Numpy normalize

Numpy normal distribution

>>> mu, sigma = 0, 0.1 # mean and standard deviation
>>> s = np.random.normal(mu, sigma, 1000)

Source: docs.scipy.org

Numpy normalize

def normalize(v):
    norm = np.linalg.norm(v)
    if norm == 0: 
       return v
    return v / norm

Source: stackoverflow.com

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.

Source: stackoverflow.com

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)

-1

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