Trying to implement Gaussian Mixture model implementation in python using the Optical Recognition of Handwritten Digits Data Set which consists of 10 training folds each of size [100𝑥64], and 10 training labels each of size [100𝑥1]. The dataset also has a testing dataset and label set of size [110𝑥64], and [110𝑥1]. Don't know how to estimate optimal parameters with this dataset configuration, especially the class conditional probability
I don't even know if my approach is correct. I've searched on either GitHub and medium for similar examples. Any help or guidance would be appreciated.