pythonnumpyprobability-distribution

Efficient way to create the probability distribution of a list of numbers with numpy

This is an example of what I am trying to do. Suppose the following numpy array:

A = np.array([3, 0, 1, 5, 7]) # in practice, this array is a huge array of float numbers: A.shape[0] >= 1000000

I need the fastest possible way to get the following result:

result = []

for a in A:
    result.append( 1 / np.exp(A - a).sum() )

result = np.array(result)

print(result)

>>> [1.58297157e-02 7.88115138e-04 2.14231906e-03 1.16966657e-01 8.64273193e-01]

Option 1 (faster than previous code):

result = 1 / np.exp(A - A[:,None]).sum(axis=1)

print(result)

>>> [1.58297157e-02 7.88115138e-04 2.14231906e-03 1.16966657e-01 8.64273193e-01]

Is there a faster way to get "result" ?

Solution

  • Yes: scipy.special.softmax did the trick

    from scipy.special import softmax

result = softmax(A)

    Thank you @j1-lee and @Karl Knechtel