ds-algo-study
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## Binary Trees
1. Explain and implement a Binary Tree.
+ A tree is a collection of nodes and edges between them.
+ It cannot have any cycles, which are edges that form a loop between nodes.
+ We also only consider rooted trees in computer science, which is a tree that has one root node that is able to access all other nodes.
+ For a tree to be a binary tree, each node can have a maximum of two children.
+ It's important to be able to identify and explain tree terminology as well. If given a tree, be able to point out each component.
- root: The single node of a tree that can access every other node through edges.
- parent node: A node that is connected to lower nodes in the tree. If a tree only has one node, it is not a parent node because there are no children.
- child node: A node that is connected to a higher node in the tree. Every node except for the root is a child node of some parent.
- sibling nodes: Nodes that have the same parent.
- leaf node: A node that has no children (at the ends of the branches of the tree)
- internal node: A non-leaf node (aka a parent)
- path: A series of nodes that can be traveled through edges.
- subtree: A smaller portion of the original tree. Any node that is not the root node is itself the root of a subtree.
+ Know the basics of each term
- A non-empty tree has to have a root.
- A tree doesn't have any parent nodes if there are no children.
- What's the min/max number of parent and leaf nodes for a tree with 5 nodes?
- Two extreme implementations:
![min-max-nodes-ll.png]
- Implementing in a chain results in max number of parents and min number of leaves: 4 parents, 1 leaf
![min-max-nodes-balanced.png]
- Implementing as a balanced tree results in min number of parents and max number of leaves: 2 parents, 3 leaves
+ All that we need in order to implement a binary tree is a TreeNode class that can store a value and references to a left and right child. We can create a tree by assigning the left and right properties to point to other TreeNode instances:
``` javascript
class TreeNode {
constructor(val) {
this.val = val;
this.left = null;
this.right = null;
}
}
```
2. Identify the three types of tree traversals: pre-order, in-order, and post-order.
+ Pre-order: Values are accessed as soon as the node is reached.
+ In-order: Values are accessed after we have fully explored the left but before we explore the right branch.
+ Post-order: Values are accessed after all of our children have been accessed.
+ *Breadth First: The previous three are types of Depth First Traversals. Breadth first accesses values of nodes by level, left to right, top to bottom.
+ Given a tree, be able to determine the order of each traversal type:
![Number tree]
- Breadth First: 20, 9, 24, 7, 11, 23, 27, 3, 10, 17, 36, 30
- Pre-order: 20, 9, 7, 3, 11, 10, 17, 24, 23, 27, 36, 30
- In-order: 3, 7, 9, 10, 11, 17, 20, 23, 24, 27, 30, 36
- Post-order: 3, 7, 10, 17, 11, 9, 23, 30, 36, 27, 24, 20
3. Explain and implement a Binary Search Tree.
+ A binary search tree is a binary tree with the added stipulation that all values to the left of a node are less than its value and all values to the right are greater than its value.
- Example of a Binary Search Tree ![Binary Search Tree]
+ Example of a BST with an insert method. You won't be asked to implement a removal:
``` javascript
class BST {
constructor() {
this.root = null;
}
insert(val, currentNode = this.root) {
if (!this.root) {
this.root = new TreeNode(val);
return;
}
if (val < currentNode.val) {
if (!currentNode.left) {
currentNode.left = new TreeNode(val);
} else {
this.insert(val, currentNode.left);
}
} else {
if (!currentNode.right) {
currentNode.right = new TreeNode(val);
} else {
this.insert(val, currentNode.right);
}
}
}
}
```
## Graphs
1. Know the differences between graphs and trees
* A graph can:
+ Consist of any collection of nodes and edges (no limits on connections)
+ Have cycles
+ Have disconnected portions (a forest, with multiple trees, for example)
+ Be missing a root node (don't have to have one node that connects to everything)
* In a tree, we had an idea of children and parents, in a graph we have neighbors (no hierarchy)
2. What are three ways that we can implement a graph? What are each implementations' advantages or disadvantages?
* Adjacency Matrix - 2D Array
+ Visually clear what's going on
+ One axis (outside array) has an entry (inner array) for each node in the graph. If one node is connected to another node in the graph, our entry in the inner array is set to true. Otherwise the entry is false.
``` javascript
let matrix = [
/* A B C D E F */
/*A*/
[true, true, true, false, true, false],
/*B*/
[false, true, false, false, false, false],
/*C*/
[false, true, true, true, false, false],
/*D*/
[false, false, false, true, false, false],
/*E*/
[true, false, false, false, true, false],
/*F*/
[false, false, false, false, true, true]
];
```
* Adjacency List - POJO
+ Object where every value in the graph has a key
+ Value for the key is an array with each other node that it is connected to (neighbors)
+ Easy to iterate through
+ Doesn't take up as much space as an Adjacency Matrix or Node
+ Can refer to the entire graph by referencing the object
``` javascript
let list = {
a: ['b', 'c', 'e'],
b: [],
c: ['b', 'd'],
d: [],
e: ['a'],
f: ['e']
};
```
* Object-Oriented (ex: using Nodes)
+ Similar to our linked list or tree implementations
+ Track the value and the neighbors array as instance variables on the node
+ We don't have a reference to the overall graph with this implementation
``` javascript
class GraphNode {
constructor(val) {
this.val = val;
this.neighbors = [];
}
}
```
3. Given a graph in one of the above implementations, be able to traverse the graph in a breadth-first or depth-first manner.
