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  • WHat does information_schema database means in databse

    - by Mirage
    I have one dtabase in mysql. But when i log into phpmyadmin , it shows another database called information_schema. Is that databse always present with one database. I mean to say there is copy of information_schema for evry database present in mysql or in one mysql there is one table called inforemation_schema. If i do something wronmg with table , how will that affect my current database

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  • what means subject in certificate?

    - by George2
    Hi guys, The related link is this MSDN article. I am always confused about the term "subject", for example, sk option "Specifies the subject's key container location", sr option "Specifies the subject's certificate store location". What exactly mean subject? The certificate owner? The certificate issuer (e.g. the root CA which issues the certificate)? Or something else? regards, George

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  • Best means to store data locally when offline

    - by mickartz
    I am in the midst of writing a small program (more to experiment with vs 2010 than anything else) Despite being an experiment it has some practical use for our local athletics club. My thought was to access the DB (currently online) to download the current members and store locally on a laptop (this is a MS sql table, used to power the club's website). take the laptop to the event (yes there ARE places that don't have internet coverage), add members to that days race (also a row from a sql table (though no changes would be made to this), record results (new records in 3rd table) Once home, showered and within internet access again, upload/edit the tables as per the race results/member changes etc. So I was thinking i'd do something like write xml files locally with the data, including a field to indicate changes etc? If anyone can point me in a direction i would appreciate it...hell if anyone could tell me if this has a name, I'd appreciate it.

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  • CopyLzfileName/CreatLZFileName..what does LZ means?

    - by kiddo
    I am gathering small information regarding kernel dll functions..and I found this function called CopyLZFileName/CreateLZFileName and some other functions related to this LZ..I googled for it..I cant find a proper source for it.From the name I guess it copies/creates file but am not sure what kind of file..does LZ mean something to it.Please let me know.

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  • what means "not enclossing class" hier in scala

    - by echo
    Hoi ,i am learning scala and trying to translate some java code to scala. Here are some of the code below in java that I want to translate public class Note{ protected void addNote(Meeting n) { //add n to a list } } public abstract class Meeting{ public Meeting(String name,Note note){ note.addNote(this) } } when i translate them to scala class Note{ protected[Meeting] addNote(n:Meeting){ //add n to list } } abstract class Meeting(name:String,note:Note){ note.addNote(this) } then i got an error in class Note : Meeting is not a enclossing class. what does it mean? I have tried packagename instead of Meeting,like this:protected[packagename] addNote(n:Meeting) ,but i doesnt work.

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  • onUpdate in MySQL means?

    - by ajsie
    i know that if you create a foreign key on a field (parent_id) in a child table that refer to a parent table's primary key (id), then if this parent table is deleted the child class will be deleted as well if you set onDelete to cascade when creating the foreign key in the child class. but what happens if i set it to onUpdate = cascade?

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  • Inverse Logistic Function / Reverse Sigmoid Function

    - by Chanq
    I am currently coding up a fuzzy logic library in java. I have found the equations for all the standard functions - Grade, inverseGrade, Triangle, Trapezoid, Gaussian. However, I can't find the inverse of the sigmoid/ logistic function. The way I have written the logistic function is java is : //f(x) = 1/(1+e(-x)) public double logistic(double x){ return (1/(1+(Math.exp(-x))); } But I can't work out or find the inverse anywhere. My algebraic/calculus abilities are fairly limited, hence why I haven't been able to work out the inverse of the function. Any hints or pointers would be a big help. Thanks

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  • What free expert system can You recommend (with higher functionality then CLIPS)?

    - by Martin
    Hi, I'm trying to find best free expert system, with the highest functionality. I know about CLIPS, but is there another system, for example being able to accept percent of confidence for each rule (fuzzy logic). I need it to know will I be able to do fast a short project using expert system, with highest functionality. But anyways, it's interesting is there an open source program that aims to gather different AI methods (whitch there are plenty of), and use them together. So I would be extremely thankeful for any info about more robust CLIPS, or similar programs. Thanks!

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  • Test descriptions/name, say what the test is? or what it means when it fails?

