Word frequency: Difference between revisions
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=={{header|AppleScript}}== |
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<lang applescript>(* |
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For simplicity here, words are considered to be uninterrupted sequences of letters and/or digits. |
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The set text is too messy to warrant faffing around with anything more sophisticated. |
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The first letter in each word is upper-cased and the rest lower-cased for case equivalence and presentation. |
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Where more than n words qualify for the top n or fewer places, all are included in the result. |
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*) |
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use AppleScript version "2.4" -- OS X 10.10 (Yosemite) or later |
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use framework "Foundation" |
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use scripting additions |
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on wordFrequency(filePath, n) |
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set |⌘| to current application |
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-- Get the text and "capitalize" it (lower-case except for the first letters in words). |
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set theText to |⌘|'s class "NSString"'s stringWithContentsOfFile:(filePath) usedEncoding:(missing value) |error|:(missing value) |
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set theText to theText's capitalizedStringWithLocale:(|⌘|'s class "NSLocale"'s currentLocale()) -- Yosemite compatible. |
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-- Split it at the non-word characters. |
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set nonWordCharacters to |⌘|'s class "NSCharacterSet"'s alphanumericCharacterSet()'s invertedSet() |
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set theWords to theText's componentsSeparatedByCharactersInSet:(nonWordCharacters) |
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-- Use a counted set to count the individual words' occurrences. |
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set countedSet to |⌘|'s class "NSCountedSet"'s alloc()'s initWithArray:(theWords) |
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-- Build a list of word/frequency records, excluding any empty strings left over from the splitting above. |
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set mutableSet to |⌘|'s class "NSMutableSet"'s setWithSet:(countedSet) |
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tell mutableSet to removeObject:("") |
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script o |
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property discreteWords : mutableSet's allObjects() as list |
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property wordsAndFrequencies : {} |
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end script |
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set discreteWordCount to (count o's discreteWords) |
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repeat with i from 1 to discreteWordCount |
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set thisWord to item i of o's discreteWords |
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set end of o's wordsAndFrequencies to {thisWord:thisWord, frequency:(countedSet's countForObject:(thisWord)) as integer} |
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end repeat |
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-- Convert to NSMutableArray, reverse-sort the result on the frequencies, and convert back to list. |
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set wordsAndFrequencies to |⌘|'s class "NSMutableArray"'s arrayWithArray:(o's wordsAndFrequencies) |
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set descendingByFrequency to |⌘|'s class "NSSortDescriptor"'s sortDescriptorWithKey:("frequency") ascending:(false) |
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tell wordsAndFrequencies to sortUsingDescriptors:({descendingByFrequency}) |
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set o's wordsAndFrequencies to wordsAndFrequencies as list |
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if (discreteWordCount > n) then |
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-- If there are more than n records, check for any immediately following the nth which may have the same frequency as it. |
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set nthHighestFrequency to frequency of item n of o's wordsAndFrequencies |
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set qualifierCount to n |
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repeat with i from (n + 1) to discreteWordCount |
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if (frequency of item i of o's wordsAndFrequencies = nthHighestFrequency) then |
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set qualifierCount to i |
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else |
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exit repeat |
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end if |
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end repeat |
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else |
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-- Otherwise reduce n to the actual number of discrete words. |
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set n to discreteWordCount |
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set qualifierCount to discreteWordCount |
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end if |
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-- Compose a text report from the qualifying words and frequencies. |
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if (qualifierCount = n) then |
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set output to {"The " & n & " most frequently occurring words in the file are:"} |
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else |
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set output to {(qualifierCount as text) & " words share the " & ((n as text) & " highest frequencies in the file:")} |
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end if |
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repeat with i from 1 to qualifierCount |
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set {thisWord:thisWord, frequency:frequency} to item i of o's wordsAndFrequencies |
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set end of output to thisWord & ": " & (tab & frequency) |
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end repeat |
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set astid to AppleScript's text item delimiters |
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set AppleScript's text item delimiters to linefeed |
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set output to output as text |
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set AppleScript's text item delimiters to astid |
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return output |
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end wordFrequency |
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-- Test code: |
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set filePath to POSIX path of ((path to desktop as text) & "www.rosettacode.org:Word frequency:135-0.txt") |
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set n to 10 |
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return wordFrequency(filePath, n)</lang> |
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{{output}} |
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<lang applescript>"The 10 most frequently occurring words in the file are: |
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The: 41092 |
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Of: 19954 |
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And: 14943 |
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A: 14545 |
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To: 13953 |
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In: 11219 |
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He: 9649 |
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Was: 8622 |
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That: 7924 |
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It: 6661"</lang> |
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=={{header|AutoHotkey}}== |
=={{header|AutoHotkey}}== |
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Revision as of 07:01, 25 June 2020
You are encouraged to solve this task according to the task description, using any language you may know.
- Task
Given a text file and an integer n, print the n most common words in the file (and the number of their occurrences) in decreasing frequency.
For the purposes of this task:
- A word is a sequence of one or more contiguous letters
- You are free to define what a letter is. Underscores, accented letters, apostrophes, and other special characters can be handled at the example writer's discretion. For example, you may treat a compound word like "well-dressed" as either one word or two. The word "it's" could also be one or two words as you see fit. You may also choose not to support non US-ASCII characters. Feel free to explicitly state the thoughts behind the program decisions.
- Assume words will not span multiple lines.
- Do not worry about normalization of word spelling differences. Treat "color" and "colour" as two distinct words.
- Uppercase letters are considered equivalent to their lowercase counterparts
- Words of equal frequency can be listed in any order
Show example output using Les Misérables from Project Gutenberg as the text file input and display the top 10 most used words.
- History
This task was originally taken from programming pearls from Communications of the ACM June 1986 Volume 29 Number 6 where this problem is solved by Donald Knuth using literate programming and then critiqued by Doug McIlroy, demonstrating solving the problem in a 6 line Unix shell script (provided as an example below).
- References
Ada
This version uses a character set to match valid characters in a token. Another version could use a pointer to a function returning a boolean to match valid characters (allowing to use functions such as Is_Alphanumeric), but AFAIK there is no "Find_Token" method that uses one.
<lang Ada>with Ada.Command_Line; with Ada.Text_IO; with Ada.Integer_Text_IO; with Ada.Strings.Maps; with Ada.Strings.Fixed; with Ada.Characters.Handling; with Ada.Containers.Indefinite_Ordered_Maps; with Ada.Containers.Indefinite_Ordered_Sets; with Ada.Containers.Ordered_Maps;
procedure Word_Frequency is
package TIO renames Ada.Text_IO;
package String_Counters is new Ada.Containers.Indefinite_Ordered_Maps(String, Natural);
package String_Sets is new Ada.Containers.Indefinite_Ordered_Sets(String);
package Sorted_Counters is new Ada.Containers.Ordered_Maps
(Natural,
String_Sets.Set,
"=" => String_Sets."=",
"<" => ">");
-- for sorting by decreasing number of occurrences and ascending lexical order
procedure Increment(Key : in String; Element : in out Natural) is
begin
Element := Element + 1;
end Increment;
path : constant String := Ada.Command_Line.Argument(1);
how_many : Natural := 10;
set : constant Ada.Strings.Maps.Character_Set := Ada.Strings.Maps.To_Set(ranges => (('a', 'z'), ('0', '9')));
F : TIO.File_Type;
first : Positive;
last : Natural;
from : Positive;
counter : String_Counters.Map;
sorted_counts : Sorted_Counters.Map;
C1 : String_Counters.Cursor;
C2 : Sorted_Counters.Cursor;
tmp_set : String_Sets.Set;
begin
-- read file and count words
TIO.Open(F, name => path, mode => TIO.In_File);
while not TIO.End_Of_File(F) loop
declare
line : constant String := Ada.Characters.Handling.To_Lower(TIO.Get_Line(F));
begin
from := line'First;
loop
Ada.Strings.Fixed.Find_Token(line(from .. line'Last), set, Ada.Strings.Inside, first, last);
exit when last < First;
C1 := counter.Find(line(first .. last));
if String_Counters.Has_Element(C1) then
counter.Update_Element(C1, Increment'Access);
else
counter.Insert(line(first .. last), 1);
end if;
from := last + 1;
end loop;
end;
end loop;
TIO.Close(F);
-- fill Natural -> StringSet Map
C1 := counter.First;
while String_Counters.Has_Element(C1) loop
if sorted_counts.Contains(String_Counters.Element(C1)) then
tmp_set := sorted_counts.Element(String_Counters.Element(C1));
tmp_set.Include(String_Counters.Key(C1));
else
sorted_counts.Include(String_Counters.Element(C1), String_Sets.To_Set(String_Counters.Key(C1)));
end if;
String_Counters.Next(C1);
end loop;
-- output
C2 := sorted_counts.First;
while Sorted_Counters.Has_Element(C2) loop
for Item of Sorted_Counters.Element(C2) loop
Ada.Integer_Text_IO.Put(TIO.Standard_Output, Sorted_Counters.Key(C2), width => 9);
TIO.Put(TIO.Standard_Output, " ");
TIO.Put_Line(Item);
end loop;
Sorted_Counters.Next(C2);
how_many := how_many - 1;
exit when how_many = 0;
end loop;
end Word_Frequency; </lang>
- Output:
$ ./word_frequency 135-0.txt
41093 the
19954 of
14943 and
14558 a
13953 to
11219 in
9649 he
8622 was
7924 that
6661 it
AppleScript
<lang applescript>(*
For simplicity here, words are considered to be uninterrupted sequences of letters and/or digits. The set text is too messy to warrant faffing around with anything more sophisticated. The first letter in each word is upper-cased and the rest lower-cased for case equivalence and presentation. Where more than n words qualify for the top n or fewer places, all are included in the result.
- )
use AppleScript version "2.4" -- OS X 10.10 (Yosemite) or later use framework "Foundation" use scripting additions
on wordFrequency(filePath, n)
set |⌘| to current application
-- Get the text and "capitalize" it (lower-case except for the first letters in words).
set theText to |⌘|'s class "NSString"'s stringWithContentsOfFile:(filePath) usedEncoding:(missing value) |error|:(missing value)
set theText to theText's capitalizedStringWithLocale:(|⌘|'s class "NSLocale"'s currentLocale()) -- Yosemite compatible.
