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Commit 9b356d9

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Merge pull request #67 from amejiarosario/feat/add-exercises-solutions
Feat/add exercises solutions
2 parents 3901188 + dbdef21 commit 9b356d9

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‎.vscode/settings.json

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{
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"workbench.colorCustomizations": {
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"activityBar.activeBackground": "#fbed80",
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"activityBar.activeBorder": "#06b9a5",
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"activityBar.background": "#fbed80",
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"activityBar.foreground": "#15202b",
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"activityBar.inactiveForeground": "#15202b99",
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"activityBarBadge.background": "#06b9a5",
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"activityBarBadge.foreground": "#15202b",
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"titleBar.activeBackground": "#f9e64f",
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"titleBar.activeForeground": "#15202b",
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"titleBar.inactiveForeground": "#15202b99",
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"statusBar.background": "#f9e64f",
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"statusBarItem.hoverBackground": "#f7df1e",
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"statusBar.foreground": "#15202b"
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"titleBar.activeBackground": "#f9e64f",
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"titleBar.activeForeground": "#15202b",
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"titleBar.inactiveBackground": "#f9e64f99",
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"titleBar.inactiveForeground": "#15202b99"
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},
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"peacock.color": "#f9e64f"
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"peacock.remoteColor": "#f9e64f"
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}

‎book/A-time-complexity-cheatsheet.asc

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[appendix]
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[[a-time-complexity-cheatsheet]]
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== Cheatsheet
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== Cheatsheet [[a-time-complexity-cheatsheet]]
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This section summerize what we are going to cover in the rest of this book.
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‎book/B-self-balancing-binary-search-trees.asc

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[appendix]
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[[b-self-balancing-binary-search-trees]]
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== Self-balancing Binary Search Trees
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== Self-balancing Binary Search Trees [[b-self-balancing-binary-search-trees]]
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Binary Search Trees (BST) are an excellent data structure to find elements very fast _O(log n)_.
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However, when the BST branches have different branch sizes, then the performance suffers.
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To be more specific we rotated node `1` to the left to balance the tree.
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Let's examine all the possible rotation we can do to balance a tree.
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[[tree-rotations]]
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=== Tree Rotations
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=== Tree Rotations [[tree-rotations]]
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(((Tree Rotations)))
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We can do single rotations left and right and also we can do double rotations.
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Let's go one by one.

‎book/C-AVL-tree.asc

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[appendix]
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[[c-avl-tree]]
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== AVL Tree
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== AVL Tree [[c-avl-tree]]
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(((AVL Tree)))
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(((Tree, AVL)))
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AVL Tree is named after their inventors (**A**delson-**V**elsky and **L**andis).
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The first thing we do is to see if one subtree is longer than the other.
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If so, then we check the children balance to determine if need a single or double rotation and in which direction.
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You can review <<b-self-balancing-binary-search-trees#tree-rotations>> in case you want a refresher.
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You can review <<tree-rotations>> in case you want a refresher.
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[appendix]
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[[d-interview-questions-solutions]]
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== Interview Questions Solutions
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(((Interview Questions Solutions)))
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=== Solutions for Array Questions
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(((Interview Questions Solutions, Arrays)))
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:leveloffset: -1
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[#array-q-max-subarray]
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include::content/part02/array.asc[tag=array-q-max-subarray]
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The first step is making sure we understand the problem well. Let's do basic examples:
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----
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A = [-5, 6, 9, -8]
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B = [-1, 6, -3, 8]
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----
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What's the subarray with the maximum sum? For A, it will be `[6, 9]` and for B, it will be `[6, -3, 8]`.
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We could generate all possible subarrays, add them up, and then pick the max number.
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[source, javascript]
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----
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include::interview-questions/max-subarray.js[tag=maxSubArrayBrute1]
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----
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This code is simple to understand; however, not very efficient. The runtime is `O(n^3)`.
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If you noticed we adding up the numbers from `i` to `j` on each cycle. But, we can optimize this. We can keep a local variable and add the new number to it. That way, we don't have to revisit previous numbers.
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[source, javascript]
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----
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include::interview-questions/max-subarray.js[tag=maxSubArrayBrute2]
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----
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The runtime is much better: `O(n)`. Can we still do better?
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We can use a greedy approach, where do one pass through the array. We only add the numbers if their sum is larger than just taking the current element.
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[source, javascript]
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----
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include::interview-questions/max-subarray.js[tag=description]
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include::interview-questions/max-subarray.js[tag=solution]
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----
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The runtime is `O(n)` and a space complexity of `O(1)`.
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[#array-q-buy-sell-stock]
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include::content/part02/array.asc[tag=array-q-buy-sell-stock]
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There are multiple examples that we can simulate: bear markets (when prices are going down), bullish markets (when prices are going up), and zig-zag markets (when prices are going up and down).
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[source, javascript]
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----
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// zig-zag market
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maxProfit([5, 10, 5, 10]); // => 5
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// bullish market
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maxProfit([1, 2, 3]); // => 2
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// bearish market
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maxProfit([3, 2, 1]); // => 0
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----
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During the bearish markets, the profit will always be 0. Since if you buy, we are only going to lose.
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We can do a brute force solution doing all combinations:
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[source, javascript]
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----
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include::interview-questions/buy-sell-stock.js[tag=maxProfitBrute1]
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----
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The runtime for this solution is `O(n^2)`.
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A better solution is to eliminate the 2nd for loop and only do one pass.
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Algorithm:
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- Do one pass through all the prices
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- Keep track of the minimum price seen so far.
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- calculate `profit = currentPrice - minPriceSoFar`
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- Keep track of the maximun profit seen so far.
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- Return maxProfit.
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[source, javascript]
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----
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include::interview-questions/buy-sell-stock.js[tag=description]
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include::interview-questions/buy-sell-stock.js[tag=solution]
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----
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The runtime is `O(n)` and a space complexity of `O(1)`.

