For example, sum all the digits of 1253, 1+2+5+3 which is 11. This is two digits, so sum those again to get 2. The final number left is a single digit.
This is what I have so far:
defmodule Kata do
def digital_root(n) do
n |> Integer.digits() |> Enum.reduce(0, &Kernel.+/2)
end
end
n = 1253
You can use a multi clause function for this: in the first one, return n unchanged if it's a single digit. Otherwise, compute the sum of digits (like you've already figured out) and recursively call the function on it again. Also, your reduce can be replaced with Enum.sum/1.
defmodule Kata do
def digital_root(n) when n < 10, do: n
def digital_root(n) do
n |> Integer.digits() |> Enum.sum() |> digital_root()
end
end
Test:
iex(1)> Kata.digital_root(0)
0
iex(2)> Kata.digital_root(1)
1
iex(3)> Kata.digital_root(9)
9
iex(4)> Kata.digital_root(91)
1
iex(5)> Kata.digital_root(912)
3
iex(6)> Kata.digital_root(9123)
6
iex(7)> Kata.digital_root(1253)
2
Just for fun, here's a version that doesn't use Integer.digits/2 or Enum, and instead uses div/2 and rem/2 to calculate the least significant digit for each iteration:
defmodule Kata do
# Function header
def digital_root(to_sum, acc \\ 0)
# Base case. All digits added, acc is a single digit
def digital_root(0, acc) when acc < 10, do: acc
# Finished a round. All digits added, acc is multiple digits. Sum again.
def digital_root(0, acc), do: digital_root(acc, 0)
# Split off the least significant digit, add it, and recurse
def digital_root(to_sum, acc) do
digit = rem(to_sum, 10)
next_to_sum = div(to_sum, 10)
digital_root(next_to_sum, acc + digit)
end
end
Output:
iex> Kata.digital_root(1523)
2
In a benchmark, this version is twice as fast as dogbert's, and three times faster than 7stud's answers.
Name ips average deviation median 99th %
adam 14.26 M 70.13 ns ±1898.51% 66.70 ns 83.30 ns
dogbert 7.08 M 141.28 ns ±14510.93% 125 ns 167 ns
sevenstud 4.83 M 206.98 ns ±15193.15% 167 ns 292 ns
Comparison:
adam 14.26 M
dogbert 7.08 M - 2.01x slower +71.14 ns
sevenstud 4.83 M - 2.95x slower +136.85 ns
Operating System: macOS
CPU Information: Apple M1 Pro
Number of Available Cores: 10
Available memory: 16 GB
Elixir 1.13.4
Erlang 24.3.4
def digit_sum(number) when number < 10, do: number
def digit_sum(number) when is_integer(number) do
digit_sum(
for digit <- Integer.digits(number), reduce: 0 do
acc -> acc + digit
end
)
end
I like Dogbert's better.
Related
I came across this question in a coding competition. Given a number n, concatenate the binary representation of first n positive integers and return the decimal value of the resultant number formed. Since the answer can be large return answer modulo 10^9+7.
N can be as large as 10^9.
Eg:- n=4. Number formed=11011100(1=1,10=2,11=3,100=4). Decimal value of 11011100=220.
I found a stack overflow answer to this question but the problem is that it only contains a O(n) solution.
Link:- concatenate binary of first N integers and return decimal value
Since n can be up to 10^9 we need to come up with solution that is better than O(n).
Here's some Python code that provides a fast solution; it uses the same ideas as in Abhinav Mathur's post. It requires Python >= 3.8, but it doesn't use anything particularly fancy from Python, and could easily be translated into another language. You'd need to write algorithms for modular exponentiation and modular inverse if they're not already available in the target language.
First, for testing purposes, let's define the slow and obvious version:
# Modulus that results are reduced by,
M = 10 ** 9 + 7
def slow_binary_concat(n):
"""
Concatenate binary representations of 1 through n (inclusive).
Reinterpret the resulting binary string as an integer.
"""
concatenation = "".join(format(k, "b") for k in range(n + 1))
return int(concatenation, 2) % M
Checking that we get the expected result:
>>> slow_binary_concat(4)
220
>>> slow_binary_concat(10)
462911642
Now we'll write a faster version. First, we split the range [1, n) into subintervals such that within each subinterval, all numbers have the same length in binary. For example, the range [1, 10) would be split into four subintervals: [1, 2), [2, 4), [4, 8) and [8, 10). Here's a function to do that splitting:
def split_by_bit_length(n):
"""
Split the numbers in [1, n) by bit-length.
Produces triples (a, b, 2**k). Each triple represents a subinterval
[a, b) of [1, n), with a < b, all of whose elements has bit-length k.
