### Install WordSegment

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/index.md

Install the WordSegment library using pip. This is the first step to using the library in your Python projects.

```bash
$ pip install wordsegment
```

--------------------------------

### Install wordsegment with pip

Source: https://github.com/grantjenks/python-wordsegment/blob/master/README.rst

Install the wordsegment library using pip. This is the recommended method for adding the library to your project.

```bash
pip install wordsegment
```

--------------------------------

### Run wordsegment as a Server Process

Source: https://github.com/grantjenks/python-wordsegment/blob/master/README.rst

Execute the wordsegment module with unbuffered output for use in a server-like process. This example demonstrates writing to stdin and reading from stdout.

```python
import subprocess as sp
wordsegment = sp.Popen(
        ['python', '-um', 'wordsegment'],
        stdin=sp.PIPE, stdout=sp.PIPE, stderr=sp.STDOUT)
wordsegment.stdin.write('thisisatest\n')
wordsegment.stdout.readline()
wordsegment.stdin.write('workswithotherlanguages\n')
wordsegment.stdout.readline()
wordsegment.stdin.close()
wordsegment.wait()  # Process exit code.
```

--------------------------------

### Server Process with Unbuffered Output

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/index.md

Run WordSegment as a server process using Python's -u option for unbuffered output. This example demonstrates piping input and reading output from the subprocess.

```python
>>> import subprocess as sp
>>> wordsegment = sp.Popen(
        ['python', '-um', 'wordsegment'],
        stdin=sp.PIPE, stdout=sp.PIPE, stderr=sp.STDOUT)
>>> wordsegment.stdin.write('thisisatest\n')
>>> wordsegment.stdout.readline()
'this is a test\n'
>>> wordsegment.stdin.write('workswithotherlanguages\n')
>>> wordsegment.stdout.readline()
'works with other languages\n'
>>> wordsegment.stdin.close()
>>> wordsegment.wait()  # Process exit code.
0
```

--------------------------------

### Get CPU and Python Version

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/python-load-dict-fast-from-file.md

Retrieves and prints the CPU brand string and the Python version. Useful for performance benchmarking context.

```python
import subprocess
print subprocess.check_output([
    '/usr/sbin/sysctl',
    '-n',
    'machdep.cpu.brand_string'
])

import sys
print sys.version
```

--------------------------------

### Access Specific Bigram Counts

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/index.md

Retrieve the count for specific bigrams from the BIGRAMS dictionary. The '<s>' prefix indicates the start of a bigram.

```python
>>> ws.BIGRAMS['<s> where']
15419048.0
>>> ws.BIGRAMS['<s> what']
11779290.0
```

--------------------------------

### View Sample Dictionary Entries

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/using-a-different-corpus.md

Displays the first few entries from the UNIGRAMS and BIGRAMS dictionaries to show their structure (word/phrase and count).

```python
print wordsegment.UNIGRAMS.items()[:3]
print wordsegment.BIGRAMS.items()[:2]
```

--------------------------------

### Import izip for Dictionary Creation

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/python-load-dict-fast-from-file.md

Imports the izip function from itertools, aliased as zip, for efficient pairing of keys and values.

```python
from itertools import izip as zip
```

--------------------------------

### Load Dictionary from Text and Binary Files

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/python-load-dict-fast-from-file.md

Reads words from a text file and counts from a binary file, then constructs a dictionary. This method is optimized for speed by using str.split for words and the array module for binary counts.

```python
with open('words.txt', 'rb') as lines, open('counts.bin', 'rb') as counts:
    words = lines.read().split('\n')
    values = array('d')
    values.fromfile(counts, 333333)
    dict(zip(words, values))
```

--------------------------------

### Construct Dictionary using __setitem__

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/python-load-dict-fast-from-file.md

Benchmarks the time taken to construct a dictionary by repeatedly calling __setitem__.

```python
# %%timeit
with open('../wordsegment_data/unigrams.txt') as reader:
    lines = (line.split('\t') for line in reader)
    result = dict()
    for word, number in lines:
        result[word] = float(number)
```

--------------------------------

### Access Documentation in Interpreter

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/index.md

Access the built-in help documentation for the wordsegment library directly within the Python interpreter.

```python
>>> import wordsegment
>>> help(wordsegment)
```

--------------------------------

### Load WordSegment

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/using-a-different-corpus.md

Initializes the WordSegment library. This should be called before accessing or modifying its dictionaries.

```python
import wordsegment
wordsegment.load()
```