* We can use recursion or iteration to traverse each node.
* We generally want to keep track of each node that we've visited already so that we don't get trapped in cycles. Easiest way to do this is to keep a Set variable that we update as we traverse to each node.
* The projects from W08D02 and their solutions are a great resource here.
+ Be comfortable with taking either an iterative or a recursive approach to traversing a graph, as well as being able to work with either an adjacency list (like in the friendsOf problem) or a node class (like in the breadthFirstSearch or maxValue problems).
+ Practice taking the implementation that you did in the project and converting it to a different implementation. You probably used recursion for friendsOf, so try using iteration with a stack array, etc.
4. Be able to make conclusions from these traversals
+ Is it possible to get from node A to node B?
- Here we're really implementing a search, like the breadthFirstSearch problem.
+ What is the maximum/minimum value we can encounter if we start at node X?
- Instead of returning a boolean, we want to compare values of nodes and return the appropriate value
- If we do this recursively we can compare this node and to each of its neighbors values and return the maximum up the call stack.
- If we do this iteratively, we can keep a currentMax variable as we traverse and update it if we find a new max value.
+ etc.
## **Whiteboarding Tips and Tricks**
* Companies whiteboard because steps for solving wb problems are almost nearly identical to solving real-world practical situations.
+ They are looking to test a few things:
- Can you think algorithmically? Think efficiently?
- Are you a good communicator?
- Can you code neatly and correctly?
**`Correct Steps`**
1. Repeat and clarify the problem.
2. Identify Edge Cases & I/O.
3. Pseudocode IF NECESSARY.
4. Implement Code.
5. Walk through an example input.
6. Det. Time and Space (Theta).
- Time is expensive, space is cheap.
- Ask interviewer is optimization is even necessary.
**`Strategies`**
* Hash Maps.
* Divide and Conquer DP Programming.
* Using Math Properties.
* **`Amortized Analysis`** : Method for analyzing a given algorithm's complexity, or how much a resource, especially time or or memory, it takes to execute.
* Keeping a Stack or Queue on the side to track values.
* Keep two pointers for the same iteration.
* Perform an operation twice.
* Sort the input.
* Approach the problem from the other end.
* Use binary numbers instead of decimal numbers.
* Use binary search instead of incrementation.
**If you have seen the problem before, just tell them you have**.
---
# **Notes**
## **Binary Trees**
* **`Graph`** : A collection of nodes and any edges between those nodes. (Linked Lists and Trees are both considered subclasses of graphs)
* **`Tree`** : Graph that does not contain any cycles.
+ In CS we only refer to trees that are "`rooted`", or a tree where there exists a node that is accessible from every other node.
* **`Binary Tree`** : Tree where nodes have at most 2 children.
``` js
class TreeNode {
constructor(val) {
this.val = val;
this.left = null;
this.right = null;
}
}
a.left = b;
a.right = c;
b.left = d;
b.right = e;
c.right = f;
```
* Common way to implement a binary tree is by using a node class with OOP.
**Basic Tree Terminology**
* **`Tree`** : Graph with no cycles.
+ Cycle is a path through edges that begins and ends at the same node.
+ In CS, trees must have roots!
* **`Binary Tree`** : Tree where nodes have at most 2 nodes.
* **`Root`** : The ultimate parent, the single node s tree that can access every other node through edges; root does not have a parent.
* **`Internal Node`** : Node that has children.
* **`Leaf`** : Node that does not have any children.
* **`Path`** : A series of nodes that can be traveled through edges.
**Traversing Trees**
* Trees can be traveresed in multiple ways: Depth-First or Breadth-First.
* Three common ways to go depth-first:
+ In-Order
+ Pre-Order
+ Post-Order
* **`Breadth First`** : Traversing level by level, visiting every node at each stage.
``` js
A
/
\
B C /
/ \
D E F
A, B, C, D, E, F
```
* **`Depth-First`** :
``` js
A
/
\
B C /
/ \
D E F
A, B, D, C, E, F
```
* **`Pre-Order Traversal`** :
+ F, B, A, D, C, E, G, I, H
+ Access data of the current Node
+ Recursively visit the left sub tree
+ Recursively visit the right sub tree
* **`In-Order Traversal`** :
+ A, B, C, D, E, F, G, H, I
+ Recursively visit the left sub tree
+ Access the data of the current node
+ Recursively visit the right sub tree
* **`Post-Order Traversal`** :
+ A, C, E, D, B, H, I, G, F
+ Recursively visit the left sub tree
+ Recursively visit the right sub tree
+ Access the data of the current node
**`Binary Search Trees`**
* A Binary Tree is a **Binary Search Tree** if:
+ The left subtree contains values less than the root.
+ AND the right subtree contains values greater than or equal to the root
+ AND the left subtree is a Binary Search Tree
+ AND the right subtree is a Binary Search Tree
* BSTs are sorted Data Structures
* If this printing function is called on a BST, the values will be print out in ascending order.
``` js
function inOrderPrint(root) {
if (!root) return;
inOrderPrint(root.left);
console.log(root.val);
inOrderPrint(root.right);
}
// BST 1: 42
// BST 2: 4, 5, 6
// BST 3: 1, 5, 7, 10, 16, 16
```
* The best BSTs are **height balanced**.
