Merge conflict resolved

[cipher-training.git] / slides / caesar-break.html

diff --git a/slides/caesar-break.html b/slides/caesar-break.html
index 7a2fbf6d550cbd8e8dc90bcea3b694c0e4dcd293..81a8396f6d7d8d67b841d5925e17c2a545e4405f 100644 (file)
--- a/slides/caesar-break.html
+++ b/slides/caesar-break.html
@@ -112,6 +112,8 @@ How do we define "closeness"?
 
 ## Abstraction: frequency of letter counts
 
+.float-right[![right-aligned Letter frequencies](letter-frequency-treemap.png)]
+
 Letter | Count
 -------|------
 a | 489107
@@ -146,7 +148,7 @@ Letter      | i       | f       | m       | m       | p       | ifmmp
 ------------|---------|---------|---------|---------|---------|-------
 Probability | 0.06723 | 0.02159 | 0.02748 | 0.02748 | 0.01607 | 1.76244520 × 10<sup>-8</sup>
 
-(Implmentation issue: this can often underflow, so get in the habit of rephrasing it as `\( \sum_i \log p_i \)`)
+(Implmentation issue: this can often underflow, so we rephrase it as `\( \sum_i \log p_i \)`)
 
 Letter      | h       | e       | l       | l       | o       | hello
 ------------|---------|---------|---------|---------|---------|-------
@@ -207,6 +209,8 @@ Text encodings will bite you when you least expect it.
 # Five minutes on StackOverflow later...
 
 ```python
+import unicodedata
+
 def unaccent(text):
     """Remove all accents from letters. 
     It does this by converting the unicode string to decomposed compatibility
@@ -246,8 +250,6 @@ with open('count_1l.txt', 'w') as f:
 
 # Reading letter probabilities
 
-New file: `language_models.py`
-
 1. Load the file `count_1l.txt` into a dict, with letters as keys.
 
 2. Normalise the counts (components of vector sum to 1): `$$ \hat{\mathbf{x}} = \frac{\mathbf{x}}{\| \mathbf{x} \|} = \frac{\mathbf{x}}{ \mathbf{x}_1 + \mathbf{x}_2 + \mathbf{x}_3 + \dots }$$`