Most frequent k chars distance
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In information theory, the MostFreqKDistance is a string metric for quickly estimating how similar two ordered sets or strings are. The scheme was invented by Sadi Evren SEKER,[1] and initially used in text mining applications like author recognition.[1] This method is originally based on a hashing function, MaxFreqKChars[2] classical author recognition problem and idea first came out while studying data stream mining.[3] The string distance
- Definition
Method has two steps.
- Hash input strings str1 and str2 separately using MostFreqKHashing and output hstr1 and hstr2 respectively
- Calculate string distance (or string similarity coefficient) of two hash outputs, hstr1 and hstr2 and output an integer value
- Most Frequent K Hashing
The first step of algorithm is calculating the hashing based on the most frequent k characters. The hashing algorithm has below steps:
string function MostFreqKHashing (string inputString, int K) def string outputString for each distinct characters count occurrence of each character for i from 0 to K char c = next most frequent ith character (if two chars have same frequency than get the first occurrence in inputString) int count = number of occurrence of the character append to outputString, c and count end for return outputString
Aim of 'Most Frequent K Hashing' function is calculating the most count of each character and returning the K most frequent character with the character and count. Rules of hash can be listed as below:
- Output will hold the character and count
- Most frequent character and count will appear before the least frequent at the output
- if two characters have equal frequency, the first appearing in input will appear before at the output
Similar to the most of hashing functions, Most Frequent K Hashing is also a wp:one way function.
- Most Frequent K Distance
Distance calculation between two strings is based on the hash outputs of two strings.
int function MostFreqKSimilarity (string inputStr1, string inputStr2) def int similarity for each c = next character from inputStr1 lookup c in inputStr2 if c is not null similarity += frequency of c in inputStr1 + frequency of c in inputStr2 return similarity
Above function, simply gets two input strings, previously outputted from the MostFreqKHashing function. From the most frequent k hashing function, the characters and their frequencies are returned. So, the similarity function calculates the similarity based on characters and their frequencies by checking if the same character appears on both strings and if their frequencies are equal.
In some implementations, the distance metric is required instead of similarity coefficient. In order to convert the output of above similarity coefficient to distance metric, the output can be subtracted from any constant value (like the maximum possible output value). For the case, it is also possible to implement a wp:wrapper function over above two functions.
- String Distance Wrapper Function
In order to calculate the distance between two strings, below function can be implemented
int function MostFreqKSDF (string inputStr1, string inputStr2, int K, int maxDistance) return maxDistance - MostFreqKSimilarity(MostFreqKHashing(inputStr1,K), MostFreqKHashing(inputStr2,K))
Any call to above string distance function will supply two input strings and a maximum distance value. The function will calculate the similarity and subtract that value from the maximum possible distance. It can be considered as a simple wp:additive inverse of similarity.
- Examples
Let's consider maximum 2 frequent hashing over two strings ‘research’ and ‘seeking’. <lang javascript>MostFreqKHashing('research',2) = 'r2e2'</lang> because we have 2 'r' and 2 'e' characters with the highest frequency and we return in the order they appear in the string. <lang javascript>MostFreqKHashing('seeking',2) = 'e2s1'</lang> Again we have character 'e' with highest frequency and rest of the characters have same frequency of 1, so we return the first character of equal frequencies, which is 's'. Finally we make the comparison: <lang javascript>MostFreqKSimilarity('r2e2','e2s1') = 2</lang> We simply compared the outputs and only character occurring in both input is character 'e' and the occurrence in both input is 2. Instead running the sample step by step as above, we can simply run by using the string distance wrapper function as below: <lang javascript>MostFreqKSDF('research', 'seeking',2) = 2</lang>
Below table holds some sample runs between example inputs for K=2:
Inputs | Hash Outputs | SDF Output (max from 10) |
---|---|---|
'night'
'nacht' |
n1i1
n1a1 |
9 |
'my'
'a' |
m1y1
a1NULL0 |
10 |
‘research’
‘research’ |
r2e2
r2e2 |
8 |
‘aaaaabbbb’
‘ababababa’ |
a5b4
a5b4 |
1 |
‘significant’
‘capabilities’ |
i3n2
i3a2 |
5 |
Method is also suitable for bioinformatics to compare the genetic strings like in wp:fasta format
Str1= LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV Str2 = EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG MostFreqKHashing(str1,2) = L9T8 MostFreqKHashing(str2,2) = F9L8 MostFreqKSDF(str1,str2,2,100) = 83
C++
<lang cpp>#include <string>
- include <vector>
- include <map>
- include <iostream>
- include <algorithm>
- include <utility>
- include <sstream>
std::string mostFreqKHashing ( const std::string & input , int k ) {
std::ostringstream oss ; std::map<char, int> frequencies ; for ( char c : input ) { frequencies[ c ] = std::count ( input.begin( ) , input.end( ) , c ) ; } std::vector<std::pair<char , int>> letters ( frequencies.begin( ) , frequencies.end( ) ) ; std::sort ( letters.begin( ) , letters.end( ) , [input] ( std::pair<char, int> a ,
std::pair<char, int> b ) { char fc = std::get<0>( a ) ; char fs = std::get<0>( b ) ; int o = std::get<1>( a ) ; int p = std::get<1>( b ) ; if ( o != p ) { return o > p ; } else { return input.find_first_of( fc ) < input.find_first_of ( fs ) ; } } ) ;
