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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.
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You can determine the mean of the signal, and just subtract that value from all the entries. That will give you a zero mean result. To get unit variance, determine the standard deviation of the signal, and divide all entries by that value.
In order to normalize a vector in NumPy, we can use the np. linalg. norm() function, which returns the vector's norm value. We can then use the norm value to divide each value in the array to get the normalized array.
The standard deviation is the square root of the average of the squared deviations from the mean, i.e., std = sqrt(mean(x)) , where x = abs(a - a. mean())**2 . The average squared deviation is typically calculated as x. sum() / N , where N = len(x) .
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])
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