While learning the programming language Rust, I want to make a numerology calculator based on a person's full name. This numerology calculator would, later on, provide computations on other pieces of texts as well as other numbers.
The numerology calculator would take a name such as John Edward Smith and calculate his life path like this, for example:
1 + 6 + 8 + 4 + 5 + 4 + 5 + 1 + 9 + 4 + 1 + 4 + 9 + 2 + 8
J O H N E D W A R D S M I T H
Adding up all the letters yields 71 and 7+1 = 8
Another calculation uses the vowels in the name, and here is the problem: most algorithms detect whether a letter in question is AEIO or U and declares it to be a vowel. Otherwise, it's a consonant. However, Y could be either a vowel or a consonant. W is also sometimes a vowel but for the purposes of numerology, it is always a consonant.
I came across a cargo package called Eudex (supposedly better than Soundex) but I don't know how to use it to detect whether Y is a vowel or consonant. Could someone point me in the right direction? Thanks.
fn main() {
assert!((Hash::new("jumpo") - Hash::new("jumbo")).similar());
assert!(!(Hash::new("Horse") - Hash::new("Norse")).similar());
println!("{:?}", Hash::new("hello"));
println!(Hash::new("Sydney")).listVowels());
}
Hash { hash: 144115188075855872 }
Fantasy result of the last print line:
ye
Related
I have a problem where I have a string of length N, where (1 ≤ N ≤ 10^5). This string will only have lower case letters.
We have to rewrite the string so that it has a series of "streaks", where the same letter is included at least K (1 ≤ K ≤ N) times in a row.
It costs a_ij to change a single specific letter in the string from i to j. There are M different possible letters you can change each letter to.
Example: "abcde" is the input string. N = 5 (length of "abcde"), M = 5 (letters are A, B, C, D, E), and K = 2 (each letter must be repeated at least 2 times) Then we are given a M×M matrix of values a_ij, where a_ij is an integer in the range 0…1000 and a_ii = 0 for all i.
0 1 4 4 4
2 0 4 4 4
6 5 0 3 2
5 5 5 0 4
3 7 0 5 0
Here, it costs 0 to change from A to A, 1 to change from A to B, 4 to change from A to C, and so on. It costs 2 to change from B to A.
The optimal solution in this example is to change the a into b, change the d into e, and then change both e’s into c’s. This will take 1 + 4 + 0 + 0 = 5 moves, and the final combo string will be "bbccc".
It becomes complicated as it might take less time to switch from using button i to an intermediate button k and then from button k to button j rather than from i to j directly (or more generally, there may be a path of changes starting with i and ending with j that gives the best overall cost for switching from button i ultimately to button j).
To solve for this issue, I am treating the matrix as a graph, and then performing Floyd Warshall to find the fastest time to switch letters. This will take O(M^3) which is only 26^3.
My next step is to perform dynamic programming on each additional letter to find the answer. If someone could give me advice on how to do this, I would be thankful!
Here are some untested ideas. I'm not sure if this is efficient enough (or completely worked out) but it looks like 26 * 3 * 10^5. The recurrence could be converted to a table, although with higher Ks, memoisation might be more efficient because of reduced state possibilities.
Assume we've recorded 26 prefix arrays for conversion of the entire list to each of the characters using the best conversion schedule, using a path-finding method. This lets us calculate the cost of a conversion of a range in the string in O(1) time, using a function, cost.
A letter in the result can be one of three things: either it's the kth instance of character c, or it's before the kth, or it's after the kth. This leads to a general recurrence:
f(i, is_kth, c) ->
cost(i - k + 1, i, c) + A
where
A = min(
f(i - k, is_kth, c'),
f(i - k, is_after_kth, c')
) forall c'
A takes constant time since the alphabet is constant, assuming earlier calls to f have been tabled.
f(i, is_before_kth, c) ->
cost(i, i, c) + A
where
A = min(
f(i - 1, is_before_kth, c),
f(i - 1, is_kth, c'),
f(i - 1, is_after_kth, c')
) forall c'
Again A is constant time since the alphabet is constant.
f(i, is_after_kth, c) ->
cost(i, i, c) + A
where
A = min(
f(i - 1, is_after_kth, c),
f(i - 1, is_kth, c)
)
A is constant time in the latter. We would seek the best result of the recurrence applied to each character at the end of the string with either state is_kth or state is_after_kth.
