string-searching | Fast string searching algorithms | Learning library
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kandi X-RAY | string-searching Summary
Fast string searching algorithms.
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QUESTION
The answer probably exists somewhere but I can't find it. I came to this question from an algorithm I am creating. Essentially a .contains(String s1, String s2) that returns true if s1 contains s2, ignoring Greek/English character difference. For example the string 'nai, of course' contains the string 'ναι'. However, this is kindly irrelevant to my question.
The contains() method of a String uses the naive approach and I use the same for my algorithm. What contains() essentially does, is to call the static indexOf(char[] source, int sourceOffset, int sourceCount, char[] target, int targetOffset, int targetCount, int fromIndex) which exists in java.lang.String.class with the correct parameters.
While I was doing different kind of benchmarking and tests to my algorithm, I removed all the Greek - English logic to see how fast it behaves with only English strings. And it was slower. About 2 times slower than a s1.contains(s2) that comes from JDK.
So, I took the time to copy and paste this indexOf method to my class, and call it 15 million times in the same way JDK calls it for a string.
The class is the following:
...ANSWER
Answered 2021-Feb-17 at 16:37Because String.indexOf() is an intrinsic method, making the JDK call a native implementation and your call a Java implementation.
The JVM doesn't actually execute the Java code you see, it knows that it can replace it with a far more efficient version. When you copy the code it goes through regular JIT compilation, making it less efficient. Just one of the dozens of tricks the JVM does to make things more performant, without the developer often even realizing.
QUESTION
As far as I understand, when I do 'foo' in 'abcfoo' in Python, the interpreter tries to invoke 'abcfoo'.__contains_('foo') under the hood.
This is a string matching (aka searching) operation that accepts multiple algorithms, e.g.:
How do I know which algorithm a given implementation may be using? (e.g. Python 3.8 with CPython). I'm unable to this information looking at e.g. the source code for CPython for string. I'm not familiar with its code base, and e.g. I can't find the __contains__ defined for it.
ANSWER
Answered 2020-Apr-12 at 20:35According to the source code:
QUESTION
Let's say I have the following string:
...ANSWER
Answered 2019-Aug-18 at 19:46I think your basic version is the fastest (a combination of Boyer-Moore and Horspoo) available (sublinear search behaviour in good cases (O(n/m)), I will add small changes to your basic version:
QUESTION
Sklearn algorithm require a feature and a label for it to learn.
I have a CSV file which contain some data. These data is actually a challenge from hackerearth website in which participant need to create a learning algorithm that learn from data on massive amount of individuals from affiliate network and their ad click performance which then predict future performance of other individuals in the affiliate network which allow the company to optimize their ad performance.
The features in these data include id,date,siteid, offerid, category, merchant, countrycode,type of browser, type of device and the number of clicks their ads have gotten.
So my plan is to use the first 7 information as my feature and ad click as label. Unfortunately, countrycode,browser and device information is in text (Google Chrome, Desktop) and not integers which can be turned into array.
Q1: Is there a way for sklearn to accept not just numpy arrays but also words as features? Am I support to use vectorizer for this? If so, how would I do it? If not, can I just replace the wording data into numbers (Google Chrome replaced by 1, firefox replaced by 2) and still have it to work? (I am using Naive Bayes algorithm)
Q2: Would Naive Bayes algorithm be suitable for this task? Since this competition require participant to create a program that predict the probability of individuals in affiliate network have their ads click, I assume Naive Bayes would be best suited.
Training data : https://drive.google.com/open?id=1vWdzm0uadoro3WcpWmJ0SVEebeaSsHvr
Testing data : https://drive.google.com/open?id=1M8gR1ZSpNEyVi5W19y0d_qR6EGUeGBQl
My messy coding and horrible attempt at this challenge which I don't think will be much help:
...ANSWER
Answered 2017-Dec-15 at 02:41Answer for Question1: No. Sklearn only works with numerical data. So you need to convert your text to numbers.
Now to convert text to numbers you can follow multiple approaches. First is as you said just assign numbers to them. But you need to to take in account if the text data shows any order like the numbers assigned to them or not. In that case, most often one-hot encoding is used. Please see the below scikit-learn documentation for that: - http://scikit-learn.org/stable/modules/preprocessing.html#encoding-categorical-features
Answer to Question 2: It depends on the data and task at hand.
No single algorithm is capable of handling every type of data optimally.
Most of the times we need to compare multiple algorithms and see what gives best result for our data. See this example:
Even in a single algorithm we need to check for various parameter values, tune those values for maximum score. This is called grid-search. See this example:
Hope this clears your doubts. Make sure to go through the scikit-learn documentation and examples:
- http://scikit-learn.org/stable/user_guide.html
- http://scikit-learn.org/stable/auto_examples/index.html
They are one of the best out there.
QUESTION
I have a list of 1M to 10M strings and I want to see which ones of them can be found in a single document (say 1 page of text).
I know I can use Lucene (Solr/Elasticsearch) to find all documents containing a string. But this is the opposite.
I could program some ad-hoc solution based on one of the string-searching algorithms such as Aho-Corasic, tries, etc., but I assume I would be reinventing the wheel. Is there any library/framework for this?
(I am fine with splitting the strings and the documents into words, if it makes any difference)
...ANSWER
Answered 2017-Oct-16 at 11:15This use case is usually solved by a "Percolator" component . Both Apache Solr[1] and Elasticsearch[2] offer the functionality. Basically you index the "queries" Q and then build a query D out of a document to verify which queries Q match.
[1] https://github.com/flaxsearch/luwak , http://www.flax.co.uk/what-we-do/luwak/
[2] https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-percolate-query.html
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