for ( int i = 0 ; i < letters.size( ) ; i++ ) { oss << std::get<0>( letters[ i ] ) ; oss << std::get<1>( letters[ i ] ) ; } std::string output ( oss.str( ).substr( 0 , 2 * k ) ) ; if ( letters.size( ) >= k ) { return output ; } else { return output.append( "NULL0" ) ; }
}
int mostFreqKSimilarity ( const std::string & first , const std::string & second ) {
int i = 0 ; while ( i < first.length( ) - 1 ) { auto found = second.find_first_of( first.substr( i , 2 ) ) ; if ( found != std::string::npos )
return std::stoi ( first.substr( i , 2 )) ;
else
i += 2 ;
} return 0 ;
}
int mostFreqKSDF ( const std::string & firstSeq , const std::string & secondSeq , int num ) {
return mostFreqKSimilarity ( mostFreqKHashing( firstSeq , num ) , mostFreqKHashing( secondSeq , num ) ) ;
}
int main( ) {
std::string s1("LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV" ) ; std::string s2( "EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG" ) ; std::cout << "MostFreqKHashing( s1 , 2 ) = " << mostFreqKHashing( s1 , 2 ) << '\n' ; std::cout << "MostFreqKHashing( s2 , 2 ) = " << mostFreqKHashing( s2 , 2 ) << '\n' ; return 0 ;
} </lang>
- Output:
MostFreqKHashing( s1 , 2 ) = L9T8 MostFreqKHashing( s2 , 2 ) = F9L8
Go
<lang go>package main
import (
"fmt" "sort"
)
type cf struct {
c rune f int
}
func reverseStr(s string) string {
runes := []rune(s) for i, j := 0, len(runes)-1; i < j; i, j = i+1, j-1 { runes[i], runes[j] = runes[j], runes[i] } return string(runes)
}
func indexOfCf(cfs []cf, r rune) int {
for i, cf := range cfs { if cf.c == r { return i } } return -1
}
func minOf(i, j int) int {
if i < j { return i } return j
}
func mostFreqKHashing(input string, k int) string {
var cfs []cf for _, r := range input { ix := indexOfCf(cfs, r) if ix >= 0 { cfs[ix].f++ } else { cfs = append(cfs, cf{r, 1}) } } sort.SliceStable(cfs, func(i, j int) bool { return cfs[i].f > cfs[j].f // descending, preserves order when equal }) acc := "" min := minOf(len(cfs), k) for _, cf := range cfs[:min] { acc += fmt.Sprintf("%c%c", cf.c, cf.f) } return acc
}
func mostFreqKSimilarity(input1, input2 string) int {
similarity := 0 runes1, runes2 := []rune(input1), []rune(input2) for i := 0; i < len(runes1); i += 2 { for j := 0; j < len(runes2); j += 2 { if runes1[i] == runes2[j] { freq1, freq2 := runes1[i+1], runes2[j+1] if freq1 != freq2 { continue // assuming here that frequencies need to match } similarity += int(freq1) } } } return similarity
}
func mostFreqKSDF(input1, input2 string, k, maxDistance int) {
fmt.Println("input1 :", input1) fmt.Println("input2 :", input2) s1 := mostFreqKHashing(input1, k) s2 := mostFreqKHashing(input2, k) fmt.Printf("mfkh(input1, %d) = ", k) for i, c := range s1 { if i%2 == 0 { fmt.Printf("%c", c) } else { fmt.Printf("%d", c) } } fmt.Printf("\nmfkh(input2, %d) = ", k) for i, c := range s2 { if i%2 == 0 { fmt.Printf("%c", c) } else { fmt.Printf("%d", c) } } result := maxDistance - mostFreqKSimilarity(s1, s2) fmt.Printf("\nSDF(input1, input2, %d, %d) = %d\n\n", k, maxDistance, result)
}
func main() {
pairs := [][2]string{ {"research", "seeking"}, {"night", "nacht"}, {"my", "a"}, {"research", "research"}, {"aaaaabbbb", "ababababa"}, {"significant", "capabilities"}, } for _, pair := range pairs { mostFreqKSDF(pair[0], pair[1], 2, 10) }
s1 := "LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV" s2 := "EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG" mostFreqKSDF(s1, s2, 2, 100) s1 = "abracadabra12121212121abracadabra12121212121" s2 = reverseStr(s1) mostFreqKSDF(s1, s2, 2, 100)
}</lang>
- Output:
input1 : research input2 : seeking mfkh(input1, 2) = r2e2 mfkh(input2, 2) = e2s1 SDF(input1, input2, 2, 10) = 8 input1 : night input2 : nacht mfkh(input1, 2) = n1i1 mfkh(input2, 2) = n1a1 SDF(input1, input2, 2, 10) = 9 input1 : my input2 : a mfkh(input1, 2) = m1y1 mfkh(input2, 2) = a1 SDF(input1, input2, 2, 10) = 10 input1 : research input2 : research mfkh(input1, 2) = r2e2 mfkh(input2, 2) = r2e2 SDF(input1, input2, 2, 10) = 6 input1 : aaaaabbbb input2 : ababababa mfkh(input1, 2) = a5b4 mfkh(input2, 2) = a5b4 SDF(input1, input2, 2, 10) = 1 input1 : significant input2 : capabilities mfkh(input1, 2) = i3n2 mfkh(input2, 2) = i3a2 SDF(input1, input2, 2, 10) = 7 input1 : LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV input2 : EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG mfkh(input1, 2) = L9T8 mfkh(input2, 2) = F9L8 SDF(input1, input2, 2, 100) = 100 input1 : abracadabra12121212121abracadabra12121212121 input2 : 12121212121arbadacarba12121212121arbadacarba mfkh(input1, 2) = 112a10 mfkh(input2, 2) = 112210 SDF(input1, input2, 2, 100) = 88
Haskell
<lang Haskell>module MostFrequentK
where
import Data.List ( nub , sortBy ) import qualified Data.Set as S
count :: Eq a => [a] -> a -> Int count [] x = 0 count ( x:xs ) k
|x == k = 1 + count xs k |otherwise = count xs k
orderedStatistics :: String -> [(Char , Int)] orderedStatistics s = sortBy myCriterion $ nub $ zip s ( map (\c -> count s c ) s )
where myCriterion :: (Char , Int) -> (Char , Int) -> Ordering myCriterion (c1 , n1) (c2, n2)
|n1 > n2 = LT |n1 < n2 = GT |n1 == n2 = compare ( found c1 s ) ( found c2 s )
found :: Char -> String -> Int found e s = length $ takeWhile (/= e ) s
mostFreqKHashing :: String -> Int -> String mostFreqKHashing s n = foldl ((++)) [] $ map toString $ take n $ orderedStatistics s
where toString :: (Char , Int) -> String toString ( c , i ) = c : show i
mostFreqKSimilarity :: String -> String -> Int mostFreqKSimilarity s t = snd $ head $ S.toList $ S.fromList ( doublets s ) `S.intersection`
S.fromList ( doublets t ) where toPair :: String -> (Char , Int) toPair s = ( head s , fromEnum ( head $ tail s ) - 48 ) doublets :: String -> [(Char , Int)] doublets str = map toPair [take 2 $ drop start str | start <- [0 , 2 ..length str - 2]]
mostFreqKSDF :: String -> String -> Int ->Int mostFreqKSDF s t n = mostFreqKSimilarity ( mostFreqKHashing s n ) (mostFreqKHashing t n ) </lang>
- Output:
mostFrequentKHashing "LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV" 2 "L9T8" *MostFrequentK> mostFrequentKHashing "EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG" 2 "F9L8"
J
Solution:<lang j>NB. String Distance Wrapper Function mfksDF =: {:@:[ - (mfks@:(mfkh&.>)~ {.)~
NB. Most Frequent K Distance mfks =: score@:(charMap@:[ {"1 charVals@:])/@:kHashes
score =. ([ +/ .* =)/ NB. (+ +/ .* *.&:*)/ for sum += freq_in_left + freq_in_right charMap =. (,&< i.&> <@:~.@:,)&;/ charVals =. (; , 0:)"1 kHashes =. 0 1 |: ({.&>~ [: <./ #&>)
NB. Most Frequent K Hashing mfkh =: _&$: : (takeK freqHash) NB. Default LHA of _ means "return complete frequency table"
takeK =. (<.#) {. ] freqHash =. ~. (] \:~ ,.&:(<"0)) #/.~
NB. No need to fix mfksDF mfkh =: mfkh f. mfks =: mfks f.</lang>
Examples:<lang j>verb define fkh =. ;@:,@:(":&.>) NB. format k hash
assert. 'r2e2 e2s1' (-: [: fkh 2&mfkh)&>&;: 'research seeking' assert. 2 = mfks 2 mfkh&.> 'research';'seeking'
assert. 'n1i1 n1a1' (-: [: fkh 2&mfkh)&>&;: 'night nacht' assert. 9 = 2 10 mfksDF 'night';'nacht'
assert. 'm1y1 a1' (-: [: fkh 2&mfkh)&>&;: 'my a' assert. 10 = 2 10 mfksDF 'my';'a'
assert. 'r2e2' -: fkh 2 mfkh 'research' assert. 6 = 2 10 mfksDF 'research';'research' NB. task says 8; right answer is 6
assert. 'a5b4 a5b4' (-: [: fkh 2&mfkh)&>&;: 'aaaaabbbb ababababa' assert. 1 = 2 10 mfksDF 'aaaaabbbb';'ababababa'
assert. 'i3n2 i3a2' (-: [: fkh 2&mfkh)&>&;: 'significant capabilities' assert. 7 = 2 10 mfksDF 'significant';'capabilities' NB. task says 5; right answer is 7
assert. 'L9T8 F9L8' (-: [: fkh 2&mfkh)&>&;: 'LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG' assert. 100 = 2 100 mfksDF 'LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV';'EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG' NB. task says 83; right answer is 100
'pass' )
pass</lang>
Notes: As of press time, there are significant discrepancies between the task description, its pseudocode, the test cases provided, and the two other existing implementations. See the talk page for the assumptions made in this implementation to reconcile these discrepancies (in particular, in the scoring function).
Java
Translation of the pseudo-code of the Wikipedia article wp:Most frequent k characters to wp:java implementation of three functions given in the definition section are given below with wp:JavaDoc comments:
<lang java>import java.util.Collections; import java.util.Comparator; import java.util.HashMap; import java.util.LinkedHashMap; import java.util.ArrayList; import java.util.List; import java.util.Map;
public class SDF {
/** Counting the number of occurrences of each character * @param character array * @return hashmap : Key = char, Value = num of occurrence */ public static HashMap<Character, Integer> countElementOcurrences(char[] array) {
HashMap<Character, Integer> countMap = new HashMap<Character, Integer>();
for (char element : array) { Integer count = countMap.get(element); count = (count == null) ? 1 : count + 1; countMap.put(element, count); }
return countMap; } /** * Sorts the counted numbers of characters (keys, values) by java Collection List * @param HashMap (with key as character, value as number of occurrences) * @return sorted HashMap */ private static <K, V extends Comparable<? super V>> HashMap<K, V> descendingSortByValues(HashMap<K, V> map) {
List<Map.Entry<K, V>> list = new ArrayList<Map.Entry<K, V>>(map.entrySet()); // Defined Custom Comparator here Collections.sort(list, new Comparator<Map.Entry<K, V>>() { public int compare(Map.Entry<K, V> o1, Map.Entry<K, V> o2) { return o2.getValue().compareTo(o1.getValue()); } });
// Here I am copying the sorted list in HashMap // using LinkedHashMap to preserve the insertion order HashMap<K, V> sortedHashMap = new LinkedHashMap<K, V>(); for (Map.Entry<K, V> entry : list) { sortedHashMap.put(entry.getKey(), entry.getValue()); } return sortedHashMap;
} /** * get most frequent k characters * @param array of characters * @param limit of k * @return hashed String */ public static String mostOcurrencesElement(char[] array, int k) { HashMap<Character, Integer> countMap = countElementOcurrences(array); System.out.println(countMap); Map<Character, Integer> map = descendingSortByValues(countMap); System.out.println(map); int i = 0; String output = ""; for (Map.Entry<Character, Integer> pairs : map.entrySet()) {
if (i++ >= k) break;
output += "" + pairs.getKey() + pairs.getValue(); } return output; } /** * Calculates the similarity between two input strings * @param input string 1 * @param input string 2 * @param maximum possible limit value * @return distance as integer */ public static int getDiff(String str1, String str2, int limit) { int similarity = 0;
int k = 0; for (int i = 0; i < str1.length() ; i = k) { k ++; if (Character.isLetter(str1.charAt(i))) { int pos = str2.indexOf(str1.charAt(i));
if (pos >= 0) { String digitStr1 = ""; while ( k < str1.length() && !Character.isLetter(str1.charAt(k))) { digitStr1 += str1.charAt(k); k++; }
int k2 = pos+1; String digitStr2 = ""; while (k2 < str2.length() && !Character.isLetter(str2.charAt(k2)) ) { digitStr2 += str2.charAt(k2); k2++; }
similarity += Integer.parseInt(digitStr2) + Integer.parseInt(digitStr1);
} } } return Math.abs(limit - similarity);
} /** * Wrapper function * @param input string 1 * @param input string 2 * @param maximum possible limit value * @return distance as integer */ public static int SDFfunc(String str1, String str2, int limit) { return getDiff(mostOcurrencesElement(str1.toCharArray(), 2), mostOcurrencesElement(str2.toCharArray(), 2), limit); }
public static void main(String[] args) { String input1 = "LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV"; String input2 = "EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG"; System.out.println(SDF.SDFfunc(input1,input2,100));
}
}</lang>
JavaScript
Defines the functions for counting and frequency outside of the hashing function, as it is easier to calculate the comparison from a structured result rather than parsing a hash.
