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+<article id="content">
+<header>
+<h1 class="title">Module <code>szyfrow.support.language_models</code></h1>
+</header>
+<section id="section-intro">
+<p>Descriptive models of a natural language (in this case, English).</p>
+<p>The functions <code><a title="szyfrow.support.language_models.Pwords" href="#szyfrow.support.language_models.Pwords">Pwords()</a></code>, <code><a title="szyfrow.support.language_models.Pletters" href="#szyfrow.support.language_models.Pletters">Pletters()</a></code>, <code><a title="szyfrow.support.language_models.Pbigrams" href="#szyfrow.support.language_models.Pbigrams">Pbigrams()</a></code>, and <code><a title="szyfrow.support.language_models.Ptrigrams" href="#szyfrow.support.language_models.Ptrigrams">Ptrigrams()</a></code> return the
+log probability of a section of text.</p>
+<p>If you want to use a different language, replace the data files in
+<a href="../language_model_files/index.html"><code>szyfrow/language_model_files</code></a>.</p>
+<ul>
+<li><code>count_1l.txt</code>: counts of single letters</li>
+<li><code>count_2l.txt</code>: counts of pairs letters, bigrams</li>
+<li><code>count_3l.txt</code>: counts of triples of letters, triagrams</li>
+<li><code>words.txt</code>: a dictionary of words, used for keyword-based cipher breaking.
+These words should only contain characters cointained in
+<code>string.ascii_letters</code>.</li>
+</ul>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">"""Descriptive models of a natural language (in this case, English).
+
+The functions `Pwords`, `Pletters`, `Pbigrams`, and `Ptrigrams` return the
+log probability of a section of text.
+
+If you want to use a different language, replace the data files in
+[`szyfrow/language_model_files`](../language_model_files/index.html).
+
+* `count_1l.txt`: counts of single letters
+* `count_2l.txt`: counts of pairs letters, bigrams
+* `count_3l.txt`: counts of triples of letters, triagrams
+* `words.txt`: a dictionary of words, used for keyword-based cipher breaking.
+ These words should only contain characters cointained in
+ `string.ascii_letters`.
+
+"""
+
+import string
+import random
+import collections
+import itertools
+from math import log10
+import os
+import importlib.resources as pkg_resources
+
+import szyfrow.support.norms
+from szyfrow.support.utilities import sanitise, deduplicate
+from szyfrow import language_model_files
+
+
+def datafile(name, sep='\t'):
+ """Read key,value pairs from file.
+ """
+ with pkg_resources.open_text(language_model_files, name) as f:
+ # with open(p name), 'r') as f:
+ for line in f:
+ splits = line.split(sep)
+ yield [splits[0], int(splits[1])]
+
+english_counts = collections.Counter(dict(datafile('count_1l.txt')))
+"""Counts of single letters in English."""
+normalised_english_counts = szyfrow.support.norms.normalise(english_counts)
+"""Normalised counts of single letters in English (the sum of all counts
+adds to 1)."""
+
+english_bigram_counts = collections.Counter(dict(datafile('count_2l.txt')))
+"""Counts of letter bigrams in English."""
+normalised_english_bigram_counts = szyfrow.support.norms.normalise(english_bigram_counts)
+"""Normalised counts of letter bigrams in English (the sum of all counts
+adds to 1)."""
+
+english_trigram_counts = collections.Counter(dict(datafile('count_3l.txt')))
+"""Counts of letter trigrams in English."""
+normalised_english_trigram_counts = szyfrow.support.norms.normalise(english_trigram_counts)
+"""Normalised counts of letter trigrams in English (the sum of all counts
+adds to 1)."""
+
+keywords = []
+"""A sample list of keywords, to act as a dictionary for
+dictionary-based cipher breaking attempts."""
