I was looking at various problems associated with game of Scrabble. I came across this problem "Find eligible words in a game of Scrabble based on first and last characters in optimal time." If i use trie DS i can get all words starting with specific character and compare last character of each of those words with given last character. But in that case i am not using information that it is ending with specific character in building DS and in using it brut force way. Is there any better way to organize such that in place of walking all words starting with first characters, i can use that information that it is ending with given last characters.
The programme is about getting the number of letters in words of your programme with their ratings to know which appeared most.example "I stay in Ohio" from the sentence we can see it is made up of a group of letters and we are to know the number times a letter appeared ahbd their ratings in percentage
You can create std::vector<std::pair> that contains the pair of information, first would be a letter and the second one would be the number how many they are in sentence. You can go through each char in sentence and check if it is equal 0x0 if not, then add the letter to first field in std::pair in std::vector and count how many the letter appears in sentence changing the letter with 0x0. After that go to the next letter until the sentence ends. There you will have a vector containing pairs of information: first - the letter, second - how many times it appeared.
I have 3 text files. One with a set of text to be searched through
(ex. ABCDEAABBCCDDAABC)
One contains a number of patterns to search for in the text
(ex. AB, EA, CC)
And the last containing the frequency of each character
(ex.
A 4
B 4
C 4
D 3
E 1
)
I am trying to write an algorithm to find the least frequent occurring character for each pattern and search a string for those occurrences, then check the surrounding letters to see if the string is a match. Currently, I have the characters and frequencies in their own vectors, respectively. (Where i=0 for each vector would be A 4, respectively.
Is there a better way to do this? Maybe a faster data structure? Also, what are some efficient ways to check the pattern string against the piece of the text string once the least frequent letter is found?
You can run the Aho-Corasick algorithm. Its complexity (once the preprocessing - whose complexity is unrelated to the text - is done), is Θ(n + p), where
n is the length of the text
p is the total number of matches found
This is essentially optimal. There is no point in trying to skip over letters that appear to be frequent:
If the letter is not part of a match, the algorithm takes unit time.
If the letter is part of a match, then the match includes all letters, irrespective of their frequency in the text.
You could run an iteration loop that keeps a count of instances and has a check to see if a character has appeared more than a percentage of times based on total characters searched for and total length of the string. i.e. if you have 100 characters and 5 possibilities, any character that has appeared more than 20% of the hundred can be discounted, increasing efficiency by passing over any value matching that one.
As of right now, I decided to take a dictionary and iterate through the entire thing. Every time I see a newline, I make a string containing from that newline to the next newline, then I do string.find() to see if that English word is somewhere in there. This takes a VERY long time, each word taking about 1/2-1/4 a second to verify.
It is working perfectly, but I need to check thousands of words a second. I can run several windows, which doesn't affect the speed (Multithreading), but it still only checks like 10 a second. (I need thousands)
I'm currently writing code to pre-compile a large array containing every word in the English language, which should speed it up a lot, but still not get the speed I want. There has to be a better way to do this.
The strings I'm checking will look like this:
"hithisisastringthatmustbechecked"
but most of them contained complete garbage, just random letters.
I can't check for impossible compinations of letters, because that string would be thrown out because of the 'tm', in between 'thatmust'.
You can speed up the search by employing the Knuth–Morris–Pratt (KMP) algorithm.
Go through every dictionary word, and build a search table for it. You need to do it only once. Now your search for individual words will proceed at faster pace, because the "false starts" will be eliminated.
There are a lot of strategies for doing this quickly.
Idea 1
Take the string you are searching and make a copy of each possible substring beginning at some column and continuing through the whole string. Then store each one in an array indexed by the letter it begins with. (If a letter is used twice store the longer substring.
So the array looks like this:
a - substr[0] = "astringthatmustbechecked"
b - substr[1] = "bechecked"
c - substr[2] = "checked"
d - substr[3] = "d"
e - substr[4] = "echecked"
f - substr[5] = null // since there is no 'f' in it
... and so forth
Then, for each word in the dictionary, search in the array element indicated by its first letter. This limits the amount of stuff that has to be searched. Plus you can't ever find a word beginning with, say 'r', anywhere before the first 'r' in the string. And some words won't even do a search if the letter isn't in there at all.
Idea 2
Expand upon that idea by noting the longest word in the dictionary and get rid of letters from those strings in the arrays that are longer than that distance away.
So you have this in the array:
a - substr[0] = "astringthatmustbechecked"
But if the longest word in the list is 5 letters, there is no need to keep any more than:
a - substr[0] = "astri"
If the letter is present several times you have to keep more letters. So this one has to keep the whole string because the "e" keeps showing up less than 5 letters apart.
e - substr[4] = "echecked"
You can expand upon this by using the longest words starting with any particular letter when condensing the strings.
Idea 3
This has nothing to do with 1 and 2. Its an idea that you could use instead.
You can turn the dictionary into a sort of regular expression stored in a linked data structure. It is possible to write the regular expression too and then apply it.
Assume these are the words in the dictionary:
arun
bob
bill
billy
body
jose
Build this sort of linked structure. (Its a binary tree, really, represented in such a way that I can explain how to use it.)
a -> r -> u -> n -> *
|
b -> i -> l -> l -> *
| | |
| o -> b -> * y -> *
| |
| d -> y -> *
|
j -> o -> s -> e -> *
The arrows denote a letter that has to follow another letter. So "r" has to be after an "a" or it can't match.
The lines going down denote an option. You have the "a or b or j" possible letters and then the "i or o" possible letters after the "b".
