benhoyt

# AWK program to compare time complexity of joining strings using a
# simple O(N^2) algorithm and a slightly more complex O(N log N) one.

# Join array elements, separated by sep: O(N^2) version
function join1(a, sep,    i, s) {
  for (i = 1; i+1 in a; i++) {
    s = s a[i] sep
  }
  if (i in a) {
    s = s a[i]

benhoyt

def distance(s, t, cache=None):
    """Return minimum edit distance between s and t, where an edit
    is a character substitution, deletion, or addition.
    """
    if not s:
        return len(t)
    if not t:
        return len(s)

    if cache is None:

benhoyt

// Flatten JSON input
//
// Example:
//
// $ echo '{"user":"Ben","ints":[true,false,null],"sub":{"x":1,"y":2}}' | go run flattenjson.go
// _.ints[0] = true
// _.ints[1] = false
// _.ints[2] = null
// _.sub.x = 1
// _.sub.y = 2

benhoyt

diff --git a/internal/markdown/markdown.go b/internal/markdown/markdown.go
index a729b9f..94d29c1 100644
--- a/internal/markdown/markdown.go
+++ b/internal/markdown/markdown.go
@@ -13,8 +13,11 @@ import (
 	"bytes"
 
 	"github.com/yuin/goldmark"
+	"github.com/yuin/goldmark/ast"
 	"github.com/yuin/goldmark/parser"

benhoyt

200 constant max-line
create line max-line allot        \ Buffer for read-line
wordlist constant counts          \ Hash table of words to count
variable num-uniques  0 num-uniques !

\ Allocate space for new string and copy bytes, return new string.
: copy-string ( addr u -- addr' u )
    dup >r dup allocate throw
    dup >r swap move r> r> ;

benhoyt

I'm going to demo a bunch of Python builtin and stdlib functions. There's a lot to get through, so I'll be going fast, but please stop me and ask questions as we go. The goal is to give you a taste of Python's power and expressivity if you're not a Python person, or maybe teach you a few new tricks if you are already.

Built-in functions

# enumerate: iterate with index *and* item
>>> strings = ['123', '0', 'x']
>>> for i, s in enumerate(strings):
...     print(f'{i} - {s}')  # f-strings!

benhoyt

"""Quick performance tests comparing "match" with "if...elif".

See:
https://benhoyt.com/writings/python-pattern-matching/
https://news.ycombinator.com/item?id=28601616

# Enum switch with match:
$ python3.10 -m timeit -s 'import mt' -c 'mt.enum_match(mt.FileType.FILE)'
1000000 loops, best of 5: 356 nsec per loop
$ python3.10 -m timeit -s 'import mt' -c 'mt.enum_match(mt.FileType.SYMLINK)'

benhoyt

package client

func IntPtr(n int) *int { return &n }

type FooArgsPtr struct {
	UserID  *int
	User    string
	GroupID *int
	Group   string
}

benhoyt

     static void* prog[] = {
-        // loop:
         &&i_pushvar0,                       // pushvar i
         &&i_pushnum, (void*)100000000,      // pushnum 100000000
         &&i_jge, (void*)5,                  // jge end
+        // loop:
         &&i_pushvar0,                       // push i
         &&i_addvar1,                        // addvar s
         &&i_incvar0,                        // incvar i
-        &&i_jmp, (void*)((long long)-10),   // jmp loop

benhoyt

"""Print most frequent N-grams in given file.

Usage: python ngrams.py filename

Problem description: Build a tool which receives a corpus of text,
analyses it and reports the top 10 most frequent bigrams, trigrams,
four-grams (i.e. most frequently occurring two, three and four word
consecutive combinations).

NOTES