-- Split it at the non-word characters.
set nonWordCharacters to |⌘|'s class "NSCharacterSet"'s alphanumericCharacterSet()'s invertedSet()
set theWords to theText's componentsSeparatedByCharactersInSet:(nonWordCharacters)
-- Use a counted set to count the individual words' occurrences.
set countedSet to |⌘|'s class "NSCountedSet"'s alloc()'s initWithArray:(theWords)
-- Build a list of word/frequency records, excluding any empty strings left over from the splitting above.
set mutableSet to |⌘|'s class "NSMutableSet"'s setWithSet:(countedSet)
tell mutableSet to removeObject:("")
script o
property discreteWords : mutableSet's allObjects() as list
property wordsAndFrequencies : {}
end script
set discreteWordCount to (count o's discreteWords)
repeat with i from 1 to discreteWordCount
set thisWord to item i of o's discreteWords
set end of o's wordsAndFrequencies to {thisWord:thisWord, frequency:(countedSet's countForObject:(thisWord)) as integer}
end repeat
-- Convert to NSMutableArray, reverse-sort the result on the frequencies, and convert back to list.
set wordsAndFrequencies to |⌘|'s class "NSMutableArray"'s arrayWithArray:(o's wordsAndFrequencies)
set descendingByFrequency to |⌘|'s class "NSSortDescriptor"'s sortDescriptorWithKey:("frequency") ascending:(false)
tell wordsAndFrequencies to sortUsingDescriptors:({descendingByFrequency})
set o's wordsAndFrequencies to wordsAndFrequencies as list
if (discreteWordCount > n) then
-- If there are more than n records, check for any immediately following the nth which may have the same frequency as it.
set nthHighestFrequency to frequency of item n of o's wordsAndFrequencies
set qualifierCount to n
repeat with i from (n + 1) to discreteWordCount
if (frequency of item i of o's wordsAndFrequencies = nthHighestFrequency) then
set qualifierCount to i
else
exit repeat
end if
end repeat
else
-- Otherwise reduce n to the actual number of discrete words.
set n to discreteWordCount
set qualifierCount to discreteWordCount
end if
-- Compose a text report from the qualifying words and frequencies.
if (qualifierCount = n) then
set output to {"The " & n & " most frequently occurring words in the file are:"}
else
set output to {(qualifierCount as text) & " words share the " & ((n as text) & " highest frequencies in the file:")}
end if
repeat with i from 1 to qualifierCount
set {thisWord:thisWord, frequency:frequency} to item i of o's wordsAndFrequencies
set end of output to thisWord & ": " & (tab & frequency)
end repeat
set astid to AppleScript's text item delimiters
set AppleScript's text item delimiters to linefeed
set output to output as text
set AppleScript's text item delimiters to astid
return output
end wordFrequency
-- Test code: set filePath to POSIX path of ((path to desktop as text) & "www.rosettacode.org:Word frequency:135-0.txt") set n to 10 return wordFrequency(filePath, n)</lang>
- Output:
<lang applescript>"The 10 most frequently occurring words in the file are: The: 41092 Of: 19954 And: 14943 A: 14545 To: 13953 In: 11219 He: 9649 Was: 8622 That: 7924 It: 6661"</lang>
AutoHotkey
<lang AutoHotkey>URLDownloadToFile, http://www.gutenberg.org/files/135/135-0.txt, % A_temp "\tempfile.txt" FileRead, H, % A_temp "\tempfile.txt" FileDelete, % A_temp "\tempfile.txt" words := [] while pos := RegExMatch(H, "\balpha:+\b", m, A_Index=1?1:pos+StrLen(m)) words[m] := words[m] ? words[m] + 1 : 1 for word, count in words list .= count "`t" word "`r`n" Sort, list, RN loop, parse, list, `n, `r { result .= A_LoopField "`r`n" if A_Index = 10 break } MsgBox % "Freq`tWord`n" result return</lang>
Outputs:
Freq Word 41036 The 19946 of 14940 and 14589 A 13939 TO 11204 in 9645 HE 8619 WAS 7922 THAT 6659 it
AWK
<lang AWK>
- syntax: GAWK -f WORD_FREQUENCY.AWK [-v show=x] LES_MISERABLES.TXT
- A word is anything separated by white space.
- Therefor "this" and "this." are different.
- But "This" and "this" are identical.
- As I am "free to define what a letter is" I have chosen to allow
- numerics and all special characters as they are usually considered
- parts of words in text processing applications.
{ nbytes += length($0) + 2 # +2 for CR/LF
nfields += NF
$0 = tolower($0)
for (i=1; i<=NF; i++) {
arr[$i]++
}
} END {
show = (show == "") ? 10 : show
width1 = length(show)
PROCINFO["sorted_in"] = "@val_num_desc"
for (i in arr) {
if (width2 == 0) { width2 = length(arr[i]) }
if (n++ >= show) { break }
printf("%*d %*d %s\n",width1,n,width2,arr[i],i)
}
printf("input: %d records, %d bytes, %d words of which %d are unique\n",NR,nbytes,nfields,length(arr))
exit(0)
} </lang>
- Output:
1 40372 the 2 19868 of 3 14472 and 4 14278 a 5 13589 to 6 11024 in 7 9213 he 8 8347 was 9 7250 that 10 6414 his input: 73829 records, 3369772 bytes, 568744 words of which 50394 are unique
Batch File
This takes a very long time per word thus I have chosen to feed it a 200 line sample and go from there.
You could cut the length of this down drastically if you didn't need to be able to recall the word at nth position and wished only to display the top 10 words.
<lang dos> @echo off
call:wordCount 1 2 3 4 5 6 7 8 9 10 42 101
pause>nul exit
- wordCount
setlocal enabledelayedexpansion
set word=100000 set line=0 for /f "delims=" %%i in (input.txt) do ( set /a line+=1 for %%j in (%%i) do ( if not !skip%%j!==true ( echo line !line! ^| word !word:~-5! - "%%~j"
type input.txt | find /i /c "%%~j" > count.tmp set /p tmpvar=<count.tmp
set tmpvar=000000000!tmpvar! set tmpvar=!tmpvar:~-10! set count[!word!]=!tmpvar! %%~j
set "skip%%j=true" set /a word+=1 ) ) ) del count.tmp
set wordcount=0 for /f "tokens=1,2 delims= " %%i in ('set count ^| sort /+14 /r') do ( set /a wordcount+=1 for /f "tokens=2 delims==" %%k in ("%%i") do ( set word[!wordcount!]=!wordcount!. %%j - %%k ) )
cls for %%i in (%*) do echo !word[%%i]! endlocal goto:eof
</lang>
Output
1. - 0000000140 I 2. - 0000000140 a 3. - 0000000118 He 4. - 0000000100 the 5. - 0000000080 an 6. - 0000000075 in 7. - 0000000066 at 8. - 0000000062 is 9. - 0000000058 on 10. - 0000000058 as 42. - 0000000010 with 101. - 0000000004 ears
C
Known issue: this code assumes the ASCII character set, but the input file is UTF-8. <lang c>#include <ctype.h>
- include <stdbool.h>
- include <stdio.h>
- include <glib.h>
// A word is defined to be any run of characters for which isalpha // returns true, i.e. upper or lower case letters. All words are // converted to lower case. bool get_word(FILE* in, GString* word) {
int c;
g_string_set_size(word, 0);
while ((c = getc(in)) != EOF) {
if (isalpha(c)) {
g_string_append_c(word, tolower(c));
break;
}
}
if (word->len == 0)
return false;
while ((c = getc(in)) != EOF) {
if (!isalpha(c))
break;
g_string_append_c(word, tolower(c));
}
return true;
}
typedef struct word_count_tag {
const char* word; size_t count;
} word_count;
int compare_word_count(const void* p1, const void* p2) {
const word_count* w1 = p1;
const word_count* w2 = p2;
if (w1->count > w2->count)
return -1;
if (w1->count < w2->count)
return 1;
return 0;
}
void get_top_words(FILE* in, size_t count) {
GHashTable* ht = g_hash_table_new_full(g_str_hash, g_str_equal,
g_free, g_free);
// Store word counts in the hash table
GString* word = g_string_sized_new(64);
while (get_word(in, word)) {
gpointer value = g_hash_table_lookup(ht, word->str);
if (value != NULL) {
size_t* count = value;
++*count;
} else {
size_t* count = g_new(size_t, 1);
*count = 1;
g_hash_table_insert(ht, g_strdup(word->str), count);
}
}
g_string_free(word, TRUE);
// Sort words in decreasing order of frequency
size_t size = g_hash_table_size(ht);
word_count* words = g_new(word_count, size);
GHashTableIter iter;
gpointer key, value;
g_hash_table_iter_init(&iter, ht);
for (size_t i = 0; g_hash_table_iter_next(&iter, &key, &value); ++i) {
words[i].word = (char*)key;
words[i].count = *(size_t*)value;
}
qsort(words, size, sizeof(word_count), compare_word_count);
// Print the most common words
if (count > size)
count = size;
printf("Top %lu words\n", count);
printf("Rank\tCount\tWord\n");
for (size_t i = 0; i < count; ++i)
printf("%lu\t%lu\t%s\n", i + 1, words[i].count, words[i].word);
g_free(words);
g_hash_table_destroy(ht);
}
int main(int argc, char** argv) {
if (argc != 2) {
fprintf(stderr, "usage: %s file\n", argv[0]);
return 1;
}
FILE* in = fopen(argv[1], "r");
if (in == NULL) {
perror(argv[1]);
return 1;
}
get_top_words(in, 15);
fclose(in);
return 0;
}</lang>
- Output:
Top 15 words Rank Count Word 1 41093 the 2 19954 of 3 14943 and 4 14558 a 5 13953 to 6 11219 in 7 9649 he 8 8622 was 9 7924 that 10 6661 it 11 6470 his 12 6195 is 13 6182 had 14 5149 which 15 4530 with
C#
<lang csharp>using System; using System.Collections.Generic; using System.IO; using System.Linq; using System.Text.RegularExpressions;
namespace WordCount {
class Program {
static void Main(string[] args) {
var text = File.ReadAllText("135-0.txt").ToLower();
var match = Regex.Match(text, "\\w+");
Dictionary<string, int> freq = new Dictionary<string, int>();
while (match.Success) {
string word = match.Value;
if (freq.ContainsKey(word)) {
freq[word]++;
} else {
freq.Add(word, 1);
}
match = match.NextMatch();
}
Console.WriteLine("Rank Word Frequency");
Console.WriteLine("==== ==== =========");
int rank = 1;
foreach (var elem in freq.OrderByDescending(a => a.Value).Take(10)) {
Console.WriteLine("{0,2} {1,-4} {2,5}", rank++, elem.Key, elem.Value);
}
}
}
}</lang>
- Output:
Rank Word Frequency ==== ==== ========= 1 the 41035 2 of 19946 3 and 14940 4 a 14577 5 to 13939 6 in 11204 7 he 9645 8 was 8619 9 that 7922 10 it 6659
C++
<lang cpp>#include <algorithm>
- include <iostream>
- include <fstream>
- include <map>
- include <regex>
- include <string>
- include <vector>
int main() {
using namespace std;
regex wordRgx("\\w+");
map<string, int> freq;
string line;
ifstream in("135-0.txt");
if (!in.is_open()) {
cerr << "Failed to open file\n";
return 1;
}
while (getline(in, line)) {
auto words_itr = sregex_iterator(line.cbegin(), line.cend(), wordRgx);
auto words_end = sregex_iterator();
while (words_itr != words_end) {
auto match = *words_itr;
auto word = match.str();
if (word.size() > 0) {
auto entry = freq.find(word);
if (entry != freq.end()) {
entry->second++;
} else {
freq.insert(make_pair(word, 1));
}
}
words_itr = next(words_itr);
}
}
in.close();
vector<pair<string, int>> pairs;
for (auto iter = freq.cbegin(); iter != freq.cend(); ++iter) {
pairs.push_back(*iter);
}
sort(pairs.begin(), pairs.end(), [=](pair<string, int>& a, pair<string, int>& b) {
return a.second > b.second;
});
cout << "Rank Word Frequency\n";
cout << "==== ==== =========\n";
int rank = 1;
for (auto iter = pairs.cbegin(); iter != pairs.cend() && rank <= 10; ++iter) {
printf("%2d %4s %5d\n", rank++, iter->first.c_str(), iter->second);
}
return 0;
}</lang>
- Output:
Rank Word Frequency ==== ==== ========= 1 the 36636 2 of 19615 3 and 14079 4 to 13535 5 a 13527 6 in 10256 7 was 8543 8 that 7324 9 he 6814 10 had 6139
Clojure
<lang clojure>(defn count-words [file n]
(->> file slurp clojure.string/lower-case (re-seq #"\w+") frequencies (sort-by val >) (take n)))</lang>
- Output:
user=> (count-words "135-0.txt" 10) (["the" 41036] ["of" 19946] ["and" 14940] ["a" 14589] ["to" 13939] ["in" 11204] ["he" 9645] ["was" 8619] ["that" 7922] ["it" 6659])
COBOL
<lang COBOL>
IDENTIFICATION DIVISION.
PROGRAM-ID. WordFrequency.
AUTHOR. Bill Gunshannon.
DATE-WRITTEN. 30 Jan 2020.