The regular expression looks sort of like: /(arun)|(b(ill(y+))|(o(b|dy)))|(jose)/ (though I might have slipped a paren). This gives the gist of creating it as a regex.
Once you build this structure, you apply it to your string starting at the first column. Try to run the match by checking for the alternatives and if one matches, more forward tentatively and try the letter after the arrow and its alternatives. If you reach the star/asterisk, it matches. If you run out of alternatives, including backtracking, you move to the next column.
This is a lot of work but can, sometimes, be handy.
Side note I built one of these some time back by writing a program that wrote the code that ran the algorithm directly instead of having code looking at the binary tree data structure.
Think of each set of vertical bar options being a switch statement against a particular character column and each arrow turning into a nesting. If there is only one option, you don't need a full switch statement, just an if.
That was some fast character matching and really handy for some reason that eludes me today.
How about a Bloom Filter?
A Bloom filter, conceived by Burton Howard Bloom in 1970 is a
space-efficient probabilistic data structure that is used to test
whether an element is a member of a set. False positive matches are
possible, but false negatives are not; i.e. a query returns either
"inside set (may be wrong)" or "definitely not in set". Elements can
be added to the set, but not removed (though this can be addressed
with a "counting" filter). The more elements that are added to the
set, the larger the probability of false positives.
The approach could work as follows: you create the set of words that you want to check against (this is done only once), and then you can quickly run the "in/not-in" check for every sub-string. If the outcome is "not-in", you are safe to continue (Bloom filters do not give false negatives). If the outcome is "in", you then run your more sophisticated check to confirm (Bloom filters can give false positives).
It is my understanding that some spell-checkers rely on bloom filters to quickly test whether your latest word belongs to the dictionary of known words.
This code was modified from How to split text without spaces into list of words?:
from math import log
words = open("english125k.txt").read().split()
wordcost = dict((k, log((i+1)*log(len(words)))) for i,k in enumerate(words))
maxword = max(len(x) for x in words)
def infer_spaces(s):
"""Uses dynamic programming to infer the location of spaces in a string
without spaces."""
# Find the best match for the i first characters, assuming cost has
# been built for the i-1 first characters.
# Returns a pair (match_cost, match_length).
def best_match(i):
candidates = enumerate(reversed(cost[max(0, i-maxword):i]))
return min((c + wordcost.get(s[i-k-1:i], 9e999), k+1) for k,c in candidates)
# Build the cost array.
cost = [0]
for i in range(1,len(s)+1):
c,k = best_match(i)
cost.append(c)
# Backtrack to recover the minimal-cost string.
costsum = 0
i = len(s)
while i>0:
c,k = best_match(i)
assert c == cost[i]
costsum += c
i -= k
return costsum
Using the same dictionary of that answer and testing your string outputs
>>> infer_spaces("hithisisastringthatmustbechecked")
294.99768817854056
The trick here is finding out what threshold you can use, keeping in mind that using smaller words makes the cost higher (if the algorithm can't find any usable word, it returns inf, since it would split everything to single-letter words).
In theory, I think you should be able to train a Markov model and use that to decide if a string is probably a sentence or probably garbage. There's another question about doing this to recognize words, not sentences: How do I determine if a random string sounds like English?
The only difference for training on sentences is that your probability tables will be a bit larger. In my experience, though, a modern desktop computer has more than enough RAM to handle Markov matrices unless you are training on the entire Library of Congress (which is unnecessary- even 5 or so books by different authors should be enough for very accurate classification).
Since your sentences are mashed together without clear word boundaries, it's a bit tricky, but the good news is that the Markov model doesn't care about words, just about what follows what. So, you can make it ignore spaces, by first stripping all spaces from your training data. If you were going to use Alice in Wonderland as your training text, the first paragraph would, perhaps, look like so:
alicewasbeginningtogetverytiredofsittingbyhersisteronthebankandofhavingnothingtodoonceortwiceshehadpeepedintothebookhersisterwasreadingbutithadnopicturesorconversationsinitandwhatistheuseofabookthoughtalicewithoutpicturesorconversation
It looks weird, but as far as a Markov model is concerned, it's a trivial difference from the classical implementation.
I see that you are concerned about time: Training may take a few minutes (assuming you have already compiled gold standard "sentences" and "random scrambled strings" texts). You only need to train once, you can easily save the "trained" model to disk and reuse it for subsequent runs by loading from disk, which may take a few seconds. Making a call on a string would take a trivially small number of floating point multiplications to get a probability, so after you finish training it, it should be very fast.
I have a list of numbers that I want to find at least 3 of...
here is an example
I have a large list of numbers in a sql database in the format of (for example)
01-02-03-04-05-06
06-08-19-24-25-36
etc etc
basically 6 random numbers between 0 and 99.
Now I want to find the strings where at least 3 of a set of given numbers occurs.
For example:
given: 01-02-03-10-11-12
return the strings that have at least 3 of those numbers in them.
eg
01-05-06-09-10-12 would match
03-08-10-12-18-22 would match
03-09-12-18-22-38 would not
I am thinking that there might be some algorithm or even regular expression that could match this... but my lack of computer science textbook experience is tripping me up I think.
No - this is not a homework question! This is for an actual application!
I am developing in ruby, but any language answer would be appreciated
You can use a string replacement to replace - with | to turn 01-02-03-10-11-12 into 01|02|03|10|11|12. Then wrap it like this:
((01|02|03|10|11|12).*){3}
This will find any of the digit pairs, then ignore any number of characters... 3 times. If it matches, then success.