    - by xenoterracide
    The API docs for Test::More::ok is ok($got eq $expected, $test_name); right now in one of my apps I have $test_name print what the test is testing. So for example in one of my tests I have set this to 'filename exists'. What I realized after I got a bug report recently, and realized that the only time I ever see this message is when the test is failing, if the test is failing that means the file doesn't exist. In your opinion, do you think these $test_name's should say what the test means if successful? what it means if it failed? or do you think it should say something else? please explain why?

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  • How do I print out objects in an array in python?

    - by Jonathan
    I'm writing a code which performs a k-means clustering on a set of data. I'm actually using the code from a book called collective intelligence by O'Reilly. Everything works, but in his code he uses the command line and i want to write everything in notepad++. As a reference his line is >>>kclust=clusters.kcluster(data,k=10) >>>[rownames[r] for r in k[0]] Here is my code: from PIL import Image,ImageDraw def readfile(filename): lines=[line for line in file(filename)] # First line is the column titles colnames=lines[0].strip( ).split('\t')[1:] rownames=[] data=[] for line in lines[1:]: p=line.strip( ).split('\t') # First column in each row is the rowname rownames.append(p[0]) # The data for this row is the remainder of the row data.append([float(x) for x in p[1:]]) return rownames,colnames,data from math import sqrt def pearson(v1,v2): # Simple sums sum1=sum(v1) sum2=sum(v2) # Sums of the squares sum1Sq=sum([pow(v,2) for v in v1]) sum2Sq=sum([pow(v,2) for v in v2]) # Sum of the products pSum=sum([v1[i]*v2[i] for i in range(len(v1))]) # Calculate r (Pearson score) num=pSum-(sum1*sum2/len(v1)) den=sqrt((sum1Sq-pow(sum1,2)/len(v1))*(sum2Sq-pow(sum2,2)/len(v1))) if den==0: return 0 return 1.0-num/den class bicluster: def __init__(self,vec,left=None,right=None,distance=0.0,id=None): self.left=left self.right=right self.vec=vec self.id=id self.distance=distance def hcluster(rows,distance=pearson): distances={} currentclustid=-1 # Clusters are initially just the rows clust=[bicluster(rows[i],id=i) for i in range(len(rows))] while len(clust)>1: lowestpair=(0,1) closest=distance(clust[0].vec,clust[1].vec) # loop through every pair looking for the smallest distance for i in range(len(clust)): for j in range(i+1,len(clust)): # distances is the cache of distance calculations if (clust[i].id,clust[j].id) not in distances: distances[(clust[i].id,clust[j].id)]=distance(clust[i].vec,clust[j].vec) #print 'i' #print i #print #print 'j' #print j #print d=distances[(clust[i].id,clust[j].id)] if d<closest: closest=d lowestpair=(i,j) # calculate the average of the two clusters mergevec=[ (clust[lowestpair[0]].vec[i]+clust[lowestpair[1]].vec[i])/2.0 for i in range(len(clust[0].vec))] # create the new cluster newcluster=bicluster(mergevec,left=clust[lowestpair[0]], right=clust[lowestpair[1]], distance=closest,id=currentclustid) # cluster ids that weren't in the original set are negative currentclustid-=1 del clust[lowestpair[1]] del clust[lowestpair[0]] clust.append(newcluster) return clust[0] def kcluster(rows,distance=pearson,k=4): # Determine the minimum and maximum values for each point ranges=[(min([row[i] for row in rows]),max([row[i] for row in rows])) for i in range(len(rows[0]))] # Create k randomly placed centroids clusters=[[random.random( )*(ranges[i][1]-ranges[i][0])+ranges[i][0] for i in range(len(rows[0]))] for j in range(k)] lastmatches=None for t in range(100): print 'Iteration %d' % t bestmatches=[[] for i in range(k)] # Find which centroid is the closest for each row for j in range(len(rows)): row=rows[j] bestmatch=0 for i in range(k): d=distance(clusters[i],row) if d<distance(clusters[bestmatch],row): bestmatch=i bestmatches[bestmatch].append(j) # If the results are the same as last time, this is complete if bestmatches==lastmatches: break lastmatches=bestmatches # Move the centroids to the average of their members for i in range(k): avgs=[0.0]*len(rows[0]) if len(bestmatches[i])>0: for rowid in bestmatches[i]: for m in range(len(rows[rowid])): avgs[m]+=rows[rowid][m] for j in range(len(avgs)): avgs[j]/=len(bestmatches[i]) clusters[i]=avgs return bestmatches