+with pkg_resources.open_text(language_model_files, 'words.txt') as f:
+ keywords = [line.rstrip() for line in f]
+
+
+def transpositions_of(keyword):
+ """Finds the transpostions given by a keyword. For instance, the keyword
+ 'clever' rearranges to 'celrv', so the first column (0) stays first, the
+ second column (1) moves to third, the third column (2) moves to second,
+ and so on.
+
+ If passed a tuple, assume it's already a transposition and just return it.
+
+ >>> transpositions_of('clever')
+ (0, 2, 1, 4, 3)
+ >>> transpositions_of('fred')
+ (3, 2, 0, 1)
+ >>> transpositions_of((3, 2, 0, 1))
+ (3, 2, 0, 1)
+ """
+ if isinstance(keyword, tuple):
+ return keyword
+ else:
+ key = deduplicate(keyword)
+ transpositions = tuple(key.index(l) for l in sorted(key))
+ return transpositions
+
+transpositions = collections.defaultdict(list)
+"""A sample dict of transpositions, to act as a dictionary for
+dictionary-based cipher breaking attempts. Each key is a transposition,
+each value is a list of words that give that transposition."""
+for word in keywords:
+ transpositions[transpositions_of(word)] += [word]
+
+
+def weighted_choice(d):
+ """Generate random item from a dictionary of item counts
+ """
+ delems, dweights = list(zip(*d.items()))
+ return random.choices(delems, dweights)[0]
+ # target = random.uniform(0, sum(d.values()))
+ # cuml = 0.0
+ # for (l, p) in d.items():
+ # cuml += p
+ # if cuml > target:
+ # return l
+ # return None
+
+def random_english_letter():
+ """Generate a random letter based on English letter counts
+ """
+ return weighted_choice(normalised_english_counts)
+
+
+def ngrams(text, n):
+ """Returns all n-grams of a text
+
+ >>> ngrams(sanitise('the quick brown fox'), 2) # doctest: +NORMALIZE_WHITESPACE
+ ['th', 'he', 'eq', 'qu', 'ui', 'ic', 'ck', 'kb', 'br', 'ro', 'ow', 'wn',
+ 'nf', 'fo', 'ox']
+ >>> ngrams(sanitise('the quick brown fox'), 4) # doctest: +NORMALIZE_WHITESPACE
+ ['theq', 'hequ', 'equi', 'quic', 'uick', 'ickb', 'ckbr', 'kbro', 'brow',
+ 'rown', 'ownf', 'wnfo', 'nfox']
+ """
+ return [text[i:i+n] for i in range(len(text)-n+1)]
+
+
+class Pdist(dict):
+ """A probability distribution estimated from counts in datafile.
+ Values are stored and returned as log probabilities.
+ """
+ def __init__(self, data=[], estimate_of_missing=None):
+ data1, data2 = itertools.tee(data)
+ self.total = sum([d[1] for d in data1])
+ for key, count in data2:
+ self[key] = log10(count / self.total)
+ self.estimate_of_missing = estimate_of_missing or (lambda k, N: 1./N)
+ def __missing__(self, key):
+ return self.estimate_of_missing(key, self.total)
+
+def log_probability_of_unknown_word(key, N):
+ """Estimate the probability of an unknown word.
+ """
+ return -log10(N * 10**((len(key) - 2) * 1.4))
+
+Pw = Pdist(datafile('count_1w.txt'), log_probability_of_unknown_word)
+"""A [Pdist](#szyfrow.support.language_models.Pdist) holding log probabilities
+of words. Unknown words have their probability estimated by
+[log_probability_of_unknown_word](#szyfrow.support.language_models.log_probability_of_unknown_word)"""
+Pl = Pdist(datafile('count_1l.txt'), lambda _k, _N: 0)
+"""A [Pdist](#szyfrow.support.language_models.Pdist) holding log probabilities
+of single letters. Unknown words have their probability estimated as zero."""
+P2l = Pdist(datafile('count_2l.txt'), lambda _k, _N: 0)
+"""A [Pdist](#szyfrow.support.language_models.Pdist) holding log probabilities
+of letter bigrams. Unknown words have their probability estimated as zero."""
