Swift中处理概率和马尔科夫模型的简单工具。
ProbabilityVector：将项目映射到概率的映射（总和为1）ProbabilityMatrix：输入状态到输出状态的转换表MarkovModel：输入和输出状态相同的ProbabilityMatrix。可以生成链。HiddenMarkovModel：实现维特比算法以从观察序列中获得可能的隐藏状态序列在Podfile中
pod 'MarkovKit', '~> 0.6.0'
感谢DictionaryLiteralConvertible，初始化向量化很简单
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概率矩阵是将输入状态映射到描述可能的输出状态的概率向量的映射。同样，它们可以使用字典字面量轻松初始化
let matrix: ProbabilityMatrix<Int, String> = [
1: ["output1": 1]
2: ["output2": 0.5, "output3": 0.5]
]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"]要启动不带初始状态的链，必须给定初始概率
model.initialProbabilities = ["x": 1]
let newChain = model.generateChain(maximumLength: 5)
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"]