# Markov Chains

Below is a demonstration of my implementation of auto-completion using Markov Chains.

Though it is written in Rust and compiled to WebAssembly, it is not particularly efficient. To find out why, continue down the page to my detailed explanation of the implementation.

## Controls

You may use either "Choose Word" or your right arrow key [→] to let the system choose the next word. Alternatively, you can tap any of the [Possible Next Words] to do so yourself.

• another
• hand
• time
• her
• that
• the

# Explanation

Markov chains, named after their inventor, Andrey Markov, are often used to model sequences of probabilistic events. That is, systems that cannot be modeled deterministically.

## Example

Alice is at the grocery store. For every hour she is there, she has a 70% chance of leaving and going to the planetarium. Conversely, she has a 30% chance of staying.

If Alice is already at the planetarium, she has a 10% chance of leaving and going to the grocery store and a 90% chance of staying.

We can represent these probabilities as a table, where each column belongs to a start location, and each row belongs to a end location:

Start at Grocery Store Start at Planetarium
End at Grocery Store 30% 10%
End at Planetarium 70% 90%

If we already know Alice's location for sure, we can simply perform table lookups to predict her most likely next move.

For example, we know she is at the grocery store right now. So by looking at row 2, column 1, we can be 70% confident she will be at the planetarium next hour.

However, this doesn't work if we aren't sure of her location, or we want to predict more than one hour in advance. How do we predict her next move if we aren't certain of her current location?

In the latter case, we might express her current location as another table.

Location % Alice Present
Grocery Store 25%
Planetarium 75%

How do we estimate Alice's location in this new plane of possibility? In particular, how likely will Alice be at the Planetarium next hour?

Since there is a 25% probability Alice is at the grocery store, we multiply that with the probility of her transitioning to the Planetarium: $25\% * 75\%$. Next, we add the result with the probability of being at the Planetarium multiplied with the probability of her staying: $75\% * 90\%$.

In full, $25\% * 75\% + 75\% * 90\% = 85\%$.

To see the probabilities as a table:

Next Location Calculation % Alice Present
Grocery Store $25\% * 30\% + 75\% * 10\%$ 15%
Planetarium $25\% * 70\% + 75\% * 90\%$ 85%

The keen-eyed among you may have noticed that these operations look a lot like matrix multiplication.

Instead of a table, we may represent these possible transitions as a matrix $T$, and the Alice's current location as a vector $\vec{s}$.

$T = \begin{bmatrix} 0.3 & 0.1 \\ 0.7 & 0.9 \end{bmatrix}$
$\vec{s} = \begin{bmatrix} .25 \\ .75 \\ \end{bmatrix}$

Note: The location of each element remains the same as the table, even if we aren't explicitly labeling the rows and columns.

Finding the next state matrix becomes as easy as multiplying the current location vector $\vec{s}$ by $T$. To find further hours in the future, we do it more than once. For example, to estimate three hours in the future: $TTT\vec{s}$. We can condense this with an exponent: $T^3\vec{s}$ or generalize it to $n$ hours with: $T^n\vec{s}$.

## Application to Text-Completion

The principles above can be applied to a variety of probabilistic situations. Most relavant to this particular webpage, is text completion.

We want to estimate the most likely next word to the user. Given the last word, what are the most likely next words? First, we need a dictionary.

### The Dictionary

It is trivial to build a dictionary from sample text. For the purposes of the explanation, we are going to start with an arbitrary dictionary.

Index Word
0 orange
1 fruit
2 passion
3 cheese
4 not
5 is

### Building the Transition Matrix

To build our transition matrix, we need to count all the transitions that occur between possible words in our dictionary.

In the interest of performance, my implementation converts the dictionary into a HashMap<String, usize>.

Next, I go through the training text and match each word to it's index in the dictionary, effectively transforming the String into a Vec<usize>.

For example, the phrase, "passion fruit is not orange, cheese is orange," becomes, [ 2, 1, 5, 4, 0, 3, 5, 0 ].

Next, the implementation iterates through each element in this vector, counting each transition. The counts are stored in another HashMap in the interest of performance, but is eventually converted into a matrix $C$. Each row is the output word's index, and the column is the input word's index.

For example, the transition "fruit" (index 1) -> "is" (index 5) occurs exactly once, so we record 1 in column 1, row 5.

$C = \begin{bmatrix} 0 & 0 & 0 & 0 & 1 & 1 \\ 0 & 0 & 1 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 \\ 1 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 1 \\ 0 & 1 & 0 & 1 & 0 & 0 \end{bmatrix}$

Not a very interesting matrix, is it?

Each element needs to be converted into a probability. Take the sum of each column:

$\begin{bmatrix} 1 & 1 & 1 & 1 & 1 & 2 \end{bmatrix}$

Create a diagonal matrix $D$ composed of $\frac{1}{\text{column sum}}$

$C = \begin{bmatrix} 0 & 0 & 0 & 0 & 1 & 0.5 \\ 0 & 0 & 1 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 \\ 1 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0.5 \\ 0 & 1 & 0 & 1 & 0 & 0 \end{bmatrix}$

To finalize our Markov (a.k.a. transition) matrix $M$, we simply perform:

$M = DC$

### Using the transition matrix

There are two possible situations: the user is in the process of typing, or they have finished their last word.

The latter is the easiest to implement.

Scan the user's text, and isolate the last word. Perform a lookup on the wordlist to identify it's index. Create a new vector containing 0s except for that index, which should contain a 1.

For example, if the last word was 'is',

$\vec{s} = \begin{bmatrix} 0 & 0 & 0 & 0 & 0 & 1 \end{bmatrix}$

Run it through our transition matrix:

$M\vec{s} = \begin{bmatrix} 0.5 & 0 & 0 & 0 & 0.5 & 0 \end{bmatrix}$

Meaning the most probable next choices are at indices 0 and 4, which correspond to "orange" and "not" respectively.

This is great for autocomplete. We can simply list the most probable options to the user.

Create a square diagonal matrix $R$ with a side length equal to the length of $\vec{s}$. Fill the diagonal elements with random numbers between $0$ and $1$. Then choose the word whose index corresponds with the highest value of $R\vec{s}$