scores = [3.0, 1.0, 0.2]

import numpy as np

def softmax(x):
    """"""
    return np.exp(x) / np.sum(np.exp(x),axis=0)

print(softmax(scores))

import matplotlib.pyplot as plt

x = np.arange(-2.0, 6.0, 0.1)

scores = np.vstack([x,np.ones_like(x), 0.2*np.ones_like(x)])

plt.plot(x, softmax(scores).T, linewidth=2)
plt.show()