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  • Algorithm detect repeating/similiar strings in a corpus of data -- say email subjects, in Python

    - by RizwanK
    I'm downloading a long list of my email subject lines , with the intent of finding email lists that I was a member of years ago, and would want to purge them from my Gmail account (which is getting pretty slow.) I'm specifically thinking of newsletters that often come from the same address, and repeat the product/service/group's name in the subject. I'm aware that I could search/sort by the common occurrence of items from a particular email address (and I intend to), but I'd like to correlate that data with repeating subject lines.... Now, many subject lines would fail a string match, but "Google Friends : Our latest news" "Google Friends : What we're doing today" are more similar to each other than a random subject line, as is: "Virgin Airlines has a great sale today" "Take a flight with Virgin Airlines" So -- how can I start to automagically extract trends/examples of strings that may be more similar. Approaches I've considered and discarded ('because there must be some better way'): Extracting all the possible substrings and ordering them by how often they show up, and manually selecting relevant ones Stripping off the first word or two and then count the occurrence of each sub string Comparing Levenshtein distance between entries Some sort of string similarity index ... Most of these were rejected for massive inefficiency or likelyhood of a vast amount of manual intervention required. I guess I need some sort of fuzzy string matching..? In the end, I can think of kludgy ways of doing this, but I'm looking for something more generic so I've added to my set of tools rather than special casing for this data set. After this, I'd be matching the occurring of particular subject strings with 'From' addresses - I'm not sure if there's a good way of building a data structure that represents how likely/not two messages are part of the 'same email list' or by filtering all my email subjects/from addresses into pools of likely 'related' emails and not -- but that's a problem to solve after this one. Any guidance would be appreciated.

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  • Simplifying a four-dimensional rule table in Matlab: addressing rows and columns of each dimension

    - by Cate
    Hi all. I'm currently trying to automatically generate a set of fuzzy rules for a set of observations which contain four values for each observation, where each observation will correspond to a state (a good example is with Fisher's Iris Data). In Matlab I am creating a four dimensional rule table where a single cell (a,b,c,d) will contain the corresponding state. To reduce the table I am following the Hong and Lee method of row and column similarity checking but I am having difficulty understanding how to address the third and fourth dimensions' rows and columns. From the method it is my understanding that each dimension is addressed individually and if the rule is true, the table is simplified. The rules for merging are as follows: If all cells in adjacent columns or rows are the same. If two cells are the same or if either of them is empty in adjacent columns or rows and at least one cell in both is not empty. If all cells in a column or row are empty and if cells in its two adjacent columns or rows are the same, merge the three. If all cells in a column or row are empty and if cells in its two adjacent columns or rows are the same or either of them is empty, merge the three. If all cells in a column or row are empty and if all the non-empty cells in the column or row to its left have the same region, and all the non-empty cells in the column or row to its right have the same region, but one different from the previously mentioned region, merge these three columns into two parts. Now for the confusing bit. Simply checking if the entire row/column is the same as the adjacent (rule 1) seems simple enough: if (a,:,:,:) == (a+1,:,:,:) (:,b,:,:) == (:,b+1,:,:) (:,:,c,:) == (:,:,c+1,:) (:,:,:,d) == (:,:,:,d+1) is this correct? but to check if the elements in the row/column match, or either is zero (rules 2 and 4), I am a bit lost. Would it be something along these lines: for a = 1:20 for i = 1:length(b) if (a+1,i,:,:) == (a,i,:,:) ... else if (a+1,i,:,:) == 0 ... else if (a,i,:,:) == 0 etc. and for the third and fourth dimensions: for c = 1:20 for i = 1:length(a) if (i,:,c,:) == (i,:,c+1,:) ... else if (i,:,c+1,:) == 0 ... else if (i,:,c,:) == 0 etc. for d = 1:20 for i = 1:length(a) if (i,:,:,d) == (i,:,:,d+1) ... else if (i,:,:,d+1) == 0 ... else if (i,:,:,d) == 0 etc. even any help with four dimensional arrays would be useful as I'm so confused by the thought of more than three! I would advise you look at the paper to understand my meaning - they themselves have used the Iris data but only given an example with a 2D table. Thanks in advance, hopefully!

