P.R.E.P

Parameters, Returns, Examples, Pseudo Code

Methods Questions

Brute Force

ITS ALL YOU NEED

But... Let's Open More Doors

A data structure is simply an organized way of storing data  

It is a pattern of collecting and organizing data for performing various operations correctly and efficiently.

BUT WHY?

What if I wanted to store information about a stock trader?

 

Object or Array?

Object Makes The Most Sense

let stockTrader = {
    firstName: 'Bob',
    lastName: 'Wehadababyitsaboy',
    location: 'New York, New York, USA',
    jobTitle: 'Trading Associate',
    yoe: 1,
    recentTrades: ['tsla','gme','amc']
}

What if I wanted to store recent tsla purchase prices?

 

Object or Array?

Array Makes The Most Sense

let tslaPurchasePrices = 
	[208.17, 205.05, 217.57, 220.48]

 An algorithm is just    the steps you take to solve a problem

Thinking through appropriate

algorithms is how we can efficiently solve problems

We care about
Space
&

Time

Space
How much memory is being used

Time
How many operations are executed per input

When taking about the efficiency of a solution we can use

Big-O Notation

BUT WHY?

Big-O notation mathematically describes the complexity of an algorithm in terms of time and space

BUT WHY?

All computers, environments, ect... are different

We care about

rough estimates

Common Complexities

Git Connected Big O Article

O(1)

For all inputs to our algorithm there is and will always be only one operation required

  • Order 1
  • Constant Time

O(1) Example

No matter how many inputs are located in num there will only ever be one operation needed!

const nums = [1,2,3,4,5] 
const firstNumber = nums[0]

O(N)

For all inputs to our algorithm there will be one

operation per input

  • Order n
  • Linear
  • Linear Scailing

O(N) Example

Here we sum the numbers in the array. We have to add each number to a running sum, so we have to operate on each one. This means one operation per input.

const nums = [1,2,3,4,5]
let sum = 0
for(let num of nums){
	sum += num
}

O(1) VS O(N)

Summing function for a sorted, contiguous array of integers that starts with the number 1? Could easily be O(n) but...

const sumContiguousArray = function(ary){ 
  //get the last item
  const lastItem = ary[ary.length - 1]
  //Gauss's trick
  return lastItem * (lastItem + 1) / 2
}
const nums = [1,2,3,4,5]
const sumOfArray = sumContiguousArray(nums)

O(N^2)

Quadratic

Text

const hasDuplicates = function(num){ 
  for(let i = 0; i < nums.length; i++){
    const thisNum = nums[i]
    for(let j = 0; j < nums.length; j++){
      if(j !== i){ 
        const otherNum = nums[j]
       }
    }  
    if(otherNum === thisNum) return true
  }
  return false
}

const nums = [1,2,3,4,5,5]
hasDuplicates(nums) //true

O(N^2)

Here we’re iterating over our array, which we already know is O(n), and another iteration inside it, which is another O(n). For every item in our list, we have to loop our list again to calculate what we need.

O(LOG N)

Logarithmic Time
As you loop your operations are halved

Divide and Conquer?

Git Connected Big O Article

Committing these to memory is important

I have a problem

The price of boxes is 

TOO DAMN HIGH

Let's take a look

I want to create a bidding platform

How can we find the min and max bid?

Let's loop through

all the bids

Text

const findMaxBid = function(bids){
  let maxBid = bids[0],
      minBid = bids[0]
  for(let i = 0; i < bids.length; i++){
    for(let j = 0; j < bids.length; j++){
      if(bids[i] > bids[j] && bids[i] > maxBid ){ 
        maxBid = bids[i]
       }else if(bids[i] < bids[j] && bids[i] < minBid){
         minBid = bids[i]
       }
    }  
  }
  return [minBid, maxBid]
}

const allBids = [2,7,3,1,4,5,5]
console.log(findMaxBid(allBids))

Really bad code?,

but what is the complexity?

Git Connected Big O Article

What if one loop found the min and the other found the max

Finding the max bid

Text

const findMaxBid = function(bids){
  let maxBid = bids[0]
  for(let i = 0; i < bids.length; i++){
    if(bids[i] > maxBid){
      maxBid = bids[i]
    }
  }
  return maxBid
}

const allBids = [1,2,7,3,4,5,5]
console.log(findMaxBid(allBids))

2n?

What if our data was already sorted?

2?

What is an example of constant time?

What is an example of linear time?

What is an example of quadratic time?

What is an example of logarithmic time?

Let's explore some JS complexities

.pop() ?

.shift() ?

.length ?

forEach, map, reduce ?