numpy 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)numpy 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)normalize 2d numpy array
import numpy as np
def scale(X, x_min, x_max):
nom = (X-X.min(axis=0))*(x_max-x_min)
denom = X.max(axis=0) - X.min(axis=0)
denom[denom==0] = 1
return x_min + nom/denom
X = np.array([
[ 0, 1],
[ 2, 3],
[ 4, 5],
[ 6, 7],
[ 8, 9],
[10, 11],
[12, 13],
[14, 15]
])
X_scaled = scale(X, -1, 1)
print(X_scaled)Copyright © 2021 Codeinu
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