I saw in tutorial (there were no further explanation) that we can process data to zero mean with x -= np.mean(x, axis=0)
and normalize data with x /= np.std(x, axis=0)
. Can anyone elaborate on these two pieces on code, only thing I got from documentations is that np.mean
calculates arithmetic mean calculates mean along specific axis and np.std
does so for standard deviation.
This is also called zscore
.
SciPy has a utility for it:
>>> from scipy import stats
>>> stats.zscore([ 0.7972, 0.0767, 0.4383, 0.7866, 0.8091,
... 0.1954, 0.6307, 0.6599, 0.1065, 0.0508])
array([ 1.1273, -1.247 , -0.0552, 1.0923, 1.1664, -0.8559, 0.5786,
0.6748, -1.1488, -1.3324])