************************************************************
** Program Abstract:
** Given a text file and an integer n, print the n most
** common words in the file (and the number of their
** occurrences) in decreasing frequency.
**
** A file named Parameter.txt provides this information.
** Format is:
** 12345678901234567890123456789012345678901234567890
** |------------------|----|
** ^^^^^^^^^^^^^^^^ ^^^^
** | |
** Source Text File Number of words with count
** 20 Characters 5 digits with leading zeroes
**
**
************************************************************
ENVIRONMENT DIVISION.
INPUT-OUTPUT SECTION.
FILE-CONTROL.
SELECT Parameter-File ASSIGN TO "Parameter.txt"
ORGANIZATION IS LINE SEQUENTIAL.
SELECT Input-File ASSIGN TO Source-Text
ORGANIZATION IS LINE SEQUENTIAL.
SELECT Word-File ASSIGN TO "Word.txt"
ORGANIZATION IS LINE SEQUENTIAL.
SELECT Output-File ASSIGN TO "Output.txt"
ORGANIZATION IS LINE SEQUENTIAL.
SELECT Print-File ASSIGN TO "Printer.txt"
ORGANIZATION IS LINE SEQUENTIAL.
SELECT Sort-File ASSIGN TO DISK.
DATA DIVISION.
FILE SECTION.
FD Parameter-File
DATA RECORD IS Parameter-Record.
01 Parameter-Record.
05 Source-Text PIC X(20).
05 How-Many PIC 99999.
FD Input-File
DATA RECORD IS Input-Record.
01 Input-Record.
05 Input-Line PIC X(80).
FD Word-File
DATA RECORD IS Word-Record.
01 Word-Record.
05 Input-Word PIC X(20).
FD Output-File
DATA RECORD IS Output-Rec.
01 Output-Rec.
05 Output-Rec-Word PIC X(20).
05 Output-Rec-Word-Cnt PIC 9(5).
FD Print-File
DATA RECORD IS Print-Rec.
01 Print-Rec.
05 Print-Rec-Word PIC X(20).
05 Print-Rec-Word-Cnt PIC 9(5).
SD Sort-File.
01 Sort-Rec.
05 Sort-Word PIC X(20).
05 Sort-Word-Cnt PIC 9(5).
WORKING-STORAGE SECTION.
01 Eof PIC X VALUE 'F'.
01 InLine PIC X(80).
01 Word1 PIC X(20).
01 Current-Word PIC X(20).
01 Current-Word-Cnt PIC 9(5).
01 Pos PIC 99
VALUE 1.
01 Cnt PIC 99.
01 Report-Rank.
05 IRank PIC 99999
VALUE 1.
05 Rank PIC ZZZZ9.
PROCEDURE DIVISION.
Main-Program.
**
** Read the Parameters
**
OPEN INPUT Parameter-File.
READ Parameter-File.
CLOSE Parameter-File.
**
** Open Files for first stage
**
OPEN INPUT Input-File.
OPEN OUTPUT Word-File.
**
** Pare\se the Source Text into a file of invidual words
**
PERFORM UNTIL Eof = 'T'
READ Input-File
AT END MOVE 'T' TO Eof
END-READ
PERFORM Parse-a-Words
MOVE SPACES TO Input-Record
MOVE 1 TO Pos
END-PERFORM.
**
** Cleanup from the first stage
**
CLOSE Input-File Word-File
**
** Sort the individual words in alphabetical order
**
SORT Sort-File
ON ASCENDING KEY Sort-Word
USING Word-File
GIVING Word-File.
**
** Count each time a word is used
**
PERFORM Collect-Totals.
**
** Sort data by number of usages per word
**
SORT Sort-File
ON DESCENDING KEY Sort-Word-Cnt
USING Output-File
GIVING Print-File.
**
** Show the work done
**
OPEN INPUT Print-File.
DISPLAY " Rank Word Frequency"
PERFORM How-Many TIMES
READ Print-File
MOVE IRank TO Rank
DISPLAY Rank " " Print-Rec
ADD 1 TO IRank
END-PERFORM.
**
** Cleanup
**
CLOSE Print-File.
CALL "C$DELETE" USING "Word.txt" ,0
CALL "C$DELETE" USING "Output.txt" ,0
STOP RUN.
Parse-a-Words.
INSPECT Input-Record CONVERTING '-.,"();:/[]{}!?|' TO SPACE
PERFORM UNTIL Pos > FUNCTION STORED-CHAR-LENGTH(Input-Record)
UNSTRING Input-Record DELIMITED BY SPACE INTO Word1
WITH POINTER Pos TALLYING IN Cnt
MOVE FUNCTION TRIM(FUNCTION LOWER-CASE(Word1)) TO Word-Record
IF Word-Record NOT EQUAL SPACES AND Word-Record IS ALPHABETIC
THEN WRITE Word-Record
END-IF
END-PERFORM.
Collect-Totals.
MOVE 'F' to Eof
OPEN INPUT Word-File
OPEN OUTPUT Output-File
READ Word-File
MOVE Input-Word TO Current-Word
MOVE 1 to Current-Word-Cnt
PERFORM UNTIL Eof = 'T'
READ Word-File
AT END MOVE 'T' TO Eof
END-READ
IF FUNCTION TRIM(Word-Record)
EQUAL
FUNCTION TRIM(Current-Word)
THEN
ADD 1 to Current-Word-Cnt
ELSE
MOVE Current-Word TO Output-Rec-Word
MOVE Current-Word-Cnt TO Output-Rec-Word-Cnt
WRITE Output-Rec
MOVE 1 to Current-Word-Cnt
MOVE Word-Record TO Current-Word
MOVE SPACES TO Input-Record
END-IF
END-PERFORM.
CLOSE Word-File Output-File.
END-PROGRAM.
</lang>
- Output:
Rank Word Frequency
1 the 40551
2 of 19806
3 and 14730
4 a 14351
5 to 13775
6 in 11074
7 he 09480
8 was 08613
9 that 07632
10 his 06446
11 it 06335
12 had 06181
13 is 06097
14 which 05135
15 with 04469
Common Lisp
<lang lisp> (defun count-word (n pathname)
(with-open-file (s pathname :direction :input)
(loop for line = (read-line s nil nil) while line
nconc (list-symb (drop-noise line)) into words
finally (return (subseq (sort (pair words)
#'> :key #'cdr)
0 n)))))
(defun list-symb (s)
(let ((*read-eval* nil))
(read-from-string (concatenate 'string "(" s ")"))))
(defun drop-noise (s)
(delete-if-not #'(lambda (x) (or (alpha-char-p x)
(equal x #\space)
(equal x #\-))) s))
(defun pair (words &aux (hash (make-hash-table)) ac)
(dolist (word words) (incf (gethash word hash 0))) (maphash #'(lambda (e n) (push `(,e . ,n) ac)) hash) ac)
</lang>
- Output:
> (count-word 10 "c:/temp/135-0.txt") ((THE . 40738) (OF . 19922) (AND . 14878) (A . 14419) (TO . 13702) (IN . 11172) (HE . 9577) (WAS . 8612) (THAT . 7768) (IT . 6467))
Crystal
<lang ruby>require "http/client" require "regex"
- Get the text from the internet
response = HTTP::Client.get "https://www.gutenberg.org/files/135/135-0.txt" text = response.body
text
.downcase
.scan(/[a-zA-ZáéíóúÁÉÍÓÚâêôäüöàèìòùñ']+/)
.reduce({} of String => Int32) { |hash, match|
word = match[0]
hash[word] = hash.fetch(word, 0) + 1 # using fetch to set a default value (1) to the new found word
hash
}
.to_a # convert the returned hash to an array of tuples (String, Int32) -> {word, sum}
.sort { |a, b| b[1] <=> a[1] }[0..9] # sort and get the first 10 elements
.each_with_index(1) { |(word, n), i| puts "#{i} \t #{word} \t #{n}" } # print the result
</lang>
- Output:
1 the 41092 2 of 19954 3 and 14943 4 a 14556 5 to 13953 6 in 11219 7 he 9649 8 was 8622 9 that 7924 10 it 6661
D
<lang D>import std.algorithm : sort; import std.array : appender, split; import std.range : take; import std.stdio : File, writefln, writeln; import std.typecons : Tuple; import std.uni : toLower;
//Container for a word and how many times it has been seen alias Pair = Tuple!(string, "k", int, "v");
void main() {
int[string] wcnt;
//Read the file line by line
foreach (line; File("135-0.txt").byLine) {
//Split the words on whitespace
foreach (word; line.split) {
//Increment the times the word has been seen
wcnt[word.toLower.idup]++;
}
}
//Associative arrays cannot be sort, so put the key/value in an array
auto wb = appender!(Pair[]);
foreach(k,v; wcnt) {
wb.put(Pair(k,v));
}
Pair[] sw = wb.data.dup;
//Sort the array, and display the top ten values
writeln("Rank Word Frequency");
int rank=1;
foreach (word; sw.sort!"a.v>b.v".take(10)) {
writefln("%4s %-10s %9s", rank++, word.k, word.v);
}
}</lang>
- Output:
Rank Word Frequency 1 the 40368 2 of 19863 3 and 14470 4 a 14277 5 to 13587 6 in 11019 7 he 9212 8 was 8346 9 that 7251 10 his 6414
F#
<lang fsharp> open System.IO open System.Text.RegularExpressions let g=Regex("[A-Za-zÀ-ÿ]+").Matches(File.ReadAllText "135-0.txt") [for n in g do yield n.Value.ToLower()]|>List.countBy(id)|>List.sortBy(fun n->(-(snd n)))|>List.take 10|>List.iter(fun n->printfn "%A" n) </lang>
- Output:
("the", 41088)
("of", 19949)
("and", 14942)
("a", 14596)
("to", 13951)
("in", 11214)
("he", 9648)
("was", 8621)
("that", 7924)
("it", 6661)
Factor
This program expects stdin to read from a file via the command line. ( e.g. invoking the program in Windows: >factor word-count.factor < input.txt ) The definition of a word here is simply any string surrounded by some combination of spaces, punctuation, or newlines. <lang factor> USING: ascii io math.statistics prettyprint sequences splitting ; IN: rosetta-code.word-count
lines " " join " .,?!:;()\"-" split harvest [ >lower ] map sorted-histogram <reversed> 10 head . </lang>
- Output:
{
{ "the" 41021 }
{ "of" 19945 }
{ "and" 14938 }
{ "a" 14522 }
{ "to" 13938 }
{ "in" 11201 }
{ "he" 9600 }
{ "was" 8618 }
{ "that" 7822 }
{ "it" 6532 }
}
Frink
- Frink has a Unicode-aware function, wordList[str], which intelligently enumerates through the words in a string (and correctly handles compound words, hyphenated words, accented characters, etc.) It returns words, spaces, and punctuation marks. Results that do not contain alphanumeric characters are filtered out.
- The file fetched from Project Gutenberg is supposed to be encoded in UTF-8 character encoding, but their servers incorrectly send that it is Windows-1252 encoded, so this program fixes that.
- Frink has a Unicode-aware lowercase function, lc[str] that correctly handles accented characters and may even make a string longer.
<lang frink>d = new dict for w = select[wordList[read["https://www.gutenberg.org/files/135/135-0.txt", "UTF-8"]], %r/alnum:/ ]
d.increment[lc[w], 1]
println[join["\n", first[reverse[sort[array[d], {|a,b| a@1 <=> b@1}]], 10]]]</lang>
- Output:
[the, 40802] [of, 19933] [and, 14924] [a, 14450] [to, 13719] [in, 11184] [he, 9636] [was, 8617] [that, 7901] [it, 6641]
Go
<lang go>package main
import (
"fmt" "io/ioutil" "log" "regexp" "sort" "strings"
)
type keyval struct {
key string val int
}
func main() {
reg := regexp.MustCompile(`\p{Ll}+`)
bs, err := ioutil.ReadFile("135-0.txt")
if err != nil {
log.Fatal(err)
}
text := strings.ToLower(string(bs))
matches := reg.FindAllString(text, -1)
groups := make(map[string]int)
for _, match := range matches {
groups[match]++
}
var keyvals []keyval
for k, v := range groups {
keyvals = append(keyvals, keyval{k, v})
}
sort.Slice(keyvals, func(i, j int) bool {
return keyvals[i].val > keyvals[j].val
})
fmt.Println("Rank Word Frequency")
fmt.Println("==== ==== =========")
for rank := 1; rank <= 10; rank++ {
word := keyvals[rank-1].key
freq := keyvals[rank-1].val
fmt.Printf("%2d %-4s %5d\n", rank, word, freq)
}
}</lang>
- Output:
Rank Word Frequency ==== ==== ========= 1 the 41088 2 of 19949 3 and 14942 4 a 14596 5 to 13951 6 in 11214 7 he 9648 8 was 8621 9 that 7924 10 it 6661
Groovy
Solution: <lang groovy>def topWordCounts = { String content, int n ->
def mapCounts = [:]
content.toLowerCase().split(/\W+/).each {
mapCounts[it] = (mapCounts[it] ?: 0) + 1
}
def top = (mapCounts.sort { a, b -> b.value <=> a.value }.collect{ it })[0..<n]
println "Rank Word Frequency\n==== ==== ========="
(0..<n).each { printf ("%4d %-4s %9d\n", it+1, top[it].key, top[it].value) }
}</lang>
Test: <lang groovy>def rawText = "http://www.gutenberg.org/files/135/135-0.txt".toURL().text topWordCounts(rawText, 10)</lang>
Output:
Rank Word Frequency ==== ==== ========= 1 the 41036 2 of 19946 3 and 14940 4 a 14589 5 to 13939 6 in 11204 7 he 9645 8 was 8619 9 that 7922 10 it 6659
Haskell
<lang Haskell>module Main where
import Control.Category -- (>>>) import Data.Char -- toLower, isSpace import Data.List -- sortBy, (Foldable(foldl')), filter import Data.Ord -- Down import System.IO -- stdin, ReadMode, openFile, hClose import System.Environment -- getArgs
-- containers import Data.Map.Strict (Map) import qualified Data.Map.Strict as M import qualified Data.IntMap.Strict as IM
-- text import Data.Text (Text) import qualified Data.Text as T import qualified Data.Text.IO as T
frequencies :: Ord a => [a] -> Map a Integer frequencies = foldl' (\m k -> M.insertWith (+) k 1 m) M.empty {-# SPECIALIZE frequencies :: [Text] -> Map Text Integer #-}
main :: IO () main = do
args <- getArgs
(n,hand,filep) <- case length args of
0 -> return (10,stdin,False)
1 -> return (read $ head args,stdin,False)
_ -> let (ns:fp:_) = args
in fmap (\h -> (read ns,h,True)) (openFile fp ReadMode)
T.hGetContents hand >>=
(T.map toLower
>>> T.split isSpace
>>> filter (not <<< T.null)
>>> frequencies
>>> M.toList
>>> sortBy (comparing (Down <<< snd)) -- sort the opposite way
>>> take n
>>> print)
when filep (hClose hand)</lang>
- Output:
$ ./word_count 10 < ~/doc/les_miserables*
[("the",40368),("of",19863),("and",14470),("a",14277),("to",13587),("in",11019),("he",9212),("was",8346),("that",7251),("his",6414)]
Or, perhaps a little more simply:
<lang haskell>import qualified Data.Text.IO as T
import qualified Data.Text as T
import Data.List (group, sort, sortBy) import Data.Ord (comparing)
frequentWords :: T.Text -> [(Int, T.Text)] frequentWords =
sortBy (flip $ comparing fst) . fmap ((,) . length <*> head) . group . sort . T.words . T.toLower
main :: IO () main = T.readFile "miserables.txt" >>= (mapM_ print . take 10 . frequentWords)</lang>
- Output:
(40370,"the") (19863,"of") (14470,"and") (14277,"a") (13587,"to") (11019,"in") (9212,"he") (8346,"was") (7251,"that") (6414,"his")
J
Text acquisition: store the entire text from the web page http://www.gutenberg.org/files/135/135-0.txt (the plain text UTF-8 link) into a file. This linux example uses ~/downloads/books/LesMis.txt .
Program: Reading from left to right, 10 {. "ten take" from an array computed by words to the right. \:~ "sort descending" by items of the array computed by whatever is to the right. (#;{.)/.~ "tally linked with item" key ;: "words" parses the argument to its right as a j sentence. tolower changes to a common case
Hence the remainder of the j sentence must clean after loading the file.
The parenthesized expression (a.-.Alpha_j_,' ') computes to a vector of the j alphabet excluding [a-zA-Z ] ((e.&(a.-.Alpha_j_,' '))`(,:&' '))} substitutes space character for the unwanted characters. 1!:1 reads the file named in the box <
10{.\:~(#;{.)/.~;:tolower((e.&(a.-.Alpha_j_,' '))`(,:&' '))}1!:1<jpath'~/downloads/books/LesMis.txt'
┌─────┬────┐
│41093│the │
├─────┼────┤
│19954│of │
├─────┼────┤
│14943│and │
├─────┼────┤
│14558│a │
├─────┼────┤
│13953│to │
├─────┼────┤
│11219│in │
├─────┼────┤
│9649 │he │
├─────┼────┤
│8622 │was │
├─────┼────┤
│7924 │that│
├─────┼────┤
│6661 │it │
└─────┴────┘
Java
<lang Java>import java.io.IOException; import java.nio.file.Files; import java.nio.file.Path; import java.nio.file.Paths; import java.util.HashMap; import java.util.List; import java.util.Map; import java.util.regex.Matcher; import java.util.regex.Pattern; import java.util.stream.Collectors;
public class WordCount {
public static void main(String[] args) throws IOException {
Path path = Paths.get("135-0.txt");
byte[] bytes = Files.readAllBytes(path);
String text = new String(bytes);
text = text.toLowerCase();
Pattern r = Pattern.compile("\\p{javaLowerCase}+");
Matcher matcher = r.matcher(text);
Map<String, Integer> freq = new HashMap<>();
while (matcher.find()) {
String word = matcher.group();
Integer current = freq.getOrDefault(word, 0);
freq.put(word, current + 1);
}
List<Map.Entry<String, Integer>> entries = freq.entrySet()
.stream()
.sorted((i1, i2) -> Integer.compare(i2.getValue(), i1.getValue()))
.limit(10)
.collect(Collectors.toList());
System.out.println("Rank Word Frequency");
System.out.println("==== ==== =========");
int rank = 1;
for (Map.Entry<String, Integer> entry : entries) {
String word = entry.getKey();
Integer count = entry.getValue();
System.out.printf("%2d %-4s %5d\n", rank++, word, count);
}
}
}</lang>
- Output:
Rank Word Frequency ==== ==== ========= 1 the 41088 2 of 19949 3 and 14942 4 a 14596 5 to 13951 6 in 11214 7 he 9648 8 was 8621 9 that 7924 10 it 6661
Julia
<lang julia> using FreqTables
txt = read("les-mis.txt", String) words = split(replace(txt, r"\P{L}"i => " ")) table = sort(freqtable(words); rev=true) println(table[1:10])</lang>
- Output:
Dim1 │ ───────┼────── "the" │ 36671 "of" │ 19618 "and" │ 14081 "to" │ 13541 "a" │ 13529 "in" │ 10265 "was" │ 8545 "that" │ 7326 "he" │ 6816 "had" │ 6140
Kotlin
The author of the Raku entry has given a good account of the difficulties with this task and, in the absence of any clarification on the various issues, I've followed a similar 'literal' approach.
So, after first converting the text to lower case, I've assumed that a word is any sequence of one or more lower-case Unicode letters and obtained the same results as the Raku version.
There is no change in the results if the numerals 0-9 are also regarded as letters. <lang scala>// version 1.1.3
import java.io.File
fun main(args: Array<String>) {
val text = File("135-0.txt").readText().toLowerCase()
val r = Regex("""\p{javaLowerCase}+""")
val matches = r.findAll(text)
val wordGroups = matches.map { it.value }
.groupBy { it }
.map { Pair(it.key, it.value.size) }
.sortedByDescending { it.second }
.take(10)
println("Rank Word Frequency")
println("==== ==== =========")
var rank = 1
for ((word, freq) in wordGroups)
System.out.printf("%2d %-4s %5d\n", rank++, word, freq)
}</lang>
- Output:
Rank Word Frequency ==== ==== ========= 1 the 41088 2 of 19949 3 and 14942 4 a 14596 5 to 13951 6 in 11214 7 he 9648 8 was 8621 9 that 7924 10 it 6661
Liberty BASIC
<lang lb>dim words$(100000,2)'words$(a,1)=the word, words$(a,2)=the count dim lines$(150000) open "135-0.txt" for input as #txt while EOF(#txt)=0 and total < 150000
input #txt, lines$(total) total=total+1
wend for a = 1 to total
token$ = "?"
index=0
new=0
while token$ <> ""
new=0
index = index + 1
token$ = lower$(word$(lines$(a),index))
token$=replstr$(token$,".","")
token$=replstr$(token$,",","")
token$=replstr$(token$,";","")
token$=replstr$(token$,"!","")
token$=replstr$(token$,"?","")
token$=replstr$(token$,"-","")
token$=replstr$(token$,"_","")
token$=replstr$(token$,"~","")
token$=replstr$(token$,"+","")
token$=replstr$(token$,"0","")
token$=replstr$(token$,"1","")
token$=replstr$(token$,"2","")
token$=replstr$(token$,"3","")
token$=replstr$(token$,"4","")
token$=replstr$(token$,"5","")
token$=replstr$(token$,"6","")
token$=replstr$(token$,"7","")
token$=replstr$(token$,"8","")
token$=replstr$(token$,"9","")
token$=replstr$(token$,"/","")
token$=replstr$(token$,"<","")
token$=replstr$(token$,">","")
token$=replstr$(token$,":","")
for b = 1 to newwordcount
if words$(b,1)=token$ then
num=val(words$(b,2))+1
num$=str$(num)
if len(num$)=1 then num$="0000"+num$
if len(num$)=2 then num$="000"+num$
if len(num$)=3 then num$="00"+num$
if len(num$)=4 then num$="0"+num$
words$(b,2)=num$
new=1
exit for
end if
next b
if new<>1 then newwordcount=newwordcount+1:words$(newwordcount,1)=token$:words$(newwordcount,2)="00001":print newwordcount;" ";token$
wend
next a print sort words$(), 1, newwordcount, 2 print "Count Word" print "===== =================" for a = newwordcount to newwordcount-10 step -1
print words$(a,2);" ";words$(a,1)
next a print "-----------------------" print newwordcount;" unique words found." print "End of program" close #txt end </lang>
- Output:
Count Word ===== ================= 40292 the 19825 of 14703 and 14249 a 13594 to 122613 11061 in 09436 he 08579 was 07530 that 06428 his ----------------------- 29109 unique words found.
Lua
<lang lua> -- This program takes two command line arguments. The first (arg[1]) -- specifies the input file; if it is absent, then standard input is used. -- The second (arg[2]) refers to the number of results to show; if it is -- absent, default to the top 10 words.
local freq = {} for line in io.lines(arg[1]) do local lowerline = string.lower(line) for word in string.gmatch(lowerline, "%a+") do if not freq[word] then freq[word] = 1 else freq[word] = freq[word] + 1 end end end
local array = {} for word, count in pairs(freq) do table.insert(array, {word, count}) end
table.sort(array, function (a, b) return a[2] > b[2] end)
for i = 1, arg[2] or 10 do io.write(string.format('%7d %s\n', array[i][2] , array[i][1])) end </lang>
- Output:
❯ ./wordcount.lua 135-0.txt 41093 the 19954 of 14943 and 14558 a 13953 to 11219 in 9649 he 8622 was 7924 that 6661 it
Nim
<lang Nim>import tables, strutils, sequtils, httpclient
proc take[T](s: openArray[T], n: int): seq[T] = s[0 ..< min(n, s.len)]
var client = newHttpClient() var text = client.getContent("https://www.gutenberg.org/files/135/135-0.txt")
var wordFrequencies = text.toLowerAscii.splitWhitespace.toCountTable wordFrequencies.sort for (word, count) in toSeq(wordFrequencies.pairs).take(10):
echo alignLeft($count, 8), word</lang>
- Output:
40372 the 19868 of 14472 and 14278 a 13589 to 11024 in 9213 he 8347 was 7250 that 6414 his
Objeck
<lang objeck>use System.IO.File; use Collection; use RegEx;
class Rosetta {
function : Main(args : String[]) ~ Nil {
if(args->Size() <> 1) {
return;
};
input := FileReader->ReadFile(args[0]);
filter := RegEx->New("\\w+");
words := filter->Find(input);
word_counts := StringMap->New();
each(i : words) {
word := words->Get(i)->As(String);
if(word <> Nil & word->Size() > 0) {
word := word->ToLower();
if(word_counts->Has(word)) {
count := word_counts->Find(word)->As(IntHolder);
count->Set(count->Get() + 1);
}
else {
word_counts->Insert(word, IntHolder->New(1));
};
};
};
count_words := IntMap->New();
words := word_counts->GetKeys();
each(i : words) {
word := words->Get(i)->As(String);
count := word_counts->Find(word)->As(IntHolder);
count_words->Insert(count->Get(), word);
};
counts := count_words->GetKeys();
counts->Sort();
index := 1;
"Rank\tWord\tFrequency"->PrintLine();
"====\t====\t===="->PrintLine();
for(i := count_words->Size() - 1; i >= 0; i -= 1;) {
if(count_words->Size() - 10 <= i) {
count := counts->Get(i);
word := count_words->Find(count)->As(String);
"{$index}\t{$word}\t{$count}"->PrintLine();
index += 1;
};
};
}
}</lang>
Output:
Rank Word Frequency ==== ==== ==== 1 the 41036 2 of 19946 3 and 14940 4 a 14589 5 to 13939 6 in 11204 7 he 9645 8 was 8619 9 that 7922 10 it 6659
OCaml
<lang ocaml>let () =
let n =
try int_of_string Sys.argv.(1)
with _ -> 10
in
let ic = open_in "135-0.txt" in
let h = Hashtbl.create 97 in
let w = Str.regexp "[^A-Za-zéèàêâôîûœ]+" in
try
while true do
let line = input_line ic in
let words = Str.split w line in
List.iter (fun word ->
let word = String.lowercase_ascii word in
match Hashtbl.find_opt h word with
| None -> Hashtbl.add h word 1
| Some x -> Hashtbl.replace h word (succ x)
) words
done
with End_of_file ->
close_in ic;
let l = Hashtbl.fold (fun word count acc -> (word, count)::acc) h [] in
let s = List.sort (fun (_, c1) (_, c2) -> compare c2 c1) l in
let r = List.init n (fun i -> List.nth s i) in
List.iter (fun (word, count) ->
Printf.printf "%d %s\n" count word
) r</lang>
- Output:
$ ocaml str.cma word_freq.ml 41092 the 19954 of 14943 and 14554 a 13953 to 11219 in 9649 he 8622 was 7924 that 6661 it
Perl
<lang perl>$top = 10;
open $fh, "<", '135-0.txt'; ($text = join , <$fh>) =~ tr/A-Z/a-z/;
@matcher = (
qr/[a-z]+/, # simple 7-bit ASCII qr/\w+/, # word characters with underscore qr/[a-z0-9]+/, # word characters without underscore
);
for $reg (@matcher) {
print "\nTop $top using regex: " . $reg . "\n";
@matches = $text =~ /$reg/g;
my %words;
for $w (@matches) { $words{$w}++ };
$c = 0;
for $w ( sort { $words{$b} <=> $words{$a} } keys %words ) {
printf "%-7s %6d\n", $w, $words{$w};
last if ++$c >= $top;
}
}</lang>
- Output:
Top 10 using regex: (?^:[a-z]+) the 41089 of 19949 and 14942 a 14608 to 13951 in 11214 he 9648 was 8621 that 7924 it 6661 Top 10 using regex: (?^:\w+) the 41036 of 19946 and 14940 a 14589 to 13939 in 11204 he 9645 was 8619 that 7922 it 6659 Top 10 using regex: (?^:[a-z0-9]+) the 41089 of 19949 and 14942 a 14608 to 13951 in 11214 he 9648 was 8621 that 7924 it 6661
Phix
<lang Phix>?"loading..." constant subs = "\t\r\n_.,\"\'!;:?][()|=<>#/*{}+@%&$",
reps = repeat(' ',length(subs)),
fn = open("135-0.txt","r")
string text = lower(substitute_all(get_text(fn),subs,reps)) close(fn) sequence words = append(sort(split(text,no_empty:=true)),"") constant wf = new_dict() string last = words[1] integer count = 1 for i=2 to length(words) do
if words[i]!=last then
setd({count,last},0,wf)
count = 0
last = words[i]
end if
count += 1
end for count = 10 function visitor(object key, object /*data*/, object /*user_data*/)
?key count -= 1 return count>0
end function traverse_dict(routine_id("visitor"),0,wf,true)</lang>
- Output:
loading...
{40743,"the"}
{19925,"of"}
{14881,"and"}
{14474,"a"}
{13704,"to"}
{11174,"in"}
{9623,"he"}
{8613,"was"}
{7867,"that"}
{6612,"it"}
PHP
<lang php> <?php
preg_match_all('/\w+/', file_get_contents($argv[1]), $words); $frecuency = array_count_values($words[0]); arsort($frecuency);
echo "Rank\tWord\tFrequency\n====\t====\t=========\n"; $i = 1; foreach ($frecuency as $word => $count) {
echo $i . "\t" . $word . "\t" . $count . "\n";
if ($i >= 10) {
break;
}
$i++;
}</lang>
- Output:
Rank Word Frequency ==== ==== ========= 1 the 36636 2 of 19615 3 and 14079 4 to 13535 5 a 13527 6 in 10256 7 was 8543 8 that 7324 9 he 6814 10 had 6139
PicoLisp
<lang PicoLisp>(setq *Delim " ^I^J^M-_.,\"'*[]?!&@#$%^\(\):;") (setq *Skip (chop *Delim))
(de word+ NIL
(prog1
(lowc (till *Delim T))
(while (member (peek) *Skip) (char)) ) )
(off B) (in "135-0.txt"
(until (eof)
(let W (word+)
(if (idx 'B W T) (inc (car @)) (set W 1)) ) ) )
(for L (head 10 (flip (by val sort (idx 'B))))
(println L (val L)) )</lang>
- Output:
"the" 41088 "of" 19949 "and" 14942 "a" 14545 "to" 13950 "in" 11214 "he" 9647 "was" 8620 "that" 7924 "it" 6661
Prolog
<lang prolog>print_top_words(File, N):-
read_file_to_string(File, String, [encoding(utf8)]),
re_split("\\w+", String, Words),
lower_case(Words, Lower),
sort(1, @=<, Lower, Sorted),
merge_words(Sorted, Counted),
sort(2, @>, Counted, Top_words),
writef("Top %w words:\nRank\tCount\tWord\n", [N]),
print_top_words(Top_words, N, 1).
lower_case([_], []):-!. lower_case([_, Word|Words], [Lower - 1|Rest]):-
string_lower(Word, Lower), lower_case(Words, Rest).
merge_words([], []):-!. merge_words([Word - C1, Word - C2|Words], Result):-
!, C is C1 + C2, merge_words([Word - C|Words], Result).
merge_words([W|Words], [W|Rest]):-
merge_words(Words, Rest).
print_top_words([], _, _):-!. print_top_words(_, 0, _):-!. print_top_words([Word - Count|Rest], N, R):-
writef("%w\t%w\t%w\n", [R, Count, Word]),
N1 is N - 1,
R1 is R + 1,
print_top_words(Rest, N1, R1).
main:-
print_top_words("135-0.txt", 15).</lang>
- Output:
Top 15 words: Rank Count Word 1 41040 the 2 19951 of 3 14942 and 4 14539 a 5 13941 to 6 11209 in 7 9646 he 8 8620 was 9 7922 that 10 6659 it 11 6469 his 12 6195 is 13 6181 had 14 5149 which 15 4530 with
Python
Collections
Python2.7
<lang python>import collections import re import string import sys
def main():
counter = collections.Counter(re.findall(r"\w+",open(sys.argv[1]).read().lower())) print counter.most_common(int(sys.argv[2]))
if __name__ == "__main__":
main()</lang>
- Output:
$ python wordcount.py 135-0.txt 10
[('the', 41036), ('of', 19946), ('and', 14940), ('a', 14589), ('to', 13939),
('in', 11204), ('he', 9645), ('was', 8619), ('that', 7922), ('it', 6659)]
Python3.6
<lang python>from collections import Counter from re import findall
les_mis_file = 'les_mis_135-0.txt'
def _count_words(fname):
with open(fname) as f:
text = f.read()
words = findall(r'\w+', text.lower())
return Counter(words)
def most_common_words_in_file(fname, n):
counts = _count_words(fname) for word, count in 'WORD', 'COUNT' + counts.most_common(n): print(f'{word:>10} {count:>6}')
if __name__ == "__main__":
n = int(input('How many?: '))
most_common_words_in_file(les_mis_file, n)</lang>
- Output:
How many?: 10
WORD COUNT
the 41036
of 19946
and 14940
a 14586
to 13939
in 11204
he 9645
was 8619
that 7922
it 6659
Sorted and groupby
<lang python>""" Word count task from Rosetta Code http://www.rosettacode.org/wiki/Word_count#Python """ from itertools import (groupby,
starmap)
from operator import itemgetter from pathlib import Path from typing import (Iterable,
List,
Tuple)
FILEPATH = Path('lesMiserables.txt')
COUNT = 10
def main():
words_and_counts = most_frequent_words(FILEPATH) print(*words_and_counts[:COUNT], sep='\n')
def most_frequent_words(filepath: Path,
*,
encoding: str = 'utf-8') -> List[Tuple[str, int]]:
"""
A list of word-frequency pairs sorted by their occurrences.
The words are read from the given file.
"""
def word_and_frequency(word: str,
words_group: Iterable[str]) -> Tuple[str, int]:
return word, capacity(words_group)
file_contents = filepath.read_text(encoding=encoding) words = file_contents.lower().split() grouped_words = groupby(sorted(words)) words_and_frequencies = starmap(word_and_frequency, grouped_words) return sorted(words_and_frequencies, key=itemgetter(1), reverse=True)
def capacity(iterable: Iterable) -> int:
"""Returns a number of elements in an iterable""" return sum(1 for _ in iterable)
if __name__ == '__main__':
main()
</lang>
- Output:
('the', 40372)
('of', 19868)
('and', 14472)
('a', 14278)
('to', 13589)
('in', 11024)
('he', 9213)
('was', 8347)
('that', 7250)
('his', 6414)
R
I chose to remove apostrophes only if they're followed by an s (so "mom" and "mom's" will show up as the same word but "they" and "they're" won't). I also chose not to remove hyphens. <lang R> wordcount<-function(file,n){
punctuation=c("`","~","!","@","#","$","%","^","&","*","(",")","_","+","=","{","[","}","]","|","\\",":",";","\"","<",",",">",".","?","/","'s")
wordlist=scan(file,what=character())
wordlist=tolower(wordlist)
for(i in 1:length(punctuation)){
wordlist=gsub(punctuation[i],"",wordlist,fixed=T)
}
df=data.frame("Word"=sort(unique(wordlist)),"Count"=rep(0,length(unique(wordlist))))
for(i in 1:length(unique(wordlist))){
df[i,2]=length(which(wordlist==df[i,1]))
}
df=df[order(df[,2],decreasing = T),]
row.names(df)=1:nrow(df)
return(df[1:n,])
} </lang>
- Output:
> wordcount("MobyDick.txt",10)
Read 212793 items
Word Count
1 the 14346
2 of 6590
3 and 6340
4 a 4611
5 to 4572
6 in 4130
7 that 2903
8 his 2516
9 it 2308
10 i 1845
Racket
<lang racket>#lang racket
(define (all-words f (case-fold string-downcase))
(map case-fold (regexp-match* #px"\\w+" (file->string f))))
(define (l.|l| l) (cons (car l) (length l)))
(define (counts l (>? >)) (sort (map l.|l| (group-by values l)) >? #:key cdr))
(module+ main
(take (counts (all-words "data/les-mis.txt")) 10))</lang>
- Output:
'(("the" . 41036)
("of" . 19946)
("and" . 14940)
("a" . 14589)
("to" . 13939)
("in" . 11204)
("he" . 9645)
("was" . 8619)
("that" . 7922)
("it" . 6659))
Raku
(formerly Perl 6)
Note: much of the following exposition is no longer critical to the task as the requirements have been updated, but is left here for historical and informational reasons.
This is slightly trickier than it appears initially. The task specifically states: "A word is a sequence of one or more contiguous letters", so contractions and hyphenated words are broken up. Initially we might reach for a regex matcher like /\w+/ , but \w includes underscore, which is not a letter but a punctuation connector; and this text is full of underscores since that is how Project Gutenberg texts denote italicized text. The underscores are not actually parts of the words though, they are markup.
We might try /A-Za-z/ as a matcher but this text is bursting with French words containing various accented glyphs. Those are letters, so words will be incorrectly split up; (Misérables will be counted as 'mis' and 'rables', probably not what we want.)
Actually, in this case /A-Za-z/ returns very nearly the correct answer. Unfortunately, the name "Alèthe" appears once (only once!) in the text, gets incorrectly split into Al & the, and incorrectly reports 41089 occurrences of "the". The text has several words like "Panathenæa", "ça", "aérostiers" and "Keksekça" so the counts for 'a' are off too. The other 8 of the top 10 are "correct" using /A-Za-z/, but it is mostly by accident.
A more accurate regex matcher would be some kind of Unicode aware /\w/ minus underscore. It may also be useful, depending on your requirements, to recognize contractions with embedded apostrophes, hyphenated words, and hyphenated words broken across lines.
Here is a sample that shows the result when using various different matchers. <lang perl6>sub MAIN ($filename, $top = 10) {
my $file = $filename.IO.slurp.lc.subst(/ (<[\w]-[_]>'-')\n(<[\w]-[_]>) /, {$0 ~ $1}, :g );
my @matcher = (
rx/ <[a..z]>+ /, # simple 7-bit ASCII
rx/ \w+ /, # word characters with underscore
rx/ <[\w]-[_]>+ /, # word characters without underscore
rx/ <[\w]-[_]>+[["'"|'-'|"'-"]<[\w]-[_]>+]* / # word characters without underscore but with hyphens and contractions
);
for @matcher -> $reg {
say "\nTop $top using regex: ", $reg.perl;
.put for $file.comb( $reg ).Bag.sort(-*.value)[^$top];
}
}</lang>
- Output:
Passing in the file name and 10:
Top 10 using regex: rx/ <[a..z]>+ / the 41089 of 19949 and 14942 a 14608 to 13951 in 11214 he 9648 was 8621 that 7924 it 6661 Top 10 using regex: rx/ \w+ / the 41035 of 19946 and 14940 a 14577 to 13939 in 11204 he 9645 was 8619 that 7922 it 6659 Top 10 using regex: rx/ <[\w]-[_]>+ / the 41088 of 19949 and 14942 a 14596 to 13951 in 11214 he 9648 was 8621 that 7924 it 6661 Top 10 using regex: rx/ <[\w]-[_]>+[["'"|'-'|"'-"]<[\w]-[_]>+]* / the 41081 of 19930 and 14934 a 14587 to 13735 in 11204 he 9607 was 8620 that 7825 it 6535
REXX
version 1
This REXX version doesn't need to sort the list of words.
Currently, this version recognizes all the accented (non-Latin) accented letters that are present in the text (file) that is specified to be used (and some other non-Latin letters as well). This means that the word Alèthe is treated as one word, not as two words Al the (and not thereby adding two words).
This version also supports words that contain embedded apostrophes ( ' ) [that is, within a word, but not those words that start or end with an apostrophe; for those words, the apostrophe is elided].
Thus, it's is counted separately from it or its.
Since REXX doesn't support UTF-8 encodings, code was added to this REXX version to support the accented letters in the mandated input file. <lang rexx>/*REXX pgm displays top 10 words in a file (includes foreign letters), case is ignored.*/ parse arg fID top . /*obtain optional arguments from the CL*/ if fID== | fID=="," then fID= 'les_mes.TXT' /*None specified? Then use the default.*/ if top== | top=="," then top= 10 /* " " " " " " */ @.=0; c=0; abcL="abcdefghijklmnopqrstuvwxyz'" /*initialize word list, count; alphabet*/ q= "'"; abcU= abcL; upper abcU /*define uppercase version of alphabet*/ totW=0; accL= 'üéâÄàÅÇêëèïîìéæôÖòûùÿáíóúÑ' /* " " of some accented chrs*/
accU= 'ÜéâäàåçêëèïîìÉÆôöòûùÿáíóúñ' /* " lowercase accented characters.*/
accG= 'αßΓπΣσµτΦΘΩδφε' /* " some upper/lower Greek letters*/
a=abcL || abcL ||accL ||accL || accG /* " char string of after letters.*/ b=abcL || abcU ||accL ||accU || accG || xrange() /* " char string of before " */ x= 'Çà åÅ çÇ êÉ ëÉ áà óâ ªæ ºç ¿è ⌐é ¬ê ½ë «î »ï ▒ñ ┤ô ╣ù ╗û ╝ü' /*list of 16-bit chars.*/ xs= words(x) /*num. " " " */ !.= /*define the original word instances. */
do #=0 while lines(fID)\==0; $=linein(fID) /*loop whilst there are lines in file. */
if pos('├', $)\==0 then do k=1 for xs; _=word(x, k) /*any 16-bit chars? */
$=changestr('├'left(_, 1), $, right(_, 1) ) /*convert.*/
end /*k*/
$=translate( $, a, b) /*remove superfluous blanks in the line*/
do while $\=; parse var $ z $ /*now, process each word in the $ list.*/
parse var z z1 2 zr -1 zL /*extract: first, middle, & last char.*/
if z1==q then do; z=zr; if z== then iterate; end /*starts with apostrophe? */
if zL==q then z=strip(left(z, length(z) - 1)) /*ends " " */
if z== then iterate /*if Z is now null, skip.*/
if @.z==0 then do; c=c+1; !.c=z; end /*bump word count; assign word to array*/
totW=totW + 1; @.z=@.z + 1 /*bump total words & count of the word.*/
end /*while*/
end /*#*/
say commas(totW) ' words found ('commas(c) "unique) in " commas(#),
' records read from file: ' fID; say
say right('word', 40) " " center(' rank ', 6) " count " /*display title for output*/ say right('════', 40) " " center('══════', 6) " ═══════" /* " title separator.*/ tops=1
do until otops==tops | tops>top /*process enough words to satisfy TOP.*/
WL=; mk=0; otops=tops /*initialize the word list (to a NULL).*/
do n=1 for c; z=!.n; k=@.z /*process the list of words in the file*/
if k==mk then WL=WL z /*handle cases of tied number of words.*/
if k> mk then do; mk=k; WL=z; end /*this word count is the current max. */
end /*n*/
wr=max( length(' rank '), length(top) ) /*find the maximum length of the rank #*/
do d=1 for words(WL); _=word(WL, d) /*process all words in the word list. */
if d==1 then w=max(10, length(@._) ) /*use length of the first number used. */
say right(@._, 40) right(commas(tops), wr) right(commas(@._), w)
@._= -1 /*nullify word count for next go around*/
end /*d*/ /* [↑] this allows a non-sorted list. */
tops=tops + words(WL) /*correctly handle any tied rankings.*/
end /*until*/
exit /*stick a fork in it, we're all done. */ /*──────────────────────────────────────────────────────────────────────────────────────*/ commas: procedure; parse arg _; n=_'.9'; #=123456789; b=verify(n, #, "M")
e=verify(n, #'0', , verify(n, #"0.", 'M') ) - 4
do j=e to b by -3; _=insert(',', _, j); end /*j*/; return _</lang>
- output when using the default inputs:
574,122 words found (23,414 unique) in 67,663 records read from file: les_mes.TXT
word rank count
════ ══════ ═══════
the 1 41,088
of 2 19,949
and 3 14,942
a 4 14,595
to 5 13,950
in 6 11,214
he 7 9,607
was 8 8,620
that 9 7,826
it 10 6,535
To see a list of the top 1,000 words that show (among other things) words like it's and other accented words, see the discussion page.