---
# **Notes**
## **Graphs**
* **Graph** : Any collection of nodes and edges, it is much more relaxed compared to other trees.
+ May lack a root node.
+ May have cycles.
+ May have any number of edges leaving a node.
**GraphNode Class**
``` js
class GraphNode {
constructor(val) {
this.val = val;
this.neighbors = [];
}
}
let a = new GraphNode("a");
let b = new GraphNode("b");
let c = new GraphNode("c");
let d = new GraphNode("d");
let e = new GraphNode("e");
let f = new GraphNode("f");
a.neighbors = [b, c, e];
c.neighbors = [b, d];
e.neighbors = [a];
f.neighbors = [e];
```
* **Adjacency Matrix** : Mathematician's preferred way of representing a graph.
``` js
let matrix = [
/* A B C D E F */
/*A*/
[true, true, true, false, true, false],
/*B*/
[false, true, false, false, false, false],
/*C*/
[false, true, true, true, false, false],
/*D*/
[false, false, false, true, false, false],
/*E*/
[true, false, false, false, true, false],
/*F*/
[false, false, false, false, true, true],
];
```
* **Adjacency List** : Using an object to represent node labels.
``` js
let graph = {
a: ["b", "c", "e"],
b: [],
c: ["b", "d"],
d: [],
e: ["a"],
f: ["e"],
};
```
---
## **Graph Traversal**
**Traversal with Graph Node**
**Depthfirst Recursion**
``` js
class GraphNode {
constructor(val) {
this.val = val;
this.neighbors = [];
}
}
let a = new GraphNode("a");
let b = new GraphNode("b");
let c = new GraphNode("c");
let d = new GraphNode("d");
let e = new GraphNode("e");
let f = new GraphNode("f");
a.neighbors = [e, c, b];
c.neighbors = [b, d];
e.neighbors = [a];
f.neighbors = [e];
```
``` js
function depthFirstRecur(node, visited = new Set()) {
// if this node has already been visited, then return early
if (visited.has(node.val)) return;
// otherwise it hasn't yet been visited,
// so print it's val and mark it as visited.
console.log(node.val);
visited.add(node.val);
// then explore each of its neighbors
node.neighbors.forEach((neighbor) => {
depthFirstRecur(neighbor, visited);
});
}
```
``` js
function depthFirstIter(node) {
let visited = new Set();
let stack = [node];
while (stack.length) {
let node = stack.pop();
// if this node has already been visited, then skip this node
if (visited.has(node.val)) continue;
// otherwise it hasn't yet been visited,
// so print it's val and mark it as visited.
console.log(node.val);
visited.add(node.val);
// then add its neighbors to the stack to be explored
stack.push(...node.neighbors);
}
}
```
**Traversal with Adjacency List**
``` js
let graph = {
a: ["b", "c", "e"],
b: [],
c: ["b", "d"],
d: [],
e: ["a"],
f: ["e"],
};
```
``` js
function depthFirst(graph) {
let visited = new Set();
for (let node in graph) {
_depthFirstRecur(node, graph, visited);
}
}
function _depthFirstRecur(node, graph, visited) {
if (visited.has(node)) return;
console.log(node);
visited.add(node);
graph[node].forEach((neighbor) => {
_depthFirstRecur(neighbor, graph, visited);
});
}
depthFirst(graph);
```
# **Notes**
## **Network Knowledge**
## **OSI Network Model**
* **`OSI`** : The Open Systems Interconnection reference model is a well known Network Model created in the UK that has both conceptual layers **and** suggested protocols for each.
+ There are seven layers to the OSI Model.
+ **`Application`** : Includes information used by client-side software, data from this later interacts directly with applications. (i.e. HTTP)
+ **`Presentation`** : The syntax later that converts data from machine-readable to human-readable. Includes data compression, encryption, and character encoding. (i.e. JPEG & GIF)
+ **`Session`** : Includes protocols responsible for authentication and data continuity. Includes authorization or re-establishing a dropp connection. (i.e. RPC (Remote Procedure Call))
+ **`Transport`** : Utilization of transport protocols (i.e. TCP and UDP)
+ **`Network`** : Basically the internet layer (i.e. IP)
+ **`Data Link`** : Deal with connections from one machine's network interface to another. (i.e. ethernet)
+ **`Physical`** : Translating raw electrical signals to bits & bytes of data. (i.e. Wi-Fi & DSL)
* It is important to remember that OSI Model is highly conceptual, it's practical uses are limited.
* TCP/IP is much more practical compared to OSI, but it is purely practical.
---
## **TCP/IP Model**
* **`Reference Model`** : A High-level overview of a complex topic provided by an organization that manages it.
**Layers of the TCP/IP Model**
* **`Application`** : Includes protocols related to user-facing data. Anything that is transmitted from the Transport layer is considered Application Layer Data (i.e. HTTP & FTP)
* **`Transport`** : TCP & UDP.
* **`Internet`** : Connects separate networks together (IP)
* **`Link`** : Lower-level communication standards.
* **`Physical?`** : There is a supposed fifth layer that cinludes all the electrical concepts that span across wires, but it is not officially stated.
* We can think of the layers of our reference model as being **encapsulated**.