+P3l = Pdist(datafile('count_3l.txt'), lambda _k, _N: 0)
+"""A [Pdist](#szyfrow.support.language_models.Pdist) holding log probabilities
+of letter trigrams. Unknown words have their probability estimated as zero."""
+
+def Pwords(words):
+ """The Naive Bayes log probability of a sequence of words.
+ """
+ return sum(Pw[w.lower()] for w in words)
+
+def Pletters(letters):
+ """The Naive Bayes log probability of a sequence of letters.
+ """
+ return sum(Pl[l.lower()] for l in letters)
+
+def Pbigrams(letters):
+ """The Naive Bayes log probability of the bigrams formed from a sequence
+ of letters.
+ """
+ return sum(P2l[p] for p in ngrams(letters, 2))
+
+def Ptrigrams(letters):
+ """The Naive Bayes log probability of the trigrams formed from a sequence
+ of letters.
+ """
+ return sum(P3l[p] for p in ngrams(letters, 3))
+
+
+def cosine_distance_score(text):
+ """Finds the dissimilarity of a text to English, using the cosine distance
+ of the frequency distribution.
+
+ >>> cosine_distance_score('abcabc') # doctest: +ELLIPSIS
+ 0.73771...
+ """
+ # return szyfrow.support.norms.cosine_distance(english_counts,
+ # collections.Counter(sanitise(text)))
+ return 1 - szyfrow.support.norms.cosine_similarity(english_counts,
+ collections.Counter(sanitise(text)))
+
+
+if __name__ == "__main__":
+ import doctest
+ doctest.testmod()</code></pre>
+</details>
+</section>
+<section>
+</section>
+<section>
+<h2 class="section-title" id="header-variables">Global variables</h2>
+<dl>
+<dt id="szyfrow.support.language_models.P2l"><code class="name">var <span class="ident">P2l</span></code></dt>
+<dd>
+<div class="desc"><p>A <a href="#szyfrow.support.language_models.Pdist">Pdist</a> holding log probabilities
+of letter bigrams. Unknown words have their probability estimated as zero.</p></div>
+</dd>
+<dt id="szyfrow.support.language_models.P3l"><code class="name">var <span class="ident">P3l</span></code></dt>
+<dd>
+<div class="desc"><p>A <a href="#szyfrow.support.language_models.Pdist">Pdist</a> holding log probabilities
+of letter trigrams. Unknown words have their probability estimated as zero.</p></div>
+</dd>
+<dt id="szyfrow.support.language_models.Pl"><code class="name">var <span class="ident">Pl</span></code></dt>
+<dd>
+<div class="desc"><p>A <a href="#szyfrow.support.language_models.Pdist">Pdist</a> holding log probabilities
+of single letters. Unknown words have their probability estimated as zero.</p></div>
+</dd>
+<dt id="szyfrow.support.language_models.Pw"><code class="name">var <span class="ident">Pw</span></code></dt>
+<dd>
+<div class="desc"><p>A <a href="#szyfrow.support.language_models.Pdist">Pdist</a> holding log probabilities
+of words. Unknown words have their probability estimated by
+<a href="#szyfrow.support.language_models.log_probability_of_unknown_word">log_probability_of_unknown_word</a></p></div>
+</dd>
+<dt id="szyfrow.support.language_models.english_bigram_counts"><code class="name">var <span class="ident">english_bigram_counts</span></code></dt>
+<dd>
+<div class="desc"><p>Counts of letter bigrams in English.</p></div>
+</dd>
+<dt id="szyfrow.support.language_models.english_counts"><code class="name">var <span class="ident">english_counts</span></code></dt>
+<dd>
+<div class="desc"><p>Counts of single letters in English.</p></div>
+</dd>
+<dt id="szyfrow.support.language_models.english_trigram_counts"><code class="name">var <span class="ident">english_trigram_counts</span></code></dt>
+<dd>
+<div class="desc"><p>Counts of letter trigrams in English.</p></div>
+</dd>
+<dt id="szyfrow.support.language_models.keywords"><code class="name">var <span class="ident">keywords</span></code></dt>
+<dd>
+<div class="desc"><p>A sample list of keywords, to act as a dictionary for
+dictionary-based cipher breaking attempts.</p></div>
+</dd>