--------------------------------

### wordsegment.load()

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/api.md

Loads unigram and bigram counts from disk.

```APIDOC
## wordsegment.load()

### Description
Load unigram and bigram counts from disk.

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
None

### Response
#### Success Response (200)
None

### Response Example
None
```

--------------------------------

### Fast Dictionary Loading from Files in Python

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/python-load-dict-fast-from-file.ipynb

Measures the time taken to load a dictionary from two files: 'words.txt' for keys and 'counts.bin' for values. Uses `str.split` for words and the `array` module for binary counts. Best used when optimizing for read performance from disk.

```python
from array import array

%%timeit
with open('words.txt', 'rb') as lines, open('counts.bin', 'rb') as counts:
    words = lines.read().split('\n')
    values = array('d')
    values.fromfile(counts, 333333)
    dict(zip(words, values))
```

--------------------------------

### Time Dictionary Construction from File

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/python-load-dict-fast-from-file.ipynb

Measures the time taken to read a file, split lines, and construct a dictionary. This serves as a baseline for performance comparisons.

```python
%%timeit
with open('../wordsegment_data/unigrams.txt') as reader:
    lines = (line.split('\t') for line in reader)
    dict((word, float(number)) for word, number in lines)
```

--------------------------------

### Read All Lines from File

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/python-load-dict-fast-from-file.md

Reads all lines from the unigrams.txt file into a list. This is a benchmark for file reading performance.

```python
# %%timeit
with open('../wordsegment_data/unigrams.txt') as reader:
    lines = [line for line in reader]
```

--------------------------------

### Construct Dictionary using Dict Comprehension

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/python-load-dict-fast-from-file.md

Benchmarks the time taken to construct a dictionary using a dictionary comprehension. This is a more modern Python syntax.

```python
# %%timeit
with open('../wordsegment_data/unigrams.txt') as reader:
    lines = (line.split('\t') for line in reader)
    {word: float(number) for word, number in lines}
```

--------------------------------

### Segment Text using Command Line Interface

Source: https://github.com/grantjenks/python-wordsegment/blob/master/README.rst

Use the wordsegment module from the command line to segment text piped from stdin to stdout. Input and output can be redirected to files.

```bash
echo thisisatest | python -m wordsegment
```

--------------------------------

### Command-Line Batch Processing

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/index.md

Use the WordSegment command-line interface for batch processing text files. Input is read from stdin and output is written to stdout by default.

```bash
$ echo thisisatest | python -m wordsegment
this is a test
```

--------------------------------

### Import WordSegment Library

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/using-a-different-corpus.ipynb

Imports the necessary wordsegment library to begin manipulating its components for custom corpus usage.

```python
import wordsegment
```

--------------------------------

### Convert Strings to Floats

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/python-load-dict-fast-from-file.md

Benchmarks the time taken to convert the count strings to floating-point numbers.

```python
# %%timeit
with open('../wordsegment_data/unigrams.txt') as reader:
    lines = (line.split('\t') for line in reader)
    for word, number in lines:
        float(number)
```

--------------------------------

### Inspect Default Dictionaries

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/using-a-different-corpus.md

Prints the types of the UNIGRAMS and BIGRAMS dictionaries to confirm they are loaded correctly.

```python
print type(wordsegment.UNIGRAMS), type(wordsegment.BIGRAMS)
```

--------------------------------

### Load and Segment Text in Python

Source: https://github.com/grantjenks/python-wordsegment/blob/master/README.rst

Load the wordsegment data and segment a given phrase into a list of words. The `load` function should be called once before segmentation.

```python
from wordsegment import load, segment
load()
segment('thisisatest')
```

--------------------------------

### Read First Line of Unigram File

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/python-load-dict-fast-from-file.md

Reads and prints the first line from the unigrams.txt file to show its format.

```python
with open('../wordsegment_data/unigrams.txt', 'r') as reader:
    print repr(reader.readline())
```

--------------------------------

### Split Lines by Tab Character

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/python-load-dict-fast-from-file.md

Benchmarks the time taken to split each line in the unigrams.txt file by the tab character.

```python
# %%timeit
with open('../wordsegment_data/unigrams.txt') as reader:
    lines = (line.split('\t') for line in reader)
    for word, number in lines:
        pass
```

