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In C# is there a way to convert a linked list to a string?
I have a linked list of sentences and another linked list of words.
I want to check the sentences linked list for the words in the words linked list and was thinking a good approach would be to convert the words linked list to a string.
Also considering using a nested while loop.
was thinking a good approach would be to convert the words linked list to a string.
Any time you have a list of X and a list of Y, and you want to check whether any of the elements in X are in Y, what you need is probably a hash set (not a list)
Hashsets offer fast lookups of fixed values. Your algorithm should be:
load the list of searching-for into the set
enumerate the list of searching-in, repeatedly asking if the current item is in the set
var hs = listOfWords.ToHashSet();
foreach(var sentence in listOfSentences){
foreach(var word in sentence.Split()){
if(hs.Contains(word))
{
...
}
}
}
or in a LINQ flavored approach
var hs = listOfWords.ToHashSet();
var result = listOfSentences.Where(sentence=>
sentence.Split().Any(word =>
hs.Contains(word)
)
);
Caution: c# hashing of strings is, be default, case sensitive and every character contributes to string equality. For a word list of "hello","world","foo","bar" and a list of sentences of: "Hello world!", "Foo bar." - these sentences do NOT contain any of the words in the word list. Hello is not equal to hello, world! is not equal to world. Carefully process your sentences so you are comparing apples with apples - e.g. strip punctuation, and make case equal, for example
Working on a program that takes a CSV file and splits on each ",". The issue I have is there are thousand separators in some of the numbers. In the CSV file, the numbers render correctly. When viewed as a text document, they are shown like below:
Dog,Cat,100,100,Fish
In a CSV file, there are four cells, with the values "Dog", "Cat", "100,000", "Fish". When I split on the "," to an array of strings, it contains 5 elements, when what I want is 4. Anyone know a way to work around this?
Thanks
There are two common mistakes made when reading csv code: using a split() function and using regular expressions. Both approaches are wrong, in that they are prone to corner cases such as yours and slower than they could be.
Instead, use a dedicated parser such as Microsoft.VisualBasic.TextFieldParser, CodeProject's FastCSV or Linq2csv, or my own implemention here on Stack Overflow.
Typically, CSV files would wrap these elements in quotes, causing your line to be displayed as:
Dog,Cat,"100,100",Fish
This would parse correctly (if using a reasonable method, ie: the TextFieldParser class or a 3rd party library), and avoid this issue.
I would consider your file as an error case - and would try to correct the issue on the generation side.
That being said, if that is not possible, you will need to have more information about the data structure in the file to correct this. For example, in this case, you know you should have 4 elements - if you find five, you may need to merge back together the 3rd and 4th, since those two represent the only number within the line.
This is not possible in a general case, however - for example, take the following:
100,100,100
If that is 2 numbers, should it be 100100, 100, or should it be 100, 100100? There is no way to determine this without more information.
you might want to have a look at the free opensource project FileHelpers. If you MUST use your own code, here is a primer on the CSV "standard" format
well you could always split on ("\",\"") and then trim the first and last element.
But I would look into regular expressions that match elements with in "".
Don't just split on the , split on ", ".
Better still, use a CSV library from google or codeplex etc
Reading a CSV file in .NET?
You may be able to use Regex.Replace to get rid of specifically the third comma as per below before parsing?
Replaces up to a specified number of occurrences of a pattern specified in the Regex constructor with a replacement string, starting at a specified character position in the input string. A MatchEvaluator delegate is called at each match to evaluate the replacement.
[C#] public string Replace(string, MatchEvaluator, int, int);
I ran into a similar issue with fields with line feeds in. Im not convinced this is elegant, but... For mine I basically chopped mine into lines, then if the line didnt start with a text delimeter, I appended it to the line above.
You could try something like this : Step through each field, if the field has an end text delimeter, move to the next, if not, grab the next field, appaend it, rince and repeat till you do have an end delimeter (allows for 1,000,000,000 etc) ..
(Im caffeine deprived, and hungry, I did write some code but it was so ugly, I didnt even post it)
Do you know that it will always contain exactly four columns? If so, this quick-and-dirty LINQ code would work:
string[] elements = line.Split(',');
string element1 = elements.ElementAt(0);
string element2 = elements.ElementAt(1);
// Exclude the first two elements and the last element.
var element3parts = elements.Skip(2).Take(elements.Count() - 3);
int element3 = Convert.ToInt32(string.Join("",element3parts));
string element4 = elements.Last();
Not elegant, but it works.
I was recently asked this question during a C# interview session:
How would you efficiently find the number of occurrences of a word within a huge text like a big book (the Bible, a dictionary, etc).