"""
a = 1
while n > a:
b = 2 * a
yield (a, min(n, b), b)
a = b
Example output:
>>> list(split_by_bit_length(10))
[(1, 2, 2), (2, 4, 4), (4, 8, 8), (8, 10, 16)]
Now for each subinterval, the value of the concatenation of all numbers in that subinterval is represented by a fairly simple mathematical sum, which can be computed in exact form. Here's a function to compute that sum modulo M:
def subinterval_concat(a, b, l):
"""
Concatenation of values in [a, b), all of which have the same bit-length k.
l is 2**k.
Equivalently, sum(i * l**(b - 1 - i)) for i in range(a, b)) modulo M.
"""
n = b - a
inv = pow(l - 1, -1, M)
q = (pow(l, n, M) - 1) * inv
return (a * q + (q - n) * inv) % M
I won't go into the evaluation of the sum here: it's a bit off-topic for this site, and it's hard to express without a good way to render formulas. If you want the details, that's a topic for https://math.stackexchange.com, or a page of fairly simple algebra.
Finally, we want to put all the intervals together. Here's a function to do that.
def fast_binary_concat(n):
"""
Fast version of slow_binary_concat.
"""
acc = 0
for a, b, l in split_by_bit_length(n + 1):
acc = (acc * pow(l, b - a, M) + subinterval_concat(a, b, l)) % M
return acc
A comparison with the slow version shows that we get the same results:
>>> fast_binary_concat(4)
220
>>> fast_binary_concat(10)
462911642
But the fast version can easily be evaluated for much larger inputs, where using the slow version would be infeasible:
>>> fast_binary_concat(10**9)
827129560
>>> fast_binary_concat(10**18)
945204784
You just have to note a simple pattern. Taking up your example for n=4, let's gradually build the solution starting from n=1.
1 -> 1 #1
2 -> 2^2(1) + 2 #6
3 -> 2^2[2^2(1)+2] + 3 #27
4 -> 2^3{2^2[2^2(1)+2]+3} + 4 #220
If you expand the coefficients of each term for n=4, you'll get the coefficients as:
1 -> (2^3)*(2^2)*(2^2)
2 -> (2^3)*(2^2)
3 -> (2^3)
4 -> (2^0)
Let the N be total number of bits in the string representation of our required number, and D(x) be the number of bits in x. The coefficients can then be written as
1 -> 2^(N-D(1))
2 -> 2^(N-D(1)-D(2))
3 -> 2^(N-D(1)-D(2)-D(3))
... and so on
Since the value of D(x) will be the same for all x between range (2^t, 2^(t+1)-1) for some given t, you can break the problem into such ranges and solve for each range using mathematics (not iteration). Since the number of such ranges will be log2(Given N), this should work in the given time limit.
As an example, the various ranges become:
1. 1 (D(x) = 1)
2. 2-3 (D(x) = 2)
3. 4-7 (D(x) = 3)
4. 8-15 (D(x) = 4)
I have an array that contains repeated nonnegative integers, e.g., A=[5,5,5,0,1,1,0,0,0,3,3,0,0]. I would like to find the position of the last maximum in A. That is the largest index i such that A[i]>=A[j] for all j. In my example, i=3.
I tried to find the indices of all maximum of A then find the maximum of these indices:
A = [5,5,5,0,1,1,0,0,0,3,3,0,0];
Amax = maximum(A);
i = maximum(find(x -> x == Amax, A));
Is there any better way?
length(A) - indmax(#view A[end:-1:1]) + 1
should be pretty fast, but I didn't benchmark it.
EDIT: I should note that by definition #crstnbr 's solution (to write the algorithm from scratch) is faster (how much faster is shown in Xiaodai's response). This is an attempt to do it using julia's inbuilt array functions.
What about findlast(A.==maximum(A)) (which of course is conceptually similar to your approach)?
