I'm trying to implement the gaussian mixture classification (GMC) implementation from scratch using python. The training dataset consists of 10 folds each of size $\left[100x64\right]$. In addition, each fold has it own train label of size $\left[100x1\right]$ with classes $C=\left\{ 5,6\right\} $. I'm stuck on the parameter estimations $\mu_{i}$, which according to the book "Bishop - Pattern Recognition And Machine Learning - Springer 2006", should be $\mu_{1},\mu_{2}$ but I have been told that both should be a vector of $\left[1x64\right]$ I first need to estimate optimal parameters before maximizing likelihood but I don't know-how.


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