---
## **Binary and Hexidecimal**
* **`Binary`** : Number expressed in the base-2 numeral system or binary numeral system.
+ **`Base`** : Number System, computers use a Base 2 NS.
* **`CPU`** : Central Processing Unit, an electronic circuitry within a computer that executes instructions that make up a computer program.
* It is easy to understand binary numbers if you remember the bases.
* 1, 2, 4, 8, 16, 32, 64, 128...etc.
* **`Bit`** : A single digit represented by either 1 (ON) or 2 (OFF).
* **`Byte`** : Sequence of 8 Bits.
* **`Hexadecimal`** : Base 16, useful numeric system due to it making it easier for us to read binary.
+ Helps to make up for base 10 only going from 0 - 9, adds in A, B, C, D, E, F.
+ You will sometimes see hexadecimals pre-pended with a `0x`.
+ `FF = 255 = 11111111 = 1 byte`
```
hexadecimal: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, A, B, C, D, E, F
decimal: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15
```
```
Regular Numbers: 4 8 15 16 23 42
Binary: 00000100 00001000 00001111 00010000 00010111 00101010
Hexadecimal: 04 08 0F 10 17 2A
```
* If you provide a numerical base in JS in the .toString() method you can convert things to binary and decimal. (parseInt can also be used)
``` js
Number(42).toString(16); // 2a
Number(42).toString(2); // 101010
```
``` js
parseInt("101010", 2); // 42
parseInt("2A", 16); // 42
```
---
## **Internet Protocol**
* **`TCP`** : Transmission Control Protocol.
+ TCP is fault tolerant and end-to-end.
+ If data transmission fails it can be cached and re-sent.
+ No single central system that can take down the entire network.
* **`IP`** : Internet Protocol.
* **`Internet`** : A series of interconnected networks sharing data.
* **`Packet`** : Format that IP data is packaged in.
+ Contains metadata in headers, and body content.
* **`Pack-Switching`** : When a message is split up into separate 'packets', delivered to a destination, and reassembled as appropriate.
> A Byte is 8 Bits
>
* IPV4
* IPv4 is still the most commonly used protocol version online.
* IPV6
* Was created because we are running out of IP Addresses.
* **`Version`** : Binary representation of the version #.
* **`Traffic Class`** : Used to Identify different types of packets.
* **`Flow Label`** : An experimental option used for adding packet sequencing into IP.
* **`Payload Length`** : Let's the receivers know how large the data in the packet will be.
* **`Next Header`** : Usually identifies the protocol type of the packet's data.
* **`Hop Limit`** : Means of preventing packets from being passed around routers forever.
* **`Source Address`** : Where the pack originated.
* **`Destination Address`** : Where the packet is heading.
> All of the headers have a fixed length of 40 bytes.
**Special Addresses**
* **Local Host** : Loopback Address, or the identifier for your current machine.
+ `127.0.0.1`
* **ALl-Interfaces Address** : Used to catch any incoming requests regardless of the intended destination.
+ `0.0.0.0`
---
## **Transport Protocols**
* Transport Protocols act as our delivery person.
* IP is concerned with machine-to-machine communication.
* HTTP is designed for application-to-application communication.
* **`Port`** : Virtual interfaces that allow a single device to host lot's of different applications & services.
* **`TCP`** : Transmission Control Protocol, the most common transport protocol.
+ Connection-oriented protocol.
+ Reliable protocol because the segments cannot be lost.
+ Heavy protocol to use (hence, sometimes there is buffering)
* **`UDP`** : User Datagram Protocol.
+ Unreliable protocol.
+ Connection-less and provides no verification if data has been received.
+ Up to the sender/recipient to manage expectati
---
## **DNS**
* **`DNS`** : Domain Name System is a distributed appraoch to providing easily-understood names for internetworked devices. (Similar to a phonebook)
+ Invented as a way to distribute the work to numerous organizations, lightening the load and allowing much more rapid growth.
* **`Domain`** : A website's domain refers to the 'friendly' name for the website's host, or the server providing the site's content.
+ Top Level Domain the last part of the domain (.com, .net, .org)
+ Second Level Domain (Name of the website)
+ Subdomain (www)
* **`Resolution`** : Process of working out which name server is needed.
+ During resolving, we work from right to left.
**DNS Records**
* **`Zone File`** : Text file containing, host names, IP Addresses, and resource types.
* Common DNS Record Types:
+ **`SOA`** : Start of Authority, let's use know what name server is the primary authority (**`THE MINIMUM REQUIREMENT IN A ZONE FILE`**)
+ **`NS`** : Keeps us connected to our zone by pointing to name servers.
+ **`A/AAAA`** : A = Domain Name to IPv4 & AAAA = Domain Name to IPv6
+ **`CNAME`** : Links Domain name to Domain Name.
+ **`MX`** : Mail Exchanger, used by e-mail clients.
+ **`TTL`** : Time to live, measure of how long a record should be cached by a DNS name server.
---
## **Network Hardware**
* **HUB** : Simplest networking device aka a Signal Splitter.
+ Cheap and found in older networks.
+ Hubs do not do any filtering, every single data packet is sent to every single device all the time.
+ No one uses them anymore.
* **Switches: Traffic Control** : Intelligent Hubs, track devices connected to them, help manage network load, and can manage separate internal networks with ease.
+ The Max Address Table is the largest difference from hubs.