version 2
Inspired by version 1 and adapted for ooRexx. It ignores all characters other than a-z and A-Z (which are translated to a-z). <lang>/*REXX program reads and displays a count of words a file. Word case is ignored.*/ Call time 'R' abc='abcdefghijklmnopqrstuvwxyz' abcABC=abc||translate(abc) parse arg fID_top /*obtain optional arguments from the CL*/ Parse Var fid_top fid ',' top if fID== then fID= 'mis.TXT' /* Use default if not specified */ if top== then top= 10 /* Use default if not specified */ occ.=0 /* occurrences of word (stem) in file */ wn=0 Do While lines(fid)>0 /*loop whilst there are lines in file. */
line=linein(fID)
line=translate(line,abc||abc,abcABC||xrange('00'x,'ff'x)) /*use only lowercase letters*/
Do While line<>
Parse Var line word line /* take a word */
If occ.word=0 Then Do /* not yet in word list */
wn=wn+1
word.wn=word
End
occ.word=occ.word+1
End
End
Say 'We found' wn 'different words' say right('word',40) ' rank count ' /* header */ say right('----',40) '------ -------' /* separator. */ tops=0 Do Until tops>=top | tops>=wn /*process enough words to satisfy TOP.*/
max_occ=0
tl= /*initialize (possibly) a list of words*/
Do wi=1 To wn /*process the list of words in the file*/
word=word.wi /* take a word from the list */
Select
When occ.word>max_occ Then Do /* most occurrences so far */
tl=word /* candidate for output */
max_occ=occ.word /* current maximum occurrences */
End
When occ.word=max_occ Then Do /* tied */
tl=tl word /* add to output candidate */
End
Otherwise /* no candidate (yet) */
Nop
End
End
do d=1 for words(tl)
word=word(tl,d)
say right(word,40) right(tops+1,4) right(occ.word,8)
occ.word=0 /*nullify this word count for next time*/
End
tops=tops+words(tl) /*correctly handle the tied rankings. */
end
Say time('E') 'seconds elapsed'</lang>
- Output:
We found 22820 different words
word rank count
---- ------ -------
the 1 41089
of 2 19949
and 3 14942
a 4 14608
to 5 13951
in 6 11214
he 7 9648
was 8 8621
that 9 7924
it 10 6661
1.750000 seconds elapsed
Ring
<lang ring>
- project : Word count
fp = fopen("Miserables.txt","r") str = fread(fp, getFileSize(fp)) fclose(fp)
mis =substr(str, " ", nl) mis = lower(mis) mis = str2list(mis) count = list(len(mis)) ready = [] for n = 1 to len(mis)
flag = 0
for m = 1 to len(mis)
if mis[n] = mis[m] and n != m
for p = 1 to len(ready)
if m = ready[p]
flag = 1
ok
next
if flag = 0
count[n] = count[n] + 1
ok
ok
next
if flag = 0
add(ready, n)
ok
next for n = 1 to len(count)
for m = n + 1 to len(count)
if count[m] > count[n]
temp = count[n]
count[n] = count[m]
count[m] = temp
temp = mis[n]
mis[n] = mis[m]
mis[m] = temp
ok
next
next for n = 1 to 10
see mis[n] + " " + (count[n] + 1) + nl
next
func getFileSize fp
c_filestart = 0
c_fileend = 2
fseek(fp,0,c_fileend)
nfilesize = ftell(fp)
fseek(fp,0,c_filestart)
return nfilesize
func swap(a, b)
temp = a
a = b
b = temp
return [a, b]
</lang> Output:
the 41089 of 19949 and 14942 a 14608 to 13951 in 11214 he 9648 was 8621 that 7924 it 6661
Ruby
<lang ruby> class String
def wc
n = Hash.new(0)
downcase.scan(/[A-Za-zÀ-ÿ]+/) { |g| n[g] += 1 }
n.sort{|n,g| n[1]<=>g[1]}
end
end
open('135-0.txt') { |n| n.read.wc[-10,10].each{|n| puts n[0].to_s+"->"+n[1].to_s} } </lang>
- Output:
it->6661 that->7924 was->8621 he->9648 in->11214 to->13951 a->14596 and->14942 of->19949 the->41088
Tally and max_by
<lang ruby>RE = /alpha:+/ count = open("135-0.txt").read.downcase.scan(RE).tally.max_by(10, &:last) count.each{|ar| puts ar.join("->") } </lang>
- Output:
the->41092 of->19954 and->14943 a->14546 to->13953 in->11219 he->9649 was->8622 that->7924 it->6661
Rust
<lang Rust>use std::cmp::Reverse; use std::collections::HashMap; use std::fs::File; use std::io::{BufRead, BufReader};
extern crate regex; use regex::Regex;
fn word_count(file: File, n: usize) {
let word_regex = Regex::new("(?i)[a-z']+").unwrap();
let mut words = HashMap::new();
for line in BufReader::new(file).lines() {
word_regex
.find_iter(&line.expect("Read error"))
.map(|m| m.as_str())
.for_each(|word| {
*words.entry(word.to_lowercase()).or_insert(0) += 1;
});
}
let mut words: Vec<_> = words.iter().collect(); words.sort_unstable_by_key(|&(word, count)| (Reverse(count), word));
for (word, count) in words.iter().take(n) {
println!("{:8} {:>8}", word, count);
}
}
fn main() {
word_count(File::open("135-0.txt").expect("File open error"), 10)
}</lang>
- Output:
the 41083 of 19948 and 14941 a 14604 to 13951 in 11212 he 9604 was 8621 that 7824 it 6534
Scala
Featuring online remote file as input
- Output:
Best seen running in your browser Scastie (remote JVM). <lang Scala>import scala.io.Source
object WordCount extends App {
val url = "http://www.gutenberg.org/files/135/135-0.txt" val header = "Rank Word Frequency\n==== ======== ======"
def wordCnt =
Source.fromURL(url).getLines()
.filter(_.nonEmpty)
.flatMap(_.split("""\W+""")).toSeq
.groupBy(_.toLowerCase())
.mapValues(_.size).toSeq
.sortWith { case ((_, v0), (_, v1)) => v0 > v1 }
.take(10).zipWithIndex
println(header)
wordCnt.foreach {
case ((word, count), rank) => println(f"${rank + 1}%4d $word%-8s $count%6d")
}
println(s"\nSuccessfully completed without errors. [total ${scala.compat.Platform.currentTime - executionStart} ms]")
}</lang>
- Output:
Rank Word Frequency ==== ======== ====== 1 the 41036 2 of 19946 3 and 14940 4 a 14589 5 to 13939 6 in 11204 7 he 9645 8 was 8619 9 that 7922 10 it 6659 Successfully completed without errors. [total 4528 ms]
Seed7
The Seed7 program uses the function getHttp, to get the file 135-0.txt directly from Gutemberg. The library scanfile.s7i provides getSimpleSymbol, to get words from a fle. The words are converted to lower case, to assure that "The" and "the" are considered the same.
<lang seed7>$ include "seed7_05.s7i";
include "gethttp.s7i"; include "strifile.s7i"; include "scanfile.s7i"; include "chartype.s7i"; include "console.s7i";
const type: wordHash is hash [string] integer; const type: countHash is hash [integer] array string;
const proc: main is func
local
var file: inFile is STD_NULL;
var string: aWord is "";
var wordHash: numberOfWords is wordHash.EMPTY_HASH;
var countHash: countWords is countHash.EMPTY_HASH;
var array integer: countKeys is 0 times 0;
var integer: index is 0;
var integer: number is 0;
begin
OUT := STD_CONSOLE;
inFile := openStrifile(getHttp("www.gutenberg.org/files/135/135-0.txt"));
while hasNext(inFile) do
aWord := lower(getSimpleSymbol(inFile));
if aWord <> "" and aWord[1] in letter_char then
if aWord in numberOfWords then
incr(numberOfWords[aWord]);
else
numberOfWords @:= [aWord] 1;
end if;
end if;
end while;
countWords := flip(numberOfWords);
countKeys := sort(keys(countWords));
writeln("Word Frequency");
for index range length(countKeys) downto length(countKeys) - 9 do
number := countKeys[index];
for aWord range sort(countWords[number]) do
writeln(aWord rpad 8 <& number);
end for;
end for;
end func;</lang>
- Output:
Word Frequency the 41036 of 19946 and 14940 a 14589 to 13939 in 11204 he 9645 was 8619 that 7922 it 6659
Sidef
<lang ruby>var count = Hash() var file = File(ARGV[0] \\ '135-0.txt')
file.open_r.each { |line|
line.lc.scan(/[\pL]+/).each { |word|
count{word} := 0 ++
}
}
var top = count.sort_by {|_,v| v }.last(10).flip
top.each { |pair|
say "#{pair.key}\t-> #{pair.value}"
}</lang>
- Output:
the -> 41088 of -> 19949 and -> 14942 a -> 14596 to -> 13951 in -> 11214 he -> 9648 was -> 8621 that -> 7924 it -> 6661
Simula
<lang simula>COMMENT COMPILE WITH $ cim -m64 word-count.sim
BEGIN
COMMENT ----- CLASSES FOR GENERAL USE ;
! ABSTRACT HASH KEY TYPE ;
CLASS HASHKEY;
VIRTUAL:
PROCEDURE HASH IS
INTEGER PROCEDURE HASH;;
PROCEDURE EQUALTO IS
BOOLEAN PROCEDURE EQUALTO(K); REF(HASHKEY) K;;
BEGIN
END HASHKEY;
! ABSTRACT HASH VALUE TYPE ;
CLASS HASHVAL;
BEGIN
! THERE IS NOTHING REQUIRED FOR THE VALUE TYPE ;
END HASHVAL;
CLASS HASHMAP;
BEGIN
CLASS INNERHASHMAP(N); INTEGER N;
BEGIN
INTEGER PROCEDURE INDEX(K); REF(HASHKEY) K;
BEGIN
INTEGER I;
IF K == NONE THEN
ERROR("HASHMAP.INDEX: NONE IS NOT A VALID KEY");
I := MOD(K.HASH,N);
LOOP:
IF KEYTABLE(I) == NONE OR ELSE KEYTABLE(I).EQUALTO(K) THEN
INDEX := I
ELSE BEGIN
I := IF I+1 = N THEN 0 ELSE I+1;
GO TO LOOP;
END;
END INDEX;
! PUT SOMETHING IN ;
PROCEDURE PUT(K,V); REF(HASHKEY) K; REF(HASHVAL) V;
BEGIN
INTEGER I;
IF V == NONE THEN
ERROR("HASHMAP.PUT: NONE IS NOT A VALID VALUE");
I := INDEX(K);
IF KEYTABLE(I) == NONE THEN BEGIN
IF SIZE = N THEN
ERROR("HASHMAP.PUT: TABLE FILLED COMPLETELY");
KEYTABLE(I) :- K;
VALTABLE(I) :- V;
SIZE := SIZE+1;
END ELSE
VALTABLE(I) :- V;
END PUT;
! GET SOMETHING OUT ;
REF(HASHVAL) PROCEDURE GET(K); REF(HASHKEY) K;
BEGIN
INTEGER I;
IF K == NONE THEN
ERROR("HASHMAP.GET: NONE IS NOT A VALID KEY");
I := INDEX(K);
IF KEYTABLE(I) == NONE THEN
GET :- NONE ! ERROR("HASHMAP.GET: KEY NOT FOUND");
ELSE
GET :- VALTABLE(I);
END GET;
PROCEDURE CLEAR;
BEGIN
INTEGER I;
FOR I := 0 STEP 1 UNTIL N-1 DO BEGIN
KEYTABLE(I) :- NONE;
VALTABLE(I) :- NONE;
END;
SIZE := 0;
END CLEAR;
! DATA MEMBERS OF CLASS HASHMAP ;
REF(HASHKEY) ARRAY KEYTABLE(0:N-1);
REF(HASHVAL) ARRAY VALTABLE(0:N-1);
INTEGER SIZE;
END INNERHASHMAP;
PROCEDURE PUT(K,V); REF(HASHKEY) K; REF(HASHVAL) V;
BEGIN
IF IMAP.SIZE >= 0.75 * IMAP.N THEN
BEGIN
COMMENT RESIZE HASHMAP ;
REF(INNERHASHMAP) NEWIMAP;
REF(ITERATOR) IT;
NEWIMAP :- NEW INNERHASHMAP(2 * IMAP.N);
IT :- NEW ITERATOR(THIS HASHMAP);
WHILE IT.MORE DO
BEGIN
REF(HASHKEY) KEY;
KEY :- IT.NEXT;
NEWIMAP.PUT(KEY, IMAP.GET(KEY));
END;
IMAP.CLEAR;
IMAP :- NEWIMAP;
END;
IMAP.PUT(K, V);
END;
REF(HASHVAL) PROCEDURE GET(K); REF(HASHKEY) K;
GET :- IMAP.GET(K);
PROCEDURE CLEAR;
IMAP.CLEAR;
INTEGER PROCEDURE SIZE;
SIZE := IMAP.SIZE;
REF(INNERHASHMAP) IMAP;
IMAP :- NEW INNERHASHMAP(16); END HASHMAP;
CLASS ITERATOR(H); REF(HASHMAP) H;
BEGIN
INTEGER POS,KEYCOUNT;
BOOLEAN PROCEDURE MORE;
MORE := KEYCOUNT < H.SIZE;
REF(HASHKEY) PROCEDURE NEXT;
BEGIN
INSPECT H DO
INSPECT IMAP DO
BEGIN
WHILE KEYTABLE(POS) == NONE DO
POS := POS+1;
NEXT :- KEYTABLE(POS);
KEYCOUNT := KEYCOUNT+1;
POS := POS+1;
END;
END NEXT;
END ITERATOR;
COMMENT ----- PROBLEM SPECIFIC CLASSES ;
HASHKEY CLASS TEXTHASHKEY(T); VALUE T; TEXT T;
BEGIN
INTEGER PROCEDURE HASH;
BEGIN
INTEGER I;
T.SETPOS(1);
WHILE T.MORE DO
I := 31*I+RANK(T.GETCHAR);
HASH := I;
END HASH;
BOOLEAN PROCEDURE EQUALTO(K); REF(HASHKEY) K;
EQUALTO := T = K QUA TEXTHASHKEY.T;
END TEXTHASHKEY;
HASHVAL CLASS COUNTER;
BEGIN
INTEGER COUNT;
END COUNTER;
REF(INFILE) INF; REF(HASHMAP) MAP; REF(TEXTHASHKEY) KEY; REF(COUNTER) VAL; REF(ITERATOR) IT; TEXT LINE, WORD; INTEGER I, J, MAXCOUNT, LINENO; INTEGER ARRAY MAXCOUNTS(1:10); REF(TEXTHASHKEY) ARRAY MAXWORDS(1:10);
WORD :- BLANKS(1000); MAP :- NEW HASHMAP; COMMENT MAP WORDS TO COUNTERS ;
INF :- NEW INFILE("135-0.txt");
INF.OPEN(BLANKS(4096));
WHILE NOT INF.LASTITEM DO
BEGIN
BOOLEAN INWORD;
PROCEDURE SAVE;
BEGIN
IF WORD.POS > 1 THEN
BEGIN
KEY :- NEW TEXTHASHKEY(WORD.SUB(1, WORD.POS - 1));
VAL :- MAP.GET(KEY);
IF VAL == NONE THEN
BEGIN
VAL :- NEW COUNTER;
MAP.PUT(KEY, VAL);
END;
VAL.COUNT := VAL.COUNT + 1;
WORD := " ";
WORD.SETPOS(1);
END;
END SAVE;
LINENO := LINENO + 1;
LINE :- COPY(INF.IMAGE).STRIP; INF.INIMAGE;
COMMENT SEARCH WORDS IN LINE ;
COMMENT A WORD IS ANY SEQUENCE OF LETTERS ;
INWORD := FALSE;
LINE.SETPOS(1);
WHILE LINE.MORE DO
BEGIN
CHARACTER CH;
CH := LINE.GETCHAR;
IF CH >= 'a' AND CH <= 'z' THEN
CH := CHAR(RANK(CH) - RANK('a') + RANK('A'));
IF CH >= 'A' AND CH <= 'Z' THEN
BEGIN
IF NOT INWORD THEN
BEGIN
SAVE;
INWORD := TRUE;
END;
WORD.PUTCHAR(CH);
END ELSE
BEGIN
IF INWORD THEN
BEGIN
SAVE;
INWORD := FALSE;
END;
END;
END;
SAVE; COMMENT LAST WORD ;
END;
INF.CLOSE;
COMMENT FIND 10 MOST COMMON WORDS ;
IT :- NEW ITERATOR(MAP);
WHILE IT.MORE DO
BEGIN
KEY :- IT.NEXT;
VAL :- MAP.GET(KEY);
FOR I := 1 STEP 1 UNTIL 10 DO
BEGIN
IF VAL.COUNT >= MAXCOUNTS(I) THEN
BEGIN
FOR J := 10 STEP -1 UNTIL I + 1 DO
BEGIN
MAXCOUNTS(J) := MAXCOUNTS(J - 1);
MAXWORDS(J) :- MAXWORDS(J - 1);
END;
MAXCOUNTS(I) := VAL.COUNT;
MAXWORDS(I) :- KEY;
GO TO BREAK;
END;
END;
BREAK:
END;
COMMENT OUTPUT 10 MOST COMMON WORDS ;
FOR I := 1 STEP 1 UNTIL 10 DO
BEGIN
IF MAXWORDS(I) =/= NONE THEN
BEGIN
OUTINT(MAXCOUNTS(I), 10);
OUTTEXT(" ");
OUTTEXT(MAXWORDS(I) QUA TEXTHASHKEY.T);
OUTIMAGE;
END;
END;
END </lang>
- Output:
41089 THE
19949 OF
14942 AND
14608 A
13951 TO
11214 IN
9648 HE
8621 WAS
7924 THAT
6661 IT
6 garbage collection(s) in 0.2 seconds.
