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Bruno BELANYI | 763ee444d4 | ||
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Bruno BELANYI | 0030310952 | ||
Bruno BELANYI | dda444bdc0 | ||
Bruno BELANYI | aea5587742 | ||
Bruno BELANYI | c13abdc134 | ||
Bruno BELANYI | 987078068f | ||
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Bruno BELANYI | 6a1c074e32 | ||
Bruno BELANYI | c413bb82a4 | ||
Bruno BELANYI | 937cd8e730 | ||
Bruno BELANYI | 3ca80055e2 | ||
Bruno BELANYI | ea9fe25571 | ||
Bruno BELANYI | bbcc1f97ce | ||
Bruno BELANYI | b56078f917 |
191
content/posts/2024-07-06-gap-buffer/index.md
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191
content/posts/2024-07-06-gap-buffer/index.md
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@ -0,0 +1,191 @@
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---
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title: "Gap Buffer"
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date: 2024-07-06T21:27:19+01:00
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draft: false # I don't care for draft mode, git has branches for that
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description: "As featured in GNU Emacs"
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tags:
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- algorithms
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- data structures
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- python
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categories:
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- programming
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series:
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- Cool algorithms
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favorite: false
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disable_feed: false
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---
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The [_Gap Buffer_][wiki] is a popular data structure for text editors to
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represent files and editable buffers. The most famous of them probably being
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[GNU Emacs][emacs].
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[wiki]: https://en.wikipedia.org/wiki/Gap_buffer
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[emacs]: https://www.gnu.org/software/emacs/manual/html_node/elisp/Buffer-Gap.html
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<!--more-->
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## What does it do?
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A _Gap Buffer_ is simply a list of characters, similar to a normal string, with
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the added twist of splitting it into two side: the prefix and suffix, on either
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side of the cursor. In between them, a gap is left to allow for quick
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insertion at the cursor.
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Moving the cursor moves the gap around the buffer, the prefix and suffix getting
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shorter/longer as required.
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## Implementation
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I'll be writing a sample implementation in Python, as with the rest of the
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[series]({{< ref "/series/cool-algorithms/">}}). I don't think it showcases the
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elegance of the _Gap Buffer_ in action like a C implementation full of
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`memmove`s would, but it does makes it short and sweet.
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### Representation
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We'll be representing the gap buffer as an actual list of characters.
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Given that Python doesn't _have_ characters, let's settle for a list of strings,
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each representing a single character...
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```python
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Char = str
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class GapBuffer:
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# List of characters, contains prefix and suffix of string with gap in the middle
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_buf: list[Char]
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# The gap is contained between [start, end) (i.e: buf[start:end])
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_gap_start: int
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_gap_end: int
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# Visual representation of the gap buffer:
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# This is a very [ ]long string.
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# |<----------------------------------------------->| capacity
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# |<------------>| |<-------->| string
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# |<------------------->| gap
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# |<------------>| prefix
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# |<-------->| suffix
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def __init__(self, initial_capacity: int = 16) -> None:
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assert initial_capacity > 0
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# Initialize an empty gap buffer
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self._buf = [""] * initial_capacity
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self._gap_start = 0
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self._gap_end = initial_capacity
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```
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### Accessors
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I'm mostly adding these for exposition, and making it easier to write `assert`s
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later.
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```python
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@property
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def capacity(self) -> int:
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return len(self._buf)
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@property
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def gap_length(self) -> int:
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return self._gap_end - self._gap_start
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@property
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def string_length(self) -> int:
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return self.capacity - self.gap_length
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@property
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def prefix_length(self) -> int:
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return self._gap_start
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@property
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def suffix_length(self) -> int:
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return self.capacity - self._gap_end
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```
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### Growing the buffer
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I've written this method in a somewhat non-idiomatic manner, to make it closer
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to how it would look in C using `realloc` instead.
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It would be more efficient to use slicing to insert the needed extra capacity
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directly, instead of making a new buffer and copying characters over.
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```python
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def grow(self, capacity: int) -> None:
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assert capacity >= self.capacity
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# Create a new buffer with the new capacity
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new_buf = [""] * capacity
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# Move the prefix/suffix to their place in the new buffer
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added_capacity = capacity - len(self._buf)
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new_buf[: self._gap_start] = self._buf[: self._gap_start]
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new_buf[self._gap_end + added_capacity :] = self._buf[self._gap_end :]
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# Use the new buffer, account for added capacity
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self._buf = new_buf
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self._gap_end += added_capacity
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```
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### Insertion
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Inserting text at the cursor's position means filling up the gap in the middle
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of the buffer. To do so we must first make sure that the gap is big enough, or
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grow the buffer accordingly.
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Then inserting the text is simply a matter of copying its characters in place,
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and moving the start of the gap further right.