The fastest thing would probably be explicit loop implementation like this:
function lastindmax(x)
k = 1
m = x[1]
#inbounds for i in eachindex(x)
if x[i]>=m
k = i
m = x[i]
end
end
return k
end
I tried #Michael's solution and #crstnbr's solution and I found the latter much faster
a = rand(Int8(1):Int8(5),1_000_000_000)
#time length(a) - indmax(#view a[end:-1:1]) + 1 # 19 seconds
#time length(a) - indmax(#view a[end:-1:1]) + 1 # 18 seconds
function lastindmax(x)
k = 1
m = x[1]
#inbounds for i in eachindex(x)
if x[i]>=m
k = i
m = x[i]
end
end
return k
end
#time lastindmax(a) # 3 seconds
#time lastindmax(a) # 2.8 seconds
Michael's solution doesn't support Strings (ERROR: MethodError: no method matching view(::String, ::StepRange{Int64,Int64})) or sequences so I add another solution:
julia> lastimax(x) = maximum((j,i) for (i,j) in enumerate(x))[2]
julia> A="abžcdž"; lastimax(A) # unicode is OK
6
julia> lastimax(i^2 for i in -10:7)
1
If you more like don't catch exception for empty Sequence:
julia> lastimax(x) = !isempty(x) ? maximum((j,i) for (i,j) in enumerate(x))[2] : 0;
julia> lastimax(i for i in 1:3 if i>4)
0
Simple(!) benchmarks:
This is up to 10 times slower than Michael's solution for Float64:
julia> mlastimax(A) = length(A) - indmax(#view A[end:-1:1]) + 1;
julia> julia> A = rand(Float64, 1_000_000); #time lastimax(A); #time mlastimax(A)
0.166389 seconds (4.00 M allocations: 91.553 MiB, 4.63% gc time)
0.019560 seconds (6 allocations: 240 bytes)
80346
(I am surprised) it is 2 times faster for Int64!
julia> A = rand(Int64, 1_000_000); #time lastimax(A); #time mlastimax(A)
0.015453 seconds (10 allocations: 304 bytes)
0.031197 seconds (6 allocations: 240 bytes)
423400
it is 2-3 times slower for Strings
julia> A = ["A$i" for i in 1:1_000_000]; #time lastimax(A); #time mlastimax(A)
0.175117 seconds (2.00 M allocations: 61.035 MiB, 41.29% gc time)
0.077098 seconds (7 allocations: 272 bytes)
999999
EDIT2:
#crstnbr solution is faster and works with Strings too (doesn't work with generators). There difference between lastindmax and lastimax - first return byte index, second return character index:
julia> S = "1š3456789ž"
julia> length(S)
10
julia> lastindmax(S) # return value is bigger than length
11
julia> lastimax(S) # return character index (which is not byte index to String) of last max character
10
julia> S[chr2ind(S, lastimax(S))]
'ž': Unicode U+017e (category Ll: Letter, lowercase)
julia> S[chr2ind(S, lastimax(S))]==S[lastindmax(S)]
true
I was curious how quick and accurate, algorithm from Rosseta code ( https://rosettacode.org/wiki/Ackermann_function ) for (4,2) parameters, could be. But got StackOverflowError.
julia> using Memoize
#memoize ack3(m, n) =
m == 0 ? n + 1 :
n == 0 ? ack3(m-1, 1) :
ack3(m-1, ack3(m, n-1))
# WARNING! Next line has to calculate and print number with 19729 digits!
julia> ack3(4,2) # -> StackOverflowError
# has to be -> 2003529930406846464979072351560255750447825475569751419265016973710894059556311
# ...
# 4717124577965048175856395072895337539755822087777506072339445587895905719156733
EDIT:
Oscar Smith is right that trying ack3(4,2) is unrealistic. This is version translated from Rosseta's C++:
module Ackermann
function ackermann(m::UInt, n::UInt)
function ack(m::UInt, n::BigInt)
if m == 0
return n + 1
elseif m == 1
return n + 2
elseif m == 2
return 3 + 2 * n;
elseif m == 3
return 5 + 8 * (BigInt(2) ^ n - 1)
else
if n == 0
return ack(m - 1, BigInt(1))
else
return ack(m - 1, ack(m, n - 1))
end
end
end
return ack(m, BigInt(n))
end
end
julia> import Ackermann;Ackermann.ackermann(UInt(1),UInt(1));#time(a4_2 = Ackermann.ackermann(UInt(4),UInt(2)));t = "$a4_2"; println("len = $(length(t)) first_digits=$(t[1:20]) last digits=$(t[end-20:end])")
0.000041 seconds (57 allocations: 33.344 KiB)
len = 19729 first_digits=20035299304068464649 last digits=445587895905719156733
Julia itself does not have an internal limit to the stack size, but your operating system does. The exact limits here (and how to change them) will be system dependent. On my Mac (and I assume other POSIX-y systems), I can check and change the stack size of programs that get called by my shell with ulimit:
$ ulimit -s
8192
$ julia -q
julia> f(x) = x > 0 ? f(x-1) : 0 # a simpler recursive function
f (generic function with 1 method)
julia> f(523918)
0
julia> f(523919)
ERROR: StackOverflowError:
Stacktrace:
[1] f(::Int64) at ./REPL[1]:1 (repeats 80000 times)
$ ulimit -s 16384
$ julia -q
julia> f(x) = x > 0 ? f(x-1) : 0
f (generic function with 1 method)
julia> f(1048206)
0
julia> f(1048207)
ERROR: StackOverflowError:
Stacktrace:
[1] f(::Int64) at ./REPL[1]:1 (repeats 80000 times)
I believe the exact number of recursive calls that will fit on your stack will depend upon both your system and the complexity of the function itself (that is, how much each recursive call needs to store on the stack). This is the bare minimum. I have no idea how big you'd need to make the stack limit in order to compute that Ackermann function.
