Develop Reference

In JavaScript, is there a way to make 0.84729347293923 into an integer without using any string or regex manipulation?

Given any number between 0 and 1, such as 0.84729347293923, is there a simple way to make it into 84729347293923 without string or regex manipulation? I can think of using a loop, which probably is no worse than using a string because it is O(n) with n being the number of digits. But is there a better way?
function getRandom() {
let r = Math.random();
while (Math.floor(r) !== r) r *= 10;
return r;
}
for (let i = 0; i < 10; i++)
console.log(getRandom());

Integers mod 1 = 0, non integers mod 1 != 0.
while ((r*=10) % 1);

Ok, just want to refactor my code (i realized that was bad so this is what i discovered to correctly get the value as you requested).
NOTE: As the question says that "given any number between 0 and 1", this solution only works for values between 0 and 1:
window.onload = ()=>{
function getLen(num){
let currentNumb = num;
let integratedArray = [];
let realLen = 0;
/*While the number is not an integer, we will multiply the copy of the original
*value by ten, and when the loop detects that the number is already an integer
*the while simply breaks, in this process we are storing each transformations
*of the number in an array called integratedArray*/
while(!(Number.isInteger(currentNumb))){
currentNumb *= 10;
integratedArray.push(currentNumb);
}
/*We iterate over the array and compare each value of the array with an operation
*in which the resultant value should be exactly the same as the actual item of the
*array, in the case that both are equal we assign the var realLen to i, and
*in case that the values were not the same, we simply breaks the loop, if the
*values are not the same, this indicates that we found the "trash numbers", so
*we simply skip them.*/
for(let i = 0; i < integratedArray.length; i++){
if(Math.floor(integratedArray[i]) === Math.floor(num * Math.pow(10, i + 1))){
realLen = i;
}else{
break;
}
}
return realLen;
}
//Get the float value of a number between 0 and 1 as an integer.
function getShiftedNumber(num){
//First we need the length to get the float part of the number as an integer
const len = getLen(num);
/*Once we have the length of the number we simply multiply the number by
*(10) ^ numberLength, this eliminates the comma (,), or point (.), and
*automatically transforms the number to an integer in this case a large integer*/
return num * (Math.pow(10, len));
}
console.log(getShiftedNumber(0.84729347293923));
}
So the explanation is the next:
Because we want to convert this number without using any string, regex or any another thing, first we need to get the length of the number, this is a bit hard to do without using string conversions... so i did the function getLen for this purpose.
In the function getLen, we have 3 variables:
currentNumb: This var is a copy of the original value (the original number), this value help us to found the length of the number and we can do some transforms to this value whitout changing the original reference of the number.
We need to multiply this value any times is needed to transform the number to an integer and then multiplyng this value by ten to ten.
with the help of a while (this method makes the number a false integer).
NOTE: I saw "False integer" because when i was making the tests i realized that in the number is being adding more digits than normal... (Very very strange), so this stupid but important thing makes neccesary the filter of these "trash numbers", so later we proccess them.
integratedArray: This array stores the values of the result of the first while operations, so the last number stored in this array is an integer, but this number is one of the "fake integers", so with this array we need to iterate later to compare what of those stored values are different to the original value multiplied by (10 * i + 1), so here is the hint:
In this case the first 12 values of this array are exactly the same with the operation of Math.floor(num * Math.pow(10, i + 1))), but in the 13th value of the array these values are not the same so... yes!, there are those "trash numbers" that we were searching for.
realLen: This is the variable where we will store the real length of the number converting the float part of this number in an integer.

Some binary search approach:
Its useless if avarage length < 8;
It contains floating point issues.
But hey it is O(log n) with tons of wasted side computations - i guess if one counts them its event worse than just plain multiplication.
I prefer #chiliNUT answer. One line stamp.
function floatToIntBinarySearch(number){
const max_safe_int_length = 16;
const powers = [
1,
10,
100,
1000,
10000,
100000,
1000000,
10000000,
100000000,
1000000000,
10000000000,
100000000000,
1000000000000,
10000000000000,
100000000000000,
1000000000000000,
10000000000000000
]
let currentLength = 16
let step = 16
let _number = number * powers[currentLength]
while(_number % 1 != 0 || (_number % 10 | 0) == 0){
step /= 2
if( (_number % 10 | 0) == 0 && !(_number % 1 != 0)){
currentLength = currentLength - step;
} else {
currentLength = step + currentLength;
}
if(currentLength < 1 || currentLength > max_safe_int_length * 2) throw Error("length is weird: " + currentLength)
_number = number * powers[currentLength]
console.log(currentLength, _number)
if(Number.isNaN(_number)) throw Error("isNaN: " + ((number + "").length - 2) + " maybe greater than 16?")
}
return number * powers[currentLength]
}
let randomPower = 10 ** (Math.random() * 10 | 0)
let test = (Math.random() * randomPower | 0) / randomPower
console.log(test)
console.log(floatToIntBinarySearch(test))

