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Latest 0.6.1 https://github.com/sadawi/MarkovKit MIT ios 8.0, requires ARC Sam Williams

Some simple tools for working with probabilities and Markov models in Swift.

• `ProbabilityVector`: A mapping of items to probabilities (summing to 1)
• `ProbabilityMatrix`: A transition table mapping input states to output states
• `MarkovModel`: A `ProbabilityMatrix` where the input and output states are the same. Can generate chains.
• `HiddenMarkovModel`: Implementation of the Viterbi algorithm for obtaining a likely sequence of hidden states from a sequence of observations

Installation

``pod 'MarkovKit', '~> 0.6.0'``

Probability Vectors

Thanks to `DictionaryLiteralConvertible`, it’s simple to initialize a vector:

``````let vector: ProbabilityVector<String> = ["red": 0.25, "blue": 0.5, "green": 0.25]
let item = vector.randomItem()  // should return "blue" about 50% of the time``````

Probability Matrices

A probability matrix is a mapping of input states to probability vectors describing possible output states. Again, they can be initialized easily with dictionary literals:

``````let matrix: ProbabilityMatrix<Int, String> = [
1: ["output1": 1]
2: ["output2": 0.5, "output3": 0.5]
]``````

Markov Chains

``````let model: MarkovModel<String> = [
"x": ["y": 1],
"y": ["x": 1],
]
let chain = model.generateChain(from: "x", maximumLength: 5)
// always returns ["x", "y", "x", "y", "x"]``````

To start a chain without an initial state, initial probabilities must be given:

``````model.initialProbabilities = ["x": 1]
let newChain = model.generateChain(maximumLength: 5)
``````

Hidden Markov Models

``````let states = ["healthy", "sick"]
let initialProbabilities:ProbabilityVector<String> = ["healthy": 0.6, "sick": 0.4]

let transitionProbabilities:MarkovModel<String> = [
"healthy":  ["healthy": 0.7, "sick": 0.3],
"sick":     ["healthy": 0.4, "sick": 0.6],
]

// Note that the emission type isn't necessarily the same as the state type.
let emissionProbabilities: ProbabilityMatrix<String, String> = [
"healthy":  ["normal": 0.5, "cold": 0.4, "dizzy": 0.1],
"sick":     ["normal": 0.1, "cold": 0.3, "dizzy": 0.6],
]

let hmm = HiddenMarkovModel(states:states,
initialProbabilities: initialProbabilities,
transitionProbabilities: transitionProbabilities,
emissionProbabilities: emissionProbabilities)

let observations = ["normal", "cold", "dizzy"]
let prediction = hmm.calculateStates(observations)

// ["healthy", "healthy", "sick"]``````

Latest podspec

```{
"name": "MarkovKit",
"version": "0.6.1",
"summary": "Tools for working with Markov models",
"authors": {
"Sam Williams": "[email protected]"
},
"source": {