--------------------------------

### wordsegment.UNIGRAMS

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/api.md

A mapping of unigram counts, loaded from 'wordsegment/unigrams.txt'.

```APIDOC
## wordsegment.UNIGRAMS

### Description
Mapping of (unigram, count) pairs.
Loaded from the file ‘wordsegment/unigrams.txt’.

### Parameters
None

### Request Example
None

### Response
#### Success Response (200)
- **UNIGRAMS** (dict) - A dictionary where keys are unigrams and values are their counts.

### Response Example
None
```

--------------------------------

### Read File and Split by Newline

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/python-load-dict-fast-from-file.md

Reads the entire file content and splits it into lines based on the newline character. This is a benchmark for reading and splitting performance.

```python
# %%timeit
with open('../wordsegment_data/unigrams.txt') as reader:
    lines = reader.read().split('\n')
```

--------------------------------

### Convert Unigrams to Binary Format

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/python-load-dict-fast-from-file.md

Converts the unigrams.txt file into two separate files: 'words.txt' (ASCII words) and 'counts.bin' (binary counts) for potentially faster loading.

```python
with open('../wordsegment_data/unigrams.txt') as reader:
    pairs = [line.split('\t') for line in reader]
    words = [pair[0] for pair in pairs]
    counts = [float(pair[1]) for pair in pairs]

    with open('words.txt', 'wb') as writer:
        writer.write('\n'.join(words))

    from array import array
    values = array('d')
    values.fromlist(counts)
    with open('counts.bin', 'wb') as writer:
        values.tofile(writer)
```

--------------------------------

### Explore Bigram Counts in Python

Source: https://github.com/grantjenks/python-wordsegment/blob/master/README.rst

Load the wordsegment data and explore the bigram counts using `heapq.nlargest`. Bigrams are phrases of two words joined by a space.

```python
import heapq
from pprint import pprint
from operator import itemgetter
ws.load()
pprint(heapq.nlargest(10, ws.BIGRAMS.items(), itemgetter(1)))
```

--------------------------------

### Time Converting Strings to Floats

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/python-load-dict-fast-from-file.ipynb

Measures the time taken to convert the number strings obtained after splitting lines into floating-point numbers. This highlights another performance bottleneck.

```python
%%timeit
with open('../wordsegment_data/unigrams.txt') as reader:
    lines = (line.split('\t') for line in reader)
    for word, number in lines:
        float(number)
```

--------------------------------

### Time Reading File and Splitting Lines

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/python-load-dict-fast-from-file.ipynb

Measures the time taken to read the entire file content and then split it into lines. This method is often faster than reading line by line.

```python
%%timeit
with open('../wordsegment_data/unigrams.txt') as reader:
    lines = reader.read().split('\n')
```

--------------------------------

### Time Reading All Lines from File

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/python-load-dict-fast-from-file.ipynb

Measures the time taken to read all lines from the unigrams.txt file into a list. This is a faster alternative to line-by-line reading for certain use cases.

```python
%%timeit
with open('../wordsegment_data/unigrams.txt') as reader:
    lines = [line for line in reader]
```

--------------------------------

### Explore Bigram Counts

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/index.md

Examine the bigram counts using heapq.nlargest to find the most frequent bigrams. Bigrams are represented as strings with words joined by a space.

```python
>>> import heapq
>>> from pprint import pprint
>>> from operator import itemgetter
>>> pprint(heapq.nlargest(10, ws.BIGRAMS.items(), itemgetter(1)))
[('of the', 2766332391.0),
 ('in the', 1628795324.0),
 ('to the', 1139248999.0),
 ('on the', 800328815.0),
 ('for the', 692874802.0),
 ('and the', 629726893.0),
 ('to be', 505148997.0),
 ('is a', 476718990.0),
 ('with the', 461331348.0),
 ('from the', 428303219.0)]
```

--------------------------------

### Inspect Default Counts

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/using-a-different-corpus.ipynb

Displays the types and initial entries of the unigram and bigram count dictionaries within the wordsegment library. This helps understand the structure before modification.

```python
print type(wordsegment.unigram_counts), type(wordsegment.bigram_counts)
```

```python
print wordsegment.unigram_counts.items()[:3]
print wordsegment.bigram_counts.items()[:3]
```

--------------------------------

### Access Unigram Counts in Python

Source: https://github.com/grantjenks/python-wordsegment/blob/master/README.rst

Load the wordsegment data and access the unigram counts dictionary. This allows exploration of word frequencies.

```python
import wordsegment as ws
ws.load()
ws.UNIGRAMS['the']
ws.UNIGRAMS['gray']
ws.UNIGRAMS['grey']
```

