Made a few tweaks

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

diff --git a/slides/caesar-break.html b/slides/caesar-break.html
index 4d2ebfa0d01d556dbdd37599f3e4320f92fc4031..7a2fbf6d550cbd8e8dc90bcea3b694c0e4dcd293 100644 (file)
--- a/slides/caesar-break.html
+++ b/slides/caesar-break.html
@@ -128,11 +128,11 @@ Use this to predict the probability of each letter, and hence the probability of
 
 ---
 
-# An infinite number of monkeys
+.float-right[![right-aligned Typing monkey](typingmonkeylarge.jpg)]
 
-What is the probability that this string of letters is a sample of English?
+# Naive Bayes, or the bag of letters
 
-## Naive Bayes, or the bag of letters
+What is the probability that this string of letters is a sample of English?
 
 Ignore letter order, just treat each letter individually.
 
@@ -234,13 +234,20 @@ def unaccent(text):
 
 1. Read from `shakespeare.txt`, `sherlock-holmes.txt`, and `war-and-peace.txt`.
 2. Find the frequencies (`.update()`)
-3. Sort by count 
-4. Write counts to `count_1l.txt` (`'text{}\n'.format()`)
+3. Sort by count (read the docs...)
+4. Write counts to `count_1l.txt` 
+```python
+with open('count_1l.txt', 'w') as f:
+    for each letter...:
+        f.write('text\t{}\n'.format(count))
+```
 
 ---
 
 # 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): `$$ \hat{\mathbf{x}} = \frac{\mathbf{x}}{\| \mathbf{x} \|} = \frac{\mathbf{x}}{ \mathbf{x}_1 + \mathbf{x}_2 + \mathbf{x}_3 + \dots }$$`
@@ -257,6 +264,8 @@ def unaccent(text):
 
 # Breaking caesar ciphers
 
+New file: `cipherbreak.py`
+
 ## Remember the basic idea
 
 ```