Databases
StackOverflow2013
Postsdbo
PO
 

Note that there are some explanatory texts on larger screens.

plurals
  1. PO
    primarykey
    data
    text
    <p>Assuming the maximum Levenshtein distance allowed is small, this can be done in a single-pass while keeping a list of candidates for fuzzy matches.</p> <p>Here is an example implementation I just worked up. It's not thoroughly tested, documented or optimized. But at least it works for simple examples (see below). I've tried to avoid having it return several matches due to skipping characters at the edges of the sub-sequence, but as I've said, I haven't thoroughly tested this.</p> <p>If you're interested I'll be happy to clean this up, write some tests, do basic optimization and make this available as an open-source library.</p> <pre><code>from collections import namedtuple Candidate = namedtuple('Candidate', ['start', 'subseq_index', 'dist']) Match = namedtuple('Match', ['start', 'end', 'dist']) def find_near_matches(subsequence, sequence, max_l_dist=0): prev_char = None candidates = [] for index, char in enumerate(sequence): for skip in range(min(max_l_dist+1, len(subsequence))): candidates.append(Candidate(index, skip, skip)) if subsequence[skip] == prev_char: break new_candidates = [] for cand in candidates: if char == subsequence[cand.subseq_index]: if cand.subseq_index + 1 == len(subsequence): yield Match(cand.start, index + 1, cand.dist) else: new_candidates.append(cand._replace( subseq_index=cand.subseq_index + 1, )) else: if cand.dist == max_l_dist or cand.subseq_index == 0: continue # add a candidate skipping a sequence char new_candidates.append(cand._replace(dist=cand.dist + 1)) # try skipping subsequence chars for n_skipped in range(1, max_l_dist - cand.dist + 1): if cand.subseq_index + n_skipped == len(subsequence): yield Match(cand.start, index + 1, cand.dist + n_skipped) break elif subsequence[cand.subseq_index + n_skipped] == char: # add a candidate skipping n_skipped subsequence chars new_candidates.append(cand._replace( dist=cand.dist + n_skipped, subseq_index=cand.subseq_index + n_skipped, )) break candidates = new_candidates prev_char = char </code></pre> <p>Now:</p> <pre><code>&gt;&gt;&gt; list(find_near_matches('bde', 'abcdefg', 0)) [] &gt;&gt;&gt; list(find_near_matches('bde', 'abcdefg', 1)) [Match(start=1, end=5, dist=1), Match(start=3, end=5, dist=1)] &gt;&gt;&gt; list(find_near_matches('cde', 'abcdefg', 0)) [Match(start=2, end=5, dist=0)] &gt;&gt;&gt; list(find_near_matches('cde', 'abcdefg', 1)) [Match(start=2, end=5, dist=0)] &gt;&gt;&gt; match = _[0] &gt;&gt;&gt; 'abcdefg'[match.start:match.end] 'cde' </code></pre> <p><strong>EDIT:</strong></p> <p>Following this question, I'm writing a Python library for searching for nearly matching sub-sequences: <a href="http://github.com/taleinat/fuzzysearch" rel="nofollow"><code>fuzzysearch</code></a>. It it still very much a work-in-progress.</p> <p>For now, try the <a href="http://github.com/taleinat/fuzzysearch/blob/cf5bbd62691c1361ca790e154dc745fc7dba8e26/fuzzysearch/fuzzysearch.py#L148" rel="nofollow"><code>find_near_matches_with_ngrams()</code></a> function! It should perform particularly well in your use-case.</p>
    singulars
    1. This table or related slice is empty.
    1. POGet position of subsequence using Levenshtein-Distance
      singulars
      1. PTQuestion
    1. PTAnswer
    1. UStaleinat
    1. UStaleinat
    plurals
    1. This table or related slice is empty.
    1. This table or related slice is empty.
    1. POGet position of subsequence using Levenshtein-Distance
      singulars
      1. PTQuestion
    1. This table or related slice is empty.
    1. VO
      singulars
      1. PO
      1. This table or related slice is empty.
      1. VTUpMod
    2. VO
      singulars
      1. PO
      1. This table or related slice is empty.
      1. VTUpMod
    3. VO
      singulars
      1. PO
      1. This table or related slice is empty.
      1. VTUpMod
    1. COHi @taleinat, this is great! Usually we expect to find the small sequences in the records without any modifications (distance = 0) or very few (1 or 2). I'm updating the question with the naive code I generated. I would like know how much faster is your solution. And it will be great to have a library, there's more people interested in this algorithm.
      singulars
      1. PO
      1. USbiojl
    2. CO>>> list(find_near_matches(pattern,text,1)) [Match(start=3, end=24, dist=1), Match(start=5, end=24, dist=1)] In this case the real one is the first one, as you expect to find the whole pattern, not a piece.
      singulars
      1. PO
      1. USbiojl
    3. COWell, that's an example of issues with the edge of the subsequence. What if the entire subsequence is there except the first letter? The second match is just as valid as the first if your only criterion is Levenshtein distance. In any case, I'm still working on this, and will add filtering out of such duplicate matches, returning only the "best" one.
      singulars
      1. PO
      1. UStaleinat
 

Querying!

 
Guidance
A row detail

Detail views are divided into sections. All the information in the data section comes from columns in the selected row. The other sections display data from other, related rows.

Related data can be related in a to-one or a to-many fashion. Captions of data related in a to-many fashion link to a list view showing a filtered view of the table.

Try moving around until you find a non-empty to-many entry and click on the label to get to one. You can move back to the root by clicking on the database name in the header.

SQuiL has stopped working due to an internal error.

If you are curious you may find further information in the browser console, which is accessible through the devtools (F12).

Reload