Given a integer N (0 < N < 10^1000), find a minimum number M greater than N, M has the same length with N and the sum of the digits are equal. if M not exists return -1
example:
N=134 , M=143, // 1+3+4=1+4+3
N=020, M = 101, //2=1+1 it seems 0 can be added to make length equal
N=120, M = 201, //2=1+1
The question is a written test question today i did, I have no idea how to solve it in a huge scope of N.
Represent the numbers as strings of digits. N and M are just two strings of equal length.
Let's number N's digits as Nk, where N0 is the last (rightmost) digit.
Set M0 = N0 - 1, M1 = N1 + 1, Mi = Ni otherwise. The sum of digits of M stays the same as in N, because you've just moved a 1 from one digit to another, but now M > N.
Of course, this trick does not work if N0 = 0 or if N1 = 9. In this case, shift to N1 and N2 to transfer a 1, etc.
As an exercise, prove that M created this way is really the smallest number satisfying the conditions. (Or disprove — I may be wrong, especially in the case of N0 = 0, though I don't see where I'm wrong there.)
Related
Looking within the xcorr function, most of it is pretty straightforward, except for one function within xcorr called "findTransformLength".
function m = findTransformLength(m)
m = 2*m;
while true
r = m;
for p = [2 3 5 7]
while (r > 1) && (mod(r, p) == 0)
r = r / p;
end
end
if r == 1
break;
end
m = m + 1;
end
With no comments, i fail to understand what this function is meant to acheive and what is the significance of p = [2 3 5 7]. Why those numbers specifically? Why not take a fixed FFT size instead? Is there a disadvantage(cause errors) to taking a fixed FFT size?
This part is used to get the integer closest to 2*m that can be written in the form:
Either:
m is already of this form, then the loop
for p = [2 3 5 7]
while (r > 1) && (mod(r, p) == 0)
r = r / p;
end
end
Will decrease r down to 1 and the break will be reached.
Or m has at least one other prime factor, and r will not reach 1. You go back to the look with m+1 and so on until you reach a number of the right form.
As per why they do this, you can see on the fft doc, in the Input arguments section:
n — Transform length [] (default) | nonnegative integer scalar
Transform length, specified as [] or a nonnegative integer scalar.
Specifying a positive integer scalar for the transform length can
increase the performance of fft. The length is typically specified as
a power of 2 or a value that can be factored into a product of small
prime numbers. If n is less than the length of the signal, then fft
ignores the remaining signal values past the nth entry and returns the
truncated result. If n is 0, then fft returns an empty matrix.
Example: n = 2^nextpow2(size(X,1))
I want to find the smallest integer P, such that the number of primes in the set {1,2,..., P} is less than P/6.
I think have the answer via (long) trial and error but would like to know how to verify this through MATLAB.
You can use isprime to check if any value in an array is a prime number. If we want to check all integers up until the integer N we can do
% You can change this to the maximum number that you'd like to consider for P
N = 2000;
possible_P_values = 2:N; % We omit 1 here since it's not a prime number
primes = isprime(possible_P_values);
To determine how many primes have occured up to a given integer N we can use cumsum of this logical matrix (the cumulative sum)
nPrimes_less_than_or_equal_to_P = cumsum(primes);
Then we can divide possible_P_values by 6 and check where the number of primes up to a certain point is less than that number.
is_less_than_P_over_6 = nPrimes_less_than_or_equal_to_P < (possible_P_values ./ 6);
Then we can identify the first occurance with find
possible_P_values(find(is_less_than_P_over_6, 1, 'first'))
% 1081
Given an integer m, a hash function defined on T is a map T -> {0, 1, 2, ..., m - 1}. If k is an element of T and m is a positive integer, we denote hash(k, m) its hashed value.
For simplicity, most hash functions are of the form hash(k, m) = f(k) % m where f is a map from T to the set of integers.
In the case where m = 2^p (which is often used to the modulo m operation is cheap) and T is a set of integers, I have seen many people using f(k) = c * k with c being a prime number.
I understand if you want to choose a function of the form f(k) = c * k, you need to have gcd(c, m) = 1 for every hash table size m. Even though using a prime number fits the bill, c = 1 is also good.
So my question is the following: why do people still use f(k) = prime * k as their hash function? What kind of nice property does it have?