Trying to optimize my code to either remove nested loop or make it more efficient

A friend of mine takes a sequence of numbers from 1 to n (where n > 0)
Within that sequence, he chooses two numbers, a and b
He says that the product of a and b should be equal to the sum of all numbers in the sequence, excluding a and b
Given a number n, could you tell me the numbers he excluded from the sequence?
Have found the solution to this Kata from Code Wars but it times out (After 12 seconds) in the editor when I run it; any ideas as too how I should further optimize the nested for loop and or remove it?
function removeNb(n) {
var nArray = [];
var sum = 0;
var answersArray = [];
for (let i = 1; i <= n; i++) {
nArray.push(n - (n - i));
sum += i;
}
var length = nArray.length;
for (let i = Math.round(n / 2); i < length; i++) {
for (let y = Math.round(n / 2); y < length; y++) {
if (i != y) {
if (i * y === sum - i - y) {
answersArray.push([i, y]);
break;
}
}
}
}
return answersArray;
}
console.log(removeNb(102));
.as-console-wrapper { max-height: 100% !important; top: 0; }

I think there is no reason for calculating the sum after you fill the array, you can do that while filling it.
function removeNb(n) {
let nArray = [];
let sum = 0;
for(let i = 1; i <= n; i++) {
nArray.push(i);
sum += i;
}
}
And since there could be only two numbers a and b as the inputs for the formula a * b = sum - a - b, there could be only one possible value for each of them. So, there's no need to continue the loop when you find them.
if(i*y === sum - i - y) {
answersArray.push([i,y]);
break;
}
I recommend looking at the problem in another way.
You are trying to find two numbers a and b using this formula a * b = sum - a - b.
Why not reduce the formula like this:
a * b + a = sum - b
a ( b + 1 ) = sum - b
a = (sum - b) / ( b + 1 )
Then you only need one for loop that produces the value of b, check if (sum - b) is divisible by ( b + 1 ) and if the division produces a number that is less than n.
for(let i = 1; i <= n; i++) {
let eq1 = sum - i;
let eq2 = i + 1;
if (eq1 % eq2 === 0) {
let a = eq1 / eq2;
if (a < n && a != i) {
return [[a, b], [b, a]];
}
}
}

You can solve this in linear time with two pointers method (page 77 in the book).
In order to gain intuition towards a solution, let's start thinking about this part of your code:
for(let i = Math.round(n/2); i < length; i++) {
for(let y = Math.round(n/2); y < length; y++) {
...
You already figured out this is the part of your code that is slow. You are trying every combination of i and y, but what if you didn't have to try every single combination?
Let's take a small example to illustrate why you don't have to try every combination.
Suppose n == 10 so we have 1 2 3 4 5 6 7 8 9 10 where sum = 55.
Suppose the first combination we tried was 1*10.
Does it make sense to try 1*9 next? Of course not, since we know that 1*10 < 55-10-1 we know we have to increase our product, not decrease it.
So let's try 2*10. Well, 20 < 55-10-2 so we still have to increase.
3*10==30 < 55-3-10==42
4*10==40 < 55-4-10==41
But then 5*10==50 > 55-5-10==40. Now we know we have to decrease our product. We could either decrease 5 or we could decrease 10, but we already know that there is no solution if we decrease 5 (since we tried that in the previous step). So the only choice is to decrease 10.
5*9==45 > 55-5-9==41. Same thing again: we have to decrease 9.
5*8==40 < 55-5-8==42. And now we have to increase again...
You can think about the above example as having 2 pointers which are initialized to the beginning and end of the sequence. At every step we either
move the left pointer towards right
or move the right pointer towards left
In the beginning the difference between pointers is n-1. At every step the difference between pointers decreases by one. We can stop when the pointers cross each other (and say that no solution can be obtained if one was not found so far). So clearly we can not do more than n computations before arriving at a solution. This is what it means to say that the solution is linear with respect to n; no matter how large n grows, we never do more than n computations. Contrast this to your original solution, where we actually end up doing n^2 computations as n grows large.