+<dt id="szyfrow.support.language_models.normalised_english_bigram_counts"><code class="name">var <span class="ident">normalised_english_bigram_counts</span></code></dt>
+<dd>
+<div class="desc"><p>Normalised counts of letter bigrams in English (the sum of all counts
+adds to 1).</p></div>
+</dd>
+<dt id="szyfrow.support.language_models.normalised_english_counts"><code class="name">var <span class="ident">normalised_english_counts</span></code></dt>
+<dd>
+<div class="desc"><p>Normalised counts of single letters in English (the sum of all counts
+adds to 1).</p></div>
+</dd>
+<dt id="szyfrow.support.language_models.normalised_english_trigram_counts"><code class="name">var <span class="ident">normalised_english_trigram_counts</span></code></dt>
+<dd>
+<div class="desc"><p>Normalised counts of letter trigrams in English (the sum of all counts
+adds to 1).</p></div>
+</dd>
+<dt id="szyfrow.support.language_models.transpositions"><code class="name">var <span class="ident">transpositions</span></code></dt>
+<dd>
+<div class="desc"><p>A sample dict of transpositions, to act as a dictionary for
+dictionary-based cipher breaking attempts. Each key is a transposition,
+each value is a list of words that give that transposition.</p></div>
+</dd>
+</dl>
+</section>
+<section>
+<h2 class="section-title" id="header-functions">Functions</h2>
+<dl>
+<dt id="szyfrow.support.language_models.Pbigrams"><code class="name flex">
+<span>def <span class="ident">Pbigrams</span></span>(<span>letters)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>The Naive Bayes log probability of the bigrams formed from a sequence
+of letters.</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def Pbigrams(letters):
+ """The Naive Bayes log probability of the bigrams formed from a sequence
+ of letters.
+ """
+ return sum(P2l[p] for p in ngrams(letters, 2))</code></pre>
+</details>
+</dd>
+<dt id="szyfrow.support.language_models.Pletters"><code class="name flex">
+<span>def <span class="ident">Pletters</span></span>(<span>letters)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>The Naive Bayes log probability of a sequence of letters.</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def Pletters(letters):
+ """The Naive Bayes log probability of a sequence of letters.
+ """
+ return sum(Pl[l.lower()] for l in letters)</code></pre>
+</details>
+</dd>
+<dt id="szyfrow.support.language_models.Ptrigrams"><code class="name flex">
+<span>def <span class="ident">Ptrigrams</span></span>(<span>letters)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>The Naive Bayes log probability of the trigrams formed from a sequence
+of letters.</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def Ptrigrams(letters):
+ """The Naive Bayes log probability of the trigrams formed from a sequence
+ of letters.
+ """
+ return sum(P3l[p] for p in ngrams(letters, 3))</code></pre>
+</details>
+</dd>
+<dt id="szyfrow.support.language_models.Pwords"><code class="name flex">
+<span>def <span class="ident">Pwords</span></span>(<span>words)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>The Naive Bayes log probability of a sequence of words.</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def Pwords(words):
+ """The Naive Bayes log probability of a sequence of words.
+ """
+ return sum(Pw[w.lower()] for w in words)</code></pre>
+</details>
+</dd>
+<dt id="szyfrow.support.language_models.cosine_distance_score"><code class="name flex">
+<span>def <span class="ident">cosine_distance_score</span></span>(<span>text)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Finds the dissimilarity of a text to English, using the cosine distance
+of the frequency distribution.</p>
+<pre><code class="language-python-repl">>>> cosine_distance_score('abcabc') # doctest: +ELLIPSIS
+0.73771...
