Could you explain how to convert from lz77 to huffman on the example in the below picture?
Easy:
In the first step your output is essentially 3 numbers:
prev index
number of characters to repeat
next character (be it ascii or unicode)
The algorithm demands that you specify a sliding window up front. That means you know how big (1) and (2) can be at most.
In other words, you know how many bits (1) and (2) will take up.
Since (3) is essentially also a character from a fixed length alphabet, you also know the bit-length of (3)
That means it's safe to simply concatenate them.
So, the output of the first algorithm can be thought of as outputting a bit-sequence, where every item in the sequence has a fixed length.
That's ideal for applying huffman.
Of course the specifics are not mentioned, and you can choose from a lot of options.
normalized huffman table
1 on left-branch vs 0 on left-branch
priorities when merging items of similar count
etc
So I can not readily explain the exact output values you are showing.
But I hope I can at least explain how to get from A to B.
You can't. The coding shown is, well, figurative. Not literal. The symbols A, B, and C are all coded to the single bit 0. Obviously that's not going to be very helpful on the decoding end.
I am trying to develop a search engine in my free time modeled after google.
I am using the original google research paper listed here: http://infolab.stanford.edu/~backrub/google.html
However I am having a few problems here. To be exact I am having problem developing the forward index.
In the paper it says:
If a document contains words that fall into a particular barrel, the docID is recorded into the barrel, followed by a list of wordID's with hitlists which correspond to those words.
Now there are two problem with in this statement. First who decides which words out of the huge lexicon goes into the Forward Barrels? Do all of them go. Second is the meaning of the word corresponding. Does it mean words that actually appear in that document after the previous word or something else?
I am really new to Search Engines and would really appreciate any Information Retrival Expert helping me on this. If moderators think that this question belong in some other Stack Exchange site please do so.
First Question:
The string value of every word is mapped into an integer (by a hash function). This is because integers are far more easier to handle than strings. You can then define ranges (buckets or bins or whatever else you might want to call them) over these integer values, e.g.
term ids 0 to 1000 => Bin-1
term ids 1001 to 2000 => Bin-2
and so on.
Second question:
The context information is typically not used. A word is simply a term present in a document, such as the terms "the", "quick", "brown" etc.
Since you said you are new to IR, a good way to start would be to read an introductory book to IR, e.g. the book by Manning and Schutze.
I present a tricky question that I am not sure how to approach. So, I have formulated a plist containing dictionaries which contain two objects:
The Country Name
The Plug Size Of The Country
There are only 210 countries/facts though.
And, I have enabled to search through a list of many many countries, in which there might be a fact or not. But here is my problem, I am using a web service called Geonames and the user can use a search bar display controller to search for countries, and these plist country names paired with plug sizes are actually from a Wikipedia article.
Now, the country naming in Geonames and in my plist from Wikipedia might be named slightly different, maybe with an extra space, an extra dash, an extra letter. This is why I want to see if the geoname country string is very similar to the one in the plist.
So, this would not be isEqualToString: because that finds if it is exact, can the compare: method work?
How can I approach this? Here is an example:
Geoname returns (not a real country just an example):
Yiting
But plist may return:
Yitting
So with 1 extra 't', but there are other circumstances. I would like these to be compared as exact, or at least similar, so I could consider them as a match.
Are there any tutorials, resources, projects etc. you could point me towards?
Thank you! Bye!
The Soundex algorithm is useful in cases like this.
I found a sample implementation on github.
You need to implement an algorithm for approximate matching of strings. One of the most popular such algorithms is Levenshtein distance, one of several Edit distance algorithms. The distance is calculated as the number of editing operations required to transform string A into string B - inserting, deleting, or changing a character counts as one edit operation. The closer are the strings, the smaller is the edit distance between them. You can calculate pairwise edit distances, and find the smallest one to identify the match.
You may find this post about auto update/complete useful:
I've tested that UITextViews work well while adhering to the UITextViewDelegate protocol in your UIViewController class and will produce a result similar to what you'll find in the Messages app. I haven't checked if UITextField and UITextFieldDelegate do as well.
The problem is as follows:
By replacing each of the letters in the word CARE with 1, 2, 9, and 6 respectively, we form a square number: 1296 = 36^(2). What is remarkable is that, by using the same digital substitutions, the anagram, RACE, also forms a square number: 9216 = 96^(2). We shall call CARE (and RACE) a square anagram word pair and specify further that leading zeroes are not permitted, neither may a different letter have the same digital value as another letter.
Using words.txt (right click and 'Save Link/Target As...'), a 16K text file containing nearly two-thousand common English words, find all the square anagram word pairs (a palindromic word is NOT considered to be an anagram of itself).
What is the largest square number formed by any member of such a pair?
NOTE: All anagrams formed must be contained in the given text file.
I don't understand the mapping of CARE to 1296? How does that work? or are all permutation mappings meant to be tried i.e. all letters to 1-9?
All assignments of digits to letters are allowed. So C=1, A=2, R=3, E=4 would be a possible assignment ... except that 1234 is not a square, so that would be no good.
Maybe another example would help make it clear? If we assign A=6, E=5, T=2, then TEA = 256 = 16² and EAT = 625 = 25². So (TEA=256, EAT=625) is a square anagram word pair.
(Just because all assignments of digits to letters are allowed, does not mean that actually trying out all such assignments is the best way to solve the problem. There may be some other, cleverer, way to do it.)
In short: yes, all permutations need to be tried.
If you test all substitutions letter for digit, than you are looking for pairs of squares with properties:
have same length
have same digits with number of occurrences as in input string.
It is faster to find all these pairs of squares. There are 68 squares with length 4, 216 squares with length 5, ... Filtering all squares of same length by upper properties will generate 'small' number of pairs, which are solutions you are looking for.
These data is 'static', and doesn't depend on input strings. It can be calculated once and used for all input strings.
Hmm. How to put this. The people who put together Project Euler promise that there is a solution that is under one minute for every problem, and there is only one problem that I think might fail this promise, but this is not it.
Yes, you could permute the digits, and try all permutations against all squares, but that would be a very large search space, not at all likely to be the (TM) right thing. In general, when you see that your "look" at the problem is going to generate a search that will take too long, you need to search something else.
Like, suppose you were asked to determine what numbers would be the result of multiplying two primes between 1 and a zillion. You could factor every number between 1 and a zillion, but it might be faster to take all combinations of two primes and multiply them. Since you are looking at combinations, you can start with two and go until your results are too large, then do the same with three, etc. By comparison, this should be much faster - and you don't have to multiply all the numbers out, you could take logs of all the primes and then just add them and find the limit for every prime, giving you a list of numbers you could add up.
There are a bunch of innovative solutions, but the first one you think of - especially the one you think of when Project Euler describes the problem, is likely to be wrong.
So, how can you approach this problem? There are probably too many permutations to look at, but maybe you can figure out something with mappings and comparing mappings?
(Trying to avoid giving it all away.)
I have Persons table in SQL Server 2008.
My goal is to find Persons who have almost similar addresses.
The address is described with columns state, town, street, house, apartment, postcode and phone.
Due to some specific differences in some states (not US) and human factor (mistakes in addresses etc.), address is not filled in the same pattern.
Most common mistakes in addresses
Case sensitivity
Someone wrote "apt.", another one "apartment" or "ap." (although addresses aren't written in English)
Spaces, dots, commas
Differences in writing street names, like 'Dr. Jones str." or "Doctor Jones street" or "D. Jon. st." or "Dr Jones st" etc.
The main problem is that data isn't in the same pattern, so it's really difficult to find similar addresses.
Is there any algorithm for this kind of issue?
Thanks in advance.
UPDATE
As I mentioned address is separated into different columns. Should I generate a string concatenating columns or do your steps for each column?
I assume I shouldn't concatenate columns, but if I'll compare columns separately how should I organize it? Should I find similarities for each column an union them or intersect or anything else?
Should I have some statistics collecting or some kind of educating algorithm?
Suggest approaching it thus:
Create word-level n-grams (a trigram/4-gram might do it) from the various entries
Do a many x many comparison for string comparison and cluster them by string distance. Someone suggested Levenshtein; there are better ones for this kind of task, Jaro-Winkler Distance and Smith-Waterman work better. A libraryt such as SimMetrics would make life a lot easier
Once you have clusters of n-grams, you can resolve the whole string using the constituent subgrams i.e. D.Jones St => Davy Jones St. => DJones St.
Should not be too hard, this is an all-too-common problem.
Update: Based on your update above, here are the suggested steps
Catenate your columns into a single string, perhaps create a db "view" . For example,
create view vwAddress
as
select top 10000
state town, street, house, apartment, postcode,
state+ town+ street+ house+ apartment+ postcode as Address
from ...