Hassan is correct, here is a full solution:
function removeNb (n) {
var a = 1;
var d = 1;
// Calculate the sum of the numbers 1-n without anything removed
var S = 0.5 * n * (2*a + (d *(n-1)));
// For each possible value of b, calculate a if it exists.
var results = [];
for (let numB = a; numB <= n; numB++) {
let eq1 = S - numB;
let eq2 = numB + 1;
if (eq1 % eq2 === 0) {
let numA = eq1 / eq2;
if (numA < n && numA != numB) {
results.push([numA, numB]);
results.push([numB, numA]);
}
}
}
return results;
}

In case it's of interest, CY Aries pointed this out:
ab + a + b = n(n + 1)/2
add 1 to both sides
ab + a + b + 1 = (n^2 + n + 2) / 2
(a + 1)(b + 1) = (n^2 + n + 2) / 2
so we're looking for factors of (n^2 + n + 2) / 2 and have some indication about the least size of the factor. This doesn't necessarily imply a great improvement in complexity for the actual search but still it's kind of cool.

This is part comment, part answer.
In engineering terms, the original function posted is using "brute force" to solve the problem, iterating every (or more than needed) possible combinations. The number of iterations is n is large - if you did all possible it would be
n * (n-1) = bazillio n
Less is More
So lets look at things that can be optimized, first some minor things, I'm a little confused about the first for loop and nArray:
// OP's code
for(let i = 1; i <= n; i++) {
nArray.push(n - (n - i));
sum += i;
}
??? You don't really use nArray for anything? Length is just n .. am I so sleep deprived I'm missing something? And while you can sum a consecutive sequence of integers 1-n by using a for loop, there is a direct and easy way that avoids a loop:
sum = ( n + 1 ) * n * 0.5 ;
THE LOOPS
// OP's loops, not optimized
for(let i = Math.round(n/2); i < length; i++) {
for(let y = Math.round(n/2); y < length; y++) {
if(i != y) {
if(i*y === sum - i - y) {
Optimization Considerations:
I see you're on the right track in a way, cutting the starting i, y values in half since the factors . But you're iterating both of them in the same direction : UP. And also, the lower numbers look like they can go a little below half of n (perhaps not because the sequence start at 1, I haven't confirmed that, but it seems the case).
Plus we want to avoid division every time we start an instantiation of the loop (i.e set the variable once, and also we're going to change it). And finally, with the IF statements, i and y will never be equal to each other the way we're going to create the loops, so that's a conditional that can vanish.
But the more important thing is the direction of transversing the loops. The smaller factor low is probably going to be close to the lowest loop value (about half of n) and the larger factor hi is probably going to be near the value of n. If we has some solid math theory that said something like "hi will never be less than 0.75n" then we could make a couple mods to take advantage of that knowledge.
The way the loops are show below, they break and iterate before the hi and low loops meet.
Moreover, it doesn't matter which loop picks the lower or higher number, so we can use this to shorten the inner loop as number pairs are tested, making the loop smaller each time. We don't want to waste time checking the same pair of numbers more than once! The lower factor's loop will start a little below half of n and go up, and the higher factor's loop will start at n and go down.
// Code Fragment, more optimized:
let nHi = n;
let low = Math.trunc( n * 0.49 );
let sum = ( n + 1 ) * n * 0.5 ;
// While Loop for the outside (incrementing) loop
while( low < nHi ) {
// FOR loop for the inside decrementing loop
for(let hi = nHi; hi > low; hi--) {
// If we're higher than the sum, we exit, decrement.
if( hi * low + hi + low > sum ) {
continue;
}
// If we're equal, then we're DONE and we write to array.
else if( hi * low + hi + low === sum) {
answersArray.push([hi, low]);
low = nHi; // Note this is if we want to end once finding one pair
break; // If you want to find ALL pairs for large numbers then replace these low = nHi; with low++;
}
// And if not, we increment the low counter and restart the hi loop from the top.
else {
low++;
break;
}
} // close for
} // close while
Tutorial:
So we set the few variables. Note that low is set slightly less than half of n, as larger numbers look like they could be a few points less. Also, we don't round, we truncate, which is essentially "always rounding down", and is slightly better for performance, (though it dosenit matter in this instance with just the single assignment).
The while loop starts at the lowest value and increments, potentially all the way up to n-1. The hi FOR loop starts at n (copied to nHi), and then decrements until the factor are found OR it intercepts at low + 1.
The conditionals:
First IF: If we're higher than the sum, we exit, decrement, and continue at a lower value for the hi factor.
ELSE IF: If we are EQUAL, then we're done, and break for lunch. We set low = nHi so that when we break out of the FOR loop, we will also exit the WHILE loop.
ELSE: If we get here it's because we're less than the sum, so we need to increment the while loop and reset the hi FOR loop to start again from n (nHi).

Factorial in JS at my interview at Sina Weibo

how much 0 at the end of 1*2*3*....*100?
for example1 zero 10,2 zero at the end of 10100,0 zero at 10001
The interviewer gave me 3 mins.
front-end develope intern was the job i went for.
i tried to solve it in JavaScript,it was wrong because 100! is so big for a JS variable.Here is my code.
var factorial = function(x){
if(x<0){
return error;
}else if (x ===1 || x ===0) {
return x;
}else if (x > 1) {
return (factorial(x - 1) * x);
}
}
var counter = 0;
var countZero = function(x){
while((x%10) === 0){
counter = counter + 1;
x = x/10;
}
return counter;
}
console.log(countZero(factorial(100)));
Any ideas?I solved it without computer in a pure-math way,but i want to know how to solve it in JS.

A number gets a zero at the end of it if the number has 10 as a factor. For instance, 10 is a factor of 50, 120, and 1234567890. So you need to find out how many times 10 is a factor in the expansion of 100!.
But since 5×2 = 10, we need to account for all the products of 5 and 2. Looking at the factors in the above expansion, there are many more numbers that are multiples of 2 (2, 4, 6, 8, 10, 12, 14,...) than are multiples of 5 (5, 10, 15,...). That is, if we take all the numbers with 5 as a factor, We'll have way more than enough even numbers to pair with them to get factors of 10 (and another trailing zero on my factorial). So to find the number of times 10 is a factor, all we really need to worry about is how many times 5 is a factor in all of the numbers between 1 and 100
Okay, how many multiples of 5 are there in the numbers from 1 to 100? There's 5, 10, 15, 20, 25,...
Let's do this the short way: 100 is the closest multiple of 5 below or equal to 100, and 100 ÷ 5 = 20, so there are twenty multiples of 5 between 1 and 100.
But wait: 25 is 5×5, so each multiple of 25 has an extra factor of 5 that we need to account for. How many multiples of 25 are between 1 and 100? Since 100 ÷ 25 = 4, there are four multiples of 25 between 1 and 100.
Adding these, We get 20 + 4 = 24 trailing zeroes in 100!
It was more of a math question that a Javascript specific question and you don't need to calculate the actual factorial to find the number of trailing zeros
More details here

Zeros at the end of a number result from 2s and 5s being multiplied together. So, one way to count up the total number of zeros would be to figure out how many total 2s and 5s go into 100! - don't calculate that number, just add up the number of factors - and then return whichever count (2s or 5s) is smallest.
let twos = 0;
let fives = 0;
for (let i = 1; i <= 100; i++) {
let num = i;
while (num % 2 === 0) {
twos++;
num /= 2;
}
while (num % 5 === 0) {
fives++;
num /= 5;
}
}
console.log(Math.min(twos, fives));

For the numbers less or equal than 1000000000 as your case here you could use the following algorithm :
function numberOfZeros(n) {
if (n == 0) return 0;
return parseInt(n * (n - 1) / (4 * n));
}
console.log(numberOfZeros(100));

Decompose a number in prime numbers

How can I find a number within the interval (from 1 to given number) which is when decomposed into prime numbers has the maximum amount of them.
Example:
Input: 9
Output: 8
Explanation:
8 = 2 * 2 * 2 (3 primes)
7 = 7 * 1 (1 prime)
6 = 3 * 2 ( 2 primes)
And so on... At the end, we'll see that 8 has the greatest amount of primes in decomposition.
Specification:
If there're several numbers having the same amount of primes in decomposition, return the greatest of them.