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  • Using MinHash to find similiarities between 2 images

    - by Sung Meister
    I am using MinHash algorithm to find similar images between images. I have run across this post, How can I recognize slightly modified images? which pointed me to MinHash algorithm. Being a bit mathematically challenged, I was using a C# implementation from this blog post, Set Similarity and Min Hash. But while trying to use the implementation, I have run into 2 problems. What value should I set universe value to? When passing image byte array to HashSet, it only contains distinct byte values; thus comparing values from 1 ~ 256. What is this universe in MinHash? And what can I do to improve the C# MinHash implementation? Since HashSet<byte> contains values upto 256, similarity value always come out to 1. Here is the source that uses the C# MinHash implementation from Set Similarity and Min Hash: class Program { static void Main(string[] args) { var imageSet1 = GetImageByte(@".\Images\01.JPG"); var imageSet2 = GetImageByte(@".\Images\02.TIF"); //var app = new MinHash(256); var app = new MinHash(Math.Min(imageSet1.Count, imageSet2.Count)); double imageSimilarity = app.Similarity(imageSet1, imageSet2); Console.WriteLine("similarity = {0}", imageSimilarity); } private static HashSet<byte> GetImageByte(string imagePath) { using (var fs = new FileStream(imagePath, FileMode.Open, FileAccess.Read)) using (var br = new BinaryReader(fs)) { //List<int> bytes = br.ReadBytes((int)fs.Length).Cast<int>().ToList(); var bytes = new List<byte>(br.ReadBytes((int) fs.Length).ToArray()); return new HashSet<byte>(bytes); } } }

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  • Is this a variation of the traveling salesman problem?

    - by Ville Koskinen
    I'm interested in a function of two word lists, which would return an order agnostic edit distance between them. That is, the arguments would be two lists of (let's say space delimited) words and return value would be the minimum sum of the edit (or Levenshtein) distances of the words in the lists. Distance between "cat rat bat" and "rat bat cat" would be 0. Distance between "cat rat bat" and "fat had bad" would be the same as distance between "rat bat cat" and "had fat bad", 4. In the case the number of words in the lists are not the same, the shorter list would be padded with 0-length words. My intuition (which hasn't been nurtured with computer science classes) does not find any other solution than to use brute force: |had|fat|bad| a solution ---+---+---+---+ +---+---+---+ cat| 2 | 1 | 2 | | | 1 | | ---+---+---+---+ +---+---+---+ rat| 2 | 1 | 2 | | 3 | | | ---+---+---+---+ +---+---+---+ bat| 2 | 1 | 1 | | | | 4 | ---+---+---+---+ +---+---+---+ Starting from the first row, pick a column and go to the next rows without ever revisiting a column you have already visited. Do this over and over again until you've tried all combinations. To me this sounds a bit like the traveling salesman problem. Is it, and how would you solve my particular problem?

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  • How to calculate this string-dissimilarity function efficiently?