+ **`Flood`** : If a destination address is unknown, the switch will flood received data out to all connect devices.
+ **`Forward`** : If the destination is known, it will send data directly to that device.
+ **`Filter`** : When data is dropped entirely from a transfer.
* **`Routers`** : Connect separate networks with each other.
+ Participate in a process called NAT (Network Address Translation)
---
## Big O Notation
### time complexity
it allow us to talk formally about how the runtime of an algorithm grows as the input grows.
n = number of operation the computer has to do can be:
f(n) = n
f(n) = n^2
f(n) = 1
f(n) = could be something entirely different !
O(n):
``` typescript
function addUpToSimple(n: number) {
let total = 0;
for (let i = 0; i < n; i++) {
total += i;
}
return total;
}
```
O(1):
``` typescript
function addUpComplex(n: number) {
return (n * (n + 1)) / 2;
}
```
O(n): maybe thinking O(2n) but we see big picture! BigONotation doesn't care about precision only about general trends _linear? quadric? constant?_
``` typescript
function printUpAndDown(n: number) {
console.log("Going up");
for (let i = 0; i < n; i++) {
console.log(i);
}
console.log("Going down");
for (let j = n - 1; j > 0; j--) {
console.log(j);
}
}
```
O(n^2)
``` typescript
function printAllPairs(n: number) {
for (let i = 0; i < n; i++) {
console.log(i);
for (let j = 0; j < n; j++) {
console.log(j);
}
}
}
```
O(n) : cuz as soon as n grows complexity grows too
``` typescript
function logAtLeastFive(n: number) {
for (let i = 0; i <= Math.max(5, n); i++) {
console.log(i);
}
}
```
O(1)
``` typescript
function logAtMostFive(n: number) {
for (let i = 0; i <= Math.min(5, n); i++) {
console.log(i);
}
}
```
### space complexity
Rules of Thumb
* most primitive _booleans numbers undefined null_ are constant space.
* strings and reference types like objects an arrays require O(n) space _n is string length or number of keys_
O(1)
``` typescript
function sum(arr: number[]) {
let total = 0;
for (let i = 0; i < arr.length; i++) {
total += arr[i];
}
}
```
O(n)
``` typescript
function double(arr: number[]) {
const newArr = [];
for (let i = 0; i < arr.length; i++) {
array.push(arr[i] * 2);
}
return newArr;
}
```
### quick note around object, array through BigO lens!
object:
``` typescript
const person = { name: "John", age: 22, hobbies: ["reading", "sleeping"] };
Object.keys(person); // ["name", "age", "hobbies"] O(n)
Object.values(person); // ["John", 22, Array(2)] O(n)
Object.entries(person); // [Array(2), Array(2), Array(2)] O(n)
person.hasOwnProperty("name"); // true O(1)
```
array:
_push() and pop()_ are always faster from _unshift() and shift()_ cuz inserting or removing element from beginning of an array needs reIndexing all elements
## Common Patterns
### frequency counter
O(n^3)
``` typescript
function same(arrOne: number[], arrTwo: number[]): boolean {
if (arrOne.length !== arrTwo.length) {
return false;
}
for (let element of arrOne) {
// for O(n)
if (!arrTwo.includes(element ** 2)) {
// includes cuz iterate over all indexes O(n)
return false;
}
arrTwo.splice(arrTwo.indexOf(element ** 2), 1); // indexOf cuz iterate over all indexes O(n)
}
return true;
}
```
frequencyCounter:
O(n)
``` typescript
function same(arr1: number[], arr2: number[]) {
if (arr1.length !== arr2.length) {
return false;
}
const frequencyCounter1 = {};
const frequencyCounter2 = {};
for (let val of arr1) {
frequencyCounter1[val] = (frequencyCounter1[val] || 0) + 1;
}
for (let val of arr2) {
frequencyCounter2[val] = (frequencyCounter2[val] || 0) + 1;
}
for (let key in frequencyCounter1) {
const sqrtKey = parseInt(key, 10) ** 2;
if (
!(sqrtKey in frequencyCounter2) || // interesting ** in ** check if object contains key
frequencyCounter2[sqrtKey] !== frequencyCounter1[key]
) {
return false;
}
}
return true;
}
```
O(n)
``` typescript
// approach one
function validAnagram(str1: string, str2: string): boolean {
if (str1.length !== str2.length) {
return false;
}
const frequencyCount1 = {};
const frequencyCount2 = {};
for (let value of str1) {
frequencyCount1[value] = (frequencyCount1[value] || 0) + 1;
}
for (let value of str2) {
frequencyCount2[value] = (frequencyCount2[value] || 0) + 1;
}
for (let key in frequencyCount1) {
if (frequencyCount1[key] !== frequencyCount2[key]) {
return false;
}
}
return true;
}
// approach two
function validAnagram(str1: string, str2: string): boolean {
if (str1.length !== str2.length) {
return false;
}
const frequencyCount = {};
for (let i = 0; i < str1.length; i++) {
const currentElement = str1[i];
frequencyCount[currentElement]
? (frequencyCount[currentElement] += 1)
: (frequencyCount[currentElement] = 1);
