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)how to normalize a 1d numpy array
# Foe 1d array
an_array = np.array([0.1,0.2,0.3,0.4,0.5])
norm = np.linalg.norm(an_array)
normal_array = an_array/norm
print(normal_array)
#[0.2,0.4,0.6,0.8,1] (Should be, I didin't run the code)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)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
Forgot your account's password or having trouble logging into your Account? Don't worry, we'll help you to get back your account. Enter your email address and we'll send you a recovery link to reset your password. If you are experiencing problems resetting your password contact us