--------------------------------

### Load Unigrams into Dictionary

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/python-load-dict-fast-from-file.md

Loads unigram data from a file into a Python dictionary, mapping words to their counts. This is the standard method used by the wordsegment module.

```python
# %%timeit
with open('../wordsegment_data/unigrams.txt') as reader:
    lines = (line.split('\t') for line in reader)
    dict((word, float(number)) for word, number in lines)
```

--------------------------------

### Download Corpus Text

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/using-a-different-corpus.ipynb

Fetches the text content of a Gutenberg ebook using the requests library. This is the first step in preparing a custom corpus for word segmentation.

```python
import requests

response = requests.get('https://www.gutenberg.org/ebooks/1342.txt.utf-8')

text = response.text

print len(text)
```

--------------------------------

### Load and Segment Text

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/index.md

Load the necessary data and segment a given phrase into a list of its constituent parts. The load function should be called once before segmenting text.

```python
>>> from wordsegment import load, segment
>>> load()
>>> segment('thisisatest')
['this', 'is', 'a', 'test']
```

--------------------------------

### Explore Unigram Counts

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/index.md

Access and inspect the unigram counts stored in the UNIGRAMS dictionary. This allows exploration of word frequencies.

```python
>>> import wordsegment as ws
>>> ws.load()
>>> ws.UNIGRAMS['the']
23135851162.0
>>> ws.UNIGRAMS['gray']
21424658.0
>>> ws.UNIGRAMS['grey']
18276942.0
```

--------------------------------

### Time Splitting Lines by Tab

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/python-load-dict-fast-from-file.ipynb

Measures the time taken specifically for splitting each line of the file by the tab character. This isolates a performance bottleneck.

```python
%%timeit
with open('../wordsegment_data/unigrams.txt') as reader:
    lines = (line.split('\t') for line in reader)
    for word, number in lines:
        pass
```

--------------------------------

### wordsegment.divide(text)

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/api.md

Generates (prefix, suffix) pairs from the input text, where the length of the prefix does not exceed a specified limit.

```APIDOC
## wordsegment.divide(text)

### Description
Yield (prefix, suffix) pairs from text with len(prefix) not exceeding limit.

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
None

### Response
#### Success Response (200)
- **pairs** (iterator) - An iterator yielding (prefix, suffix) pairs.

### Response Example
None
```

--------------------------------

### wordsegment.BIGRAMS

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/api.md

A mapping of bigram counts, where bigram keys are joined by a space, loaded from 'wordsegment/bigrams.txt'.

```APIDOC
## wordsegment.BIGRAMS

### Description
Mapping of (bigram, count) pairs.
Bigram keys are joined by a space.
Loaded from the file ‘wordsegment/bigrams.txt’.

### Parameters
None

### Request Example
None

### Response
#### Success Response (200)
- **BIGRAMS** (dict) - A dictionary where keys are bigrams (space-joined) and values are their counts.

### Response Example
None
```

--------------------------------

### wordsegment.segment(text)

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/api.md

Returns a list of words representing the best segmentation of the input text.

```APIDOC
## wordsegment.segment(text)

### Description
Return a list of words that is the best segmenation of text.

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
None

### Response
#### Success Response (200)
- **words** (list) - A list of the segmented words.

### Response Example
None
```

--------------------------------

### Time Dictionary Construction via Iteration

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/python-load-dict-fast-from-file.ipynb

Measures the time taken to construct a dictionary by iterating through lines, splitting them, converting to float, and assigning to dictionary keys. This approach avoids tuple creation.

```python
%%timeit
with open('../wordsegment_data/unigrams.txt') as reader:
    lines = (line.split('\t') for line in reader)
    result = dict()
    for word, number in lines:
        result[word] = float(number)
```

--------------------------------

### Build Custom Count Dictionaries

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/using-a-different-corpus.ipynb

Updates the wordsegment library's unigram and bigram count dictionaries using tokenized text from a custom corpus. It also defines a helper function `pairs` to generate bigrams.

```python
from collections import Counter

wordsegment.unigram_counts = Counter(tokenize(text))

def pairs(iterable):
    iterator = iter(iterable)
    values = [next(iterator)]
    for value in iterator:
        values.append(value)
        yield ' '.join(values)
        del values[0]

wordsegment.bigram_counts = Counter(pairs(tokenize(text)))
```