<lang javascript>//returns an object of counts keyed by character const kCounts = str => {
const counts = {}; for (let char of str) { counts[char] = counts[char] ? counts[char] + 1 : 1; } return counts;
};
//returns an array of length k containing the characters with the highest counts const frequentK = (counts, k) => {
//note that this is written for clarity rather than speed, //as it sorts all of the counts when only the top k are needed return Object.keys(counts) .sort((a, b) => counts[b] - counts[a]) .slice(0, k);
};
//returns a hashed string of the most frequent k characters and their frequencies const mostFreqKHashing = (str, k) => {
const counts = kCounts(str); return frequentK(counts, k) .map(char => char + counts[char]) .join("");
};
//numeric score of similarity based on the sum of counts of characters appearing in the top k of both strings const mostFreqKSimilarity = (str1, str2, k) => {
const counts1 = kCounts(str1); const counts2 = kCounts(str2); const freq1 = frequentK(counts1, k); const freq2 = frequentK(counts2, k); let similarity = 0; for (let char of freq1) { //only considers a character if it is in the top k of both strings if (freq2.includes(char)) { //the examples on this page are inconsistent in whether the similarity //should be the sum of the two counts or only the shared count (the minimum of the two) //this code uses the sum similarity += counts1[char] + counts2[char]; } } return similarity;
};
//subtracts the similarity score from the maxDifference const mostFreqKSDF = (str1, str2, k, maxDistance) => {
return maxDistance - mostFreqKSimilarity(str1, str2, k);
};</lang>
Testing with Jest <lang javascript>test('hash of "LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV" is "L9T8"', () => {
expect(mostFreqKHashing(str1, 2)).toBe("L9T8");
});
test('hash of "EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG" is "F9L8"', () => {
expect(mostFreqKHashing(str2, 2)).toBe("F9L8");
});
test("SDF of strings 1 and 2 with k=2 and max=100 is 83", () => {
expect(mostFreqKSDF(str1, str2, 2, 100)).toBe(83);
});</lang>
Testing in Console <lang javascript>const str1 = "LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV"; const str2 = "EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG"; const K = 2; const maxDistance = 100; console.log(mostFreqKHashing(str1, K)); console.log(mostFreqKHashing(str2, K)); console.log(mostFreqKSDF(str1, str2, K, maxDistance));</lang>
- Output:
L9T8 F9L8 83
Kotlin
The code for the MostFreqKSimilarity function differs in this task from that in the associated Wikipedia article. Also the description is inconsistent with the code in both cases.
What I've decided to assume is that the frequency for commonly occurring characters must be the same in both strings for it to be added to the 'similarity' variable which seems to be the implication of both descriptions. This gives the same results as the Wikipedia article for all except the last example where it gives 100 rather than 83.
It's also evident that you can't just add the frequency of each character to the output string of the MostFreqKHashing function and then expect to be able to parse it afterwards. This is because, in the general case, any printing characters (including digits) could occur in the input string and the frequency could be more than 9. I've therefore encoded the frequency as the character with the same unicode value rather than the frequency itself.