}
for (let i = 0; i < str2.length; i++) {
const currentElement = str2[i];
if (!frequencyCount[currentElement]) {
return false;
} else {
frequencyCount[currentElement] -= 1;
}
}
return true;
}
```
## multiple pointers
O(n^2)
``` typescript
function sumZero(arr: number[]) {
for (let i = 0; i < arr.length; i++) {
for (let j = i + 1; j < arr.length; j++) {
if (arr[i] + arr[j] === 0) {
return [arr[i], arr[j]];
}
}
}
}
```
multiple pointers:
O(n)
``` typescript
function sumZero(arr: number[]) {
let left = 0;
let right = arr.length - 1;
while (left < right) {
const sum = arr[left] + arr[right];
if (sum === 0) {
return [arr[left], arr[right]];
} else if (sum > 0) {
right--;
} else {
left++;
}
}
}
```
O(n)
``` typescript
// my approach
function countUniqueValues(arr: number[]): number {
let pointer = 0;
let count = 0;
while (pointer < arr.length) {
if (arr[pointer] === arr[pointer + 1]) {
pointer++;
} else {
count++;
pointer++;
}
}
return count;
}
// steele approach
function countUniqueValues(arr: number[]): number {
if (arr.length === 0) {
return 0;
}
let i = 0;
for (let j = 1; j < arr.length; j++) {
if (arr[i] !== arr[j]) {
i++;
arr[i] = arr[j];
}
}
return i + 1;
}
```
### sliding window
O(n^2)
``` typescript
function maxSubArraySum(arr: number[], n: number): number | null {
if (arr.length < n) {
return null;
}
let max = -Infinity;
for (let i = 0; i < arr.length - n + 1; i++) {
let tmp = 0;
for (let j = 0; j < n; j++) {
tmp += arr[i + j];
}
if (tmp > max) {
max = tmp;
}
}
return max;
}
```
O(n)
sliding window:
``` typescript
function maxSubArraySum(arr: number[], n: number): number | null {
if (arr.length < n) {
return null;
}
let maxSum = 0;
let tmpSum = 0;
for (let i = 0; i < n; i++) {
maxSum += arr[i];
}
for (let i = n; i < arr.length; i++) {
tmpSum = tmpSum - arr[i - n] + arr[i];
maxSum = Math.max(tmpSum, maxSum);
}
return maxSum;
}
```
### divide and conquer
linearSearch
O(n)
``` typescript
function linearSearch(arr, val): number {
for (let i = 0; i < arr.length; i++) {
if (arr[i] === val) {
return i;
}
}
return -1;
}
```
divide an conquer:
binarySearch
O (Log n)
``` typescript
function binarySearch(sortedArr: number[], value: number): number {
let min = 0;
let max = sortedArr.length - 1;
while (min <= max) {
let middle = Math.floor((min + max) / 2);
if (sortedArr[middle] < value) {
min = middle + 1;
} else if (sortedArr[middle] > value) {
max = middle - 1;
} else {
return middle;
}
}
return -1;
}
```
## Recursion
a process that calls itself
quick note around callStack
``` typescript
function wakeUp() {
// callStack [wakeUp]
takeShower();
eatBreakfast();
console.log("Ready to go ... ");
} // callStack []
function takeShower() {
// callStack [takeShower, wakeUp]
console.log("taking shower");
} // callStack[wakeUp]
function eatBreakfast() {
// callStack [eatBreakfast, wakeUp]
const meal = cookBreakFast();
console.log(`eating ${meal}`);
} // callStack [wakeUp]
function cookBreakFast() {
// callStack [cookBreakFast, eatBreakfast, wakeUp]
const meals = ["Cheese", "Protein Shake", "Coffee"];
return meals[Math.floor(Math.random() * meals.length)]; // callStack [eatBreakFast, wakeUp]
}
wakeUp();
```
two essential part of recursive functions
* base case : end of the line
* different input : recursive should call by different piece of data
``` typescript
function sumRange(num: number) {
if (num === 1) return 1;
return num + sumRange(num - 1);
}
function factorial(num: number) {
if (num === 1) return 1;
return num * factorial(num - 1);
}
```
helper method recursion vs pure recursion
``` typescript
// helper method recursion approach
function collectOdd(arr: number[]) {
const result = [];
function helper(helperArr: number[]) {
if (!helperArr.length) {
return;
}
if (helperArr[0] % 2 !== 0) {
result.push(helperArr[0]);
}
helper(helperArr.slice(1));
}
helper(arr);
return result;
}
// pure recursion approach
function collectOdd(arr: number[]): number[] {
let result = [];
if (!arr.length) {
return result;
}
if (arr[0] % 2 !== 0) {
result.push(arr[0]);
}
result = collectOdd(result.concat(arr.slice(1)));
return result;
}
```
## Searching Algorithms
### linear search
_indexOf() includes() find() findIndex()_ all this methods doing linear search behind the scene
O(n)
``` typescript
function linearSearch(arr: number[], value: number): number {
for (let i = 0; i < arr.length; i++) {
if (arr[i] === value) {
return i;
}
return -1;
}
}
```
### binary search
O(Log n)
``` typescript
function binarySearch(sortedArr: number[], value: number): number {
let left = 0;
let right = sortedArr.length - 1;
while (left <= right) {
const middle = Math.round((right + left) / 2);
if (sortedArr[middle] > value) {
right = middle - 1;
} else if (sortedArr[middle] < value) {
left = middle + 1;
} else {
return middle;
}
}
return -1;
}
```
### naive string search
O(n^2)
``` typescript
function naiveStringSearch(long: string, pattern: string): number {
let count = 0;
for (let i = 0; i < long.length; i++) {
for (let j = 0; j < pattern.length; j++) {
if (pattern[j] !== long[i + j]) {
break;
}
if (j === pattern.length - 1) {
count++;
}
}
}
return count;
}
```
## Sorting Algorithms
### array.sort()
array.sort(cb?) will turn all values to _string_ then sort it based on it's _unicode_
``` typescript
["a", "c", "b", "f", "d"].sort(); // (5) ["a", "b", "c", "d", "f"]
[1, 10, 6, 8, 2, 3, 5].sort(); //(7) [1, 10, 2, 3, 5, 6, 8]
/*
also receive callback function by two arguments:
a: previous number
b: next number
*/
// if callback return NEGATIVE number a will placed before b
[1, 10, 6, 8, 2, 3, 5].sort((a, b) => a - b); // (7) [1, 2, 3, 5, 6, 8, 10]
// if callback return POSITIVE number a will placed after b
(7)[(1, 2, 3, 5, 6, 8, 10)].sort((a, b) => b - a); // (7) [10, 8, 6, 5, 3, 2, 1]
// if callback return ZERO a and b will placed at the same position
```
## Quadric
### bubble sort
general: O(n^2)
nearlySortedData: O(n)
``` typescript
function bubbleSort(arr: number[]): number[] {
for (let i = 0; i < arr.length; i++) {
let noSwap = true;
for (let j = 0; j < arr.length - i; j++) {
if (arr[j] > arr[j + 1]) {
[arr[j], arr[j + 1]] = [arr[j + 1], arr[j]];
noSwap = false;
}
}
if (noSwap) break;
}
return arr;
}
// or
function bubbleSort(arr: number[]): number[] {
for (let i = arr.length; i > 0; i--) {
let noSwap = true;
for (let j = 0; j < i - 1; j++) {
if (arr[j] > arr[j + 1]) {
[arr[j], arr[j + 1]] = [arr[j + 1], arr[j]];
noSwap = false;
}
}
if (noSwap) break;
}
return arr;
}
```
### selection sort
O(n^2)
``` typescript
function selectionSort(arr: number[]) {
for (let i = 0; i < arr.length; i++) {
let min = i;
for (let j = i + 1; j < arr.length; j++) {
if (arr[j] < arr[min]) {
min = j;
}
}
if (min !== i) {
[arr[i], arr[min]] = [arr[min], arr[i]];
}
}
return arr;
}
```
### insertion sort
general: O(n^2)
nearlySortedData: O(n)
``` typescript
function insertionSort(arr) {
var currentVal;
for (let i = 1; i < arr.length; i++) {
currentVal = arr[i];
for (var j = i - 1; j >= 0 && arr[j] > currentVal; j--) {
arr[j + 1] = arr[j];
}
arr[j + 1] = currentVal;
}
return arr;
}
```
### quadric sorting algorithms comparison
| Algorithm | Time Complexity (Best) | Time Complexity (Average) | Time Complexity (worst) | Space Complexity |
| :------------: | :--------------------: | :-----------------------: | :---------------------: | :--------------: |
| bubble sort | O(n) | O(n^2) | O(n^2) | O(1) |
| insertion sort | O(n) | O(n^2) | O(n^2) | O(1) |
| selection sort | O(n^2) | O(n^2) | O(n^2) | O(1) |
## Fancy
### merge sort
O(n Log n)
``` typescript
// merge two sorted array
function merge(arr1: number[], arr2: number[]): number[] {
let result = [];
let i = 0;
let j = 0;
while (i < arr1.length && j < arr2.length) {
if (arr1[i] < arr2[j]) {
result.push(arr1[i]);
i++;
} else {
result.push(arr2[j]);
j++;
}
}
while (i < arr1.length) {
result.push(arr1[i]);
i++;
}
while (j < arr2.length) {
result.push(arr2[j]);
j++;
}
return result;
}
function mergeSort(arr: number[]): number[] {
if (arr.length <= 1) return arr;
const middle = Math.floor(arr.length / 2);
const left = mergeSort(arr.slice(0, middle));
const right = mergeSort(arr.slice(middle));
return merge(left, right);
}
```
### quick sort
in following implementation we always assume _first item_ as pivot
general: O(n Log n)
sorted: O(n^2)
``` typescript
// place pivot in the right index and return pivot index
function pivot(arr: number[], start = 0, end = arr.length - 1) {
const pivot = arr[start];
let pivotIndex = start;
for (let i = start + 1; i < end; i++) {
if (arr[i] < pivot) {
pivotIndex++;
[arr[pivotIndex], arr[i]] = [arr[i], arr[pivotIndex]];
}
}
[arr[start], arr[pivotIndex]] = [arr[pivotIndex], arr[start]];
}
function quickSort(arr: number[], start = 0, end = arr.length - 1) {
if (left < right) {
const pivot = pivot(arr, start, end);
// left
quickSort(arr, start, pivotIndex - 1);
// right
quickSort(arr, pivotIndex + 1, end);
}
return arr;
}
```
### radix sort
O(nk)
n: the number of items we sorting
k: average length of those numbers
``` typescript
// get the actual number at the given index
function getDigit(num: number, i: number): number {
return Math.floor(Math.abs(num) / Math.pow(10, i)) % 10;
}
// get number length
function digitCount(num: number): number {
if (num === 0) return 1;
return Math.floor(Math.log10(Math.abs(num))) + 1;
}
// return number by most length
