build K-Means model with unsupervised learning k: number of clusters max_iter: max iterations p: decide the distance, parameter of minkowski distance default: 2, i.e., Euclidean distance init_clusters: you can give a init clusters default: it will random choose k clusters from the samples early_stop: decide the tolerance error of the distance between the last two iterations default: 1e-3 seed: see
(k: usize, max_iter: usize, p: Option<usize>, init_clusters: Option<Vec<Vec<f32>>>, early_stop: Option<f32>, seed: Option<usize>, features: &Vec<Vec<f32>>)
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