* Read a file into a string
```python
open()
-read()
+.read()
```
* Count them
```python
import collections
```
+Create the `language_models.py` file for this.
+
---
# Canonical forms
# Accents
```python
->>> caesar_encipher_letter('é', 1)
+>>> 'é' in string.ascii_letters
+>>> 'e' in string.ascii_letters
```
-What does it produce?
-
-What should it produce?
## Unicode, combining codepoints, and normal forms
Text encodings will bite you when you least expect it.
+- **é** : LATIN SMALL LETTER E WITH ACUTE (U+00E9)
+
+- **e** + ** ́** : LATIN SMALL LETTER E (U+0065) + COMBINING ACUTE ACCENT (U+0301)
+
* urlencoding is the other pain point.
---
---
+# Find the frequencies of letters in English
+
+1. Read from `shakespeare.txt`, `sherlock-holmes.txt`, and `war-and-peace.txt`.
+2. Find the frequencies
+3. Sort by count (`sorted(, key=)` ; `.items()`, `.keys()`, `.values()`, `.get()`)
+4. Write counts to `count_1l.txt`
+
+---
+
# Vector distances
.float-right[![right-aligned Vector subtraction](vector-subtraction.svg)]
* L<sub>∞</sub> norm:
`\(\|\mathbf{a} - \mathbf{b}\| = \max_i{(\mathbf{a}_i - \mathbf{b}_i)} \)`
-neither of which will be that useful.)
+neither of which will be that useful here, but they keep cropping up.)
---
# Normalisation of vectors
## Computing is an empircal science
+Let's do some experiments to find the best solution!
+
+---
+
+## Step 1: get **some** codebreaking working
+
+Let's start with the letter probability norm, because it's easy.
+
+## Step 2: build some other scoring functions
+
+We also need a way of passing the different functions to the keyfinding function.
+
+## Step 3: find the best scoring function
+
+Try them all on random ciphertexts, see which one works best.
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