Write a separate application (say in Java or C#/VB.NET) and Use an algorithm like JaroWinkler to estimate the string distance for the combined address, to create a many x many comparison. and write into a separate table
address1 | address n | similarity
You can use Simmetrics to get the similarity thus:
JaroWinnkler objJw = new JaroWinkler()
double sim = objJw.GetSimilarity (address1, addres n);
You could also trigram it so that an address such as "1 Jones Street, Sometown, SomeCountry" becomes "1 Jones Street", "Jones Street Sometown", and so on....
and compare the trigrams. (or even 4-grams) for higher accuracy.
Finally you can order by similarity to get a cluster of most similar addresses and decide an approprite threshold. Not sure why you are stuck
I would try to do the following:
split up the address in multiple words, get rid of punctuation at the same time
check all the words for patterns that are typically written differently and replace them with a common name (e.g. replace apartment, ap., ... by apt, replace Doctor by Dr., ...)
put all the words back in one string alphabetically sorted
compare all the addresses using a fuzzy string comparison algorithm, e.g. Levenshtein
tweak the parameters of the Levenshtein algorithm (e.g. you want to allow more differences on longer strings)
finally do a manual check of the strings
Of course, the solution to keep your data 'in shape' is to have explicit fields for each of your characteristics in your database. Otherwise, you will end up doing this exercise every few months.
The main problem I see here is to exactly define equality.
Even if someone writes Jon. and another Jone. - you will never be able to say if they are the same. (Jon-Jonethan,Joneson,Jonedoe whatever ;)
I work in a firm where we have to handle exact this problem - I'm afraid I have to tell you this kind of checking the adress lists for navigation systems is done "by hand" most of the time. Abbrevations are sometimes context dependend, and there are other things that make this difficult. Ofc replacing string etc is done with python - but telling you the MEANING of such an abbr. can only done by script in a few cases. ("St." -> Can be "Saint" and "Street". How to decide? impossible...this is human work.).
Another big problem is as you said "Is there a street "DJones" or a person? Or both? Which one is ment here? Is this DJones the same as Dr Jones or the same as Don Jones? Its impossible to decide!
You can do some work with lists as presented by another answer here - but it will give you enough "false positives" or so.
You have a postcode field!!!
So, why don't you just buy a postcode table for your country
and use that to clean up your street/town/region/province information?
I did a project like this in the last centuary. Basicly it was a consolidation of two customer files after a merger, and, involved names and addresses from three different sources.
Firstly as many posters have suggested, convert all the common words and abbreveations and spelling mistakes to a common form "Apt." "Apatment" etc. to "Apt".
Then look through the name and identifiy the first letter of the first name, plus the first surname. (Not that easy consider "Dr. Med. Sir Henry de Baskerville Smythe") but dont worry where there are amiguities just take both! So if you lucky you get HBASKERVILLE and HSMYTHE. Now get rid of all the vowels as thats where most spelling variations occur so now you have HBSKRVLL HSMTH.
You would also get these strings from "H. Baskerville","Sir Henry Baskerville Smith" and unfortunately "Harold Smith" but we are talking fuzzy matching here!
Perform a similar exercise on the street, and apartment and postcode fields. But do not throw away the original data!
You now come to the interesting bit first you compare each of the original strings and give say 50 points for each string that matches exactly. Then go through you "normalised" strings and give say 20 points for each one that matches exactly. Then go through all the strings and give say 5 points for each four character or more substring they have in common. For each pair compared you will end up with some with scores > 150 which you can consider as a certain match, some with scores less than 50 which you can consider not matched and some inbetween which have some probability of matching.
You need some more tweaking to improve this by adding various rules like "subtract 20 points for a surname of 'smith'". You really have to keep running and tweaking until you get happy with the resulting matches, but, once you look at the results you get a pretty good feel which score to consider a "match" and which are the false positives you need to get rid of.
I think the amount of data could affect what approach works best for you.
I had a similar problem when indexing music from compilation albums with various artists. Sometimes the artist came first, sometimes the song name, with various separator styles.
What I did was to count the number of occurrences on other entries with the same value to make an educated guess wether it was the song name or an artist.
Perhaps you can use soundex or similar algorithm to find stuff that are similar.
EDIT: (maybe I should clarify that I assumed that artist names were more likely to be more frequently reoccurring than song names.)
One important thing that you mention in the comments is that you are going to do this interactively.