Okay, I think I understood your requirement.
Here's a simple script to do what you have asked.
//source of this function: https://jsfiddle.net/JamesOR/RC7SY/
function getAllFactorsFor(remainder) {
var factors = [], i;
for (i = 2; i <= remainder; i++) {
while ((remainder % i) === 0) {
factors.push(i);
remainder /= i;
}
}
return factors;
}
function calculate(x) {
lastFactorCount = 0;
highestNumber = 0;
while (x) {
currentCount = getAllFactorsFor(x).length;
if (currentCount > lastFactorCount) {
lastFactorCount = currentCount;
highestNumber = x;
}
x--;
}
return highestNumber;
}
console.log(calculate(7)); //output: 6
console.log(calculate(11)) //output: 8
This pass the two test cases you have given. I borrowed the getAllFactorsFor() function from a jsfiddle I found since we don't need to reinvent anything ;)
calculate() function takes in an input number, then it loops through every number from x to 0, counting how many factors it has, and it keeps track of the last factor count while decrementing x in each iteration.
Finally it outputs the number with highest factor count. Simple.
Hope it helps!!

Notice that after 2 and 3, the next prime is 5, which is bigger than 2*2 (obviously). Therefore, using 2*2 will ALWAYS be better for amount of prime factors, than any higher prime. The number with the highest amount of 2 as prime factors which is still lower or equal is 2 ** Math.floor(Math.log2(num)). The only thing we need to check is whether replacing the last prime factor 2 with a 3 will still be below the number, as that may happen, and would yield a bigger number. Note again, using more than one 3 would be 3*3 = 9 > 8 = 2*2*2, which cannot be a solution again. All of that together yields that the solution should simply be
const f = num => {
let twoEnd = 2 ** Math.floor(Math.log2(num));
let threeEnd = twoEnd / 2 * 3;
return threeEnd <= num ? threeEnd : twoEnd;
}
Some handling for numbers smaller than 2 may be necessary, depending on the circumstances.

Can someone explain this base conversion code

var ShortURL = new function() {
var _alphabet = '23456789bcdfghjkmnpqrstvwxyzBCDFGHJKLMNPQRSTVWXYZ-_',
_base = _alphabet.length;
this.encode = function(num) {
var str = '';
while (num > 0) {
str = _alphabet.charAt(num % _base) + str;
num = Math.floor(num / _base);
}
return str;
};
this.decode = function(str) {
var num = 0;
for (var i = 0; i < str.length; i++) {
num = num * _base + _alphabet.indexOf(str.charAt(i));
}
return num;
};
};
I understand encode works by converting from decimal to custom base (custom alphabet/numbers in this case)
I am not quite sure how decode works.
Why do we multiply base by a current number and then add the position number of the alphabet? I know that to convert 010 base 2 to decimal, we would do
(2 * 0^2) + (2 * 1^1) + (2 * 0 ^ 0) = 2
Not sure how it is represented in that decode algorithm
EDIT:
My own decode version
this.decode2 = function (str) {
var result = 0;
var position = str.length - 1;
var value;
for (var i = 0; i < str.length; i++) {
value = _alphabet.indexOf(str[i]);
result += value * Math.pow(_base, position--);
}
return result;
}
This is how I wrote my own decode version (Just like I want convert this on paper. I would like someone to explain more in detail how the first version of decode works. Still don't get why we multiply num * base and start num with 0.

OK, so what does 376 mean as a base-10 output of your encode() function? It means:
1 * 100 +
5 * 10 +
4 * 1
Why? Because in encode(), you divide by the base on every iteration. That means that, implicitly, the characters pushed onto the string on the earlier iterations gain in significance by a factor of the base each time through the loop.
The decode() function, therefore, multiplies by the base each time it sees a new character. That way, the first digit is multiplied by the base once for every digit position past the first that it represents, and so on for the rest of the digits.
Note that in the explanation above, the 1, 5, and 4 come from the positions of the characters 3, 7, and 6 in the "alphabet" list. That's how your encoding/decoding mechanism works. If you feed your decode() function a numeric string encoded by something trying to produce normal base-10 numbers, then of course you'll get a weird result; that's probably obvious.
edit To further elaborate on the decode() function: forget (for now) about the special base and encoding alphabet. The process is basically the same regardless of the base involved. So, let's look at a function that interprets a base-10 string of numeric digits as a number:
function decode10(str) {
var num = 0, zero = '0'.charCodeAt(0);
for (var i = 0; i < str.length; ++i) {
num = (num * 10) + (str[i] - zero);
}
return num;
}
The accumulator variable num is initialized to 0 first, because before examining any characters of the input numeric string the only value that makes sense to start with is 0.
The function then iterates through each character of the input string from left to right. On each iteration, the accumulator is multiplied by the base, and the digit value at the current string position is added.
If the input string is "214", then, the iteration will proceed as follows:
num is set to 0
First iteration: str[i] is 2, so (num * 10) + 2 is 2
Second iteration: str[i] is 1, so (num * 10) + 1 is 21
Third iteration: str[i] is 4, so (num * 10) + 4 is 214
The successive multiplications by 10 achieve what the call to Math.pow() does in your code. Note that 2 is multiplied by 10 twice, which effectively multiplies it by 100.
The decode() routine in your original code does the same thing, only instead of a simple character code computation to get the numeric value of a digit, it performs a lookup in the alphabet string.

