numpy normalize
def normalize(v):
norm = np.linalg.norm(v)
if norm == 0:
return v
return v / normnumpy 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)normalize numpy array
norm = np.linalg.norm(an_array_to_normalize)
normal_array = an_array_to_normalize/norm
or for pixels to be obtained in my case. This can be used to map values to another scale from the current scale of values.
scaled_array = (array/np.float(np.max(array)) )*255.Copyright © 2021 Codeinu
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