jbowles

using BenchmarkTools

dbl2nd(da::Array{Int64,1}) = for i in 1:2:(length(da))
    da[i] *= 2
end

mod9(da::Array{Int64,1}) = for i in 1:2:length(da)
    if da[i] > 9
        da[i] -= 9
    end

jbowles

//Luhn trait
pub trait Luhn {
    fn luhn(self) -> bool;
}
impl Luhn for String {
    fn luhn(self) -> bool {
        let s = remove_whitespace(&self);
        let d = Digits::digits(s);
        if d.len() < 16 {
            return false;

jbowles

/*
RatcliffObserhelp distance

SEE tokenizer

 [NIST Ratcliff/Obershelp pattern recognition](https://xlinux.nist.gov/dads/HTML/ratcliffObershelp.html):
Compute the similarity of two strings as the number of matching characters divided by the total number of characters in the two strings.
Matching characters are those in the longest common subsequence plus, recursively, matching characters in the unmatched region on either side of the longest common subsequence.
*/

jbowles

package metron

import "math"

// CosineSim calculate the similarity of two non-zero vectors using dot product (multiplying vector elements and summing) and deriving the cosine angle between the two vectors.
// TODO not fully tested yet!
//
// Dot Product:
//	  \vec{a} = (a_1, a_2, a_3, \ldots), \vec{b} = (b_1, b_2, b_3, \ldots); where a_n and a_b are elements of the vector.
// Cosine Similarity: