# Calculating the length needed to represent an integer in an arbitrary base

I have the length of a representation of an integer in an arbitrary base. Say the length is 15, and the base is 36. I'd then like to work out how long a representation of said integer would be in another arbitrary base. i.e, converting to base 2 might result in a length of 68.

I know it's along the lines of the below, but I can't quite get my head around what I need to floor and ceil, and I'm getting some results that are way off:

```length * log(fromBase) / log(toBase)
```

Following a Mathematica-like syntax, let

```Log[b,n]
```

represent the logarithm to base b of n. Let Log[n] represent the natural logarithm of n.

Then the ratio

```Log[b1,n]/Log[b2,n]
```

is constant, and equal to

```Log[b2]/Log[b1]
```

This ratio is a multiplier for calculating the number of digits in base b1 from the number of digits in base b2 (or vice-versa if you see things that way). For the example in the question, a 15-digit base-36 number will need

```15*Log[36]/Log[2] == 77.5489
```

base-2 digits. This is, of course, precisely what you have in your question. You only need to round the final answer up to the next integer.

I'm not sure, of course, why you seem to be getting some results that are way off.

Sadly, there is no exact solution without computing in high precision. For example, (I'll use MATLAB for my work, including tools for high precision work I've written myself) what is 2^200? In base 10, we get:

```vpij(2)^200
ans =
1606938044258990275541962092341162602522202993782792835301376
```

That number is represented in binary using 201 base 2 digits. However, 2^200-1 only needs 200 base 2 digits to represent.

```vpij(2)^200 - 1
ans =
1606938044258990275541962092341162602522202993782792835301375
```

Now, we could compute the log of these numbers, as a double, by taking only the highest order digits. We need to add 1 to the base 2 log of a number to know the number of base 2 digits are needed to represent it.

```format long g
1 + log2(vpij(2)^200)
ans =
201

1 + log2(vpij(2)^200 - 1)
ans =
201
```

Here log2 did exactly that, taking the top decimal digits to compute that log. See that it cannot tell that the second number really requires one less digit to store in binary form.

```vpij2bin(vpij(2)^200)
ans =
100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000

vpij2bin(vpij(2)^200 - 1)
ans =
11111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111
```

We can see what happens by taking a high precision log of those numbers. Thus, accurate to 100 decimal places,

```log2(hpf(2,100)^200)
ans =
200

log2(hpf(2,100)^200 - 1)
ans =
199.9999999999999999999999999999999999999999999999999999999999991022086719253476184905817230522465495
```

The difference between those two numbers is very small.

```log10(hpf(2,100)^200) - log10(hpf(2,100)^200 - 1)
ans =
2.702621195974725251000559400026211938865e-61
```

So that any computation using logs must fail here, unless a high precision log is itself taken. At best, you can come within a digit of being correct, but no more than that. So if your goal is merely to allocate sufficient space for the number, then always allocate one more digit than apparently needed. This should be sufficient until you start working with REALLY huge powers.

(VPIJ is a new variable precision integer form in MATLAB, that will directly replace my older VPI tool. HPF is available already on the file exchange.)

You can get an exact answer without using logarithms. Walk up the radixes of the arbitrary base until the number fits inside.

Python example:

```def count_digits(number, base):
while number >= base ** radix:
```

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