There are N jewellery shop(s). Each jewellery shop has three kinds of coins - Gold, Platinum, and Diamond having worth value A, B, and C respectively. You decided to go to each of N jewellery shop and take coins from each of the shop. But to do so following conditions must satisfy -
You can take at most 1 coin from an individual shop.
You can take at most X coins of Gold type.
You can take at most Y coins of Platinum type.
You can take at most Z coins of Diamond type.
You want to collect coins from shops in such a way that worth value of coins collected is maximised.
Input Format :
The first line contains an integer N. Where N is the number of jewellery shops.
The second line contains three integers X, Y, Z. Where X, Y, Z denotes the maximum number of coins you can collect of type Gold, Platinum, and diamond respectively.
Then N lines contain three space-separated integers A, B, C. Where A, B, C is the worth value of the Gold, Platinum, and diamond coin respectively.
Output Format :
Print a single integer representing the maximum worth value you can get.
Constraints :
1
<=
N
<=
200
1
<=
X
,
Y
,
Z
<=
N
1
<=
A
,
B
,
C
<
10
9
Example : -
4
2 1 1
5 4 5
4 3 2
10 9 7
8 2 9
Answer:-
27(9+9+5+4)
I tried the obvious greedy approach but it failed :-)
The question is in the following link:
http://www.spoj.com/problems/AEROLITE/
Input:
1 1 1 1
0 0 6 3
1 1 1 2
[and 7 test cases more]
Output:
6
57
8
[and 7 test cases more]
How does the output come from the input?
Consider the outputs corresponding to the following letters:
a. 1 1 1 1 = 6
b. 0 0 6 3 = 57
c. 1 1 1 2 = 8
Restating the definitions from the problem in a more tactical way, the 4 inputs correspond to the following:
The number of "{}" pairs
The number of "[]" pairs
The number of "()" pairs
The max depth when generating the output
The output is a single number representing the number of regular expressions that match the input parameters (how much depth can be used with the pairs) and how many combinations of the 3 pairs can be generating matching the prioritization rules that "()" cannot contain "{}" or "[]" and "[]" cannot contain "{}".
The walkthrough below shows how to arrive at the outputs, but it doesn't try to break the sub-problems or anything down. Hopefully, it will at least help you connect the numbers and start to find the problems to break down.
Taking those examples explicitly, start with "a" for 1 1 1 1 = 6:
The inputs mean that only do a depth of 1 and use 1 pair each of "{}", "[]", "()". This is a permutation how many arrangements of 3 can be made as permutations, so 3! = 6.
Actual: {}, {}()[], []{}(), {}, (){}[], ()[]{}
Then go to "b" for 1 1 1 2 = 8
This is just like "a" with exception that we must now allow for another level of depth (d = 2 instead of 1)
Therefore, this is 6 from "a" + any additional combinations of depth = 2
** Additional = {[()]}, {} (only 2 additional cases meet the rules)
"a" + (additional for d = 2) = 8
Finally, consider "b" where we are exploring only the d = 3 of 6 "()".
We must break down and add the depth (d) of 1, 2, and 3
Because only parenthesis exist here, this is just a Catalan number Cn where n = 6, but limited to a depth of no more than 3 levels of parenthesis (For more on this: https://en.wikipedia.org/wiki/Catalan_number) C(6) = 132, but once you exclude all the Catalan numbers for depths more than 3, you are left with 57 matches.
Alternatively and much more tediously, you can iterate over all the combinations of parenthesis that are depth of 3 or less to get to 57 records:
** Start with d = 1, so just ()()()()()()
** Then d = 2, so examples like (())()()()(), ()(())()()(), ()()(())()(), ()()()(())(), ()()()()(()), and so on
** Then d = 3, so examples like ((()))()()(), ()((()))()(), ()()((()))(), ()()()((())), and so on
Not quite sure how to describe this, but I have a word game I like to play that I'd like to implement as a computer program.
