numpy normal distribution
>>> mu, sigma = 0, 0.1 # mean and standard deviation
>>> s = np.random.normal(mu, sigma, 1000)numpy normal distribution
>>> mu, sigma = 0, 0.1 # mean and standard deviation
>>> s = np.random.normal(mu, sigma, 1000)numpy normalize
def normalize(v):
norm = np.linalg.norm(v)
if norm == 0:
return v
return v / normnumpy normalize matrix
import numpy as np
x= np.random.random((3,3))
print("Original Array:")
print(x)
xmax, xmin = x.max(), x.min()
x = (x - xmin)/(xmax - xmin)
print("After normalization:")
print(x)Copyright © 2021 Codeinu
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