Both the original and your own version of the decode function achieve the same thing, but the original version does it more efficiently.
In the following assignment:
num = num * _base + _alphabet.indexOf(str.charAt(i));
... there are two parts:
_alphabet.indexOf(str.charAt(i))
The indexOf returns the value of a digit in base _base. You have this part in your own algorithm, so that should be clear.
num * _base
This multiplies the so-far accumulated result. The rest of my answer is about that part:
In the first iteration this has no effect, as num is still 0 at that point. But at the end of the first iteration, num contains the value as if the str only had its left most character. It is the base-51 digit value of the left most digit.
From the next iteration onwards, the result is multiplied by the base, which makes room for the next value to be added to it. It functions like a digit shift.
Take this example input to decode:
bd35
The individual characters represent value 8, 10, 1 and 3. As there are 51 characters in the alphabet, we're in base 51. So bd35 this represents value:
8*51³ + 10*51² + 1*51 + 3
Here is a table with the value of num after each iteration:
8
8*51 + 10
8*51² + 10*51 + 1
8*51³ + 10*51² + 1*51 + 3
Just to make the visualisation cleaner, let's put the power of 51 in a column header, and remove that from the rows:
3 2 1 0
----------------------------
8
8 10
8 10 1
8 10 1 3
Note how the 8 shifts to the left at each iteration and gets multiplied with the base (51). The same happens with 10, as soon as it is shifted in from the right, and the same with the 1, and 3, although that is the last one and doesn't shift any more.
The multiplication num * _base represents thus a shift of base-digits to the left, making room for a new digit to shift in from the right (through simple addition).
At the last iteration all digits have shifted in their correct position, i.e. they have been multiplied by the base just enough times.
Putting your own algorithm in the same scheme, you'd have this table:
3 2 1 0
----------------------------
8
8 10
8 10 1
8 10 1 3
Here, there is no shifting: the digits are immediately put in the right position, i.e. they are multiplied with the correct power of 51 immediately.

You ask
I would like to understand how the decode function works from logical perspective. Why are we using num * base and starting with num = 0.
and write that
I am not quite sure how decode works. Why do we multiply base by a
current number and then add the position number of the alphabet? I
know that to convert 010 base 2 to decimal, we would do
(2 * 0^2) + (2 * 1^1) + (2 * 0 ^ 0) = 2
The decode function uses an approach to base conversion known as Horner's rule, used because it is computationally efficient:
start with a variable set to 0, num = 0
multiply the variable num by the base
take the value of the most significant digit (the leftmost digit) and add it to num,
repeat step 2 and 3 for as long as there are digits left to convert,
the variable num now contains the converted value (in base 10)
Using an example of a hexadecimal number A5D:
start with a variable set to 0, num = 0
multiply by the base (16), num is now still 0
take the value of the most significant digit (the A has a digit value of 10) and add it to num, num is now 10
repeat step 2, multiply the variable num by the base (16), num is now 160
repeat step 3, add the hexadecimal digit 5 to num, num is now 165
repeat step 2, multiply the variable num by the base (16), num is now 2640
repeat step 3, add the hexadecimal digit D to num (add 13)
there are no digits left to convert, the variable num now contains the converted value (in base 10), which is 2653
Compare the expression of the standard approach:
(10 × 162) + (5 × 161) + (13 × 160) = 2653
to the use of Horner's rule:
(((10 × 16) + 5) × 16) + 13 = 2653
which is exactly the same computation, but rearranged in a form making it easier to compute. This is how the decode function works.
Why are we using num * base and starting with num = 0.
The conversion algorithm needs a start value, therefore num is set to 0. For each repetition (each loop iteration), num is multiplied by base. This only has any effect on the second iteration, but is written like this to make it easier to write the conversion as a for loop.