    - by ybungalobill
    Hello, I was looking for a string metric that have the property that moving around large blocks in a string won't affect the distance so much. So "helloworld" is close to "worldhello". Obviously Levenshtein distance and Longest common subsequence don't fulfill this requirement. Using Jaccard distance on the set of n-grams gives good results but has other drawbacks (it's a pseudometric and higher n results in higher penalty for changing single character). [original research] As I thought about it, what I'm looking for is a function f(A,B) such that f(A,B)+1 equals the minimum number of blocks that one have to divide A into (A1 ... An), apply a permutation on the blocks and get B: f("hello", "hello") = 0 f("helloworld", "worldhello") = 1 // hello world -> world hello f("abba", "baba") = 2 // ab b a -> b ab a f("computer", "copmuter") = 3 // co m p uter -> co p m uter This can be extended for A and B that aren't necessarily permutations of each other: any additional character that can't be matched is considered as one additional block. f("computer", "combuter") = 3 // com uter -> com uter, unmatched: p and b. Observing that instead of counting blocks we can count the number of pairs of indices that are taken apart by a permutation, we can write f(A,B) formally as: f(A,B) = min { C(P) | P:|A|?|B|, P is bijective, ?i?dom(P) A[P(i)]=B[P(i)] } C(P) = |A| + |B| - |dom(P)| - |{ i | i,i+1?dom(P) and P(i)+1=P(i+1) }| - 1 The problem is... guess what... ... that I'm not able to calculate this in polynomial time. Can someone suggest a way to do this efficiently? Or perhaps point me to already known metric that exhibits similar properties?

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  • Anything wrong with this function for comparing floats?

    - by Michael Borgwardt
    When my Floating-Point Guide was yesterday published on slashdot, I got a lot of flak for my suggested comparison function, which was indeed inadequate. So I finally did the sensible thing and wrote a test suite to see whether I could get them all to pass. Here is my result so far. And I wonder if this is really as good as one can get with a generic (i.e. not application specific) float comparison function, or whether I still missed some edge cases. import static org.junit.Assert.assertFalse; import static org.junit.Assert.assertTrue; import org.junit.Test; public class NearlyEqualsTest { public static boolean nearlyEqual(float a, float b) { final float epsilon = 0.000001f; final float absA = Math.abs(a); final float absB = Math.abs(b); final float diff = Math.abs(a-b); if (a*b==0) { // a or b or both are zero // relative error is not meaningful here return diff < Float.MIN_VALUE / epsilon; } else { // use relative error return diff / (absA+absB) < epsilon; } } /** Regular large numbers - generally not problematic */ @Test public void big() { assertTrue(nearlyEqual(1000000f, 1000001f)); assertTrue(nearlyEqual(1000001f, 1000000f)); assertFalse(nearlyEqual(10000f, 10001f)); assertFalse(nearlyEqual(10001f, 10000f)); } /** Negative large numbers */ @Test public void bigNeg() { assertTrue(nearlyEqual(-1000000f, -1000001f)); assertTrue(nearlyEqual(-1000001f, -1000000f)); assertFalse(nearlyEqual(-10000f, -10001f)); assertFalse(nearlyEqual(-10001f, -10000f)); } /** Numbers around 1 */ @Test public void mid() { assertTrue(nearlyEqual(1.0000001f, 1.0000002f)); assertTrue(nearlyEqual(1.0000002f, 1.0000001f)); assertFalse(nearlyEqual(1.0002f, 1.0001f)); assertFalse(nearlyEqual(1.0001f, 1.0002f)); } /** Numbers around -1 */ @Test public void midNeg() { assertTrue(nearlyEqual(-1.000001f, -1.000002f)); assertTrue(nearlyEqual(-1.000002f, -1.000001f)); assertFalse(nearlyEqual(-1.0001f, -1.0002f)); assertFalse(nearlyEqual(-1.0002f, -1.0001f)); } /** Numbers between 1 and 0 */ @Test public void small() { assertTrue(nearlyEqual(0.000000001000001f, 0.000000001000002f)); assertTrue(nearlyEqual(0.000000001000002f, 0.000000001000001f)); assertFalse(nearlyEqual(0.000000000001002f, 0.000000000001001f)); assertFalse(nearlyEqual(0.000000000001001f, 0.000000000001002f)); } /** Numbers between -1 and 0 */ @Test public void smallNeg() { assertTrue(nearlyEqual(-0.000000001000001f, -0.000000001000002f)); assertTrue(nearlyEqual(-0.000000001000002f, -0.000000001000001f)); assertFalse(nearlyEqual(-0.000000000001002f, -0.000000000001001f)); assertFalse(nearlyEqual(-0.000000000001001f, -0.000000000001002f)); } /** Comparisons involving zero */ @Test public void zero() { assertTrue(nearlyEqual(0.0f, 0.0f)); assertFalse(nearlyEqual(0.00000001f, 0.0f)); assertFalse(nearlyEqual(0.0f, 0.00000001f)); } /** Comparisons of numbers on opposite sides of 0 */ @Test public void opposite() { assertFalse(nearlyEqual(1.000000001f, -1.0f)); assertFalse(nearlyEqual(-1.0f, 1.000000001f)); assertFalse(nearlyEqual(-1.000000001f, 1.0f)); assertFalse(nearlyEqual(1.0f, -1.000000001f)); assertTrue(nearlyEqual(10000f*Float.MIN_VALUE, -10000f*Float.MIN_VALUE)); } /** * The really tricky part - comparisons of numbers * very close to zero. */ @Test public void ulp() { assertTrue(nearlyEqual(Float.MIN_VALUE, -Float.MIN_VALUE)); assertTrue(nearlyEqual(-Float.MIN_VALUE, Float.MIN_VALUE)); assertTrue(nearlyEqual(Float.MIN_VALUE, 0)); assertTrue(nearlyEqual(0, Float.MIN_VALUE)); assertTrue(nearlyEqual(-Float.MIN_VALUE, 0)); assertTrue(nearlyEqual(0, -Float.MIN_VALUE)); assertFalse(nearlyEqual(0.000000001f, -Float.MIN_VALUE)); assertFalse(nearlyEqual(0.000000001f, Float.MIN_VALUE)); assertFalse(nearlyEqual(Float.MIN_VALUE, 0.000000001f)); assertFalse(nearlyEqual(-Float.MIN_VALUE, 0.000000001f)); assertFalse(nearlyEqual(1e20f*Float.MIN_VALUE, 0.0f)); assertFalse(nearlyEqual(0.0f, 1e20f*Float.MIN_VALUE)); assertFalse(nearlyEqual(1e20f*Float.MIN_VALUE, -1e20f*Float.MIN_VALUE)); } }