--------------------------------

### wordsegment.score(word, prev=None)

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/api.md

Scores a word based on its context, optionally considering the previous word.

```APIDOC
## wordsegment.score(word, prev=None)

### Description
Score a word in the context of the previous word, prev.

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
None

### Response
#### Success Response (200)
- **score** (float) - The score of the word.

### Response Example
None
```

--------------------------------

### Clean and Segment Text in Python

Source: https://github.com/grantjenks/python-wordsegment/blob/master/README.rst

Clean input text to a canonical form before segmenting. The `clean` function removes punctuation and lowercases text.

```python
from wordsegment import clean
clean('She said, "Python rocks!"')
segment('She said, "Python rocks!"')
```

--------------------------------

### Clean and Segment Text

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/index.md

Utilize the clean function to transform input text into a canonical form before segmentation. This removes punctuation and converts text to lowercase.

```python
>>> from wordsegment import clean
>>> clean('She said, "Python rocks!"')
'shesaidpythonrocks'
>>> segment('She said, "Python rocks!"')
['she', 'said', 'python', 'rocks']
```

--------------------------------

### Tokenize Text

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/using-a-different-corpus.ipynb

Defines a function to tokenize input text into words using regular expressions. This is a utility function used to prepare text for building count dictionaries.

```python
import re

def tokenize(text):
    pattern = re.compile('[a-zA-Z]+')
    return (match.group(0) for match in pattern.finditer(text))

print list(tokenize("Wait, what did you say?"))
```

--------------------------------

### wordsegment.clean(text)

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/api.md

Cleans the input text by converting it to lower case and removing non-alphanumeric characters.

```APIDOC
## wordsegment.clean(text)

### Description
Return text lower-cased with non-alphanumeric characters removed.

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
None

### Response
#### Success Response (200)
- **text** (string) - The cleaned text.

### Response Example
None
```

--------------------------------

### Segment Text with Custom Corpus

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/using-a-different-corpus.ipynb

Performs word segmentation on a given string using the updated wordsegment library, which now utilizes the custom corpus and modified cleaning function.

```python
wordsegment.segment('wantofawife')
```

--------------------------------

### Replace Cleaning Function

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/using-a-different-corpus.md

Replaces the default `clean` function within the `_segmenter` module with an identity function. This prevents input sanitation, allowing segmentation of text with capitals and punctuation.

```python
from wordsegment import _segmenter

def identity(value):
    return value

_segmenter.clean = identity
```

--------------------------------

### wordsegment.isegment(text)

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/api.md

Returns an iterator of words representing the best segmentation of the input text.

```APIDOC
## wordsegment.isegment(text)

### Description
Return iterator of words that is the best segmenation of text.

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
None

### Response
#### Success Response (200)
- **words** (iterator) - An iterator yielding the segmented words.

### Response Example
None
```

--------------------------------

### Update WordSegment Dictionaries

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/using-a-different-corpus.md

Clears the existing UNIGRAMS and BIGRAMS dictionaries and populates them with counts derived from the custom corpus text. Requires the `tokenize` function and `pairs` helper.

```python
from collections import Counter

wordsegment.UNIGRAMS.clear()
wordsegment.UNIGRAMS.update(Counter(tokenize(text)))

def pairs(iterable):
    iterator = iter(iterable)
    values = [next(iterator)]
    for value in iterator:
        values.append(value)
        yield ' '.join(values)
        del values[0]

wordsegment.BIGRAMS.clear()
wordsegment.BIGRAMS.update(Counter(pairs(tokenize(text))))
```

--------------------------------

### Update Total Unigram Count

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/using-a-different-corpus.md

Sets the `_segmenter.total` variable to the sum of all unigram counts from the custom corpus. This is necessary for accurate probability calculations during segmentation.

```python
_segmenter.total = float(sum(wordsegment.UNIGRAMS.values()))
```

--------------------------------

### Replace Cleaning Function

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/using-a-different-corpus.ipynb

Replaces the default text cleaning function in wordsegment with an identity function. This prevents any pre-processing of input strings before segmentation, useful when the corpus has specific formatting.

```python
def identity(value):
    return value

wordsegment.clean = identity
```

--------------------------------

### Update Total Count

Source: https://github.com/grantjenks/python-wordsegment/blob/master/docs/using-a-different-corpus.ipynb

Adjusts the `wordsegment.TOTAL` variable to reflect the sum of all unigram counts from the custom corpus. This is important for accurate segmentation probabilities when using a new corpus.

```python
wordsegment.TOTAL = float(sum(wordsegment.unigram_counts.values()))
```

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