Note that I doubled the stack size and it more than doubled the number of recursive calls — this is because of a constant overhead:
julia> log2(523918)
18.998981503278365
julia> 2^19 - 523918
370
julia> log2(1048206)
19.99949084151746
julia> 2^20 - 1048206
370
Just fyi, even if you change the max recursion depth, you won't get the right answer as Julia uses 64 bit integers, so integer overflow with make stuff not work. To get the right answer, you will have to use big ints to have any hope. The next problem is that you probably don't want to memoize, as almost all of the computations are not repeated, and you will be computing the function more than 10^19729 different inputs, which you really do not want to store.
Julia has a built-in function to round to n significant digits. signif(0.0229, 2) will round to two significant digits and give 0.023.
How can I chop or truncate to n significant digits so that I would get 0.022 instead?
Well, not very imaginative. Used #edit signif(0.229,2) to find the source and replace round with floor (and added a Base. for correct Module referencing). Here is the result:
function mysignif(x::Real, digits::Integer, base::Integer=10)
digits < 1 && throw(DomainError(digits, "`digits` cannot be less than 1."))
x = float(x)
(x == 0 || !isfinite(x)) && return x
og, e = Base._signif_og(x, digits, base)
if e >= 0 # for numeric stability
r = trunc(x/og)*og
else
r = trunc(x*og)/og
end
!isfinite(r) ? x : r
end
Giving:
julia> mysignif(0.0229,2)
0.022
I found a version in Maple and ported to Julia:
function signifChop(num, digits)
if num == 0.0 then
return num
else
e = ceil(log10(abs(num)))
scale = 10^(digits - e)
return trunc(num * scale) / scale
end
end
# Test cases for signifChop
println("$(signifChop(124.031, 5))")
println("$(signifChop(124.036, 5))")
println("$(signifChop(-124.031, 5))")
println("$(signifChop(-124.036, 5))")
println("$(signifChop(0.00653, 2))")
println("$(signifChop(0.00656, 2))")
println("$(signifChop(-0.00653, 2))")
println("$(signifChop(-0.00656, 2))")
Note that signif was removed as of Julia 1.0.
However, now Base.round accepts the sigdigits keyword:
julia> round(pi, digits=3)
3.142
julia> round(pi, sigdigits=3)
3.14
The same works for trunc, ceil, and floor.
Source: mforets on Github and #DNF in the comments.
I'm struggling with this code right now. I want to determine whether an integer is divsible by 11. From what I have read, an integer is divisible to 11 when the sum (one time +, one time -) of its digits is divisible by 11.
For example: 56518 is divisible by 11, because 8-1+5-6+5 = 11, and 11 is divisible by 11.
How can i write this down in Haskell? Thanks in advance.
A number x is divisible by y if it's remainder when divided by y is 0. So you can just do
divisibleBy11 x = x `rem` 11 == 0
ifan I'm sure you know that in real life you would use mod or rem for this simple example, but the algorithm you are asking about is interesting. Here's a fun way to do it that emphasizes the functional nature of Haskell:
digits = map (`mod` 10) . takeWhile (> 0) . iterate (`div` 10)
divisible11 = (== 0) . head . dropWhile (>= 11) . iterate (reduce11 . digits)
where
reduce11 [] = 0
reduce11 (d:ds) = foldl combine d $ zip (cycle [(-), (+)]) ds
combine d (op, d') = d `op` d'
Surely, div and mod are faster, but why not? I assume the problem is converting a number to a list of digits:
toDigits = map (read . (:[])) . show
56518 is converted to a String "56518", and each symbol in the string (every digit) is converted to a string itself with map (:[]), at this point we have ["5","6","5","1","8"], and we read every single-digit string as an integer value: [5,6,5,1,8]. Done.
Now we can calculate the sum of digits this way:
sumDigits x = sum (zipWith (*) (cycle [1,-1]) (reverse (toDigits x)))
cycle [1,-1] makes an infinite list [1, -1, 1, -1, ...], which we pair with the reversed list of digits (toDigit x), and multiply elements of every pair. So we have [8, -1, 5, -6, 5] and its sum.
Now we can do it recursively:
isDivisible x
| x == 11 || x == 0 = True
| x < 11 = False
| x > 11 = isDivisible (sumDigits x)
How about...
mod11 n | n < 0 = 11 - mod11 (-n)
| n < 11 = n
| otherwise = mod11 $ (n `mod` 10) - (n `div` 10)