TMG
McIlroy's Unix TMG: <lang UnixTMG>/* Input format: N text */ /* Only lowercase letters can constitute a word in text. */ /* (c) 2020, Andrii Makukha, 2-clause BSD licence. */
progrm: readn/error
table(freq) table(chain) [firstword = ~0]
loop: not(!<<>>) output
| [j=777] batch/loop loop; /* Main loop */
/* To use less stack, divide input into batches. */ /* (Avoid interpreting entire input as a single "sentence".) */ batch: [j<=0?] succ
| word/skip [j--] skip batch;
skip: string(other); not: params(1) (any($1) fail | ()); readn: string(!<<0123456789>>) readint(n) skip; error: diag(( ={ <ERROR: input must start with a number> * } ));
/* Process a word */ word: smark any(letter) string(letter) scopy
locate/new
[freq[k]++] newmax;
locate: find(freq, k); new: enter(freq, k)
[freq[k] = 1] newmax
[firstword = firstword==~0 ? k : firstword]
enter(chain, i) [chain[i]=prevword] [prevword=k];
newmax: [max = max<freq[k] ? freq[k] : max];
/* Output logic */ output: [next=max] outmax: [max=next] [next=0] [max>0?] [j = prevword] cycle/outmax; cycle: [i = j] [k = freq[i]] [n>0?]
( [max==freq[i]?] parse(wn)
| [(freq[i]<max) & (next<freq[i])?] [next = freq[i]]
| ())
[i != firstword?] [j = chain[i]] cycle;
wn: getnam(freq, i) [k = freq[i]] decimal(k) [n--]
= { 2 < > 1 * };
/* Reads decimal integer */ readint: proc(n;i) ignore(<<>>) [n=0] inta int1: [n = n*12+i] inta\int1; inta: char(i) [i<72?] [(i =- 60)>=0?];
/* Variables */ prevword: 0; /* Head of the linked list */ firstword: 0; /* First word's index to know where to stop output */ k: 0; i: 0; j: 0; n: 0; /* Number of most frequent words to display */ max: 0; /* Current highest number of occurrences */ next: 0; /* Next highest number of occurrences */
/* Tables */ freq: 0; chain: 0;
/* Character classes */ letter: <<abcdefghijklmnopqrstuvwxyz>>; other: !<<abcdefghijklmnopqrstuvwxyz>>;</lang>
Unix TMG didn't have tolower builtin. Therefore, you would use it together with tr: <lang bash>cat file | tr A-Z a-z > file1; ./a.out file1</lang>
Additionally, because 1972 TMG only understood ASCII characters, you might want to strip down the diacritics (e.g., é → e): <lang bash>cat file | uni2ascii -B | tr A-Z a-z > file1; ./a.out file1</lang>
UNIX Shell
This is derived from Doug McIlroy's original 6-line note in the ACM article cited in the task. <lang bash>#!/bin/sh cat ${1} | tr -cs A-Za-z '\n' | tr A-Z a-z | sort | uniq -c | sort -rn | sed ${2}q</lang>
- Output:
$ ./wordcount.sh 135-0.txt 10 41089 the 19949 of 14942 and 14608 a 13951 to 11214 in 9648 he 8621 was 7924 that 6661 it
VBA
In order to use it, you have to adapt the PATHFILE Const.
<lang vb> Option Explicit
Private Const PATHFILE As String = "C:\HOME\VBA\ROSETTA"
Sub Main() Dim arr Dim Dict As Object Dim Book As String, temp As String Dim T# T = Timer
Book = ExtractTxt(PATHFILE & "\les miserables.txt")
temp = RemovePunctuation(Book)
temp = UCase(temp)
arr = Split(temp, " ")
Set Dict = CreateObject("Scripting.Dictionary")
FillDictionary Dict, arr
Erase arr
SortDictByFreq Dict, arr
DisplayTheTopMostUsedWords arr, 10
Debug.Print "Words different in this book : " & Dict.Count Debug.Print "-------------------------" Debug.Print "" Debug.Print "Optionally : " Debug.Print "Frequency of the word MISERABLE : " & DisplayFrequencyOf("MISERABLE", Dict) Debug.Print "Frequency of the word DISASTER : " & DisplayFrequencyOf("DISASTER", Dict) Debug.Print "Frequency of the word ROSETTA_CODE : " & DisplayFrequencyOf("ROSETTA_CODE", Dict) Debug.Print "-------------------------" Debug.Print "Execution Time : " & Format(Timer - T, "0.000") & " sec." End Sub
Private Function ExtractTxt(strFile As String) As String 'http://rosettacode.org/wiki/File_input/output#VBA Dim i As Integer
i = FreeFile
Open strFile For Input As #i
ExtractTxt = Input(LOF(1), #i)
Close #i
End Function
Private Function RemovePunctuation(strBook As String) As String Dim T, i As Integer, temp As String Const PUNCT As String = """,;:!?."
T = Split(StrConv(PUNCT, vbUnicode), Chr(0))
temp = strBook
For i = LBound(T) To UBound(T) - 1
temp = Replace(temp, T(i), " ")
Next
temp = Replace(temp, "--", " ")
temp = Replace(temp, "...", " ")
temp = Replace(temp, vbCrLf, " ")
RemovePunctuation = Replace(temp, " ", " ")
End Function
Private Sub FillDictionary(d As Object, a As Variant) Dim L As Long
For L = LBound(a) To UBound(a)
If a(L) <> "" Then _
d(a(L)) = d(a(L)) + 1
Next
End Sub
Private Sub SortDictByFreq(d As Object, myArr As Variant) Dim K Dim L As Long
ReDim myArr(1 To d.Count, 1 To 2)
For Each K In d.keys
L = L + 1
myArr(L, 1) = K
myArr(L, 2) = CLng(d(K))
Next
SortArray myArr, LBound(myArr), UBound(myArr), 2
End Sub
Private Sub SortArray(a, Le As Long, Ri As Long, Col As Long) Dim ref As Long, L As Long, r As Long, temp As Variant
ref = a((Le + Ri) \ 2, Col)
L = Le
r = Ri
Do
Do While a(L, Col) < ref
L = L + 1
Loop
Do While ref < a(r, Col)
r = r - 1
Loop
If L <= r Then
temp = a(L, 1)
a(L, 1) = a(r, 1)
a(r, 1) = temp
temp = a(L, 2)
a(L, 2) = a(r, 2)
a(r, 2) = temp
L = L + 1
r = r - 1
End If
Loop While L <= r
If L < Ri Then SortArray a, L, Ri, Col
If Le < r Then SortArray a, Le, r, Col
End Sub
Private Sub DisplayTheTopMostUsedWords(arr As Variant, Nb As Long) Dim L As Long, i As Integer
i = 1
Debug.Print "Rank Word Frequency"
Debug.Print "==== ======= ========="
For L = UBound(arr) To UBound(arr) - Nb + 1 Step -1
Debug.Print Left(CStr(i) & " ", 5) & Left(arr(L, 1) & " ", 8) & " " & Format(arr(L, 2), "0 000")
i = i + 1
Next
End Sub
Private Function DisplayFrequencyOf(Word As String, d As Object) As Long
If d.Exists(Word) Then _
DisplayFrequencyOf = d(Word)
End Function</lang>
- Output:
Words different in this book : 25884 ------------------------- Rank Word Frequency ==== ======= ========= 1 THE 40 831 2 OF 19 807 3 AND 14 860 4 A 14 453 5 TO 13 641 6 IN 11 133 7 HE 9 598 8 WAS 8 617 9 THAT 7 807 10 IT 6 517 Optionally : Frequency of the word MISERABLE : 35 Frequency of the word DISASTER : 12 Frequency of the word ROSETTA_CODE : 0 ------------------------- Execution Time : 7,785 sec.
zkl
<lang zkl>fname,count := vm.arglist; // grab cammand line args
// words may have leading or trailing "_", ie "the" and "_the"
File(fname).pump(Void,"toLower", // read the file line by line and hash words
RegExp("[a-z]+").pump.fp1(Dictionary().incV)) // line-->(word:count,..)
.toList().copy().sort(fcn(a,b){ b[1]<a[1] })[0,count.toInt()] // hash-->list .pump(String,Void.Xplode,"%s,%s\n".fmt).println();</lang>
- Output:
$ zkl bbb ~/Documents/Les\ Miserables.txt 10 the,41089 of,19949 and,14942 a,14608 to,13951 in,11214 he,9648 was,8621 that,7924 it,6661
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