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```python
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def insert(self, val: str) -> None:
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# Ensure we have enouh space to insert the whole string
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if len(val) > self.gap_length:
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self.grow(max(self.capacity * 2, self.string_length + len(val)))
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# Fill the gap with the given string
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self._buf[self._gap_start : self._gap_start + len(val)] = val
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self._gap_start += len(val)
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```
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### Deletion
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Removing text from the buffer simply expands the gap in the corresponding
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direction, shortening the string's prefix/suffix. This makes it very cheap.
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The methods are named after the `backspace` and `delete` keys on the keyboard.
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```python
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def backspace(self, dist: int = 1) -> None:
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assert dist <= self.prefix_length
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# Extend gap to the left
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self._gap_start -= dist
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def delete(self, dist: int = 1) -> None:
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assert dist <= self.suffix_length
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# Extend gap to the right
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self._gap_end += dist
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```
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### Moving the cursor
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Moving the cursor along the buffer will shift letters from one side of the gap
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to the other, moving them accross from prefix to suffix and back.
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I find Python's list slicing not quite as elegant to read as a `memmove`, though
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it does make for a very small and efficient implementation.
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```python
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def left(self, dist: int = 1) -> None:
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assert dist <= self.prefix_length
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# Shift the needed number of characters from end of prefix to start of suffix
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self._buf[self._gap_end - dist : self._gap_end] = self._buf[
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self._gap_start - dist : self._gap_start
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]
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# Adjust indices accordingly
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self._gap_start -= dist
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self._gap_end -= dist
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def right(self, dist: int = 1) -> None:
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assert dist <= self.suffix_length
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# Shift the needed number of characters from start of suffix to end of prefix
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self._buf[self._gap_start : self._gap_start + dist] = self._buf[
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self._gap_end : self._gap_end + dist
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]
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# Adjust indices accordingly
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self._gap_start += dist
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self._gap_end += dist
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```
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97
content/posts/2024-07-14-bloom-filter/index.md
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97
content/posts/2024-07-14-bloom-filter/index.md
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@ -0,0 +1,97 @@
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---
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title: "Bloom Filter"
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date: 2024-07-14T17:46:40+01:00
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draft: false # I don't care for draft mode, git has branches for that
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description: "Probably cool"
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tags:
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- algorithms
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- data structures
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- python
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categories:
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- programming
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series:
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- Cool algorithms
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favorite: false
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disable_feed: false
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---
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The [_Bloom Filter_][wiki] is a probabilistic data structure for set membership.
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The filter can be used as an inexpensive first step when querying the actual
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data is quite costly (e.g: as a first check for expensive cache lookups or large
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data seeks).
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[wiki]: https://en.wikipedia.org/wiki/Bloom_filter
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<!--more-->
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## What does it do?
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A _Bloom Filter_ can be understood as a hash-set which can either tell you:
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* An element is _not_ part of the set.
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* An element _may be_ part of the set.
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More specifically, one can tweak the parameters of the filter to make it so that
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the _false positive_ rate of membership is quite low.
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I won't be going into those calculations here, but they are quite trivial to
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compute, or one can just look up appropriate values for their use case.
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## Implementation
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I'll be using Python, which has the nifty ability of representing bitsets
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through its built-in big integers quite easily.
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We'll be assuming a `BIT_COUNT` of 64 here, but the implementation can easily be
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tweaked to use a different number, or even change it at construction time.
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### Representation
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A `BloomFilter` is just a set of bits and a list of hash functions.
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```python
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BIT_COUNT = 64
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class BloomFilter[T]:
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_bits: int
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_hash_functions: list[Callable[[T], int]]
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def __init__(self, hash_functions: list[Callable[[T], int]]) -> None:
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# Filter is initially empty
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self._bits = 0
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self._hash_functions = hash_functions
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```
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### Inserting a key
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To add an element to the filter, we take the output from each hash function and
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use that to set a bit in the filter. This combination of bit will identify the
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element, which we can use for lookup later.
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```python
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def insert(self, val: T) -> None:
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# Iterate over each hash
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for f in self._hash_functions:
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n = f(val) % BIT_COUNT
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# Set the corresponding bit
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self._bit |= 1 << n
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```
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### Querying a key
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Because the _Bloom Filter_ does not actually store its elements, but some
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derived data from hashing them, it can only definitely say if an element _does
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not_ belong to it. Otherwise, it _may_ be part of the set, and should be checked
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against the actual underlying store.
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```python
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def may_contain(self, val: T) -> bool:
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for f in self._hash_functions:
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n = f(val) % BIT_COUNT
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# If one of the bits is unset, the value is definitely not present
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if not (self._bit & (1 << n)):
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return False
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# All bits were matched, `val` is likely to be part of the set
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return True
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```
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