I have no idea what NULL0 is supposed to mean so I've ignored that. <lang scala>// version 1.1.51
fun mostFreqKHashing(input: String, k: Int): String =
input.groupBy { it }.map { Pair(it.key, it.value.size) } .sortedByDescending { it.second } // preserves original order when equal .take(k) .fold("") { acc, v -> acc + "${v.first}${v.second.toChar()}" }
fun mostFreqKSimilarity(input1: String, input2: String): Int {
var similarity = 0 for (i in 0 until input1.length step 2) { for (j in 0 until input2.length step 2) { if (input1[i] == input2[j]) { val freq1 = input1[i + 1].toInt() val freq2 = input2[j + 1].toInt() if (freq1 != freq2) continue // assuming here that frequencies need to match similarity += freq1 } } } return similarity
}
fun mostFreqKSDF(input1: String, input2: String, k: Int, maxDistance: Int) {
println("input1 : $input1") println("input2 : $input2") val s1 = mostFreqKHashing(input1, k) val s2 = mostFreqKHashing(input2, k) print("mfkh(input1, $k) = ") for ((i, c) in s1.withIndex()) print(if (i % 2 == 0) c.toString() else c.toInt().toString()) print("\nmfkh(input2, $k) = ") for ((i, c) in s2.withIndex()) print(if (i % 2 == 0) c.toString() else c.toInt().toString()) val result = maxDistance - mostFreqKSimilarity(s1, s2) println("\nSDF(input1, input2, $k, $maxDistance) = $result\n")
}
fun main(args: Array<String>) {
val pairs = listOf( Pair("research", "seeking"), Pair("night", "nacht"), Pair("my", "a"), Pair("research", "research"), Pair("aaaaabbbb", "ababababa"), Pair("significant", "capabilities") ) for (pair in pairs) mostFreqKSDF(pair.first, pair.second, 2, 10)
var s1 = "LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV" var s2 = "EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG" mostFreqKSDF(s1, s2, 2, 100) s1 = "abracadabra12121212121abracadabra12121212121" s2 = s1.reversed() mostFreqKSDF(s1, s2, 2, 100)
}</lang>
- Output:
input1 : research input2 : seeking mfkh(input1, 2) = r2e2 mfkh(input2, 2) = e2s1 SDF(input1, input2, 2, 10) = 8 input1 : night input2 : nacht mfkh(input1, 2) = n1i1 mfkh(input2, 2) = n1a1 SDF(input1, input2, 2, 10) = 9 input1 : my input2 : a mfkh(input1, 2) = m1y1 mfkh(input2, 2) = a1 SDF(input1, input2, 2, 10) = 10 input1 : research input2 : research mfkh(input1, 2) = r2e2 mfkh(input2, 2) = r2e2 SDF(input1, input2, 2, 10) = 6 input1 : aaaaabbbb input2 : ababababa mfkh(input1, 2) = a5b4 mfkh(input2, 2) = a5b4 SDF(input1, input2, 2, 10) = 1 input1 : significant input2 : capabilities mfkh(input1, 2) = i3n2 mfkh(input2, 2) = i3a2 SDF(input1, input2, 2, 10) = 7 input1 : LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV input2 : EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG mfkh(input1, 2) = L9T8 mfkh(input2, 2) = F9L8 SDF(input1, input2, 2, 100) = 100 input1 : abracadabra12121212121abracadabra12121212121 input2 : 12121212121arbadacarba12121212121arbadacarba mfkh(input1, 2) = 112a10 mfkh(input2, 2) = 112210 SDF(input1, input2, 2, 100) = 88
Perl
<lang Perl>#!/usr/bin/perl use strict ; use warnings ;
sub mostFreqHashing {
my $inputstring = shift ; my $howmany = shift ; my $outputstring ; my %letterfrequencies = findFrequencies ( $inputstring ) ; my @orderedChars = sort { $letterfrequencies{$b} <=> $letterfrequencies{$a} || index( $inputstring , $a ) <=> index ( $inputstring , $b ) } keys %letterfrequencies ; for my $i ( 0..$howmany - 1 ) { $outputstring .= ( $orderedChars[ $i ] . $letterfrequencies{$orderedChars[ $i ]} ) ; } return $outputstring ;
}
sub findFrequencies {
my $input = shift ; my %letterfrequencies ; for my $i ( 0..length( $input ) - 1 ) { $letterfrequencies{substr( $input , $i , 1 ) }++ ; } return %letterfrequencies ;
}
sub mostFreqKSimilarity {
my $first = shift ; my $second = shift ; my $similarity = 0 ; my %frequencies_first = findFrequencies( $first ) ; my %frequencies_second = findFrequencies( $second ) ; foreach my $letter ( keys %frequencies_first ) { if ( exists ( $frequencies_second{$letter} ) ) {
$similarity += ( $frequencies_second{$letter} + $frequencies_first{$letter} ) ;
} } return $similarity ;
}
sub mostFreqKSDF {
(my $input1 , my $input2 , my $k , my $maxdistance ) = @_ ; return $maxdistance - mostFreqKSimilarity( mostFreqHashing( $input1 , $k) ,
mostFreqHashing( $input2 , $k) ) ; }
my $firststring = "LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV" ; my $secondstring = "EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG" ; print "MostFreqKHashing ( " . '$firststring , 2)' . " is " . mostFreqHashing( $firststring , 2 ) . "\n" ; print "MostFreqKHashing ( " . '$secondstring , 2)' . " is " . mostFreqHashing( $secondstring , 2 ) . "\n" ; </lang>
- Output:
MostFreqKHashing ( $firststring , 2) is L9T8 MostFreqKHashing ( $secondstring , 2) is F9L8
Phix
<lang Phix>function mostFreqKHashing(string input, integer k)
string cfs = "" sequence cfsnx = {} for i=1 to length(input) do integer r = input[i], ix = find(r,cfs) if ix>0 then cfsnx[ix][1] -= 1 else cfs &= r cfsnx = append(cfsnx,{-1,length(cfs)}) end if end for cfsnx = sort(cfsnx) -- (aside: the idx forces stable sort) sequence acc := {} for i=1 to min(length(cfs),k) do integer {n,ix} = cfsnx[i] acc &= {cfs[ix], -n} end for return acc
end function
function mostFreqKSimilarity(sequence input1, input2)
integer similarity := 0 for i=1 to length(input1) by 2 do for j=1 to length(input2) by 2 do if input1[i] == input2[j] then integer freq1 = input1[i+1], freq2 = input2[j+1] if freq1=freq2 then similarity += freq1 end if end if end for end for return similarity
end function
function flat(sequence s)
string res = "" for i=1 to length(s) by 2 do res &= sprintf("%c%d",s[i..i+1]) end for return res
end function
procedure mostFreqKSDF(string input1, input2, integer k, maxDistance)
printf(1,"input1 : %s\n", {input1}) printf(1,"input2 : %s\n", {input2}) sequence s1 := mostFreqKHashing(input1, k), s2 := mostFreqKHashing(input2, k) printf(1,"mfkh(input1, %d) = %s\n", {k,flat(s1)}) printf(1,"mfkh(input2, %d) = %s\n", {k,flat(s2)}) integer result := maxDistance - mostFreqKSimilarity(s1, s2) printf(1,"SDF(input1, input2, %d, %d) = %d\n\n", {k, maxDistance, result})
end procedure
constant tests = {{"research", "seeking"},
{"night", "nacht"}, {"my", "a"}, {"research", "research"}, {"aaaaabbbb", "ababababa"}, {"significant", "capabilities"}}
for i=1 to length(tests) do
string {t1,t2} = tests[i] mostFreqKSDF(t1, t2, 2, 10)
end for
string s1 := "LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV",
s2 := "EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG"
mostFreqKSDF(s1, s2, 2, 100) s1 = "abracadabra12121212121abracadabra12121212121" s2 = reverse(s1) mostFreqKSDF(s1, s2, 2, 100)</lang> Output matches Go and Kotlin.