function mostDigits(arr: number[]): number {
let maxDigits = 0;
for (let i = 0; i < arr.length; i++) {
maxDigits = Math.max(maxDigits, digitCount(arr[i]));
}
return maxDigits;
}
function radixSort(arr: number[]): number[] {
let maxDigitCount = mostDigits(arr);
for (let k = 0; k < maxDigitCount; k++) {
let digitBuckets = Array.from({ length: 10 }, () => []);
for (let j = 0; j < arr.length; j++) {
digitBuckets[getDigit(arr[j], k)].push(arr[j]);
}
arr = [].concat(...digitBuckets);
}
return arr;
}
```
### fancy sorting algorithms comparison
| Algorithm | Time Complexity (Best) | Time Complexity (Average) | Time Complexity (worst) | Space Complexity |
| :--------: | :--------------------: | :-----------------------: | :---------------------: | :--------------: |
| merge sort | O(n Log n) | O(n Log n) | O(n Log n) | O(n) |
| quick sort | O(n Log n) | O(n Log n) | O(n^2) | O(Log n) |
| radix sort | O(nk) | O(nk) | O(nk) | O(n + k) |
## Data Structure
### complexity comparison
| DataStructure | Insertion | Removal | Searching | Access |
| :----------------: | :-------: | :------------------------------------------------------: | :---------------------------------------: | :----: |
| Singly Linked List | O(1) | bestCase(very beginning): O(1) worstCase(very end): O(n) | O(n) | O(n) |
| Doubly Linked List | O(1) | O(1) | O(n) it is faster than Singly Linked List | O(n) |
| Stack | O(1) | O(1) | O(n) | O(n) |
| Queue | O(1) | O(1) | O(n) | O(n) |
| Binary Search Tree | O( Log n) | - | O(Log n) | - |
| Binary Heap | O( Log n) | O( Log n) | O( n ) | - |
| Hash Tables | O( 1 ) | O( 1 ) | - | O( 1 ) |
## Singly Linked list
``` typescript
class _Node {
constructor(public value: any) {}
public next: _Node | null = null;
}
class SinglyLinkedList {
private _length: number = 0;
private head: _Node | null = null;
private tail: _Node | null = null;
get length() {
return this._length;
}
get print(): null | _Node[] {
if (!this._length) return null;
const arr = [];
let currentNode = this.head;
while (currentNode) {
arr.push(currentNode.value);
currentNode = currentNode.next;
}
return arr;
}
public push(value: any): SinglyLinkedList {
const node = new _Node(value);
if (!this.head || !this.tail) {
this.head = node;
this.tail = this.head;
} else {
this.tail.next = node;
this.tail = node;
}
this._length += 1;
return this;
}
public pop(): _Node | null {
if (!this.head) return null;
let currentNode = this.head;
if (!currentNode.next) {
this.head = null;
this.tail = null;
this._length -= 1;
return currentNode;
}
while (currentNode.next && currentNode.next.next) {
currentNode = currentNode.next;
}
this.tail = currentNode;
this.tail.next = null;
this._length -= 1;
return currentNode.next as _Node;
}
public unShift(value: any): SinglyLinkedList {
const currentHead = this.head;
this.head = new _Node(value);
if (currentHead) {
this.head.next = currentHead;
} else {
this.tail = this.head;
}
this._length += 1;
return this;
}
public shift(): _Node | null {
if (!this.head) return null;
const currentHead = this.head;
this.head = currentHead.next;
this._length -= 1;
if (currentHead === this.tail) this.tail = null;
return currentHead;
}
public get(index: number): _Node | null {
if (index < 0 || index >= this._length) return null;
let currentNode = this.head;
for (let j = 0; j < index; j++) {
if (currentNode && currentNode.next) {
currentNode = currentNode.next;
}
}
return currentNode;
}
public set(index: number, value: any): _Node | null {
const node = this.get(index);
if (node) {
node.value = value;
}
return node;
}
public insert(index: number, value: any): SinglyLinkedList | null {
if (index < 0 || index >= this._length) {
return null;
} else if (index === 0) {
return this.unShift(value);
} else if (index === this._length) {
return this.push(value);
} else {
const prevNode = this.get(index - 1);
if (prevNode) {
const newNode = new _Node(value);
newNode.next = prevNode.next;
prevNode.next = newNode;
this._length += 1;
return this;
}
return prevNode;
}
}
public remove(index: number): _Node | null {
if (index === 0) {
return this.shift();
} else if (index === this._length - 1) {
return this.pop();
} else {
const prevNode = this.get(index - 1);
const currentNode = this.get(index);
if (prevNode && currentNode) {
prevNode.next = currentNode.next;
this._length -= 1;
}
return currentNode;
}
}
public reverse(): SinglyLinkedList | false {
if (this._length <= 1) return false;
let node = this.head;
this.head = this.tail;
this.tail = node;
let next: _Node | null;
let prev: _Node | null = null;
for (let i = 0; i < this._length; i++) {
if (node) {
next = node.next;
node.next = prev;
prev = node;
node = next;
}