+</code></pre></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def cosine_distance_score(text):
+ """Finds the dissimilarity of a text to English, using the cosine distance
+ of the frequency distribution.
+
+ >>> cosine_distance_score('abcabc') # doctest: +ELLIPSIS
+ 0.73771...
+ """
+ # return szyfrow.support.norms.cosine_distance(english_counts,
+ # collections.Counter(sanitise(text)))
+ return 1 - szyfrow.support.norms.cosine_similarity(english_counts,
+ collections.Counter(sanitise(text)))</code></pre>
+</details>
+</dd>
+<dt id="szyfrow.support.language_models.datafile"><code class="name flex">
+<span>def <span class="ident">datafile</span></span>(<span>name, sep='\t')</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Read key,value pairs from file.</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def datafile(name, sep='\t'):
+ """Read key,value pairs from file.
+ """
+ with pkg_resources.open_text(language_model_files, name) as f:
+ # with open(p name), 'r') as f:
+ for line in f:
+ splits = line.split(sep)
+ yield [splits[0], int(splits[1])]</code></pre>
+</details>
+</dd>
+<dt id="szyfrow.support.language_models.log_probability_of_unknown_word"><code class="name flex">
+<span>def <span class="ident">log_probability_of_unknown_word</span></span>(<span>key, N)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Estimate the probability of an unknown word.</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def log_probability_of_unknown_word(key, N):
+ """Estimate the probability of an unknown word.
+ """
+ return -log10(N * 10**((len(key) - 2) * 1.4))</code></pre>
+</details>
+</dd>
+<dt id="szyfrow.support.language_models.ngrams"><code class="name flex">
+<span>def <span class="ident">ngrams</span></span>(<span>text, n)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Returns all n-grams of a text</p>
+<pre><code class="language-python-repl">>>> ngrams(sanitise('the quick brown fox'), 2) # doctest: +NORMALIZE_WHITESPACE
+['th', 'he', 'eq', 'qu', 'ui', 'ic', 'ck', 'kb', 'br', 'ro', 'ow', 'wn',
+ 'nf', 'fo', 'ox']
+>>> ngrams(sanitise('the quick brown fox'), 4) # doctest: +NORMALIZE_WHITESPACE
+['theq', 'hequ', 'equi', 'quic', 'uick', 'ickb', 'ckbr', 'kbro', 'brow',
+ 'rown', 'ownf', 'wnfo', 'nfox']
+</code></pre></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def ngrams(text, n):
+ """Returns all n-grams of a text
+
+ >>> ngrams(sanitise('the quick brown fox'), 2) # doctest: +NORMALIZE_WHITESPACE
+ ['th', 'he', 'eq', 'qu', 'ui', 'ic', 'ck', 'kb', 'br', 'ro', 'ow', 'wn',
+ 'nf', 'fo', 'ox']
+ >>> ngrams(sanitise('the quick brown fox'), 4) # doctest: +NORMALIZE_WHITESPACE
+ ['theq', 'hequ', 'equi', 'quic', 'uick', 'ickb', 'ckbr', 'kbro', 'brow',
+ 'rown', 'ownf', 'wnfo', 'nfox']
+ """
+ return [text[i:i+n] for i in range(len(text)-n+1)]</code></pre>
+</details>
+</dd>
+<dt id="szyfrow.support.language_models.random_english_letter"><code class="name flex">
+<span>def <span class="ident">random_english_letter</span></span>(<span>)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Generate a random letter based on English letter counts</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def random_english_letter():
+ """Generate a random letter based on English letter counts
+ """
+ return weighted_choice(normalised_english_counts)</code></pre>
+</details>
+</dd>
+<dt id="szyfrow.support.language_models.transpositions_of"><code class="name flex">
+<span>def <span class="ident">transpositions_of</span></span>(<span>keyword)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Finds the transpostions given by a keyword. For instance, the keyword
+'clever' rearranges to 'celrv', so the first column (0) stays first, the
+second column (1) moves to third, the third column (2) moves to second,
+and so on.</p>
+<p>If passed a tuple, assume it's already a transposition and just return it.</p>
+<pre><code class="language-python-repl">>>> transpositions_of('clever')
+(0, 2, 1, 4, 3)
+>>> transpositions_of('fred')
+(3, 2, 0, 1)
+>>> transpositions_of((3, 2, 0, 1))
+(3, 2, 0, 1)
+</code></pre></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def transpositions_of(keyword):
+ """Finds the transpostions given by a keyword. For instance, the keyword
+ 'clever' rearranges to 'celrv', so the first column (0) stays first, the
+ second column (1) moves to third, the third column (2) moves to second,
+ and so on.
