distribución normal numpy
>>> mu, sigma = 0, 0.1 # mean and standard deviation
>>> s = np.random.normal(mu, sigma, 1000)
Difficult Dunlin
>>> mu, sigma = 0, 0.1 # mean and standard deviation
>>> s = np.random.normal(mu, sigma, 1000)
def normalize(v):
norm = np.linalg.norm(v)
if norm == 0:
return v
return v / norm
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.