Python
unoptimized and limited <lang python>import collections def MostFreqKHashing(inputString, K):
occuDict = collections.defaultdict(int) for c in inputString: occuDict[c] += 1 occuList = sorted(occuDict.items(), key = lambda x: x[1], reverse = True) outputStr = .join(c + str(cnt) for c, cnt in occuList[:K]) return outputStr
- If number of occurrence of the character is not more than 9
def MostFreqKSimilarity(inputStr1, inputStr2):
similarity = 0 for i in range(0, len(inputStr1), 2): c = inputStr1[i] cnt1 = int(inputStr1[i + 1]) for j in range(0, len(inputStr2), 2): if inputStr2[j] == c: cnt2 = int(inputStr2[j + 1]) similarity += cnt1 + cnt2 break return similarity
def MostFreqKSDF(inputStr1, inputStr2, K, maxDistance):
return maxDistance - MostFreqKSimilarity(MostFreqKHashing(inputStr1,K), MostFreqKHashing(inputStr2,K))</lang>
optimized
A version that replaces the intermediate string with OrderedDict to reduce the time complexity of lookup operation: <lang python>import collections def MostFreqKHashing(inputString, K):
occuDict = collections.defaultdict(int) for c in inputString: occuDict[c] += 1 occuList = sorted(occuDict.items(), key = lambda x: x[1], reverse = True) outputDict = collections.OrderedDict(occuList[:K]) #Return OrdredDict instead of string for faster lookup. return outputDict
def MostFreqKSimilarity(inputStr1, inputStr2):
similarity = 0 for c, cnt1 in inputStr1.items(): #Reduce the time complexity of lookup operation to about O(1). if c in inputStr2: cnt2 = inputStr2[c] similarity += cnt1 + cnt2 return similarity
def MostFreqKSDF(inputStr1, inputStr2, K, maxDistance):
return maxDistance - MostFreqKSimilarity(MostFreqKHashing(inputStr1,K), MostFreqKHashing(inputStr2,K))</lang>
Test: <lang python>str1 = "LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV" str2 = "EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG" K = 2 maxDistance = 100 dict1 = MostFreqKHashing(str1, 2) print("%s:"%dict1) print(.join(c + str(cnt) for c, cnt in dict1.items())) dict2 = MostFreqKHashing(str2, 2) print("%s:"%dict2) print(.join(c + str(cnt) for c, cnt in dict2.items())) print(MostFreqKSDF(str1, str2, K, maxDistance))</lang>
- Output:
OrderedDict([('L', 9), ('T', 8)]): L9T8 OrderedDict([('F', 9), ('L', 8)]): F9L8 83
Racket
<lang Racket>#lang racket
(define (MostFreqKHashing inputString K)
(define t (make-hash)) (for ([c (in-string inputString)] [i (in-naturals)]) (define b (cdr (hash-ref! t c (λ() (cons i (box 0)))))) (set-box! b (add1 (unbox b)))) (define l (for/list ([(k v) (in-hash t)]) (list (car v) k (unbox (cdr v))))) (map cdr (take (sort (sort l < #:key car) > #:key caddr) K)))
(define (MostFreqKSimilarity inputStr1 inputStr2) ; not strings in this impl.
(for*/sum ([c1 (in-list inputStr1)] [c2 (in-value (assq (car c1) inputStr2))] #:when c2) (+ (cadr c1) (cadr c2))))
(define (MostFreqKSDF inputStr1 inputStr2 K maxDistance)
(- maxDistance (MostFreqKSimilarity (MostFreqKHashing inputStr1 K) (MostFreqKHashing inputStr2 K))))
(MostFreqKSDF
"LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV" "EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG" 2 100)
- => 83
- (Should add more tests, but it looks like there's a bunch of mistakes
- in the given tests...)
</lang>
Raku
(formerly Perl 6)
My initial impressions of this task are registered on the discussion page under "Prank Page?".
The "most frequent k characters" hashing function is straightforward enough to implement. The distance function is incomprehensible though. The description doesn't match the pseudo-code and neither of them match the examples. I'm not going to bother trying to figure it out unless there is some possible value.
Maybe I am too hasty though. Lets give it a try. Implement a MFKC routine and run an assortment of words through it to get a feel for how it hashes different words.
<lang perl6># Fairly straightforward implementation, actually returns a list of pairs
- which can be joined to make a string or manipulated further.
sub mfkh ($string, \K = 2) {
my %h; $string.comb.kv.map: { %h{$^v}[1] //= $^k; %h{$^v}[0]++ }; %h.sort( { -$_.value[0], $_.value[1] } ).head(K).map( { $_.key => $_.value[0] } );
}
- lets try running 150 or so words from unixdic.txt through it to see
- how many unique hash values it comes up with.
my @test-words = <
aminobenzoic arginine asinine biennial biennium brigantine brinkmanship britannic chinquapin clinging corinthian declination dickinson dimension dinnertime dionysian diophantine dominican financial financier finessing fingernail fingerprint finnish giovanni hopkinsian inaction inalienable inanimate incaution incendiary incentive inception incident incidental incinerate incline inclusion incommunicable incompletion inconceivable inconclusive incongruity inconsiderable inconsiderate inconspicuous incontrovertible inconvertible incurring incursion indefinable indemnify indemnity indeterminacy indian indiana indicant indifferent indigene indigenous indigent indispensable indochina indochinese indoctrinate indonesia inequivalent inexplainable infantile inferential inferring infestation inflammation inflationary influential information infringe infusion ingenious ingenuity ingestion ingredient inhabitant inhalation inharmonious inheritance inholding inhomogeneity inkling inoffensive inordinate inorganic inputting inseminate insensible insincere insinuate insistent insomnia insomniac insouciant installation instinct instinctual insubordinate insubstantial insulin insurrection intangible intelligent intensify intensive interception interruption intestinal intestine intoxicant introduction introversion intrusion invariant invasion inventive inversion involution justinian kidnapping kingpin lineprinter liniment livingston mainline mcginnis minion minneapolis minnie pigmentation pincushion pinion quinine quintessential resignation ruination seminarian triennial wilkinson wilmington wilsonian wineskin winnie winnipeg
>;
say @test-words.map( { join , mfkh($_)».kv } ).Bag;</lang>
- Output:
Bag(i2n2(151))
One... Nope, I was right, it is pretty much worthless.