The basic gist is that you look at the values of the letters (A=1..Z=26), and consolidate the letters into the fewest possible, and that are the closest possible to each other.
As an example:
s t a c k
Sum the values
19 + 20 + 1 + 3 + 11 = 54
Find the fewest number of letters:
ceil(54/26) = 3
Choose letters closest to each other
54/3 = 18
Letters to be displayed should be rrr.
That happens to be an easy example. What would it look like when you need to have, say, rrs (if your initial string was 'a stack' instead)?
Does this already have a name that I can lookup and implement?
I think your problem boils down to this: given n and k, find numbers r1, r2, ..., rk such that sum(r1 + r2 + ... + rk) = n and max(r1, r2, ..., rk) - min(r1, r2, ..., rk) is as small as possible.
The solution is pick r = floor(n / k), and set n mod k of the numbers to be r + 1, and the rest r.
For example, if n = 55 and k = 3 (your example), we have floor(55/3) = 18 and 55 mod 3 is 1, so the solution is 19, 18, 18.
All that remains is converting between numbers and letters.
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I know that an affine cipher substitutes BD with SG. I need to find the encryption formula, in the form y = a x + b, where a and b are coefficients.
From the information above I end up having to equations:
a+b=18 and
3a+b=6
So I am working like this:
a+b=18 and 3a + b = 6-> 3a+18-a=6-> 2a= 6-18 -> 2a=14 (as it is mod 26)
b=18-a
2a=?
So, O want to multiply by the multiplicative inverse of 2 mod 26
I can't find a multiplicative inverse of number 2 with 26 (y = ax + b mod 26)
Can anyone please help me find a and b?
That's because 2 doesn't have a multiplicative inverse mod 26: since 13*2=0, there does not exist K such that K * a = 1. Your modulus must be prime. Try looking up the Chinese Remainder Theorem for more information.
To be more specific, integers mod 26 is not a field (a mathematical set where every element, except 0, has a multiplicative inverse). Any ring in which a * b = 0, for some a!=0 and b!=0, is not a field.
In fact, a field will always have p^n elements, where p is a prime number and n is a positive integer. The simplest fields are just integers mod a prime number, but for prime powers you need to construct a more elaborate system. So, in short, use a different modulus like 29.
Does a = 7 work? 2*7 = 14. Thus, b = 11.
Let's check the 2 equations to see if that works:
7+11 = 18 (check for the first equation).
3*7+11=21+11 = 32 = 6.
What is wrong with the above?
EDIT: Ok, now I see what could go wrong with trying to do a division by 2 in a non-prime modulus as it is similar to a division by 0. You could take ribond's suggestion of using the Chinese Remainder Theorem and split the equations into another pair of pairs:
mod 13: a+b=5, 3a+b=6. (2a = 1 = 14 => a=7. b = 18-7 = 11.)
mod 2: a+b=0. 3a+b=0 (Note this is the same equation and has a pair of possible solutions where a and b are either 0 or 1.)
Thus there is the unique solution for your problem I think.
Other posters are right in that there is no inverse of 2 modulo 26, so you can't solve 2a=14 mod 26 by multiplying through by the inverse of 2. But that doesn't mean that 2a=14 mod 26 isn't solvable.
Consider the general equation cx = d mod n (c=2,d=14,n=26 in your case). Let g = gcd(c,n). The equation cx=d has a solution if an only if g divides d. If g divides d, then there are in fact multiple solutions (g of them). The equation (c/g)x = d/g mod n/g has a unique solution (call it x_0) because c/g is relatively prime to n/g and therefore has an inverse. The solutions to the original equation are x_0, x_0 + n/g, ..., x_0 + (g-1)n/g.
In your case c=2,d=14,n=26, and g=2. g divides d, so first solve the equation (2/2)x = (14/2) mod (26/2) which gives 7. So both 7 and 7+13=20 solve your original equation.
Note that this means you haven't uniquely determined your affine transformation, two possibilities still exist. You need another data point...