‎book/ch02-git-basics-chapter.asc

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‎book/config

Submodule config updated from f657e9b to 8d7eb9c

‎book/content/part02/array-vs-list-vs-queue-vs-stack.asc

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|===
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.2+.^s| Data Structure 2+^s| Searching By 3+^s| Inserting at the 3+^s| Deleting from .2+.^s| Space
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^|_Index/Key_ ^|_Value_ ^|_beginning_ ^|_middle_ ^|_end_ ^|_beginning_ ^|_middle_ ^|_end_
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| <<part02-linear-data-structures#array>> ^|O(1) ^|O(n) ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|O(n) ^|O(1) ^|O(n)
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| <<array-chap>> ^|O(1) ^|O(n) ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|O(n) ^|O(1) ^|O(n)
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| <<part02-linear-data-structures#singly-linked-list>> ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|*O(n)* ^|O(1) ^|O(n) ^|*O(n)* ^|O(n)
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| <<part02-linear-data-structures#doubly-linked-list>> ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|*O(1)* ^|O(1) ^|O(n) ^|*O(1)* ^|O(n)
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| <<part02-linear-data-structures#stack>> ^|- ^|- ^|- ^|- ^|O(1) ^|- ^|- ^|O(1) ^|O(n)

‎book/content/part02/array.asc

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[[array]]
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=== Array
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=== Array [[array-chap]]
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(((Array)))
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(((Data Structures, Linear, Array)))
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Arrays are one of the most used data structures. You probably have used it a lot but are you aware of the runtimes of `splice`, `shift`, `indexOf` and other operations? In this chapter, we are going deeper into the most common operations and their runtimes.
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.Fixed vs. Dynamic Size Arrays
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****
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Some programming languages have fixed size arrays like Java and C++. Fixed size arrays might be a hassle when your collection gets full, and you have to create a new one with a bigger size. For that, those programming languages also have built-in dynamic arrays: we have `vector` in C++ and `ArrayList` in Java. Dynamic programming languages like JavaScript, Ruby, and Python use dynamic arrays by default.
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Some programming languages have fixed size arrays like Java and {cpp}.
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Fixed size arrays might be a hassle when your collection gets full, and you have to create a new one with a bigger size. For that, those programming languages also have built-in dynamic arrays: we have `vector` in {cpp} and `ArrayList` in Java. Dynamic programming languages like JavaScript, Ruby, and Python use dynamic arrays by default.
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****
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Arrays look like this:
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|===
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//end::table
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==== Array Exercises
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==== Interview Questions
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(((Interview Questions, Arrays)))
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1) Implement an efficient algorithm that rotate an array `a` an `k` number of times.
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// tag::array-q-max-subarray[]
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===== Max Subarray
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Given an array of integers, find the maximum sum of consecutive elements (subarray).
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// end::array-q-max-subarray[]
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/**
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*
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* @example
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* rotateLeft([1,2,3,4,5], 4); // [5,1,2,3,4]
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*
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* rotateLeft(Array(1e6).fill(1), 1e4); // <scale testing>
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*
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*/
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function rotateLeft(a, k) {
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include::../../interview-questions/max-subarray.js[tag=description]
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// write you code here
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}
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_Solution: <<array-q-max-subarray>>_
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// tag::array-q-buy-sell-stock[]
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===== Best Time to Buy and Sell an Stock
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2) Implement an algorithm that takes two arrays of numbers and return a new array with the sum.
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You are given an array of integers. Each value represents the closing value of the stock on that day. You are only given one chance to buy and then sell. What's the maximun profit you can obtain? (Note: you have to buy first and then sell)
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// end::array-q-buy-sell-stock[]
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*
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* @example
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*
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// write you code here
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}
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_Solution: <<array-q-buy-sell-stock>>_
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// https://leetcode.com/problemset/algorithms/?topicSlugs=array

‎book/content/part03/map.asc

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The attractive part of Maps is that they are very performant usually *O(1)* or *O(log n)* depending on the implementation. We can implement the maps using two different underlying data structures:
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* *TreeMap*: it’s a map implementation that uses a self-balanced Binary Search Tree (like <<c-avl-tree#>>). The BST nodes store the key, and the value and nodes are sorted by key guaranteeing an *O(log n)* look up.
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* *TreeMap*: it’s a map implementation that uses a self-balanced Binary Search Tree (like <<c-avl-tree>>). The BST nodes store the key, and the value and nodes are sorted by key guaranteeing an *O(log n)* look up.
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<<<
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include::hashmap.asc[]

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