I am wondering what would be the most efficient data structure to store the contents of the book in. The dirtiest soultion I could think of was to store it in a StringBuilder and find the count of the substrings, but I am sure there has to be a much better way to do this.
And for a reasonably sized string there are multiple ways of doing this using substring, regular expressions, etc but for a humongous string what is the most efficient way.
Update: What I am looking for is this:
Assuming there is a text file, lets again say the Bible, of size 20 MB, and I want to find the number of times the word "Jesus" occurs in the text, other than loading the entire 20 MB into a string or StringBuilder and using a substring or regex to find the match count, is there any other data structure that could be used to store the entire text contents. The actual search can be accomplished in multiple ways, what I am looking for is the most efficient "data structure" for the temporary storage.
Assuming you dont care about substrings, but just full words, I would use a hashtable. Can be built in linear time and the size is proportional to the number of distinct words. Dictionary<string,int> specifically. On my machine, it took about 450ms to load the entire bible into a hashtable and find all entries of the word "God".
Assuming you do a full word match (can be made to work for prefix matches too).
Construct a trie from the bible with the count information.
If you need to query a word, walk the trie, get the count.
If you need to do a substring match, you can try using a suffix tree (which is basically a trie, but you also include the suffixes).
This assumes the words to query change, the bible stays fixed...
Wikipedia has an interesting article on string searching:
http://en.wikipedia.org/wiki/String_searching_algorithm
and according to that article this algorithm is a sort of benchmark:
http://en.wikipedia.org/wiki/Boyer%E2%80%93Moore_string_search_algorithm
Something the size of the bible is not so huge as to prevent the entire string to be cached in memory, so with the assumption you can... I have used this method before but it obviously would not be lightning fast. Strictly speaking in terms of efficient from a computational standpoint this is not the fastest, but from a speed of coding and reasonable speed I think this works until nanoseconds count.
string text = "a set of text to search in. fast to implement.";
string key = "to";
MessageBox.Show(text.Split(" ',.".ToCharArray()).Where(a => a == key).Count().ToString());
Edit: doesn't solve the final version of the question and may have misinterpretted the original question. Ignore.
There is a list of banned words ( or strings to be more general) and another list with let's say users mails. I would like to excise all banned words from all mails.
Trivial example:
foreach(string word in wordsList)
{
foreach(string mail in mailList)
{
mail.Replace(word,String.Empty);
}
}
How I can improve this algorithm?
Thanks for advices. I voted few answers up but I didn't mark any as answer since it was more like discussion than solution. Some people missed banned words with bad words. In my case I don't have to bother about recognize 'sh1t' or something like that.
Simple approaches to profanity filtering won't work - complex approaches don't work, for the most part, either.
What happens when you get a work like 'password' and you want to filter out 'ass'? What happens when some clever person writes 'a$$' instead - the intent is still clear, right?
See How do you implement a good profanity filter? for extensive discussion.
You could use RegEx to make things a little cleaner:
var bannedWords = #"\b(this|is|the|list|of|banned|words)\b";
foreach(mail in mailList)
var clean = Regex.Replace(mail, bannedWords, "", RegexOptions.IgnoreCase);
Even that, though, is far from perfect since people will always figure out a way around any type of filter.
You'll get best performance by drawing up a finite state machine (FSM) (or generate one) and then parsing your input 1 character at a time and walking through the states.
You can do this pretty easily with a function that takes your next input char and your current state and that returns the next state, you also create output as you walk through the mail message's characters. You draw the FSM on a paper.
Alternatively you could look into the Windows Workflow Foundation: State Machine Workflows.
In that way you only need to walk each message a single time.
Constructing a regular expression from the words (word1|word2|word3|...) and using this instead of the outer loop might be faster, since then, every e-mail only needs to be parsed once. In addition, using regular expressions would enable you to remove only "complete words" by using the word boundary markers (\b(word1|word2|word3|...)\b).
In general, I don't think you will find a solution which is orders of magnitude faster than your current one: You will have to loop through all mails and you will have to search for all the words, there's no easy way around that.
A general algorithm would be to:
Generate a list of tokens based on the input string (ie. by treating whitespace as token separators)
Compare each token against a list of banned words
Replace matched tokens
A regular expression is convenient for identifying tokens, and a HashSet would provide quick lookups for your list of banned words. There is an overloaded Replace method on the Regex class that takes a function, where you could control the replace behavior based on your lookup.
HashSet<string> BannedWords = new HashSet<string>(StringComparer.InvariantCultureIgnoreCase)
{
"bad",
};
string Input = "this is some bad text.";
string Output = Regex.Replace(Input, #"\b\w+\b", (Match m) => BannedWords.Contains(m.Value) ? new string('x', m.Value.Length) : m.Value);
Replacing it with * is annoying, but less annoying than something that removes the context of your intention by removing the word and leaving a malformed sentence. In discussing the Battle of Hastings, I'd be irritated if I saw William given the title "Grand ******* of Normandy", but at least I'd know I was playing in the small-kids playground, while his having the title of "Grand of Normandy" just looks like a mistake, or (worse) I might think that was actually his title.