+
+ If passed a tuple, assume it's already a transposition and just return it.
+
+ >>> transpositions_of('clever')
+ (0, 2, 1, 4, 3)
+ >>> transpositions_of('fred')
+ (3, 2, 0, 1)
+ >>> transpositions_of((3, 2, 0, 1))
+ (3, 2, 0, 1)
+ """
+ if isinstance(keyword, tuple):
+ return keyword
+ else:
+ key = deduplicate(keyword)
+ transpositions = tuple(key.index(l) for l in sorted(key))
+ return transpositions</code></pre>
+</details>
+</dd>
+<dt id="szyfrow.support.language_models.weighted_choice"><code class="name flex">
+<span>def <span class="ident">weighted_choice</span></span>(<span>d)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Generate random item from a dictionary of item counts</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def weighted_choice(d):
+ """Generate random item from a dictionary of item counts
+ """
+ delems, dweights = list(zip(*d.items()))
+ return random.choices(delems, dweights)[0]
+ # target = random.uniform(0, sum(d.values()))
+ # cuml = 0.0
+ # for (l, p) in d.items():
+ # cuml += p
+ # if cuml > target:
+ # return l
+ # return None</code></pre>
+</details>
+</dd>
+</dl>
+</section>
+<section>
+<h2 class="section-title" id="header-classes">Classes</h2>
+<dl>
+<dt id="szyfrow.support.language_models.Pdist"><code class="flex name class">
+<span>class <span class="ident">Pdist</span></span>
+<span>(</span><span>data=[], estimate_of_missing=None)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>A probability distribution estimated from counts in datafile.
+Values are stored and returned as log probabilities.</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">class Pdist(dict):
+ """A probability distribution estimated from counts in datafile.
+ Values are stored and returned as log probabilities.
+ """
+ def __init__(self, data=[], estimate_of_missing=None):
+ data1, data2 = itertools.tee(data)
+ self.total = sum([d[1] for d in data1])
+ for key, count in data2:
+ self[key] = log10(count / self.total)
+ self.estimate_of_missing = estimate_of_missing or (lambda k, N: 1./N)
+ def __missing__(self, key):
+ return self.estimate_of_missing(key, self.total)</code></pre>
+</details>
+<h3>Ancestors</h3>
+<ul class="hlist">
+<li>builtins.dict</li>
+</ul>
+</dd>
+</dl>
+</section>
+</article>
+<nav id="sidebar">
+<h1>Index</h1>
+<div class="toc">
+<ul></ul>
+</div>
+<ul id="index">
+<li><h3>Super-module</h3>
+<ul>
+<li><code><a title="szyfrow.support" href="index.html">szyfrow.support</a></code></li>
+</ul>
+</li>
+<li><h3><a href="#header-variables">Global variables</a></h3>
+<ul class="">
+<li><code><a title="szyfrow.support.language_models.P2l" href="#szyfrow.support.language_models.P2l">P2l</a></code></li>
+<li><code><a title="szyfrow.support.language_models.P3l" href="#szyfrow.support.language_models.P3l">P3l</a></code></li>
+<li><code><a title="szyfrow.support.language_models.Pl" href="#szyfrow.support.language_models.Pl">Pl</a></code></li>
+<li><code><a title="szyfrow.support.language_models.Pw" href="#szyfrow.support.language_models.Pw">Pw</a></code></li>
+<li><code><a title="szyfrow.support.language_models.english_bigram_counts" href="#szyfrow.support.language_models.english_bigram_counts">english_bigram_counts</a></code></li>
+<li><code><a title="szyfrow.support.language_models.english_counts" href="#szyfrow.support.language_models.english_counts">english_counts</a></code></li>
