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@leovolving
Last active July 25, 2017 01:52
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Big O Drills
//Even or odd
function isEven(value){
if (value % 2 == 0){
return true;
}
else
return false;
}
//My answer: this function only takes a single input
//The runtime is O(1) (constant time)
//Are you here?
function areYouHere(arr1, arr2) {
for (let i=0; i<arr1.length; i++) {
const el1 = arr1[i];
for (let j=0; j<arr2.length; j++) {
const el2 = arr2[j];
if (el1 === el2) return true;
}
}
return false;
}
//My answer: this function's runtime is dependent on the length of the arrays
//The runtime is O(n) (linear time)
//THINKFUL ANSWER: O^n (*nested loops*)
//Doubler
function doubleArrayValues(array) {
for (let i=0; i<array.length; i++) {
array[i] *= 2;
}
return array;
}
//My answer: this function's runtime is dependent on the length of the array
//The runtime is O(n) (linear time)
//Naive Search
function naiveSearch(array, item) {
for (let i=0; i<array.length; i++) {
if (array[i] === item) {
return i;
}
}
}
//My answer: this function's runtime is dependent on the length of the array
//The runtime is O(n) (linear time)
//Creating pairs:
function createPairs(arr) {
for (let i = 0; i < arr.length; i++) {
for(let j = i+1; j < arr.length; j++) {
console.log(arr[i] + ", " + arr[j] );
}
}
}
//My answer: This function loops through the array twice
//The runtime is O(n^2) (Polynomial time)
//Computing fibonaccis
//A fibonacci sequence is one where every number is the sum of the previous
//two numbers in the sequence. For example the following is a fibonacci
//sequence: 1, 1, 2, 3, 5, 8, 13, 21, 34. The first number always starts at 1 (technically it is 0).
//Then the second number is 0+1 = 1, the third number is the sum of the first and the second numbers (1 + 2 = 3)
//and the sequence continues in a similar manner.
//Here, we have a function generateFib that uses iteration to generate a fibonacci sequence.
//Determine its run time complexity in big O.
function generateFib(num) {
let result = [];
for (let i = 1; i <= num; i++) {
// we're adding the first item
// to the result list, append the
// number 0 to results
if (i === 1) {
result.push(0);
}
// ...and if it's the second item
// append 1
else if (i == 2) {
result.push(1);
}
// otherwise, sum the two previous result items, and append that value to results.
else {
result.push(result[i - 2] + result[i - 3]);
}
}
// once the for loop finishes
// we return `result`.
return result;
}
//My answer: This one is more complex but most of the items are constant,
//so the runtime is mostly dependent on how high a number is given.
//The runtime is O(n) (linear time)
//An Efficient Search
//In this example, we return to the problem of searching using a more
//sophisticated approach than in naive search, above.
//Assume that the input array is always sorted.
function efficientSearch(array, item) {
let minIndex = 0;
let maxIndex = array.length - 1;
let currentIndex;
let currentElement;
while (minIndex <= maxIndex) {
currentIndex = Math.floor((minIndex + maxIndex) / 2);
currentElement = array[currentIndex];
if (currentElement < item) {
minIndex = currentIndex + 1;
}
else if (currentElement > item) {
maxIndex = currentIndex - 1;
}
else {
return currentIndex;
}
}
return -1;
}
//My answer: This one is able to cut itself in half with each iteration, if needed
//The runtime is O(log(n)) (logarithmic time)
//Random element
function findRandomElement(arr) {
return arr[Math.floor(Math.random() * arr.length)];
}
//My answer: The runtime is O(1) (constant time)
//Is it prime?
function isPrime(n) {
// if n is less than 2 or a decimal, it's not prime
if (n < 2 || n % 1 != 0) {
return false;
}
// otherwise, check if `n` is divisible by any integer
// between 2 and n.
for (let i = 2; i < n; ++i) {
if (n % i == 0) return false;
}
return true;
}
//My answer: This one is O(n) (linear time)
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