Don't try replacing words with more innocuous words unless its funny. People get the joke on 4chan, but yahoo groups about history had confused people because the medireview and mediareview periods were being discussed when eval (not profanity, but is used in some XSS attacks that yahoo had been hit by) was replaced with review in medieval and mediaeval (apparantly, medireview is the American spelling of mediareview!).
In some circumstance is possible to improve it:
Just for fun:
u can use SortedList, if ur mailing list is mailing list (because u have a delimiter like ";") u can do as bellow:
first calculate ur running time algorithm:
Words: n item. (each item has an O(1) length).
mailing list: K item.
each item in mailing list average length of Z.
each sub item in mailing list item average length of Y so the average number of subitems in mailing list items is m = Z/Y.
ur algorithm takes O(n*K*Z). // the best way with knut algorithm
1.now if u sort the words list in O(n log n).
2.1- use mailingListItem.Split(";".ToCharArray()) for each mailing list item: O(Z).
2.2- sort the items in mailing list: O(m * log m)
total sorting takes O(K * Z) in worth case with respect to (m logm << Z).
3- use merge algorithm to merge items of bad word and specific mailing list: O((m + n) * k)
total time is O((m+n)*K + m*Z + n^2) with respect to m << n, total algorithm running time is O(n^2 + Z*K) in worth case, which is smaller than O(n*K*Z) if n < K * Z ( i think so).
So if performance is very very very important, u can do this.
You might consider using Regex instead of simple string matches, to avoid replacing partial content within words. A Regex would allow you to assure you are only getting full words that match. You could use a pattern like this:
"\bBADWORD\b"
Also, you may want to iterate over the mailList on the outside, and the word list on the inner loop.
Wouldn't it be easier (and more efficient) to simply redact them by changing all their characters to * or something? That way no large string needs to be resized or moved around, and the recipents are made more aware what happened, rather than getting nonsensical sentences with missing words.
Well, you certainly don' want to make the clbuttic mistake of naive string.Replace() to do it. The regex solution could work, although you'd either be iterating or using the pipe alternator (and I don't know if/how much that would slow your operation down, particularly for a large list of banned words). You could always just...not do it, since it's entirely futile no matter what--there are ways to make your intended words quite clear even without using the exact letters.
That, and it's ridiculous to have a list of words that "people find offensive" in the first place. There's someone who will be offended by pretty much any word
/censorship is bullshit rant
I assume that you want to detect only complete words (separated by non-letter characters) and ignore words with a filter-word substring (like a p[ass]word example). In that case you should build yourself a HashSet of filter-words, scan the text for words, and for each word check its existence in HashSet. If it's a filter word then build resulting StringBuilder object without it (or with an equal number of asterisks).
I had great results using this algorithm on codeproject.com better than brute force text replacments.
We have 5mb of typical text (just plain words). We have 1000 words/phrases to use as terms to search for in this text.
What's the most efficient way to do this in .NET (ideally C#)?
Our ideas include regex's (a single one, lots of them) plus even the String.Contains stuff.
The input is a 2mb to 5mb text string - all text. Multiple hits are good, as in each term (of the 1000) that matches then we do want to know about it. Performance in terms of entire time to execute, don't care about footprint. Current algorithm gives about 60 seconds+ using naive string.contains. We don't want 'cat' to provide a match with 'category' or even 'cats' (i.e. entire term word must hit, no stemming).
We expect a <5% hit ratio in the text. The results would ideally just be the terms that matched (dont need position or frequency just yet). We get a new 2-5mb string every 10 seconds, so can't assume we can index the input. The 1000 terms are dynamic, although have a change rate of about 1 change an hour.
A naive string.Contains with 762 words (the final page) of War and Peace (3.13MB) runs in about 10s for me. Switching to 1000 GUIDs runs in about 5.5 secs.
Regex.IsMatch found the 762 words (much of which were probably in earlier pages as well) in about .5 seconds, and ruled out the GUIDs in 2.5 seconds.
I'd suggest your problem lies elsewhere...Or you just need some decent hardware.
Why reinvent the wheel? Why not just leverage something like Lucene.NET?
have you considered the following:
do you care about substring? lets say I am looking for the word "cat", nothing more or nothing less. now consider the Knuth-Morris-Pratt algorithm, or string.contains for "concatinate". both of these will return true (or an index). is this ok?
