![]() ![]() It includes a QMake project file, but you can also compile it by hand. vavg_aggregate(V): Take the arithmetic mean of many vectors.vsum_aggregate(V): Aggregate many vectors into one using ( ).cossim(V, V): Compute the cosine similarity of two vectors.dot(V, V): Compute the dot product of two vectors.vprod(V): Compute the product of the elements of a vector sqlite3-pcre.spec readme.txt This is sqlite3-pcre, an extension for sqlite3 that uses libpcre to provide the REGEXP () function.vsum(V): Compute the sum of the elements of a vector.Vector operations (operate only on vectors).Binary Operators on any combination of vector and scalars.vshow(text): Format a vector as a human readable string compatible with vread().vread(text): Read space-separated floating point values from text into a vector.Download a file, run one database command and you are good to go. Unlike other DBMS, adding extensions in SQLite is a breeze. But you can easily enable them using the regexp extension. SQLite does not support regular expressions by default. vone(length): Create a 1-vector of a specific length Regular expressions are probably the most powerful text processing tool without programming.vzero(length): Create a 0-vector of a specific length.Desired API In essence, what we want to do is to enhance the String column type with additional comparators, which will then be usable as arguments for the Query.filter method. See the Boost Regex reference above for more information. Regular expressions are not implemented there directly, but thanks to sqlite3‘s custom function ability, it’ll be easy enough to provide the necessary support in Python. ![]() \2: Replaced with the second captured group.\1: Replaced with the first captured group.sub(regular expression, format string, subject): Replace any matches of the regular expression with the format string, with may contain backticks of the form:.search(regular expression, subject): Search through the subject to see if there is a match for the regular expression anywhere within the text FROM tablename AS b WHERE EXISTS (SELECT FROM words AS w WHERE b.name LIKE CONCAT ('',wb.word,'')) ypercube at 12:03 Add a comment 2 Answers Sorted by: 3 I think this is what you are after, no need for Regex.match(regular expression, subject): Match the regular expression against the subject, only at the beginning.Perhaps indexes are also helpful, but I have only a basic idea of what they are and no idea whether they would be of help here.Sqlite > SELECT vshow(sin(vread( '3 7 6 1.2 '))) I would like to know if there's way to optimize this query using either parallelization (the query should run ideally from python) or using the knowledge that there is exactly 1 match in each Label table.įrom my understanding, multiple inner joins should take at most the sum of the individual joins, correct? How you do this will depend on your platform. It defines a REGEXP operator, but this will fail with an error message unless you or your framework define a user function called regexp (). this is really slow, especially for more label tables, using PCRE sqlite3 extension for the REGEXP function. SQLite does not contain regular expression functionality by default. Websites.path REGEXP '^' || Label2.path_re || '$') Websites.domain REGEXP '^' || Label2.domain_re || '$' AND Label2 ON (Websites.protocol REGEXP '^' || Label2.protocol_re || '$' AND Websites.path REGEXP '^' || Label1.path_re || '$') Websites.domain REGEXP '^' || Label1.domain_re || '$' AND Label1 ON (Websites.protocol REGEXP '^' || Label1.protocol_re || '$' AND I would now like to write a query that reconstructs a table like the original OldWebsites one by matching labels and automatically queried data. Since I would always extend this table with new data and remove old data, this became too much of a hazzle, so I tried a different approach, namely just loading the existing addresses into one table and then have other tables for the data where I would write regex matchers for the address components CREATE TABLE Websites (Īssume that I have already (using other queries) guaranteed, that there is exactly one match in each label table for each address in the Websites table. I found myself repeating labels over and over based on certain patterns that the address would match. The labels are then in additional columns. Initially, I would load a couple 100k worth of addresses into a table with each field of the address being a column and together building the primary key. Let's use URLs an example, the three parts being the protocol, the domain and the path. The data from the live system consists primarily of an address, comprised of 3 parts. I'm using an sqlite database to store manually created labels for some data automatically queried from a live system. ![]()
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