatures
12+
* @returns {number[]} - Array with count of days to warmer temp.
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*/
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function dailyTemperatures(t) {
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// end::description[]

‎book/interview-questions/linkedlist-same-data.js

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* hasSameData(['he', 'll', 'o'], ['hel', 'lo']); // true
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* hasSameData(['hel', 'lo'], ['hi']); // false
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*
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* @param {ListNode} l1 - The root node of list 1
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* @param {ListNode} l2 - The root node of list 2
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* @param {ListNode} l1 - The root node of list 1.
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* @param {ListNode} l2 - The root node of list 2.
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* @returns {boolean} - true if has same data, false otherwise.
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*/
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function hasSameData(l1, l2) {
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// end::description[]
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// https://leetcode.com/problems/longest-substring-without-repeating-characters/submissions/
2-
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function lengthOfLongestSubstring(s: string): number {
1+
// tag::description[]
2+
/**
3+
* Find the length of the longest substring without duplicates.
4+
* @example lenLongestSubstring('abccxyz'); // => 4 (cxyz)
5+
* @param {string} s - The string.
6+
* @returns {number} - The length of the longest unique substring.
7+
*/
8+
function lenLongestSubstring(s) {
9+
// end::description[]
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// tag::placeholder[]
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// write your code here...
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// end::placeholder[]
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// tag::solution[]
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let max = 0;
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const set = new Set();
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1121
}
1222

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return max;
14-
};
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// end::solution[]
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// tag::description[]
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}
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// end::description[]
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module.exports = { lenLongestSubstring };
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describe('',()=>{
2-
it('',()=>{
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const{ lenLongestSubstring }=require('./longest-substring-without-repeating-characters');
2+
// const { } = require('../../src/index');
33

4+
[lenLongestSubstring].forEach((fn) => {
5+
describe(`Set: ${fn.name}`, () => {
6+
it('should work with null/empty', () => {
7+
const actual = '';
8+
const expected = 0;
9+
expect(fn(actual)).toEqual(expected);
10+
});
11+
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it('should work with small case', () => {
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const actual = 'abc';
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const expected = 3;
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expect(fn(actual)).toEqual(expected);
16+
});
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18+
it('should work with other case', () => {
19+
const actual = 'abccdefg';
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const expected = 5;
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expect(fn(actual)).toEqual(expected);
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});
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});
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});

‎book/interview-questions/max-subarray.js

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* maxSubArray([-3,4,-1,2,1,-5]); // => 6
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*
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* @param {number[]} a - Array
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* @returns {number} - max sum
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*/
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function maxSubArray(a) {
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// end::description[]

‎book/interview-questions/merge-lists.js

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*
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* @param {ListNode} l1 - The root node of list 1
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* @param {ListNode} l2 - The root node of list 2
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* @returns {ListNode} - The root of the merged list.
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*/
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function mergeTwoLists(l1, l2) {
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// end::description[]

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