I'm trying to calculate Fisher's score for a feature from a data set. The fisher score for the $j$-th feature:
$$F(x^{j}) = \frac{\sum^c_{k=1} n_k(\mu^j_k - \mu^j)^2{}} {\sum^c_{k=1}n_k(\sigma^j_k)^2}$$ where $c$ is the number of different classes, $n_k$ is the size of the $k$-th class, $\mu^j_k$ and $\sigma^j_k$ are the mean and standard deviation of the $k$-th class (respectively), corresponding to the $j$-th feature, $\mu^j$ is the mean deviation of the whole data set corresponding to the $j$-th feature.
It says that $\mu^{j}_{k}$ is the mean deviation of the $k$-th class, corresponding to the $j$-th feature. Standard deviation calculation requires $X$, $\mu$, and $N$, which are value in data set, mean of the data set and the population count respectively. $$\sqrt{\frac{\sum{|x-\mu|^2}}{N}}.$$
I'm a bit confused whether I have to find $\mu$ in the standard deviation formula from the $k$-th class subset or the overall data set and the same with the count—number of data entries from the whole data set or just the subset?