You don't need it to be prime. One of the most efficient hash functions with provable collision resistance just multiplies with a random number: https://en.wikipedia.org/wiki/Universal_hashing#Avoiding_modular_arithmetic. You do however need it to be odd.
I have adapted some existing code for my program but I am coming across an error that I do not know the cause for. I have data with N observations where my goal is to break up the data into increasing smaller subsamples and do calculations on each of the subsamples. To determine the how the subsample size will change, the program finds divisors of N and stores it into an array OptN.
dmin = 2;
% Find OptN such that it has the largest number of
% divisors among all natural numbers in the interval [0.99*N,N]
N = length(x);
N0 = floor(0.99*N);
dv = zeros(N-N0+1,1);
for i = N0:N,
dv(i-N0+1) = length(divisors(i,dmin));
end
OptN = N0 + find(max(dv)==dv) - 1;
% Use the first OptN values of x for further analysis
x = x(1:OptN);
% Find the divisors >= dmin for OptN
d = divisors(OptN,dmin);
function d = divisors(n,n0)
% Find all divisors of the natural number N greater or equal to N0
i = n0:floor(n/2);
d = find((n./i)==floor(n./i))' + n0 - 1; % Problem line
In function divisors is where the problem occurs. I have 'Error using ./ Matrix dimensions must agree.' However, this worked with input data of length 60, but when I try data of length 1058 it gives me the above error.
I think that with large dataset it's possible that find(max(dv)==dv) will returns multiple numbers. So OptN will become a vector, not a scalar.
Then the length of i (BTW not a good name for variable in MATLAB, it's also a complex number i) will be unpredictable and probably different from n causing the dimension error in the next statement.
You can try find(max(dv)==dv,1) instead to get only the first match. Or add a loop.
I am trying to create or find a CoffeeScript implementation of the Levenshtein Distance formula, aka Edit Distance. Here is what I have so far, any help at all would be much appreciated.
levenshtein = (s1,s2) ->
n = s1.length
m = s2.length
if n < m
return levenshtein(s2, s1)
if not s1
return s2.length
previous_row = [s2.length + 1]
for c1, i in s1
current_row = [i + 1]
for c2, j in s2
insertions = previous_row[j + 1] + 1
deletions = current_row[j] + 1
substitutions = previous_row[j] # is this unnescessary?-> (c1 != c2)
current_row.push(Math.min(insertions,deletions,substitutions))
previous_row = current_row
return previous_row[previous_row.length-1]
#End Levenshetein Function
Btw: I know this code is wrong on many levels, I am happy to receive any and all constructive criticism. Just looking to improve, and figure out this formula!
CodeEdit1: Patched up the errors Trevor pointed out, current code above includes those changes
Update: The question I am asking is - how do we do Levenshtein in CoffeeScript?
Here is the 'steps' for the Levenshtein Distance Algorithm to help you see what I am trying to accomplish.
Steps
1
Set n to be the length of s.
Set m to be the length of t.
If n = 0, return m and exit.
If m = 0, return n and exit.
Construct a matrix containing 0..m rows and 0..n columns.
2
Initialize the first row to 0..n.
Initialize the first column to 0..m.
3 Examine each character of s (i from 1 to n).
4 Examine each character of t (j from 1 to m).
5 If s[i] equals t[j], the cost is 0.
If s[i] doesn't equal t[j], the cost is 1.
6 Set cell d[i,j] of the matrix equal to the minimum of:
a. The cell immediately above plus 1: d[i-1,j] + 1.
b. The cell immediately to the left plus 1: d[i,j-1] + 1.
c. The cell diagonally above and to the left plus the cost: d[i-1,j-1] + cost.
7 After the iteration steps (3, 4, 5, 6) are complete, the distance is found in cell d[n,m].
source:http://www.merriampark.com/ld.htm
This page (linked to from the resource you mentioned) offers a JavaScript implementation of the Levenshtein distance algorithm. Based on both that and the code you posted, here's my CoffeeScript version:
LD = (s, t) ->
n = s.length
m = t.length
return m if n is 0
return n if m is 0
d = []
d[i] = [] for i in [0..n]
d[i][0] = i for i in [0..n]
d[0][j] = j for j in [0..m]
for c1, i in s
for c2, j in t
cost = if c1 is c2 then 0 else 1
d[i+1][j+1] = Math.min d[i][j+1]+1, d[i+1][j]+1, d[i][j] + cost
d[n][m]
It seems to hold up to light testing, but let me know if there are any problems.