+<li><code><a title="szyfrow.support.language_models.english_trigram_counts" href="#szyfrow.support.language_models.english_trigram_counts">english_trigram_counts</a></code></li>
+<li><code><a title="szyfrow.support.language_models.keywords" href="#szyfrow.support.language_models.keywords">keywords</a></code></li>
+<li><code><a title="szyfrow.support.language_models.normalised_english_bigram_counts" href="#szyfrow.support.language_models.normalised_english_bigram_counts">normalised_english_bigram_counts</a></code></li>
+<li><code><a title="szyfrow.support.language_models.normalised_english_counts" href="#szyfrow.support.language_models.normalised_english_counts">normalised_english_counts</a></code></li>
+<li><code><a title="szyfrow.support.language_models.normalised_english_trigram_counts" href="#szyfrow.support.language_models.normalised_english_trigram_counts">normalised_english_trigram_counts</a></code></li>
+<li><code><a title="szyfrow.support.language_models.transpositions" href="#szyfrow.support.language_models.transpositions">transpositions</a></code></li>
+</ul>
+</li>
+<li><h3><a href="#header-functions">Functions</a></h3>
+<ul class="">
+<li><code><a title="szyfrow.support.language_models.Pbigrams" href="#szyfrow.support.language_models.Pbigrams">Pbigrams</a></code></li>
+<li><code><a title="szyfrow.support.language_models.Pletters" href="#szyfrow.support.language_models.Pletters">Pletters</a></code></li>
+<li><code><a title="szyfrow.support.language_models.Ptrigrams" href="#szyfrow.support.language_models.Ptrigrams">Ptrigrams</a></code></li>
+<li><code><a title="szyfrow.support.language_models.Pwords" href="#szyfrow.support.language_models.Pwords">Pwords</a></code></li>
+<li><code><a title="szyfrow.support.language_models.cosine_distance_score" href="#szyfrow.support.language_models.cosine_distance_score">cosine_distance_score</a></code></li>
+<li><code><a title="szyfrow.support.language_models.datafile" href="#szyfrow.support.language_models.datafile">datafile</a></code></li>
+<li><code><a title="szyfrow.support.language_models.log_probability_of_unknown_word" href="#szyfrow.support.language_models.log_probability_of_unknown_word">log_probability_of_unknown_word</a></code></li>
+<li><code><a title="szyfrow.support.language_models.ngrams" href="#szyfrow.support.language_models.ngrams">ngrams</a></code></li>
+<li><code><a title="szyfrow.support.language_models.random_english_letter" href="#szyfrow.support.language_models.random_english_letter">random_english_letter</a></code></li>
+<li><code><a title="szyfrow.support.language_models.transpositions_of" href="#szyfrow.support.language_models.transpositions_of">transpositions_of</a></code></li>
+<li><code><a title="szyfrow.support.language_models.weighted_choice" href="#szyfrow.support.language_models.weighted_choice">weighted_choice</a></code></li>
+</ul>
+</li>
+<li><h3><a href="#header-classes">Classes</a></h3>
+<ul>
+<li>
+<h4><code><a title="szyfrow.support.language_models.Pdist" href="#szyfrow.support.language_models.Pdist">Pdist</a></code></h4>
+</li>
+</ul>
+</li>
+</ul>
+</nav>
+</main>
+<footer id="footer">
+<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.9.2</a>.</p>
+</footer>
+</body>
+</html>
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projects / szyfrow.git / blobdiff