Ring
<lang ring>
- Project : Most frequent k chars distance
load "stdlib.ring" str1 = "LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV" str2 = "EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG" see "Str1 = " + str1 + nl see "Str2 = " + str2 + nl
MostFreqKHashing(str1,"str1") MostFreqKHashing(str2,"str2")
func MostFreqKHashing(str3,strp)
chr = newlist(26,2) for n = 1 to 26 str = char(n+64) cstr = count(str3,str) chr[n][1] = str chr[n][2] = cstr next chr = sortsecond(chr) chr = reverse(chr) see "MostFreqKHashing(" + strp + ",2) = " see chr[1][1] + chr[1][2] + chr[2][1] + chr[2][2] + nl
func count(cString,dString)
sum = 0 while substr(cString,dString) > 0 sum++ cString = substr(cString,substr(cString,dString)+len(string(sum))) end return sum
func sortsecond(alist)
aList = sort(alist,2) for n=1 to len(alist)-1 for m=n to len(aList)-1 if alist[m+1][2] = alist[m][2] and alist[m+1][1] < alist[m][1] temp = alist[m+1] alist[m+1] = alist[m] alist[m] = temp ok next next return aList
</lang> Output:
Str1 = LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV Str2 = EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG MostFreqKHashing(str1,2) = L9T8 MostFreqKHashing(str2,2) = F9L8
Sidef
<lang ruby>func _MostFreqKHashing(string, k) {
var seen = Hash() var chars = string.chars var freq = chars.freq var schars = freq.keys.sort_by {|c| -freq{c} }
var mfkh = [] for i in ^k { chars.each { |c| seen{c} && next if (freq{c} == freq{schars[i]}) { seen{c} = true mfkh << Hash(c => c, f => freq{c}) break } } }
mfkh << (k-seen.len -> of { Hash(c => :NULL, f => 0) }...) mfkh
}
func MostFreqKSDF(a, b, k, d) {
var mfkh_a = _MostFreqKHashing(a, k); var mfkh_b = _MostFreqKHashing(b, k);
d - gather { mfkh_a.each { |s| s{:c} == :NULL && next mfkh_b.each { |t| s{:c} == t{:c} && take(s{:f} + (s{:f} == t{:f} ? 0 : t{:f})) } } }.sum
}
func MostFreqKHashing(string, k) {
gather { _MostFreqKHashing(string, k).each { |h| take("%s%d" % (h{:c}, h{:f})) } }.join
}
var str1 = "LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV"
var str2 = "EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG"
say "str1 = #{str1.dump}" say "str2 = #{str2.dump}"
say
say("MostFreqKHashing(str1, 2) = ", MostFreqKHashing(str1, 2)) say("MostFreqKHashing(str2, 2) = ", MostFreqKHashing(str2, 2)) say("MostFreqKSDF(str1, str2, 2, 100) = ", MostFreqKSDF(str1, str2, 2, 100))
say
var arr = [
%w(night nacht), %w(my a), %w(research research), %w(aaaaabbbb ababababa), %w(significant capabilities),
]
var k = 2 var limit = 10
for s,t in arr {
"mfkh(%s, %s, #{k}) = [%s, %s] (SDF: %d)\n".printf( s.dump, t.dump, MostFreqKHashing(s, k).dump, MostFreqKHashing(t, k).dump, MostFreqKSDF(s, t, k, limit), )
}</lang>
- Output:
str1 = "LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV" str2 = "EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG" MostFreqKHashing(str1, 2) = L9T8 MostFreqKHashing(str2, 2) = F9L8 MostFreqKSDF(str1, str2, 2, 100) = 83 mfkh("night", "nacht", 2) = ["n1i1", "n1a1"] (SDF: 9) mfkh("my", "a", 2) = ["m1y1", "a1NULL0"] (SDF: 10) mfkh("research", "research", 2) = ["r2e2", "r2e2"] (SDF: 6) mfkh("aaaaabbbb", "ababababa", 2) = ["a5b4", "a5b4"] (SDF: 1) mfkh("significant", "capabilities", 2) = ["i3n2", "i3a2"] (SDF: 7)
Tcl
<lang tcl>package require Tcl 8.6
proc MostFreqKHashing {inputString k} {
foreach ch [split $inputString ""] {dict incr count $ch} join [lrange [lsort -stride 2 -index 1 -integer -decreasing $count] 0 [expr {$k*2-1}]] ""
} proc MostFreqKSimilarity {hashStr1 hashStr2} {
while {$hashStr2 ne ""} {
regexp {^(.)(\d+)(.*)$} $hashStr2 -> ch n hashStr2 set lookup($ch) $n
} set similarity 0 while {$hashStr1 ne ""} {
regexp {^(.)(\d+)(.*)$} $hashStr1 -> ch n hashStr1 if {[info exist lookup($ch)]} { incr similarity $n incr similarity $lookup($ch) }
} return $similarity
} proc MostFreqKSDF {inputStr1 inputStr2 k limit} {
set h1 [MostFreqKHashing $inputStr1 $k] set h2 [MostFreqKHashing $inputStr2 $k] expr {$limit - [MostFreqKSimilarity $h1 $h2]}
}</lang> Demonstrating: <lang tcl>set str1 "LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV" set str2 "EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG" puts [MostFreqKHashing $str1 2] puts [MostFreqKHashing $str2 2] puts [MostFreqKSDF $str1 $str2 2 100]</lang>
- Output:
L9T8 F9L8 83
- A more efficient metric calculator
This version is appreciably more efficient because it does not compute the intermediate string representation “hash”, instead working directly on the intermediate dictionaries and lists: <lang tcl>proc MostFreqKSDF {inputStr1 inputStr2 k limit} {
set c1 [set c2 {}] foreach ch [split $inputStr1 ""] {dict incr c1 $ch} foreach ch [split $inputStr2 ""] {dict incr c2 $ch} set c2 [lrange [lsort -stride 2 -index 1 -integer -decreasing $c2[set c2 {}]] 0 [expr {$k*2-1}]] set s 0 foreach {ch n} [lrange [lsort -stride 2 -index 1 -integer -decreasing $c1[set c1 {}]] 0 [expr {$k*2-1}]] {
if {[dict exists $c2 $ch]} { incr s [expr {$n + [dict get $c2 $ch]}] }
} return [expr {$limit - $s}]
}</lang> It computes the identical value on the identical inputs.