Also you will have to look into the idea of the stemmed or "Finite" state of the word. lets look for "diary" again, the test sentance is "there are many kinds of diaries". well to you and me we have the word "diaries" does this count? if so we will need to preprocess the sentance converting the words to a finite state (diaries -> diary) the sentance will become "there are many kind of diary". now we can say that Diary is in the sentance (please look at the porter Stemmer Algroithm)
Also when it comes to processing text (aka Natrual Langauge Processing) you can remove some words as noise, take for example "a, have, you, I, me, some, to" <- these could be considered as useless words, and can then be removed before any processing takes place? for example
"I have written some C# today", if i have 10,000 key works to look for I would have to scan the entire sentance 10,000 x the number of words in the sentance. removing noise before hand will shorting the processing time
"written C# today" <- removed noise, now there are lots less to look throught.
A great article on NLP can be found here. Sentance comparing
HTH
Bones
A modified Suffix tree would be very fast, though it would take up a lot of memory and I don't know how fast it would be to build it. After that however every search would take O(1).
Here's another idea: Make a class something like this:
class Word
{
string Word;
List<int> Positions;
}
For every unique word in your text you create an instance of this class. Positions array will store positions (counted in words, not characters) from the start of the text where this word was found.
Then make another two lists which will serve as indexes. One will store all these classes sorted by their texts, the other - by their positions in the text. In essence, the text index would probably be a SortedDictionary, while the position index would be a simple List<Word>.
Then to search for a phrase, you split that phrase into words. Look up the first word in the Dictionary (that's O(log(n))). From there you know what are the possible words that follow it in the text (you have them from the Positions array). Look at those words (use the position index to find them in O(1)) and go on, until you've found one or more full matches.
Are you trying to achieve a list of matched words or are you trying to highlight them in the text getting the start and length of the match position? If all you're trying to do is find out if the words exist, then you could use subset theory to fairly efficiently perform this.
However, I expect you're trying to each match's start position in the text... in which case this approach wouldn't work.
The most efficient approach I can think is to dynamically build a match pattern using a list and then use regex. It's far easier to maintain a list of 1000 items than it is to maintain a regex pattern based on those same 1000 items.
It is my understanding that Regex uses the same KMP algorithm suggested to efficiently process large amounts of data - so unless you really need to dig through and understand the minutiae of how it works (which might be beneficial for personal growth), then perhaps regex would be ok.
There's quite an interesting paper on search algorithms for many patterns in large files here: http://webglimpse.net/pubs/TR94-17.pdf
Is this a bottleneck? How long does it take? 5 MiB isn't actually a lot of data to search in. Regular expressions might do just fine, especially if you encode all the search strings into one pattern using alternations. This basically amortizes the overall cost of the search to O(n + m) where n is the length of your text and m is the length of all patterns, combined. Notice that this is a very good performance.
An alternative that's well suited for many patterns is the Wu Manber algorithm. I've already posted a very simplistic C++ implementation of the algorithm.
Ok, current rework shows this as fastest (psuedocode):
foreach (var term in allTerms)
{
string pattern = term.ToWord(); // Use /b word boundary regex
Regex regex = new Regex(pattern, RegexOptions.IgnoreCase);
if (regex.IsMatch(bigTextToSearchForTerms))
{
result.Add(term);
}
}
What was surprising (to me at least!) is that running the regex 1000 times was faster that a single regex with 1000 alternatives, i.e. "/b term1 /b | /b term2 /b | /b termN /b" and then trying to use regex.Matches.Count
How does this perform in comparison? It uses LINQ, so it may be a little slower, not sure...
List<String> allTerms = new List<String>(new String(){"string1", "string2", "string3", "string4"});
List<String> matches = allTerms.Where(item => Regex.IsMatch(bigTextToSearchForTerms, item, RegexOptions.IgnoreCase));
This uses classic predicates to implement the FIND method, so it should be quicker than LINQ:
static bool Match(string checkItem)
{
return Regex.IsMatch(bigTextToSearchForTerms, checkItem, RegexOptions.IgnoreCase);
}
static void Main(string[] args)
{
List<String> allTerms = new List<String>(new String(){"string1", "string2", "string3", "string4"});
List<String> matches = allTerms.Find(Match);
}
Or this uses the lambda syntax to implement the classic predicate, which again should be faster than the LINQ, but is more readable than the previous syntax:
List<String> allTerms = new List<String>(new String(){"string1", "string2", "string3", "string4"});
List<String> matches = allTerms.Find(checkItem => Regex.IsMatch(bigTextToSearchForTerms, checkItem, RegexOptions.IgnoreCase));
I haven't tested any of them for performance, but they all implement your idea of iteration through the search list using the regex. It's just different methods of implementing it.