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  • Splitting a set of object into several subsets of 'similar' objects

    - by doublep
    Suppose I have a set of objects, S. There is an algorithm f that, given a set S builds certain data structure D on it: f(S) = D. If S is large and/or contains vastly different objects, D becomes large, to the point of being unusable (i.e. not fitting in allotted memory). To overcome this, I split S into several non-intersecting subsets: S = S1 + S2 + ... + Sn and build Di for each subset. Using n structures is less efficient than using one, but at least this way I can fit into memory constraints. Since size of f(S) grows faster than S itself, combined size of Di is much less than size of D. However, it is still desirable to reduce n, i.e. the number of subsets; or reduce the combined size of Di. For this, I need to split S in such a way that each Si contains "similar" objects, because then f will produce a smaller output structure if input objects are "similar enough" to each other. The problems is that while "similarity" of objects in S and size of f(S) do correlate, there is no way to compute the latter other than just evaluating f(S), and f is not quite fast. Algorithm I have currently is to iteratively add each next object from S into one of Si, so that this results in the least possible (at this stage) increase in combined Di size: for x in S: i = such i that size(f(Si + {x})) - size(f(Si)) is min Si = Si + {x} This gives practically useful results, but certainly pretty far from optimum (i.e. the minimal possible combined size). Also, this is slow. To speed up somewhat, I compute size(f(Si + {x})) - size(f(Si)) only for those i where x is "similar enough" to objects already in Si. Is there any standard approach to such kinds of problems? I know of branch and bounds algorithm family, but it cannot be applied here because it would be prohibitively slow. My guess is that it is simply not possible to compute optimal distribution of S into Si in reasonable time. But is there some common iteratively improving algorithm?

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  • "Did you mean" feature on a dictionary database

    - by Hazar
    I have a ~300.000 row table; which includes technical terms; queried using PHP and MySQL + FULLTEXT indexes. But when I searching a wrong typed term; for example "hyperpext"; naturally giving no results. I need to "compansate" little writing errors and getting nearest record from database. How I can accomplish such feaure? I know (actually, learned today) about Levenshtein distance, Soundex and Metaphone algorithms but currently not having a solid idea to implement this to querying against database. Best regards. (Sorry about my poor English, I'm trying to do my best)

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