Typescript
with added type annotations
<lang typescript>//returns an object of counts keyed by character const kCounts = (str: string): Record<string, number> => {
const counts: Record<string, number> = {}; for (let char of str) { counts[char] = counts[char] ? counts[char] + 1 : 1; } return counts;
};
//returns an array of length k containing the characters with the highest counts const frequentK = ( counts: Record<string, number>, k: number ): string[] => {
//note that this is written for clarity rather than speed, //as it sorts all of the counts when only the top k are needed return Object.keys(counts) .sort((a, b) => counts[b] - counts[a]) .slice(0, k);
};
//returns a hashed string of the most frequent k characters and their frequencies const mostFreqKHashing = (str: string, k: number): string => {
const counts = kCounts(str); return frequentK(counts, k) .map(char => char + counts[char]) .join("");
};
//numeric score of similarity based on the sum of counts of characters appearing in the top k of both strings const mostFreqKSimilarity = ( str1: string, str2: string, k: number ): number => {
const counts1 = kCounts(str1); const counts2 = kCounts(str2); const freq1 = frequentK(counts1, k); const freq2 = frequentK(counts2, k); let similarity = 0; for (let char of freq1) { //only considers a character if it is in the top k of both strings if (freq2.includes(char)) { //the examples on this page are inconsistent in whether the similarity //should be the sum of the two counts or only the shared count (the minimum of the two) //this code uses the sum similarity += counts1[char] + counts2[char]; } } return similarity;
};
//subtracts the similarity score from the maxDifference const mostFreqKSDF = ( str1: string, str2: string, k: number, maxDistance: number ): number => {
return maxDistance - mostFreqKSimilarity(str1, str2, k);
};</lang>
Wren
<lang ecmascript>import "/seq" for Lst import "/sort" for Sort import "/trait" for Stepped
var mostFreqKHashing = Fn.new { |input, k|
var indivs = Lst.individuals(input.toList).map { |indiv| [indiv[0], indiv[1]] }.toList var cmp = Fn.new { |p1, p2| (p2[1] - p1[1]).sign } Sort.insertion(indivs, cmp) return indivs.take(k).reduce("") { |acc, p| acc + "%(p[0])%(String.fromByte(p[1]))" }
}
var mostFreqKSimilarity = Fn.new { |input1, input2|
var similarity = 0 for (i in Stepped.new(0...input1.count, 2)) { for (j in Stepped.new(0...input2.count, 2)) { if (input1[i] == input2[j]) { var freq1 = input1[i + 1].bytes[0] var freq2 = input2[j + 1].bytes[0] if (freq1 == freq2) similarity = similarity + freq1 } } } return similarity
}
var mostFreqKSDF = Fn.new { |input1, input2, k, maxDistance|
System.print("input1 : %(input1)") System.print("input2 : %(input2)") var s1 = mostFreqKHashing.call(input1, k) var s2 = mostFreqKHashing.call(input2, k) System.write("mfkh(input1, %(k)) = ") var i = 0 for (c in s1) { System.write((i % 2 == 0) ? c : c.bytes[0]) i = i + 1 } System.write("\nmfkh(input2, %(k)) = ") i = 0 for (c in s2) { System.write((i % 2 == 0) ? c : c.bytes[0]) i = i + 1 } var result = maxDistance - mostFreqKSimilarity.call(s1, s2) System.print("\nSDF(input1, input2, %(k), %(maxDistance)) = %(result)\n")
}
var pairs = [
["research", "seeking"], ["night", "nacht"], ["my", "a"], ["research", "research"], ["aaaaabbbb", "ababababa"], ["significant", "capabilities"]
] for (pair in pairs) mostFreqKSDF.call(pair[0], pair[1], 2, 10)
var s1 = "LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV" var s2 = "EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG" mostFreqKSDF.call(s1, s2, 2, 100) s1 = "abracadabra12121212121abracadabra12121212121" s2 = s1[-1..0] mostFreqKSDF.call(s1, s2, 2, 100)</lang>
- Output:
Sane as Kotlin entry.
- References
- ↑ 1.0 1.1 SEKER, Sadi E.; Altun, Oguz; Ayan, Ugur; Mert, Cihan (2014), "A Novel String Distance Function based on Most Frequent K Characters", wp:International Journal of Machine Learning and Computing (IJMLC), 4, wp:International Association of Computer Science and Information Technology Press (IACSIT Press), pp. 177-183, http://arxiv.org/abs/1401.6596.
- ↑ Seker, Sadi E.; Mert, Cihan (2013), "A Novel Feature Hashing For Text Mining", Journal of Technical Science and Technologies, 2, wp:International Black Sea University, pp. 37 -41, Template:Citation/identifier, http://journal.ibsu.edu.ge/index.php/jtst/article/view/428.