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A package used in DNN trainning in ATLAS analysis

## Project description

Python package use cuda to normalize input variables using cuda package in ATLAS analysis

Function use to do Guassian Normalization: Mean: $$\mu_{i}=\frac{\sum x_{i}\times w_{i}}{\sum w_{i}}$$ Variance: $$\sigma_{i}=\frac{\sum (x_{i}-\mu_{i})^{2}\times w_{i}}{\frac{N-1}{N}\times\sum w_{i}}$$ Normalized input feature: $$\bar{x_{i}}=\frac{x_{i}-\mu_{i}}{\sigma_{i}}$$

Main function: guass_normal((1),(2),(3))

Input:

(1):Numpy array contain all input features you want to normalize. (2):Numpy array used to calculate each feature's mean and variance. (3):1-d Numpy array contains each events weight in (2)

(1) and (2) must have the same number of columns.

cuda_cut((1),(2),(3)): Used to calculate event yield after applying DNN cut.

Input: (1): 1-d numpy array include the variable you want to cut. (2): 1-d numpy array include event weight. (3): cut threshold