This allows to parse user input and also at the same time validate guesses on any abbreviations and to correct a lot of mistakes (the way for example phone number entry works some contact management systems - the system does the best effort to parse and correct the country code, area code and the number, but ultimately the user is presented with the guess and has the chance to correct the input)
If you want to do it really good then keeping database/dictionaries of postcodes, towns, streets, abbreviations and their variations can improve data validation and pre-processing.
So, at least you would have fully qualified address. If you can do this for all the input you will have all the data categorized and matches can then be strict on certain field and less strict on others, with matching score calculated according weights you assign.
After you have consistently pre-processed the input then n-grams should be able to find similar addresses.
Have you looked at SQL Server Integration Services for this? The Fuzzy Lookup component allows you to find 'Near matches': http://msdn.microsoft.com/en-us/library/ms137786.aspx
For new input, you could call the package from .Net code, passing the value row to be checked as a set of parameters, you'd probably need to persist the token index for this to be fast enough for user interaction though.
There's an example of address matching here: http://msdn.microsoft.com/en-us/magazine/cc163731.aspx
I'm assuming that response time is not critical and that the problem is finding an existing address in a database, not merging duplicates. I'm also assuming the database contains a large number of addresses (say 3 million), rather than a number that could be cleaned up economically by hand or by Amazon's Mechanical Turk.
Pre-computation - Identify address fragments with high information content.
Identify all the unique words used in each database field and count their occurrences.
Eliminate very common words and abbreviations. (Street, st., appt, apt, etc.)
When presented with an input address,
Identify the most unique word and search (Street LIKE '%Jones%') for existing addresses containing those words.
Use the pre-computed statistics to estimate how many addresses will be in the results set
If the estimated results set is too large, select the second-most unique word and combine it in the search (Street LIKE '%Jones%' AND Town LIKE '%Anytown%')
If the estimated results set is too small, select the second-most unique word and combine it in the search (Street LIKE '%Aardvark%' OR Town LIKE '%Anytown')
if the actual results set is too large/small, repeat the query adding further terms as before.
The idea is to find enough fragments with high information content in the address which can be searched for to give a reasonable number of alternatives, rather than to find the most optimal match. For more tolerance to misspelling, trigrams, tetra-grams or soundex codes could be used instead of words.
Obviously if you have lists of actual states / towns / streets then some data clean-up could take place both in the database and in the search address. (I'm very surprised the Armenian postal service does not make such a list available, but I know that some postal services charge excessive amounts for this information. )
As a practical matter, most systems I see in use try to look up people's accounts by their phone number if possible: obviously whether that is a practical solution depends upon the nature of the data and its accuracy.
(Also consider the lateral-thinking approach: could you find a mail-order mail-list broker company which will clean up your database for you? They might even be willing to pay you for use of the addresses.)
I've found a great article.
Adding some dlls as sql user-defined functions we can use string comparison algorithms using SimMetrics library.
Check it
http://anastasiosyal.com/archive/2009/01/11/18.aspx
the possibilities of such variations are countless and even if such an algorithm exists, it can never be fool-proof. u can't have a spell checker for nouns after all.
what you can do is provide a drop-down list of previously entered field values, so that they can select one, if a particular name already exists.
its better to have separate fields for each value like apartments and so on.
You could throw all addresses at a web service like Google Maps (I don't know whether this one is suitable, though) and see whether they come up with identical GPS coordinates.
One method could be to apply the Levenshtein distance algorithm to the address fields. This will allow you to compare the strings for similarity.
Edit
After looking at the kinds of address differences you are dealing with, this may not be helpful after all.
Another idea is to use learning. For example you could learn, for each abbreviation and its place in the sentence, what the abbreviation means.
3 Jane Dr. -> Dr (in 3rd position (or last)) means Drive
Dr. Jones St -> Dr (in 1st position) means Doctor
You could, for example, use decision trees and have a user train the system. Probably few examples of each use would be enough. You wouldn't classify single-letter abbreviations like D.Jones that could be David Jones, or Dr. Jones as likely. But after a first level of translation you could look up a street index of the town and see if you can expand the D. into a street name.
Again, you would run each address through the decision tree before storing it.
It feels like there should be some commercial products doing this out there.
A possibility is to have a dictionary table in the database that maps all the variants to the 'proper' version of the word:
*Value* | *Meaning*
Apt. | Apartment
Ap. | Apartment
St. | Street
Then you run each word through the dictionary before you compare.
Edit: this alone is too naive to be practical (see comment).